{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "medieval-warrant", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "id": "charitable-palestinian", "metadata": {}, "source": [ "# Spatial Weights\n" ] }, { "cell_type": "markdown", "id": "academic-assistant", "metadata": {}, "source": [ "\"Spatial weights\" are one way to represent graphs in geographic data science and spatial statistics. They are widely used constructs that represent geographic relationships between the observational units in a spatially referenced dataset. Implicitly, spatial weights connect objects in a geographic table to one another using the spatial relationships between them. By expressing the notion of geographical proximity or connectedness, spatial weights are the main mechanism through which the spatial relationships in geographical data is brought to bear in the subsequent analysis.\n", "\n", "## Introduction\n", "\n", "Spatial weights often express our knowledge about spatial relationships. \n", "For example, proximity and adjacency are common spatial questions: *What neighborhoods are you surrounded by? How many gas stations are within 5 miles of my stalled car?*\n", "These are spatial questions that target specific information about the spatial configuration of a specific target (\"a neighborhood,\" \"my stalled car\") and geographically connected relevant sites (\"adjacent neighborhoods\", \"nearby gas stations\"). For us to use this information in statistical analysis, it's often necessary to compute these relationships between all pairs of observations. This means that, for many applications in geographic data science, we are building a *topology*---a mathematical structure that expresses the connectivity between observations---that we can use to examine the data. Spatial weights matrices express this topology, letting us embed all of our observations in space together, rather than asking and answering single questions about features nearby a unit. " ] }, { "cell_type": "code", "execution_count": 2, "id": "balanced-williams", "metadata": {}, "outputs": [], "source": [ "import contextily\n", "import geopandas\n", "import rioxarray\n", "import seaborn\n", "import pandas\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "from shapely.geometry import Polygon\n", "from pysal.lib import cg as geometry" ] }, { "cell_type": "markdown", "id": "specified-conspiracy", "metadata": {}, "source": [ "Since they provide a way to represent these spatial relationships, spatial weights are widely used throughout spatial and geographic data science.\n", "In this chapter, we first consider different approaches to construct spatial weights, distinguishing between those based on contiguity/adjacency relations from weights obtained from distance based relationships. We then discuss the case of hybrid weights which combine one or more spatial operations in deriving the neighbor relationships between observations. We illustrate all of these concepts through the spatial weights class in `pysal`, which provides a rich set of methods and characteristics for spatial weights and it is stored under the `weights` submodule:" ] }, { "cell_type": "code", "execution_count": 4, "id": "irish-invite", "metadata": {}, "outputs": [], "source": [ "from pysal.lib import weights" ] }, { "cell_type": "markdown", "id": "exotic-improvement", "metadata": {}, "source": [ "We also demonstrate its set-theoretic functionality, which permits the derivation of weights through the application of set operations. Throughout the chapter, we discuss common file formats used to store spatial weights of different types, and we include visual discussion of spatial weights, making these sometimes abstract constructs more intuitive." ] }, { "cell_type": "markdown", "id": "norman-hands", "metadata": {}, "source": [ "## Contiguity Weights\n", "\n", "A contiguous pair of spatial objects are those who share a common border. At first\n", "glance this seems straightforward. However, in practice this turns out to be\n", "more complicated. The first complication is that there are different ways that objects can \"share a common border.\" Let's start with the example of a three-by-three grid. We can create it as a geo-table from scratch:" ] }, { "cell_type": "code", "execution_count": 5, "id": "german-floor", "metadata": {}, "outputs": [], "source": [ "# Get points in a grid\n", "l = numpy.arange(3)\n", "xs, ys = numpy.meshgrid(l, l)\n", "# Set up store\n", "polys = []\n", "# Generate polygons\n", "for x, y in zip(xs.flatten(), ys.flatten()):\n", " poly = Polygon([(x, y), (x + 1, y), (x + 1, y + 1), (x, y + 1)])\n", " polys.append(poly)\n", "# Convert to GeoSeries\n", "polys = geopandas.GeoSeries(polys)\n", "gdf = geopandas.GeoDataFrame(\n", " {\n", " \"geometry\": polys,\n", " \"id\": [\"P-%s\" % str(i).zfill(2) for i in range(len(polys))],\n", " }\n", ")" ] }, { "cell_type": "markdown", "id": "outstanding-establishment", "metadata": {}, "source": [ "which results in the grid shown in the following figure. " ] }, { "cell_type": "code", "execution_count": 6, "id": "lesbian-lafayette", "metadata": { "caption": "A three-by-three grid of squares. Code generated for this figure is available on the web version of the book.", "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAM0klEQVR4nO3cTWxUBduH8f+x5aMtLwYhkKlIAxSUttNOZUGMNOjUBnHhRwIRTcxIWGjUDQnVJQtMpiKu3HRHikExYiiaFMQEY3QhH8oEqSaCQBBoWluaPKQfpqP3uyhtyFuevNAz5/QuXr9kEjMz53hzM9cwHWYIzEwA/LlvqgcAcHvECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxYU4yYwZM/6Tz+f/pxDnms6Ki4uVz+eneowpxx5GFRcX3xgZGZk72eODQnzZOggC40vbUhAEYg/sYczNPQSTPZ6XtYBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4FWmcRUVFSqVSqqmp0aZNmzQ4ODjhPtevX1dTU5NWrFihpqYm9ff3j9925swZPfbYY6qurlYymdTw8HCU40YmzB727dunVCo1frnvvvuUy+Vi/hUURpg9jIyMKJPJKJlMatWqVcpms3GPHz8zC30ZPc1EZWVl4//98ssv2wcffDDhPs3NzZbNZs3MLJvN2ttvv21mZiMjI5ZMJi2Xy5mZWW9vr+Xz+dv+f7yIYg+3OnPmjC1durRA00Ynij3s27fPXnzxRTMzGxgYsIqKCrt48WKBJy+sm3uYdFexvaxtaGjQ+fPnJ1x/6NAhZTIZSVImk1F7e7sk6ejRo6qtrVVdXZ0kaf78+SoqKopr3Mjc7R5u9cknn+ill16KesRY3O0egiDQwMCA8vm8hoaGNHPmTM2dOzfOkWMXS5z5fF6HDx9WMpmccFt3d7cSiYQkKZFIqKenR5L022+/KQgCrV+/Xo8++qh27doVx6iRmswebvXpp5/eE3FOZg8bN25UWVmZEomElixZou3bt+uBBx6Ide64FUd58qGhIaVSKUmjz5Rbt26942Pz+by+//57nTx5UqWlpWpsbNTq1avV2NgY0bTRCbOHMcePH1dpaalqamoKPF18wuzhxIkTKioq0rVr19Tf36+GhgY99dRTWrZsWUTTTr1I4ywpKZnw5sWWLVt0+vRplZeXq6OjQ4sWLVJXV5cSiYS6urq0cOFCSdLixYu1bt06LViwQJL0zDPP6KeffpqWcYbZw5j9+/dP+z81w+zh448/1tNPP60ZM2Zo4cKFevzxx3Xq1Kl7Os7Y/yplz549yuVy6ujokCQ9++yzamtrkyS1tbXpueeekyStX79eZ86c0eDgoPL5vL799ltVVVXFPW5k7nQPkvTPP//os88+0+bNm6dk1ijd6R6WLFmiY8eOycw0MDCgH374QY888siUzR2LMO8mjV10B+/O/Te9vb2WTqetsrLS0um09fX1jd/20UcfWVVVlVVXV1tzc/Mdvkc2daLawzfffGNr1qwp2JxRi2IPN27csI0bN1pVVZWtWrXKdu3aVdCZo6CQ79YGo+cIJwgCK8R5prsgCMQe2MOYm3sIJns8nxACnCJOwCniBJwiTsAp4gScIk7AKeIEnCJOwCniBJwiTsAp4gScKthXxoJg0h8hvKewh1HsQSouDpdXweLkg8584HsMexgV9gmKl7WAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU5HGWVRUpFQqpZqaGm3atEmDg4MT7nP9+nU1NTVpxYoVampqUn9/vyTpxIkTSqVSSqVSqqur08GDB6McNVJh9jDm8uXLmjNnjnbv3h3X2AUXZg+XLl1SSUnJ+GPi9ddfj3v82EUaZ0lJiXK5nM6ePauZM2eqtbV1wn1aWlrU2Nioc+fOqbGxUS0tLZKkmpoanTp1SrlcTkeOHNFrr72mfD4f5biRCbOHMdu2bdOGDRviGjkSYfewfPly5XI55XK52x57r4ntZW1DQ4POnz8/4fpDhw4pk8lIkjKZjNrb2yVJpaWlKi4uliQNDw8rCIK4Ro3U3e5Bktrb27Vs2TJVV1fHNWbkJrOHf5tY4szn8zp8+LCSyeSE27q7u5VIJCRJiURCPT0947cdP35c1dXVSiaTam1tHY91uprMHgYGBvTee+9px44dsc4apck+Hi5evKj6+nqtW7dO3333XWzzTpVIH+1DQ0NKpVKSRp8pt27delfHr1mzRp2dnfr111+VyWS0YcMGzZ49O4JJoxVmDzt27NC2bds0Z86ciKaLT5g9JBIJXb58WfPnz9ePP/6o559/Xp2dnZo7d25E0zpgZqEvo6eZqKysbMJ1r776qtXV1dmGDRvMzGzlypV27do1MzO7du2arVy58rbneuKJJ+zkyZO3vc2LKPawdu1aq6iosIqKCrv//vtt3rx59uGHH0b0KyiMOB4P69atmy6Ph8l3Febg8ZPcxW/G/7V9+3bLZrNmZpbNZq25udnMzC5cuGAjIyNmZnbp0iVLJBL2559/3tlWpkgUe7jVjh077P333w83ZAyi2ENPT4/l83kzM/v999+tvLzc+vr6CjRxNKZ9nL29vZZOp62ystLS6fT4wvfu3WtVVVVWV1dn9fX1dvDgwbtYy9SIYg+3+jfE+d/2cODAAauqqrLa2lqrr6+3L774oqAzRyFsnMHoOcIJgsAKcZ7pLggCsQf2MObmHib91wx8QghwijgBp4gTcIo4AaeIE3CKOAGniBNwijgBp4gTcIo4AaeIE3CKOAGnCvZl63vlnxEJiz2MYg8K/S93FCxOvoXAtzHGsIdRYZ+geFkLOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOBVpnEVFRUqlUqqpqdGmTZs0ODg44T7Xr19XU1OTVqxYoaamJvX394/fls1mVVlZqYcfflhfffVVlKNGKswe+vr69OSTT2rOnDl666234h69oMLs4euvv9bq1auVTCa1evVqHTt2LO7xYxdpnCUlJcrlcjp79qxmzpyp1tbWCfdpaWlRY2Ojzp07p8bGRrW0tEiSfvnlF+3fv1+dnZ06cuSI3njjDf39999RjhuZMHuYPXu2du7cqd27d8c9dsGF2cOCBQv05Zdf6ueff1ZbW5teeeWVuMePXWwvaxsaGnT+/PkJ1x86dEiZTEaSlMlk1N7ePn795s2bNWvWLC1dulSVlZU6ceJEXONG5m73UFZWprVr12r27Nlxjhm5u91DfX29ysvLJUnV1dUaHh7WX3/9Fdu8UyGWOPP5vA4fPqxkMjnhtu7ubiUSCUlSIpFQT0+PJOnq1at66KGHxu+3ePFiXb16NY5xIzOZPdyLwu7h888/V319vWbNmhX5rFOpOMqTDw0NKZVKSRp9pty6desdH2tmE64LgqBQo8UqzB7uJYXYQ2dnp9555x0dPXq0wNP5E2mcYz9j3GrLli06ffq0ysvL1dHRoUWLFqmrq0uJREJdXV1auHChpNE/Kf/444/x465cuTL+sma6CbOHe0nYPVy5ckUvvPCC9u7dq+XLl8c8/RQws9CX0dNMVFZWdtvrb7V9+3bLZrNmZpbNZq25udnMzM6ePWu1tbU2PDxsFy5csKVLl1o+n/9/zzeVotjDmD179tibb74ZfsgYRLGH/v5+q62ttQMHDhRu0Ijd3MPkuwpz8PhJQvxm9Pb2WjqdtsrKSkun09bX1zd+27vvvmvLli2zlStXWkdHx53uZMpEtYeKigqbN2+elZWV2YMPPmidnZ0FmzkKUexh586dVlpaanV1deOX7u7ugs5daGHjDOw2P9vdrSAIrBDnme6CILjtz8r/Nuxh1M09TPqNEj4hBDhFnIBTxAk4RZyAU8QJOEWcgFPECThFnIBTxAk4RZyAU8QJOEWcgFMF+z7ndP0idKGxh1HsQSouLr4R5viCfCsFQOHxshZwijgBp4gTcIo4AaeIE3CKOAGniBNwijgBp4gTcIo4AaeIE3CKOAGniBNwijgBp4gTcOp/AWvrSeS8pHILAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot grid geotable\n", "ax = gdf.plot(facecolor=\"w\", edgecolor=\"k\")\n", "\n", "# Loop over each cell and add the text\n", "for x, y, t in zip(\n", " [p.centroid.x - 0.25 for p in polys],\n", " [p.centroid.y - 0.25 for p in polys],\n", " [i for i in gdf[\"id\"]],\n", "):\n", " plt.text(\n", " x,\n", " y,\n", " t,\n", " verticalalignment=\"center\",\n", " horizontalalignment=\"center\",\n", " )\n", "\n", "# Remove axes\n", "ax.set_axis_off()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "academic-concept", "metadata": {}, "source": [ "A common way to express contiguity/adjacency relationships arises from an analogy to the legal moves that different chess pieces can make. *Rook* contiguity requires that the pair of polygons in\n", "question share an *edge*. According to this definition, polygon $0$ would be a rook neighbor of $1$ and $3$, while $1$ would be a rook neighbor with $0$, $2$, and $4$. Applying this rule to all nine polygons we can model our neighbor relations as:" ] }, { "cell_type": "code", "execution_count": 7, "id": "regulated-ethiopia", "metadata": {}, "outputs": [], "source": [ "# Build a rook contiguity matrix from a regular 3x3\n", "# lattice stored in a geo-table\n", "wr = weights.contiguity.Rook.from_dataframe(gdf)" ] }, { "cell_type": "markdown", "id": "third-cleaner", "metadata": {}, "source": [ "Note the pattern we use to build the `w` object, which is similar across the library: we specify the criterium we want for the weights (`weights.contiguity.Rook`) and then the \"constructor\" we will use (`from_dataframe`). We can visualise the result plotted on top of the same grid of labeled polygons, using red dotted lines to represent the edges between a pair of nodes (polygon centroids in this case). We can see this in the following figure. " ] }, { "cell_type": "code", "execution_count": 8, "id": "exposed-performance", "metadata": { "caption": "Grid cells connected by a red line are 'neighbors' under a 'Rook' contiguity rule.Code generated for this figure is available on the web version of the book.", "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Set up figure\n", "f, ax = plt.subplots(1, 1, subplot_kw=dict(aspect=\"equal\"))\n", "# Plot grid\n", "gdf.plot(facecolor=\"w\", edgecolor=\"k\", ax=ax)\n", "# Loop over each cell and add the text\n", "for x, y, t in zip(\n", " [p.centroid.x - 0.25 for p in polys],\n", " [p.centroid.y - 0.25 for p in polys],\n", " [i for i in gdf[\"id\"]],\n", "):\n", " plt.text(\n", " x,\n", " y,\n", " t,\n", " verticalalignment=\"center\",\n", " horizontalalignment=\"center\",\n", " )\n", "# Plot weights connectivity\n", "wr.plot(gdf, edge_kws=dict(color=\"r\", linestyle=\":\"), ax=ax)\n", "# Remove axes\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "id": "several-rating", "metadata": {}, "source": [ "The `neighbors` attribute of our `pysal` $W$ object encodes the neighbor\n", "relationships by expressing the *focal* observation on the left (in the `key` of the dictionary), and expressing the *neighbors* to the *focal* in the list on the right (in the `value` of the dictionary). This representation has computational advantages, as it exploits\n", "the sparse nature of contiguity weights matrices by recording only non-zero weights:" ] }, { "cell_type": "code", "execution_count": 9, "id": "about-hydrogen", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: [1, 3],\n", " 1: [0, 2, 4],\n", " 2: [1, 5],\n", " 3: [0, 4, 6],\n", " 4: [1, 3, 5, 7],\n", " 5: [8, 2, 4],\n", " 6: [3, 7],\n", " 7: [8, 4, 6],\n", " 8: [5, 7]}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wr.neighbors" ] }, { "cell_type": "markdown", "id": "behind-democrat", "metadata": {}, "source": [ "More specifically, knowing\n", "that the neighbors of polygon $0$ are $3$ and $1$ implies that polygons $2, 4,\n", "5, 6, 7, 8$ are not Rook neighbors of 0. As such, there is no reason to store\n", "the \"non-neighbor\" information and this results in significant reductions in\n", "memory requirements. However, it is possible to create the fully dense, matrix\n", "representation if needed:" ] }, { "cell_type": "code", "execution_count": 10, "id": "major-instrumentation", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8\n", "0 0 1 0 1 0 0 0 0 0\n", "1 1 0 1 0 1 0 0 0 0\n", "2 0 1 0 0 0 1 0 0 0\n", "3 1 0 0 0 1 0 1 0 0\n", "4 0 1 0 1 0 1 0 1 0\n", "5 0 0 1 0 1 0 0 0 1\n", "6 0 0 0 1 0 0 0 1 0\n", "7 0 0 0 0 1 0 1 0 1\n", "8 0 0 0 0 0 1 0 1 0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pandas.DataFrame(*wr.full()).astype(int)" ] }, { "cell_type": "markdown", "id": "demonstrated-labor", "metadata": {}, "source": [ "As you can see from the matrix above, most entries are zero. In fact out of all of the possible $9^2=81$ linkages that there could be in this matrix, there are only twenty-four non-zero entries:" ] }, { "cell_type": "code", "execution_count": 11, "id": "premium-disposal", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "24" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wr.nonzero" ] }, { "cell_type": "markdown", "id": "disabled-publisher", "metadata": {}, "source": [ "Thus, we can save a significant amount of memory and lose no information using these sparse representations, which only record the non-zero values. \n", "\n", "More generally, the spatial weights for our 3-by-3 grid can be represented as a matrix that has 9 rows and 9 columns, matching the number of polygons $(n=9)$. An important thing to note is that geography has more than one dimension. When compared to common representations of relationships *in time* used in data science, using information about spatial relationships can be more complex: spatial relationships are bi-directional, while temporal relationships are unidirectional. Further complicating things, the ordering of the observations in the weights matrix is arbitrary. The first row is not first for a specific mathematical reason, it just happens to be the first entry in the input. Here we use the alphanumeric ordering of the unit identifiers to match a polygon with a row or column of the matrix, but any arbitrary rule could be followed and the weights matrix would look different. The graph, however, would be isomorphic and retain the mapping of relationships.\n", "\n", "Spatial weights matrices may look familiar to those acquainted with social\n", "networks and graph theory in which **adjacency** matrices play a central role in\n", "expressing connectivity between nodes. Indeed, spatial weights matrices can be\n", "understood as a graph adjacency matrix where each observation is a node and\n", "the spatial weight assigned between a pair represents the weight of the edge on\n", "a graph connecting the arcs. Sometimes, this is called the **dual graph** of the input geographic data. This is advantageous, as geographic data science can\n", "borrow from the rich graph theory literature. At the same time, spatial\n", "data has numerous distinguishing characteristics that necessitate the\n", "development of specialized procedures and concepts in the handling of spatial\n", "weights. This chapter will cover many of these features.\n", "\n", "But for now, let's get back to the Rook contiguity graph. A close inspection reveals that this criterion actually places\n", "a restriction on the spatial relation. More specifically, polygons $0$ and $5$\n", "are not Rook neighbors, but they do in fact share a common border. However, in\n", "this instance the sharing is due to a common *vertex* rather than a shared\n", "*edge*. If we wanted them to be considered as neighbours, we can switch to the more inclusive notion of *Queen* contiguity, which\n", "requires the pair of polygons to only share one or more *vertices*. We can create the\n", "neighbor relations for this same configuration as follows:" ] }, { "cell_type": "code", "execution_count": 12, "id": "stock-physiology", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: [1, 3, 4],\n", " 1: [0, 2, 3, 4, 5],\n", " 2: [1, 4, 5],\n", " 3: [0, 1, 4, 6, 7],\n", " 4: [0, 1, 2, 3, 5, 6, 7, 8],\n", " 5: [1, 2, 4, 7, 8],\n", " 6: [3, 4, 7],\n", " 7: [3, 4, 5, 6, 8],\n", " 8: [4, 5, 7]}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Build a queen contiguity matrix from a regular 3x3\n", "# lattice stored in a geo-table\n", "wq = weights.contiguity.Queen.from_dataframe(gdf)\n", "wq.neighbors" ] }, { "cell_type": "markdown", "id": "disciplinary-madagascar", "metadata": {}, "source": [ "In addition to this neighbors representation, we can also express the graph visually, as done before. This is shown in the following figure." ] }, { "cell_type": "code", "execution_count": 13, "id": "radical-cherry", "metadata": { "caption": "Grid cells connected by a red line are considered 'neighbors' under 'Queen' contiguity. Code generated for this figure is available on the web version of the book.", "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Set up figure\n", "f, ax = plt.subplots(1, 1, subplot_kw=dict(aspect=\"equal\"))\n", "# Plot grid\n", "gdf.plot(facecolor=\"w\", edgecolor=\"k\", ax=ax)\n", "# Loop over each cell and add the text\n", "for x, y, t in zip(\n", " [p.centroid.x - 0.25 for p in polys],\n", " [p.centroid.y - 0.25 for p in polys],\n", " [i for i in gdf[\"id\"]],\n", "):\n", " plt.text(\n", " x,\n", " y,\n", " t,\n", " verticalalignment=\"center\",\n", " horizontalalignment=\"center\",\n", " )\n", "# Plot weights connectivity\n", "wq.plot(gdf, edge_kws=dict(color=\"r\", linestyle=\":\"), ax=ax)\n", "# Remove axes\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "id": "confirmed-joining", "metadata": {}, "source": [ "By using `Contiguity.Queen` rather than `Contiguity.Rook`, we consider observations that share a vertex to be neighbors. The result is that the neighbors of $0$ now include $4$ along with $3$ and $1$.\n", "\n", "Akin to how the `neighbors` dictionary encodes the contiguity relations, the `weights` dictionary encodes the strength of the link connecting the focal to each neighbor. For contiguity\n", "weights, observations are usually either considered \"linked\" or \"not linked,\" so the resulting weights matrix is binary. As in any `pysal` `W` object, the actual weight values are contained in the `weights` attribute:" ] }, { "cell_type": "code", "execution_count": 14, "id": "occupied-transition", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: [1.0, 1.0, 1.0],\n", " 1: [1.0, 1.0, 1.0, 1.0, 1.0],\n", " 2: [1.0, 1.0, 1.0],\n", " 3: [1.0, 1.0, 1.0, 1.0, 1.0],\n", " 4: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n", " 5: [1.0, 1.0, 1.0, 1.0, 1.0],\n", " 6: [1.0, 1.0, 1.0],\n", " 7: [1.0, 1.0, 1.0, 1.0, 1.0],\n", " 8: [1.0, 1.0, 1.0]}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wq.weights" ] }, { "cell_type": "markdown", "id": "efficient-volume", "metadata": {}, "source": [ "Similar to the `neighbors` attribute, the `weights` object is a Python\n", "dictionary that only stores the non-zero weights. Although the weights for a\n", "given observations neighbors are all the same value for contiguity weights, it\n", "is important to note that the `weights` and `neighbors` are aligned with one another; for each observation, its first neighbor in `neighbors` has the first weight in its `weights` entry. This will be important when we examine distance based weights further\n", "on, when observations will have different weights. \n", "\n", "In addition to the `neighbor` and `weights` attributes, the `w` object has a\n", "large number of other attributes and methods that can be useful. The\n", "`cardinalities` attribute reports the number of neighbors for each observation:" ] }, { "cell_type": "code", "execution_count": 15, "id": "exact-associate", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: 3, 1: 5, 2: 3, 3: 5, 4: 8, 5: 5, 6: 3, 7: 5, 8: 3}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wq.cardinalities" ] }, { "cell_type": "markdown", "id": "ceramic-mauritius", "metadata": {}, "source": [ "The related `histogram` attribute provides an overview of the distribution of\n", "these cardinalities:" ] }, { "cell_type": "code", "execution_count": 16, "id": "musical-screen", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(3, 4), (4, 0), (5, 4), (6, 0), (7, 0), (8, 1)]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wq.histogram" ] }, { "cell_type": "markdown", "id": "gentle-hotel", "metadata": {}, "source": [ "We can obtain a quick visual representation by converting the cardinalities\n", "into a `pandas.Series` and creating a histogram:" ] }, { "cell_type": "code", "execution_count": 17, "id": "blond-cuisine", "metadata": { "caption": "Histogram of cardinalities (i.e. the number of neighbors each cell has) in the Queen grid." }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pandas.Series(wq.cardinalities).plot.hist(color=\"k\");" ] }, { "cell_type": "markdown", "id": "returning-boundary", "metadata": {}, "source": [ "The `cardinalities` and `histogram` attributes help quickly spot asymmetries in\n", "the number of neighbors. This, as we will see later in the book, is relevant\n", "when using spatial weights in other analytical techniques (e.g.\n", "spatial autocorrelation analysis or spatial regression). Here we see that there are four corner\n", "observations with three neighbors, four edge observations with five neighbors,\n", "and the one central observation has eight neighbors. There are also no\n", "observations with four, six, or seven neighbors.\n", "\n", "By convention, an ordered pair of contiguous observations constitutes a *join*\n", "represented by a non-zero weight in a $W$. The attribute `s0` records the number\n", "of joins." ] }, { "cell_type": "code", "execution_count": 18, "id": "royal-swing", "metadata": { "lines_to_next_cell": 0 }, "outputs": [ { "data": { "text/plain": [ "40.0" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wq.s0" ] }, { "cell_type": "markdown", "id": "latter-cornell", "metadata": {}, "source": [ "Thus, the Queen weights here have just under twice the number of joins in this case.\n", "The `pct_nonzero` attribute provides a measure of the density (compliment of\n", "sparsity) of the spatial weights matrix (if we had it stored explicitly, which\n", "we don't):" ] }, { "cell_type": "code", "execution_count": 19, "id": "attractive-structure", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "49.382716049382715" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wq.pct_nonzero" ] }, { "cell_type": "markdown", "id": "infinite-compiler", "metadata": {}, "source": [ "which is equal to $100 \\times (\\texttt{w.s0} / \\texttt{w.n}^2)$.\n", "\n", "### Spatial Weights from real-world geographic tables\n", "\n", "The regular lattice map encountered above helps us to understand the logic and\n", "properties of `pysal`'s spatial weights class. However, the artificial nature of\n", "that geography is of limited relevance to real world research problems.\n", "`pysal` supports the construction of spatial weights objects from a\n", "number of commonly used spatial data formats. Here we demonstrate this\n", "functionality for the case of census tracts in San Diego, California. Most spatial\n", "data formats, such as shapefiles, are non-topological in that they encode the\n", "polygons as a collection of vertices defining the edges of the geometry's\n", "boundary. No information about the neighbor relations is explicitly encoded, so we\n", "must construct it ourselves. Under the hood, `pysal` uses efficient spatial indexing\n", "structures to extract these." ] }, { "cell_type": "code", "execution_count": 20, "id": "scenic-liquid", "metadata": {}, "outputs": [], "source": [ "san_diego_tracts = geopandas.read_file(\n", " \"../data/sandiego/sandiego_tracts.gpkg\"\n", ")\n", "w_queen = weights.contiguity.Queen.from_dataframe(san_diego_tracts)" ] }, { "cell_type": "markdown", "id": "frank-muslim", "metadata": {}, "source": [ "Like before, we can visualize the adjacency relationships, but they are much more difficult to see without showing a closer detail. This higher level of detail is shown in the right pane of the plot." ] }, { "cell_type": "code", "execution_count": 21, "id": "original-chuck", "metadata": { "caption": "The Queen contiguity graph for San Diego tracts. Tracts connected with a red line are neighbors. Code generated for this figure is available on the web version of the book.", "lines_to_next_cell": 0, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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/nTsXy8aNg6OjI0aPHi1iY2Mzn//AAdDVqyAiXL9+HaGhobJCocBnCgWW6XTwcXYWzs7Owmg0ws/PT9y7dy/ttzdugIzG9z6XQ4cOwcfHR6bERFD79qD5862fr2UbPx709dfp99nEx3DJFvXz80txc3NL3fHJJ5/Q/fv3FZ9//jnVqlWLNmzY8L+duUUL/nv9Ope8i4vjknd2dmx19u7NArFcOaIyZbjgeoUKbHXq9UzkiY8nun+fEk0mOkNETzQaMrRuTdWIaFP37sqBAwcqduzYQVOmTFGsWLGCIocNU9ZycaF8Li7yqSJFiDw9ae7cuRgwYAAVLFiQ21OyJFcOkmUiSWLhvHw5u4f/R8iyTB/CwP0r0adPH/r0008pMTFRFRUVpXr27Jlq7969ypkzZyrHjx//nxeYPK6yHjz/OqxaRZSYyKGKtm05napgQYqJjSWtVmt9nwYDkYMD/69UEvn40A4/P8hubkT79jGXgIiV2fRelPPniR49Iho4kMjOjtRqNc2fP18xePBgypMnD+XLl4/HbqlSPHaIiK5eJRo/nqhaNU4Za9GCHhYqRJPy5AF5eaWeWtq9m/Jv3CgqV66McePGKenaNaL27dkLtWEDkaMjrVmzhlxdXeXy5ctzta8SJTgFbfZsoqpVWcEOC6Oqx49Ty4cPlXTwYNq9ZMTWrXx/Hzgud96+TX0vXMCQrl0xbdo0m2w5u+vXyaltW/L29iYi7mP9TCYOKc2fz2TG9PyMAgU4K+A96Fq5MjySkhT+Z87QrydO0NkSJQghIWS+CFGhQkSPH6cWaklOTlbE5MjB538PFAoFUVISv7NcuYhevWKeSDqPmHmsECUkEDVqRNSuHa0+dozq1KkjunXrpoiPj8984itXiH7+mQBQ8+bNcffuXWVoaKj8XWQkddi9myLLlVNE37mjiIuLo4oVK1KZMmXSCsLkyUN08GDqtZ8/f053796lqKiotLaYv9MARG3aENWuzZXfbDH/+/Thef19+MOJnErloO7duychC6xZswZ6vR7Tpk3L6pCssXgx4OEB1K8P9OgBnD/P+1evBm7dAh4/BmrW5H19+gCNGvH/BgOwdi1w4gSQPz8A4NGQIehABHcipBDhelAQlletivzptAzs2gXs2AE8fIg9RqPY0asXACBnzpymZcuWvbutX3wBtG0LPH3K5/gdiIqKQsGCBeXhw4f/rt/9HdDpdCAi/Bva+kdhLlzwCv+AhOmPssmpdTisMGfOHBQsWNBktTMyEnjzxmpX6dKlpQkjRwLBwcD8+bwzKQnQaoFXr4Dnz4F+/YDt2zNdo3HjxggLCxOpO6pVA16/BoQAVqwAFi4E4uKAdu2A77/HmFGjUKJECatzdA8Lk88ZDCIpyTzdrF4NbN4MnDsHJCcDAAoUKCCPGjUKiI4GlixJa2cGfN6jB4aWLi1h0iTg00+B9eth9Xzu3AFcXIBr12z+PiO+//576PV6HGvbVuDzz20fdO4ctzkdnu3fj6VKJdCihe3fDBgANGmS9YWFwLOxY/GECN56vfDy8pK9vLxkIsLVq1f5mB49gKZNrX6WL18+ef78+cC9e0BC1vVm/P39pXuNGwOzZll/8eZN6vNq3ry51KFDB95/4wZQvTogy7hx4wbc3NxkvV4POzs7NG3aVIqPj8euXbvQoUMHaUy7dvK1Xr2QlJQEhUKByMhIPHjwABMmTOB+8d13wPTp/C4BlClTRhgMBqxatQpXrlzB+dat0aVSJclgMMDOzg6Ojo5Cp9PB0dERVatWlSZMmCBGDh2KAxoNsGEDnzMrmExAuXLA06eQ+b5sjqE/bNI4OzvXq1q1apbMkBYtWpCHhwc1atSIHj58SFOmTPnwk9eqRfTFF0QXL7JGsXEj51927kypmueePfx3xoy03z19yprUy5esURCRv0ZD7kTU7ZtvSDlhAgVFRtKR+/dphqMjlfrtN7q2fz+fY+RIIr2etjg5oVjBgooXL15QYmKi6v79+6B3xbOmT+e/R4/yNc+cYbJSkSKZ4pxDhw7F3LlzFZUrV5YB0ObNm1VEpGzevPmHP5u/Cd27d6d58+bRuHHjyMnJyaokXfrSdOHh4VTyQzS2bPx1yCLeHhMTQwaDwfrLnj3Z6hk7NnXX27dvFU6enuzxUSqJGjTgNK+oKPborFpF5OfHqwZlQMmSJenJkyeCuCA+Ub9+RG/fskW6bh17i0qVYmvKz49UEyaQUqlMHXN9+/al4w8eKKc2aJBGRLt4kah6dW7HyZNEOXPS7du3lb09Pdmiun6dLWUbkAC66u3NlvCNGzx+Fy8mGjSILY3r14m++YboPalyQgj69NNPsXfvXsWOHTuoXK5cCmrYkK26jJbMhAlEFSumfR40iNznzaOVWi11WLPG9gWaNWML2hb69ye6d4+a3bsnh7VvT5HLlqXWl/bw8BAJCQn8Tnv35rkoHZKTk8lgMLBn7rvviD77zOYlZFmmR0+eUPTbt/TG7EF7+vQplbt5kwKOHKEny5fT+vXrVb/88guvEBUXxx4IIsqbNy9FRUUpiYiePXtG+fLlU+7fv586duxIJUqUUEUCFLZ6Nf148SLUarXCx8cn9bqhoaHUZPBgooUL+R398AOdOHFCMX78eBo0aJAcHx+vPJCQQAXLllUdOXKEihUrRkSkEELQ6dOnad26daqt69aJ8ZcvK+bZ2aFqkyYKm5alBWo1UZMmRAkJVK9ePbFr1y7bx2UlST9kIyKlSqUyPXnyJGvJbcaFCxfg4uKCTz/99B1i3obUV6uB3r2Bly+Bhw9Z4/j2W2DRondrDDagVCoxYsAAEe/piZYqFU6OHg20b88W7KJFwNGjfODEiVjs4iJPnjwZrq6ugogwcuTID7+YLAMpKYBeDxiNbHkmJqZ+vXjxYqtSfq1bt0aPHj3w/Pnz33U/fxciIiJQpkwZER4eLhctWlQqWrSoVKRIESksLEwqXLiwZDAYUKFChX99Sb3/nIWZBYYMGSI8PT0xfPhwDB8+HCNHjsTizz7D7N69MXbsWIwbNw7ffvsttFotVqxYkfbDOXOATZuAKVOAOnWANm2yHJO9evVCnTp10qzYtm2BVauAChV4TI8cCRw/nvr19u3b4e7uLsuyjC+++AKOjo64ePEi8OAB8PYtW0VeXkB8PHDsWOrvSigUiD98mL1L70CbNm3QqlUr6z7666/sKWreHCACZsx45zkWLFgAT09POU+ePOLu3btpX2zaBBw5Yn3w8+dsYVqsuc2bgSlT8Lp/fygUiqwvsnUrkP7cQvBzP3kSWLUKl+fMgV6vx4sXL6x+5unpKZ84cQLo1Am4cCHTaf39/cXmzZvZqs8Kv/6KbQ4OwujgAB8fH8myubq6ioIODmKUQgGNRoOyZcuyqdmqFXsIskD37t1BRHB2dk7tJM9798Z8T0/RqlWrVPM+Z86c4ueff0673yFDgJ9/5vf+oYiNBerUwZ3p0+Hq4vJBc3f0hg1Y5OEh/P39BbKSeVl98SEbESmNRuPFEiVKvL19+/Z7G3Tv3j34+PiIJk2a2PYN2UKPHoC3N3eS8HAWordvA8OGsSvl2bP3nmLGjBlwd3eXiQjTWrYUhY1GfPHFF/zlqlXsUkr3olM++wxl7O2h1+vRsmVL+fHjx0hMJ/Deib17gWLF2FXl4cECv0oVftn+/sC8ecDr1xBxcfjll19QuXJlWa/Xo06dOuLBgwcf/Fj+yRg2bBgqVqz4rxeYT58+hV6vj8HfL+j+VIHZsmVL2cHBQZQvX14qX768VK5cOembPHnkdoULS6VLl5ZKlSollSxZUiIirF27NvMJnJx4Kvn+e2DqVGDSJODQIeC334AXLwBJQsWKFTFkyJC0iWvvXmDECHY31qmTyf1rMpkQFBQkPD09hYuLCy5fvsxf5M8PzJ4NHD4MDB4MhISkTfoLF+IGER5ajn0HAgMD5R9++EGkKrc//shu0Xv3AEdHvp9Vq3ie+f57QOLuHBcXh969e8PDw0N2d3fHwoULYTJZe7OxZAkyuWU7dQLGjeP/v/yS55wTJ/D27VsQER49emS7oYULAytX8v+3bwMxMbzvwAEAQKVKlUT37t0zjTV3d3e5fbt2eJA/P1aNHo25c+di1qxZmDlzJmbOnAkiwt69e1mQV61q+9rbt+NgYKBo3769za+TjhxB7O7d/OHhQ35GGZ9FOlw1E3z69euXtjMqCli3LvWjEALe3t5izZo11j/+6it+3+ndx8OGAa1bZ77Qy5es9GzahNevX0Or1SLZ7LLPChMnTkRxvV7cNxrx5vVr4M8QmGChqdLpdAP1en38pEmTJEl69zwZEREBpVKJQYMGvfO4VJQpA5QoAWzcyDFNS/xCloErV4CCBXmQvkOglS5dWuj1euiJcNVoRAgRtmzZAly6lKYVmWMIj1evxmiDQSiVSnh6eiIlJeWdzUt++xYzZ8yAVLgwD4jffuMJ46ef+P8NG4A1a/D2xx+ReOAA75s1C3B15cnkp59wdft2NGnSRLKzs0PVqlXlyMjID3s2/1CMHDkS5cqV+9cLzNevX0On0yXi7xd0f6rALFSokDRmzBjrnZUrA5MnW+1yd3fH4cOH+YMQLBjatGGBFxoKnD4N7NnD43HYMGD4cLYcc+fGQY0GV3r25L4/bx4LEAcH/t8co8qI3r17C41Gg5s3b6btvHWLj1+1ii3Trl15kp4+HXjyBANbtpS8vb3luHdYTmMbNRKt3d1FwtGjQFAQT9irVwOjRrE1M348x1cBvk737ri5ZAkOubujgk6HkiVLyhs3bswsKC2wCAHLJC0Ee8levwZGj+a4ajqeg1arRZZj/tUrntv27WPD4c6d1K9kWYajo6M4ffp0pp917NBB7u3nJxUoUEAqUKCAZG9vD09PT2HxAhUtWlR69eoVz6O1alkLIllmQ+XRIwwYMEDUqlXLdttGjwYqVeLfOjvbXMQiPU6cOAEissQI065VvTpgfsc3b96EwWDIbKDIMsdzt21Le66//AJkFKxPn3LfSieEQ0JCxMSJE2226datWwgJCZE8PDywY/t2XowjKgr4swRm6omIcjk6Op4tXLjw2+vXr7/zwR05cgR6vd62tpoRXboAZcsCM2dyxytUiAP0FsTGsqbap4+VSyc98uTJY2rQoAFCnJzwuUIBFxcXEX/nDmurhw7h3NGjCA0OlkNCQuQFGg1+LF5cXrlyJYxGI2JjYzOfMDERePMG8vLlSNTpoCNCO70ejy2uj549WXOWJMhnz+JxaCj2E6FWrVqysLisXr/mTlC1KneEI0cQ3bYt2tauLalUKvj6+iImJub9z+cfiNGjR6NMmTL/eoH59u1bqNXqFPz9gu7jbBcvsiKXoV81a9ZMrlmzprXbKimJLa90cHZ2xtmzZ1kwHj0KNGsGLFvG41KSuN/bCCvs2bEDYQYDks+eZctyyRJAqQRUKiAsjCfaDRtShZQQAqNGjZKNRqM4ls7dCoDdmmPHMiHkyy95DC1Zwor1mzcwmUyoWbOm7O/vL8+fP58ti6Qkdrc2aoSdM2Zgn1KJV40bszv3xg0+7717bLl++y2wYEGqO3b79u3IlSuX5GVnh+WlSsm3ly8HatcGWrbk55MFkQrNmqVN5t98wwJvzBi+ZwsZxwyVSmV7rF+9yoKoWDGeczI82zlz5sDPz08IW27wgwfZZW1uX9WqVaXBgwfbbqsQ1hb+q1dAkSLAy5f4/vvvER4ebvsmExNZWJpMme7J+rBE1K5dW9ZoNFCpVJkPmDGD3yuAjRs3IkeOHKlzx4oVK9C1a1d0794dP//8M0TnzqzUpKTw9X/9Ne08Dx+ySz192ADsOvf29ra6B1mW0aNHD+j1evTs2VNKVbB69GBvw58tMMFCU6nRaHobjcaEU+/RNtauXQuDwYA76TQmm9ixA+jfn7fERNb4bLFQDx9mTXfNGo5zpIOfn5+0e8sWPPH0RPlcuWSxbBkzot6+RevWreFIhHiNBrMmTMD5CROQYnbz5siRQ1ppcYeYTBwrEQJwdwdGj8aPEyYgRK1GjRo1hL+/v0xEOD1gADPr3r7F2rVrRW4iPFUoMG/2bDQxGMS4ceNsd76rVzmuExWFVyEh+I4IO9av50H9L8O4ceNQunTpf73ANJlMUCgUWcYz/nXbmzdsTXTowELKPEk1b97c1Lx5c+ubb9iQ40bp4G9nh4jVq9njs2kTC0mjkd2vAIceNm3K9BwLFCggDRkyRIYQQMeOwMWLPNGdPMmC6swZdmH27AkMHIjN1avLwQaDuGBhxafHtm0c98yRg5XnFi14okxnISU+fIiNtWrJgTlyiPsKBZ5VrgxcvozogQOR08FBLF++3Pqcly8DffuyoASAb7+FPHw4GjRoIFu4BpbNyckJ93bvRvKCBcDu3SxYtm61WCVpWLaM56yEBG5rzZrAxIlARESmW1IoFEg+fJhjjU+fArly8T2FhvIUnS8fz3lHjli5PP38/KRp06ZllpaJiSwo0wnS9u3bS61atcr8PAFWPnLm5P9XreJsATMWLlyIfPnyZTalheDNy4v7gMUiz4DZs2fD1dVVLlOmjHz27Fmo1erMB924AXzyCQBg/vz5MBqNon379ihatKgwGAwoW7asVLNmTcnJyQl9e/Zky3bNGvYwOjjwOSIiWDFZsiTT6ZOTk+Hp6Slmz54NADhw4AC8vb2l3LlzizNnzlgfvHUrh//+CoFp2ZRKZT0HB4f4TNphBnz++edSjhw5JDkrLQ0Ali9nraxTpzQt5tgxTiHJqFmlpABLl7KWsGoVIElITk6GVqvFzWXLIKpVQyM7OxFZvjwQFYWOHTsKtVqN06dPA599xtT2dJ2qUKFCcu/cuTGnTx8RNW4c4OfHndE8OPr27StXr149tRHLmjbFBa0WwuxecXJyAhHhlxUrgCdPkOLigko6HYYPH/5uYXLkCOoaDBiQLx/e6PWY9OmnmNqgAYY1a4bOnTtj5syZ7/z5342JEyeiePHi/3qBCQBKpVIQkRp/t7D7OBtDkliLDgwEHjzATzVryg0s6VkWDBzISqgZKUuX4hwRE2os1lBGy0QIjrOlU1i3bNkCBwcHvHr8mCfxRo1YQF69yoIqA06MHYspGg0ezprFSu2wYXxs+uscOsRpEmvW8N+YGN5atWJBNWMGULEiEBWFGb17izzBwZIsywgICJA7duxo3S+PHmWrcuPG1F2JCQkoWLCglCdPHhEREYH4+HjExcXhwIED0Gq1cHR0FESE7du3A2fP8mRdrhxby2/eMElHCD7vhg1sWdrbsztVCJ6Unz0D9u2DnDcviAjJ5cuzpyk5mUlU165x2tyOHbzdvcupOEWKACdPYnrbtvDz9U1LsUmPWrWAxo2tdg0fPhwVK1bMfCzA/AoLaah+feCHH1K/WrtsGUIDA024cSON51GtGtCgAf/PfGC2qI8dA6ZNA6KjYTKZEBISIrm7u2PFihUQQuDJkyfQarWZr5+UxM/uyRMkJiZiypQpolatWlLv3r3FObNSBwArV66Ev7+/BJOJlb5Bg4AnT1jpGj06TeGxgSVLlsDFxUXUrl1b1uv1mDBhgmwz3JaSYrnPv05ggoVmLYPBEH8kI1vM6jklITQ0VLyTOZucDJQuzQO8Z0/eFx3NbpEstBrcugUsXw5T/fpo7OMjFQ8Pl01jxgCdO2OlTif8fH2xfPly6PV6XLC4Uc+e5Rdg6ZxDhyK8aFFcJ8I37u7CXqvFD1OmWEn2UqVKCXd3d277wYNI/vVX5NFqUatWLXnYsGHQ6XSYPXs2x3nGjweio3Hx5El46nTvtay//fZbqXHjxlLL6tVNDWvXNh3y85MvubuL2qGhYp5aLTJa0f8kmN04/wmBqVarJSKyx98v7D6ewLRAkoDHj3EjIECsCwpiAWnJO7x+nYXQtm1A/fpIPHgQhZXKtN+ePAn4+GR2SXbvzpYm2O3l5eUlz/7uOwEnJx5jFnTokDbpmnH69Gk4OztjypQpvOPuXbaqvvwSKF+e2zJxIqDT8dS1dy+74FxceJ748kvmCKTDsWPH4OnpKTdv3lzky5fPWsAcPcpenYMHU3c9f/4crxUKdCtUSM4qJPLcXH3oano3pMUtefgw8yqWLWOPl0rF1xg1ioWREGxxbtgAPH6MhMmTodNorC/w5Zep+eM4c4YFoOU5v3qFe+PGYadSiZsjR7IwvnLF+vevX2fyss2ePRsFChTImpEzdiy3V5Y5Y6BHDwBAssGAb1xdJdy7x+0A2Pq1eL6OHOH5+eefWWA2aICBdevKW1UqfJcjh4hPx2x98OABdDqd7eufPs2ckncgNjYWRqMRP1sYs/Pns7s6zY2aJYQQKFmypHB0dHy/V7N+feCvFphgoVnDwcEh4bwt14oZt27dgsFgwM8Z3D+pkGUuRHDkCLs10rOdvv7ailKeHpFPn+ITFxd5u5+fMFWvDnh4QJ42DUW0WjQyu1dWtWolcOUKu02KFWPzfuNGYMsWoHp1NKlTB71795YBYNmyZVAoFFYkoO+//55dDI8fs/vk4EHcu3cPDRs2hF6vR3BwMAvTX35Ji7t27YrLTk5CrVaja9euckREBM6dO4f+/fuLLJly6XBs1SosVSiw9Isv8LhOHWYKy/I/ynX7X7IwdTpdChE54e8Xdh9fYJrRr18/uV7t2jz5+vuzW9DPj627Tp2An39GPMdz034UHc3x0IxITGRrJT4e/Xr3xkRXV2FKSnonISQmJgY9evSQ9Hp91mTAhASeqL/6CqlWzf79LBjeQfA5evQoXFxcYDAYcOvWrbQvNmwAevWyir1dunQJrq6u8vcVKsjJGVi76TF16lT4+fm9W8m/e5fjtESc4nLsWCYXNwC8efMmTYgkJLDCbjJZW/E9eqR6tUwmE7y8vOThw4bJePOG31H16vwupk9n1vCiRawMTZrEVvzFi4ioVAkFc+Qw4Ysv2C3+6hW7YVesYKvQ8kxfvGDFZPx4AMDmdesQEBBgeyzPmIGY0aNxNG9eHPLwQOPy5VG+fHkYDQbcGzYM0vz57LUrVw749VfcP3kS9vb2tp/Zhg3MV3kPZsyYIYq7u8vyzz+zkCfiTIRbt4C5c7kPA3x/6fD27Vt4enqKcRam8rvAit1fLzDBQrOJk5NTwruIQEuWLBFOTk7iTVadNDSU/dPTp7MmYsGgQVxZJAMuXboEJycn0aZlS8l06RLfplaLVePGoRcRTOHhiI6Ohly+PDNb+/RJ6zDDh3NspFMn9PXxkY/WqQNcuQL55EkUcXaWGzZokNp5BgwYIBoUKcKdPMP9TZ06FUWKFGG10GTiGIks8+C+fh3r166FWq0GEXEtSSLsShc7yAoPHjxAlSpV5AIFCsiVHBwEli/nGICXFzPDLl/OrHH+xRg3bhxKlSr1nxCYDg4OiUTkib9f2P1pArNPnz5yvXr1+IPJxJabVms1tt6mF5g//cSTbFY4ehSSvz8K6nRI8PFJc/elx6hREDNmYO3atXB1dUVoaKhkJdBsISICuH+frc1+/TgG6+XFDMss8j8PHDgAFxcXLFq0iA8QgmOP9epZxRO3bdsGBwcH8X3fvrLYujXLJsiyDBcXF7E6Q8WeVKxcyS5LWeYJ/OTJd97Sq1ev0gTmjz+ywMt4L4cPczwUQKtWrUBEmRm6Z8+yhajRACVL8vVLl+Z8zwsXENmgAXK5ucnYvJmNAklil3bLljw3FSsGFC2aqX3bt29nN6gNnChZEqO0WrHTy0tsrlULB8uWxdChQ2EVFzSZ2AX94gWSgoJwSKlkN+rBg9b3aanyZOs9yjILxDVrIFerhg16PTbWrs2WeKlSrExIErtkhw5lpc3envup2X3fr08fqWJAgIT3pJcA4Pb+XQITAKnV6s/c3d0TImwEvAE2l+vVqydn6cabPZtf7tq1rD2l/ZBLeM2dm7rrzJkzICIEE0HkzcuFCT77DJg7F7fMxYhjY2M5njB5Mrt2f/yRO82kSewWKFEC1xYvRhWtFnHDh/PArFYNJl9f1CFCVMmSuN2lC6YEBYmXanXmnCsA06ZNQ1hYWJq/KiwsTdhHRwOenkBEBF69eoW4uDj4+/tL222UFMsK58+fh52dHX6zuKBevOD4yYgRbCUkJnK7PrC018fE5MmTUaxYsf+EwHRzc3tLRIH4+4XdXyMwDx7kFIhu3ayswsTERKhUKk4J+O47tmyywtGjiFap0LZhwyz7QMq4cZgRFiY5OTmlkjHeCZOJyTBTpqSRWoA0IkyxYqzsZsDu3bvh4eHBTFKTidNY2rWzskrnzZsHvV6PJUuWCCxbBuTJk2Uznj9/DrVajdfpw0FC8Ljbs4cF5NSpvO/7799dGABcFtNTq+W2C2GbdfvsGRASgvjnz+Hq6ipPnjzZtnZQvDi34969TF/dvXsXRqORP7x4wa7s48d5zktMZBdnmTJsmKSLZ+/Zswe+vr6Z3qMcHw+dVstx3DJlWJGZP/+d882lX39FHoOB+1jx4kwQmz07zfKePJktTYDbdPYsC8WOHdni/eEH4MwZrFm9GuOMRgn582eZkgRJYkv//Hm8+OQT6PV6JObKxSSxiAhWah484HZnDG+xdf/3CUwAZGdnNyBXrlzxWeU1RkVFwdXVFTbzZdq14wFx/z4npKbvVM+fs4C4fx+7d++GjghjiVCdiAfYlSv8UN++xYkTJ1CVCDOJMDNPHvnpyJFpD3zaNCYKrFwJfPEF2lSrhgl16mTqvXZEKKJQoJq9vbhJBFmpZFbZwYNpA+3KFcyZOBGFCxdO+31EhPXgmTvXym3g6+srbbLBMMwKDx48gF6vt10lxELGaNeOO+DUqeyXT0jAB2lYfxBW1vW/HN7e3nFEFIK/X9j9aQKzd+/ecv369VnzL1iQ42bLlmXymqhUKsTt2ZN1zrPJBBw/jgObNqGPRoPEypXT0jbSQZZl1KpVSw4MDJSy9Cqlx40bHNa4f58/f/utFTkPAAuqs2dZqa5QITVEsW/fPp7wExL4u9atrdr/zTffwGAwfJB3x4I8efJII0eOlFPJU7LME3F6l2t8PId43lGnFWCXbCW1mq27d+WwnzuHz8LC5MqVK8s200i2bGEj4OhRjiFnELxv376FUamE/PPPPB8MHmzN7PX05Ply5052yZqR+vwyILZqVey1xLTbt+d3tHgxC9wsrP2zZ8/C2dk5bYcspxUfOHUKyJuXizLMn89zV+PG7L3LkKN65coVHFOpUpm174LJZEK+fPlEx44dReo1X75kUltUFLvl8+XjNpcsyd4HTt/5ewUmESkcHR1P/PDDD1lOpHv37s0cawA4h8nVlf9v0CBz2am4OOwcPRr1NRrMIcJbIvyi1YoLGzZgSO3aOGZvL362t5c3q1SY5uwsahHByd4ednZ2qFevniy9eMEPa86cVKZVxSJF5DcuLtwJ0mHHjh1I2LYNokgRHF6wAKtnzMCyZcsQd/s2++sBoFQpnCtWDIVCQmRUq8ba26pVHGtIj+++Q+LAgTDXqUXPnj0RFxeH6OhovHnzBq9evcLLly+ttFkL2+zp06c4duxY1kH09LhxgyeZu3eZMOHvz+SJPwmZrOt/MQICAmKIqCD+fmH3pwrMCcWLM6nDQtL49FMWMOmgUiohe3pm2p+KqVMhgoLg7eUlTZo0SaBDBxZ0GbB48WJcUqmQ3LdvVk2yRrFiPBFbsGsXW5q2EBnJsa2UFKBaNZybPh2hXl4yRo3i0Es6tG/fXjg5OVm7EDt1ShvHWWDH1q0oaG8P08WLQEAAx3xt4V3sfwB4/hwp1avDwVaqRQZs7doV6zQaPLNV2ezJE2bP3r6dKrxPbdiA8PBw+dy5c6lC4qRCgbj69TMrzUlJ/L5v3Uq7lzJlgG+/xd69e20KzJ/mzEH5nDn5Bteu5WunpLDlmgWp5tixY3Bzc2OFZckS/jtmDHv5nj/nUqIaDVu+WRE6AZiaNMEggwH9zItjvAszZ84Unp6e8jszMQBWVr77ji1kLjT/9wpMsNDM5+DgkGDzpZvRpUsXOTQ01PoFXbzIMYfERNZ+MgRup06dikgiCCLcNAvMiUQoqdHgN2dnIREhiQiNtVr4OzgIIkLZsmXlGzduwMHBQXTx9hbH/PxE8bAweYLBIMqXKwedTgcHlQpDBw7EjaAgfJc/PypUqICGROhnNIpWRqO8Ua1GDn9/QUTQ6XS4ls4dMXPmTIQHBQlUr84vIV8+Dk6nj2msX485Hh6pvyciaDQaaLVaaLVa2NnZwc7ODkSEGjVqiNGjR4uyZcsKIoJer4dWq4XLu+okxsdzx+vXjwfU6tUcm1IoWIOTJKblf4iW/zswc+ZM5M+f/z8hMHPnzh1DRMXw9wu7P01gjmrdWv7N1TXNggOYXJOBpGKnVuP548eZT7B4MbNjZRnjR41Czpw5RWqM7ehRtljNiImJgbOzMzZ++y1P9O/CuXPs+cnIVn3zJrPSnBEpKcA33+DS+PFYb2cnUKVKquUjyzKqVasme3t7i0zK+bhxbGXYgqVAQ/v2OE/E1WiyqmfdoQM/w3fh2TOk1KsHna1k/nS4ffs2nPR63K9XD0hJgRACt2/fxtKlS2UiQvcaNWSr53HnDi6WLAkigh8RIgoVAnr2RF53d7F3797MF3jyhN/R8OFpbd6yBXj0CHttuWS3bsWFZs0QGBjIN5++rxw5wpahRSg/fpyqgESVKoXZ9vYCjx8zUzgigufDzZv52NhYK+vWJqKiAAcH3PnhB+j1emxMlw6U+dAoODo6fliBHID72aZNFrbu3y8wAZCDg8PU2rVrJ9h0K4AHlIeHB8aOHZu289Yttoy2b+cXMHRoqvviq6++gooIvxAhgQiPiOCuVKIxEe4rlehAhBMqFU7Uq4eob77Bba0W1RUK+BDh0qVLSE5Oxo4WLTC+Vi3k8PfHIZUKszt1wqxZs0BECAoKEqP8/eVldeuKo4ULi/v582Px+PHYP2MGJLPFmJKSgh49ekh2dnaIiY4Gjh7FhfLlUTtfPhmenhwfbd+eyQq//QasW4dH58/j/v37KJAjh7hgIQlkgXHjxiFnzpwgIqhUKhQoUMD2wUIw6WftWraUK1bkmO+IEUwm2LKFrcwhQzi/9fVrPiYlhTV2G0m//wtWrFgBIkLLli3/9XHMfPnyRRNRafz9wu6PbxnjS0IA06fjlrc3GjdsaP3dzp3WBLvr1xFDhJfpmdwmE4c01q0DJkwAAAQGBkrz0y+p9eWXVp6V4cOHy3ny5JGwebPNWJsVWrdOJbtY4cgRdt29DxERiKxRAyvt7AS6dgXi4iD17YtS+fNLefLkETbL0b14YVsImkw8yc+dCzk6GpqsqvNYsGIFk6dsISqK3Z+PHiEhIcF29RszZFnmBZCJ8HVgII5WqyZGjx4tiAhqtRqViXCdCF8NHCgBHHMsFBoqnyPCEaMRPYOCsPKTTwBJQr58+eR58+Zlvsi337Ln7uhR64LzyclIcnFBDTc363G8cCEuVagAb29vflDjxvE5LGjYkIsZbNrEAjA0FBAC58eORSVX16wVfAtLOCs8esSlFs0M3nHjxon8+fPbnGNkWUb16tXlEiVKfNgcJMss+EuWtLy3f4bAJCKd0Wi8OnTo0CyLtO7btw8GgwFWJKFhw9IqinzxBXDtGr777juxmwjXiPCKCDUUCggiJOl0KK9W41d7e9FKpUIHT0+RrFZDEOEIEU6UK4dkIlzLlYvjNAMHAmfP4sCBA6ikUkEya75BQUE8+KOj2VVTuzZbakBavCE2Fjh6FEkXL2IvEaK6dQNmzcLxypVRtWhRZmUtX85uInN1jPjy5TGPCI4GA9REiPXzsxnrsYUBAwagRo0aaZ1AkphFN2AA0+zz5WOyz/PnPOF98glrcnv3ssuhWzfW0Dt3tp4Yhg3joHpcHCch/4FYpyzLGDx4MPR6/f98jn8KihYtGk1ElfB3C7uPsL3VaNC8Xj2pevXqcod27aRJ1avL+0NDRQ69Ho0zJLpj+vTUot+yLMOUmIjqRLhz5w6eP3+ON2/ewFSlCiTOWQMAPHnyBDqdDlZxSUliz5BZ0AYFBYn58+dzzM5G7tz9+/dxYOJE3K9UCetWrsTy5cuxYMECzJw5E1OmTMHixYvx4t49Jti8C1euAF264OLEiall0aKvXcNpnU60DguT3/70E3uu0uPNG3YJpmegnj0LFCjArupDhwCTCWfOnIGjoyOyUvohy+w2tvW9JPE2ahQgy0hKSoIyfX5rBnTt2lUEBgaKnj17opSDg7hOBAURZs+ejXFjx+LB/PnooNenlq+bN2IERmi12FqzJp41aYJalSph6NChAIBKlSpJX9myeh8+5PSaxET2RqVr941hw+Dj4SGn7ktJwctHj2AwGLDBQtC5eNE6dNW6NZA7N1vq6fIwd+zYAV9f36wF5u3bXPjeBjZt2iT/UrCgMOn1ePzwIVJSUrB48WKEhITYFIgjRoyQ3dzc3llTOBVt2zI3JiWFBXzBgsA/RWCChaang4PDoylTpmQp/Tt27ChZuWbz50/LsZk0CdubNBF6OzusIIJMhHgihOl0kIlwwd5eJCqVqS7as/nz4xURJCJ8R4QEgwExCgW+7dIFqekkGzbgzp07KEGEhPBwAMDXX3+NkmFhMtq3Z83GZEpzcRIxMaJiRdZKXr3CvAYNJG8nJ9GzZ084OTmhUqVKEtat48IFZhKCJEmYM2cOAnx8JLRuzS5RWWbL8wOEZo9u3TCgUiUJN2+yJlehAluUs2ez9Q2wED92jLXFoUPThN8PP/AkKAT/1hajzVJRRAi+53dQ7N+F+vXrC03GhOx/IUqUKBFNRNXwDxB4f3Tr36QJalWrhqZNmmBnnjw46eeHVs2bp6Y3qVQqKJVKKJVK9CZCR3O+chszUU6hUPB3SiXKKxTwJ4KWKPVZTZ06FQULFszs/Th6FPDxwdM7d6DT6RAZGYmkpCS8fv0aZ8+exbRp01C9enXJ2dkZer0eA4jwo9EoAgMDRa5cueTQ0FC5UKFCctGiReXcuXPLWo0G2+3sxNCvvrK9CsWdO5xucORIahm0wYMHw2g0iiZNmkiJiYk8Jtu3Z4vSUrhAktLG4Ny5PF6ePmVFM50HaM2aNfD09Mx64j96lAulZ0RiInMx0lnuycnJWQrMAwcOQK/X87JmZiQdOICzFuE0ezYwdCg6duyIGhUqSLh+HRF58mClu7uMJ0+AMmXwadmyKFOmDCZMmABHR0cEBgZiwIAB6NChA2bMmIEHDx6wFX/uHI/54sVZgJpx8eJF1DAaBby8WKB89x3i3d3h4+OTdv8nT3KKnwWTJvF81qcPzyXmetybN2+Geems340+5ctL4zQajNTphL29PXx9fUXjxo1RpUqVTOfbuXOndVGarGApr7dlC8+Fsszuaa4u9M8RmGChGWAwGF4uXrzY5gOMjY2Ft7e3+Prrr3lHt25AuXIQQmBE165yEhES7e1xwhyzjCZCcZUKjo6OQq/XY3dgoNhpFqSniVBfqcR3bduimb09JLNFur95c8DNjRNrr15FXLNmKEmE+MBAIDER8bt24QoRHh4/ztpnxYqsKZcpw49u9WorZpssy2jSpImsVCrxzTff4PzChcxGS7ee3eTJk0FEqFu3roz791nYXbzIVf+zWhXAsjrDTz/hdO7cOOfpKXD+PLsvMrLwUlK41mezZtb79+2zjp9aVk55F3r0YAH74EHWOVJZIFeuXFIPc7WQfzPKlCkTTUS18A8QeH94E4JJKm3asKverMSVLl0a3bp1Q0JCApKSkpCcnAzTqlWQFyzgNJJ167j0mEVo1KsHLFyIlJQUK5Z2ly5d8Mknn0gA5xfu27cP06ZNQ6dOnaRuuXLJaoUC9may3Q4iVNBo4OrqKgoUKCD16tUL5376CZe0WrHBVkGEdIiOjkaSXo8yISGyg4MDQkNDpUqVKqFKlSr4vFAh/OrkhKalSqFy5crw9vYWDg4OyJ07t7DJhD1wgJfhe/WKSX+ffcYsypEjeeLPgPXr18NgMGDatGlZx1AsE296WFy9GdqQ8RlakJiYCDc3N3nSpEnW1/nxx7Q0turVgV270LNxY1w3GoHPP8esqVMRFhbGk9Ls2Tg+fz5q1aqFUqVKCYPBAJ1OhypVqkjNmzeX8ufPL+t0Otx3d0+LCZ84YRUzfvr0KfR2djzXAYDJhH2LFlkXbnj8ONVNirg49tgBfL8HDnAceudO/Pzzz8iZM2fWk0hcHM+DGZGSgjiVCrvDw1PnoM8++0wQkXXoDsDLly/h5OSEGe9ayzQxkY2IkJC0+CnAaS1p1af+WQITLDRD9Xp9dFbpFMeOHYNer8edO3cgb9oEadIktGrVSp6iVEImwjMi9DATfJIVCjwiwqsaNXBfpRLzFQpMUauRSIRYIvxmjnHGmclBD4gw3M0Nz5YtS6u4P2IEFjo4iG3t2jGL9OuvcdxoFNdy5BAwmdiNaen4ERHYMWQIdowezSs4mHHo0CE4OjqiZ926HDexuJHNOHv2LIgIr9JXotizh+t6vnjB8VqTieMckyaxkD5wgN0Ga9eif48eqFevXuYSV69fc+rIiBG2E8XDwqxdYLt2ZRaqWeH0abZIAdZG31MjGAACAgLkRelzZv+lqFixYjQR1cPfLew+xhYTw+Xs7O3Zktq0CUhJQYkSJTBgwADriXnNGnbLbtnCzFNZZjdbOmFmcScmJydj5syZcHR0BBHB29tbaDQaeHt7y2FhYVLTpk3x/bffIiEoCLLFuurTx1phMxdMmGo0yh+U4iFJECYTrly5grlz52LE8OGY89lneOzri+8HDsTIkSMxfPhwNG7cGK6urrZX9AC46MHYsSwwLd6mr76yqRxGRERAq9Uiy6IFFnTrZh2Lk2Uu35d+lSUzsrIwK1WqJJcrVy4zuzM6msk58+cz0aZBA8wuVw79K1WSJElCgwYN5Nq1a3Pj374FChVC8osXGDNmjKzRaNC/f3+rG7tx9Sp8HRyQmgs7dChXUDMjPj4eCoWCCU69ewOurti9e3da/BJglq1lZZS9ezNnA/zyC1CgADZOnYrgoKCsFY34eA57ZURkJJ7a22NmhvnK1vJqjRs3lt5ZlvPePR4D589nfsezZqWPO//zBCZYaIbr9fq3J7JYIb1SpUpSmTJl8AURYhUKlNHp8FCphIkIL+3skGJ2yf5qForCvJnKl8daIpjM3ycRIcXsljUR4RgRzhOhCxE+KVwYEwwGnBw+HKudnIRsGTh79uDurFkoqtGkFgg4UqwYxpndV58T4bLZbZUzZ04pX758UlBQkMlFp8MILy8ZWRRJ12g0eJ5+qZ6kJHZbtGvH1y1bljvu6tXsLjLnrq5atQpKpRJ169ZN6xDJyTyAbt1iDcnWquSWogbpO0hiIhMnbBVvfheaNuUBcOYMx5IzIC4uDqVLlxYKhQLfpicC/EtRpUqVaCJqiL9b2H2Mbd++1JxkXL/OluLgwRhXvDgmNWtmPZGdOpUWz+nenfvZ+vVW/cVSJESv1yNXrlzC29sbTk5OYsuWLbZdpWPGWNeTteDuXV6X8uFD+Pr6Svv27XvPWwHHFS0MSVnmGFrVqpkWMN61axf8/Px4vDx5wori48esiI4dy4UMmjXjmH+DBuwR2rePvytThl2yZkt8+vTpcLWkt70L7dqluXnPnGHLKZ2bMz1sCcz58+fD0dERTzJaqULwhN+gAcda168Hli3Dzu3bYTAYoNVq4e3tnbqe7suXL3G1SBF0cnCAn5+f1LVr18zpXidPIsHdHXq9nhX/VauYD2HGypUr4eHhwb/Zvh2oWBEH9u/P7JKuXp0LLLx6ZTu0lJSEi/XqYbWjo3hfMYcMDwPw9ESr0qWzXp7MjI0bN8JoNIqXL19m/vLpUyZfJifbJhZ9/33GuPg/U2ACIKVS2cDNzS3huY219Hr37i2pVCq4EOEwEWKIcLR6ddwzW5eJ5m2CUokdoaH4xdFRbNLr5RsqlWipVmOcWWC+NVuXMhHemIVqEhH2ECFcq8UJIuwkQnkiJDs4AHZ23EFq1sQVDw+ULFpUPtGmDTppNKKLqyvy588vqlWrhug7d3Bq+HCsXrkSkydPxqqVKzHCzQ0/OTqKO3fu4MaNG4jPUOfVRaVC7Ny5bKkNHMgaz927bEUWKGBN7wfX2y1RooSk0WgQEBCA+5bvb95kC6BevczVKiwQgs9pCdCnx8aNWVPo34ft29NWO+/ePdVasCwSGx4ejqwqO/2bUKNGjRgiaoJ/wDj5o5u/v79k2fz8/CQ/Pz8ph4+PNIgIkU5OTPz4/nsWHitXMtEtLo7DFulWjbDg4cOHGDt2bGpqxvLly5E7d+6sC3wDbBVt3Mis9zNn2DNy715qqb0cOXJIWz8kbj59OsefkpKYS9Chg7Xyl5wMPH2KQ7NmYaO9vcC+fUzsKFOGPUrr1rESaTJxreoTJ9h7YlmpIymJFdbYWMDdHcdr1hR+5rVv169fj2+//RZt27aVwsPDpdy5c8u5cuWSc+XKJecPDpZLBgXJuXPnlnPnzi0/UKsxxt1dthwTHBwsBwcHy7ly5ZJDQkLk3Llzy5QuDvzo0SM4GAxi66pV7J1aupS3rl05lhoWxgURiKxyqU0mEx48eIBHjx5hxowZKF68uKzT6VA4KEg6Y04he/DgAbRarVU9bEREAPPnY/To0bKrq6uIuHUrlewF8Nyj1+txxVIABkBM/vyYYW9vLTCXL+dwVd26mRd1NmP25MlY5u4ucOwYK/i2oFancS4s5KkCBVCvTBlp2LBhePv2LebNm4eMZJ7Hjx/DyckJc3hpLmtERbHrtU4d24UkTCYgODijMmdzDKnpHwBZlrc5ODjMbtKkSa8jR47oVSpV6nfjx49XJScn097ly6lsUhIpiQj791OAQkGfA/SQiG4S4RMhFA+fPRP9Y2OVW4gUAUQ0hIg8iOgtETkQ0SEiqkxEMUTUWKejbcnJVDo4mJT37lHxn36i/Zs3i/GPHim0x44pKC6Obh06RL8GBdGZXLnoxcKFyqALF6hm376K5qGhpIyOVtBXXxElJ1OpbduolJ8f0cCBRGfOUFzRouSyf79ChISQ0d6eCiYm0gu9HkMliUrKsiKnEGS/bRuRry/RF18QDR9O5OhIlJxMtH49UWBg6v2npKRQ2bJlRd26dRVffvklFS5cmAIDA4m+/ppo40ai8+eJGjbM+uFKElGfPkSNGmX+LjmZ6OBBolq1fv9Lq1ePN4Dozh0ijYZoxw4qdvIkFSpUiM6fP085c+b8/ef9h0Gj0SiISPN3t+Nj4Jyvr+pl8eIkGQzkfuEC3W/Rgvz37iWFSkXqAQOIZs4kevCA6N49or17+W+FCkQnT3I/evKEyMuLSKUiUigoR44cNGzYMIXl/DqdjmRZfncjbt4k+vVXouvXiYQgCg8nWr2aqG9fIiJydHSkBw8evP9mypQhSkgg+uoropQUogUL+JzLlhF9+SVRqVJEISFk+vRTuq7ToUloqILWrUv7ffPmRFu3EtWvT3T2LFG+fES7dhH5+1tuhqhlSyIiWjdyJMYPGUK7W7cmlyVLqMkXX0iFHR2VCAtTNWvWjHx9fUmpVBIRUciBA5R33z662LQpvfXyoqO+vhSkViuHC0FKpZJUKhUJIQgAybJM4vlzGjtkCHU3GCBLEpWTJMVpT0/KbzIRzZrFc0H79ty+vHmJhgwhiosjunWL6OFD3rdjBz1SqeiTTz4RN27cUAYEBMhNmzZV7dmzh1xdXFTUogXR1q0U0KQJGQwGXLp0SVG8eHG+z4gIoqJFaXjx4soXL17IhYoWVV3S6eiuWk2ykxPJskwhISHUplAh+lWtpk0rVtDL8HAae+2awmPVKhLERlfR5cvp0eXLlDcyks7cvk1v5s8nSZIoKSmJTCYTmUwm2rhxI12MilLYTZ1Kpc6do1+aNKHX/v6k1WpJo9GQTqcj38GDKXrDBtLodFRp6FB6WaYMJXTqRL9Onqx8vns3zZ49m968eUOPHz8WY8eOVRIR3b9/n0qVKkXu7u7o2bOngtKjZ0+io0eJfvvN9hxIRLRvH/dJJ6f397usJOlfvRGRytHR8eSQIUNsppts/PlnSGa3agIRNhKhrUYjZCLIAQEYY7YaI4lwx2w9SmaLMpIIj4ngZo5dLiNCiPn7RCIYiRAaGoreQUFSdMWKzBS7cQO9PDzkhyoValWrJul0Ovym0UAsW8bkgK+/Zu0zTx425Z8+ZWuvSBGIp08R1bYt5EGDgB9/hFyuHB4vXYqZDRuiGBE0tip7HDtm06U0bNgw5MuXj2MZssyu2rZtmQloy/2aHrLMsccs3EF4+ZK1QRuxgP8JS5YAPXrg0cOHWEKEQ6tWfZzz/o1o2LBhLBG1wj9gjPzhbc0aXqT4/Hnuw5GRHLeZPp29Gl9/zRbgrFlIjecVLcpxyyZNOA/x66+5LF2XLuyG69WL3XhDh+JavXro6O0tpS71tGcPX+/ZM7Zak5LY45GYyOSayMhMxRE6dOiABg0aSIiOZmsgKYkTyU0mdvVZ6tuWLQv4+rKVFRrK7dm6leNsT56klqHcsGEDAgMDM3dwk4kt5/RVfjZsyJRq8uuvv0Kv12OzhRzy5AmPu/z5ebnBS5eYFWtBZCQ/31q12K1rQXQ0e2F27eKc1c2bmYCVKxcif/wRT3v0QA8PD1HD318kv6tyUFgYn99yzoAAPK9XD7mMRjRu3Fgk2ipbePw4W9DgxbynT5+e9l3DhogeOBDBwcEiZ86cgojwuVaLgt7ekp+fn+Tv7y/lyJFD0mq1CDUaERQUZAoICJC83d3lN0olWvj5mXLnzm2a6eoqD/P0lPr6+kr58uWTChQoIBUqVEgKDw+XS5UqJcqVKwcXFxcolUpUrFgRHQoXxsCQEHmWr68oUriwVKBAAVPevHlNgz08pNDAQFNwzpymgzqdfESrFT4+PpKTk5Pw9fWVFi9enMoeBjhM5ejoKEqUKCGIiN3YQrD7f8EC7tdZ1ZsFODQREpJ5AfCs5FRWX/wdGxF5GgyGqG1ZVHs4UaiQmKZS4TERRhLhy9KlZUGEFL0eE4mwMWdOTFIoRCwRks3CdYRKhUiz4FxiFqoSETQKBdyJ4K9S4asmTfAdETq3bi3s7ezEYXt7kc/fXyIinN61C9i/H8nR0XBychIHDhzgxgjBZJjmzVnwKBT8OD082FU0bBgnf6eLG3bt2lUm4oTjNxkX3p03LzVXLT3y5csnTZ48WWD3bnaLffNNaud/Lx49AsLD312jsmpVm+XL/hfExcWhZ8+e2LFhA44R4ZMqVWQsXWopNfWvROPGjWOJqDX+AePjI2xZQwiOgbu4sDC4fZu3ffu4qEX6SjJJScykvHGD++S1a8COHdhUu7b4KjRUYNYsdtE1acKu0nnzOPadOzencuTPz2PFz48FlIsLn6tjRyR7ecHe3h7CyYlzFY8d41SMJ0+YTFO5MrdBo+Ext2MHj4cs8obXrFmD4ODgNIFpMnFbXr3KXCxk2LBMY6FYsWJyhw4dbD+zxES+z5w5WVBOmMBpXp9/zkJtzBi+/1WrmLneuDHHHZcu5Wf27Fnq/DBnzhzh7Ows3lkMQZY5hJNOKK5btw7hGg0kpdJm8XkAfI2wMODkSXTp0gX169dPmxCiovDq3j0QERYvXow1a9YgbtIk60UuZBkH7ezEtnSuWgCsEMkyzy979rDS8g4iYY8ePdAwfUH+J084nLRnT1qdbRcXfgcNGzLz1kZqyP79+0FEKF++vGwwGNC3b18EBASIkJAQCY8e8bk6drQdhsqIly9tx9X/DQITLDTLGI1Gm/FM+cwZHHV3F8vMAvA4Eb4iQkOlEsE6HS6o1UgxC8sfzALzKHEuZgoRcqjVmEiEX4iEm50drhJhg7OzLPn5AUR4Pm8eHIlQJ1cuBAcHQ6PRQKVQQAQHA0TYGhaGasWKybC35wExbx4P4NhY1lKIWJvbtCnTAD5w4ADy5s0r9Ho9iMg6R2jLlrR8zHSYOnUqXIjw8NChtETgD4UQfN73WY8TJ6ZRwv8gtmzZAjKToFxcXJCQkMAxsa+/5kHVqlVqTta/BWaB2Qb/gLHxETbbKFaMhcXGjVwFypJ+NHgwb/Pm8YRrS8iYIYRAQEBA5pVH4uJ40ktO5uovGzdybp5ez6zu6GgWrI8esSBISICfn580Z86crNMP3rwBatTgNvfqxcI8CxbsqlWrkCtXrrRBIMu8GoetKkPdulkxwK9fvw6DwQCbVpv1zfN9lizJc4CTEz/HVauY4Z6RcJcBp06dgl6vx453VblJTmaL1fz8Hz16hF69eskGgwFr1qzhGPCkSVxZyVZ7p00DfvgB+/fvtyYu5csHvHmDwoULy6kpGrNn87JfFkRF4bidnVhnKzZ54QLf79atrCi8o2Rhhw4d0KpVK2vtXZL4tzVqpBX737UrrRC8DcTFxUGv1yNfvnxi0qRJ0Ov16NOnDytxbm48/34INmxg5c32u/l3CEwAZDQaFw8aNCiza3bIEAidDjmMRiET51d+aa7u80arhdHsrn2r0cBeo0EbImwiwksipCgUIDNpSBBhs1YLiThP84qnpyzlzIk8vr7yMWdnIfn7o2muXPIvRiPmE0F4egJEkH184EeE3EQoVLAg09+LFGFSTZ48LBxOnwaKFcPioCCh0WjQtGlTVKlShROyBwyQL168iDZt2kh6vR49evRAzhw55F+GDrV26wDo168fmjs4IMbbm8vd/d7KO7t2sRB/l3UJMAPQUoXoD+LgwYPQarW4efNm5rJhL14w4SIlhQfv+6j5/xA0adIkjoja4h8wLv7oFh0djdevXyMqKgqvzpyBKTQUL27eRNSqVYjp1g3x1arh0YULePDgAR4+fIgXM2fixbhxuHPnDm5cvYqojh0RU6kSjm7dil27dmHPnj04ffo0rl27hrVr18LR0VFIlmWVRo9mQk+XLmxdATwhWgp8x8XxhHX5MrM+f/2V+8SOHVi+fDkcHR1hk+0YE8NJ8ikpbIGOHs2TbRaF3FeuXMlEpORkLiZw+XLWL7tfPyuG5/fffy8KFiz4YaXVXr1i4VOxIisYR49yfvc7CrmbF5sXRAQnJyeRJ08eU+7cuU3BwcGmoKAgU2BgoCkgIMCUI0cOqY6Hh3RHrYavt7fk6+srkZmdnL5+taXOKooXt33BESMg37sHe3t7LlgQH8+ubVnGuHHjRGr+ZmystWs6Kgq5c+c2rVixwvZ5Dxxg0mKJEpY6rDbRsmVLfPbZZ7af548/8pzl68uKRqFCWRZgnzp1Ktzc3LBv3z7o9XpsmT2bn/Xjx1kXwreFHTtspvqY8e8RmESUU6/XJ77O+MBevQJ+/RWHdu0CEaGs2Zp8qVLhkp0dVhEhSaVCL50OQ93dhciTB1cnTEBPs6CcambC3ibCdwaDNEChwF4ifEsEdyLUN7trQYTxTk5ygr09FhPhbeXKrJWOHo0JgYFiLRH2qVQoSITh7drJwmhk1p+XFxAQgOSEBNgT4VZwMCb7+cmtW7eWLqXrSMeOHUPBggVFiRIl5PYGA/apVFi6dCkaNWqEBw8eoF+hQtI6hQJ96tZNi1f8XsTHs0vtfRCCy+d9hEWn4+Li4OjoKD755BP54sWLkLIS1oMGsZsvLo6Zth8rhvonoGnTpnFE1A7/gHHxRzetVouuajXGqVRw1+kwQaWCh1aL6SoVmmm18NPr4eDgAAcHBxgMBhiNRtS0t4e30SicnJyEu9Eo+tnZiTqurnJPFxfZ19dXcnd3l52dnYVOp0Nv4kpaeqUSV4lQSKOBQa+H0WiEs7Mzcjg64p5Wm5abXL269SokgwaxyzYqCqODguQK5cvLmfrQwoUcRgDYSvz6axaiO3aw0MxQH3bVqlUolzMnd7DFi9+9gkjv3sw8N6NevXqmnj17Zn08wAppv37cj2/eZPY4kFqkIGXsWNzp1Ak7y5ZFq+rVUaRIEcnPz08yGo1Cp9OhXr160rx587B06VIsW7YMK1aswOrVq7Fu3Tps2LABmzZtwva1a7Fj06ZUJWXfvn24ePEijEYjW5fpkZLCcV5fX17GKz1GjgQGD0bOnDml1atXszvSXFTl8uXLMBqNQpZldrkHB3OsNiUF0GrRlC3QrLXqDh1YnHTpwp8rV+ZncfEinyslBfsCAnDBUrjA2ZkVpg0bmBkLsMJBxGzWDJkFFrx58waOjo5i8+bNqFerlvR12bLM0ejZMzX97oMwd67VOso28O8RmADI0dFx9ciRI61n0uRkjmd8/z2W5cghx5qtxZfEKSMxRLhAhGEqFWR/f8DHBw8aNEACcSrJhfBw7AwPF7fVatz29cUgR0exjQhRRBhhLtj+mgh7S5ZEYzc3kWA0Yr6bGxppNChOhF6urjhKhH1ml/AyIownwigiLO7VC3HLlwOnT2Pt2rVcM3HzZo4FXLrEndMGIho0QNfAQGFnZwd3IpRSKjHf01PMDA4W9erWfc9bzwJr16bVvP0QrFv30Raa3rFjB0qUKCG0Wi1UKhXCwsLkffv24cKFC5lz827c4IElBFsK76sP+jegWbNmcUTUHv+AMfGHtyVLgJkz08gosgw0aoRfKlVClfLlbT+AsDAmCKXH6dPcv06cAB48gPD3RyGjEZcHDIA0aRLevHmDyMhIRERE4MaNG7hy5QpOnTqFk7t3o7FCgacWKyAxkSe5jHl5Fy5ADgxEETc3eV7z5nJq5ZnHj1kopSe7DR/OQlSWWQkrXdoqvWTtsmVIVCjeX9UKYM9HuqIf1apVM3355ZdZH//2LXD+PEwDBuDQ6tW4FhqK8eXKoWjRolJAQIDk7OwsVCoVSrq4YKZajYpqNX4LD8fRKVNw8+ZNZLU2cCZ068bvIQN+/PFH4eXlZa0BWBahLlKE47zpF7ePjAQOHULTmjXRrVs3GePHp1a2EULA3d0dW7Zs4WM//zytWtGzZxg0aBDKlCmT2Z26Zw+nHw0axBa8ZcmtMWPYmo+K4ucqSfi8XDlMbtGC23v+PL/3lJQ0S/LYMbYsP/kkS49XhQoV5Dp16kgAsCA4WH7u4vK/KdxVqrzLugT+bQKTiEIcHBwSYzPGvIKDgTlzkDJhAu6ZLczZRIg0GCDMFmQDs2t2X9OmkBwcIMwCDu3a4bGjIwZpNEimNFatbLYymykUiFepkGKOw60mrhLUlgiXiHCVCIlKJRKIMJiYYZtChBdE6MyxUWzWaDCeCHVr107ryH362PaVL1/OibmyDCQkIM7DAxvz5JHj4+PRt29fue7/KjAPHOBzfyhOneIO9BGRmJiIEydOoHbt2sJoNAq9Xg9nZ2eMGTPGdkHkzz7j/K+ICM45+wgu4o+B5s2bxxFRB/wDxsQf3mrUSLuxy5c5dnnrFkaPGoUKFSrYfgAREWm5a7LMlpwQ7AYtWhQoUQKPO3WCu9H47he2ciUQFoaAHDkkq+WWunXj3D0buHb1Kn5VKnGrTh1m2VarxtZcehw8CFOZMti3bx9WrVqFDYsW4XVgIE4PGIArHTuiY4cOCHFzE/v378ehQ4dw/PhxnDx5EmfPnsWFCxdw5coVXLt2DTdv3kRyUBAenjmDhw8f4smTJ6hbt65MROjduzdevnyJmzdvYtasWWjXrh0qlCghH9PpxHyzYujh7i6eabXo26qVNHnyZGzatAlXrlzhOD6A06dPI4ezs8DMmUwOGjuWn8mHhFpkOZPlDAAvXryAnZ0dx0h//ZXP5eDARK29ezm/VKXi3FKLZd2hA462aIHcuXPLuHrVqq5tp06dpDp16vB73LKFz9GiBXDhAiIjI6HT6RAVFcXK/+7dTGJq0YKvnZLC/WLr1izXKq1RowYGDRpku59ERnK7z561ea8A5/kaDAbE9ugBNGuGwV98IZcqXvwdLoMssHPn+5eW+7cJTADk6Oi4ecKECdZaTbqFmpctXgwXIrTSaNDd7E59o1DASa8XU2vWRJCrK45qteJRq1apMZWnLi5oR4RH5crhmVnwLSLClsBApJgt1AQiPDT/lYmw3CwcXxFhLXGFoAbmz/PMx0hmAWwyC9D4oCB2d0yZwtTmly+Z5tyxI3cuSeIJa/16trKWLrWyQgcOHChqZVVb9l04cOC9C+BmQlISpwy8i379EbB8+XIEBQVJvr6+YsOGDbZdtseOpU2ggwdb0/7/BrRo0eItEX2Gf8B4+AgbW5jnzrEL0VyCbtSoUahcrhxr+snJ3G+vXuX+sH07F9aYM4etOxcXntAiI1mzX7IEszt1EuNz5hTv1PQ3bgQ2b0a1atWgUqlQokQJKTg4WC7g7IwqGg0UCgV8fX3Ft99+K9KHYpYvXw6jXo+nQ4cyaSg6GoiMRHR0NH766SdUq1pVaqxWo6qbm5QrVy5TcHCwqYaPj2mzvb14rlCghF4vHBwc4OPjI3l7e8uenp6yh4eH7O7uLru6usouLi7C2dlZODk5iRtKJbzN7mh7e3uo1WoYDAY4OTnB2dkZWq0WRIRBNWtK25o1w5ERI3D50iUkJSWxe9hindmAJEkgItyzkI22buV+fvQok+5skBwBMIvU1pJcCQlI6dMHLhoNYpo04aUDAVaEzAo4AH5Pu3ax9XfqFHDuHBKHDoVGo0HSmDFWMdtt27alruyCqVPZymzQIJW9X8/HRzrVtCmXDvzuu8yrvQBsOYaG2iwQUKlSJYwYMcL2fXbvziGtYcMyfXXixAk0aNBAVNPpsG/6dPagHTiAiIgI2NnZ4VGG7IJnz55h06ZN6N+/v1y4cGHZ3d0dvXr1kl69esVhveBg7t/vxr9PYBJRQScnpwSrSjkNGgDu7kyBd3bGMa1WJBFhhEaDN2o1duj1ckKHDkBEBJLbtcOEli2Fj4+PbCpRAokbNkBesAArNBox1Wxd/ma2JiU3NwgizCHCU+KqQoUdHeWxBoMYp1DARFxZSDZblSlmARmbTrBaBOae/PkFGjZkt0ipUhwbqVyZyUGNGjGLa/ZsTkl58IAnsQyxlUGDBoka6S2CD0WbNla1ID8Yx459UI3Yj4FvvvkGbm5uso+Pjxg3blzWGmL9+lxi7PRpZh3+DWjVqtVbIuqEf8B4+MPbqFFcR5aIQxtnzgD58+NSoUJY7e/PCtyuXWw5Fi7M/aFUKT5eqWQlb/hwXkYuHWoUKSLdyp2b6xjbihF27Jjqbp8+fTqICN9++y1WrFiBw4cP4+nPP0Nq1QpLly5FSEiIZG9vjx49ekh3zfG1bp99Jm6r1YjduRMHNm9GnFqNNioVQszl3l5+911aObe4OG7z06dA8+Y4lzcv6tes+X7ijhBs9ZmVOKPRKIoXLy4WLlyIffv24eTJk3j8+DFEfDyT6TLWSV6wwDoemwEmkwlElJnIdO8et33SJJ7TMqY4zJnDFpwFI0ZwRaKkJCAsDCM7dJCcnZxE//7909i858+zdWlRYFJSOD44fjwLuQkT0MVolBO8vFJZrUIIHD16FETEAujJE07vSEri6zdvjgshIeifP7+UVXgpFbIMMXw4bq9YgR07dmD37t04evQo8uXLJ3r27IlHjx7x8nDm9kXcvYsNCxbgzuDBSHzzBq9evcLEiRNRokQJ2dnZWTgaDGj+ySdSsq9vpiXhGjZsKJUuXVrMmjULTZo0kTw9PYVOp4O/v79UtWpVMXHiRGzcuBFhYWGSk5MTti9d+k5iUjr8+wQmAHJyctrzww8/pJnxFy5wKsSrV8DGjTjRpg1maLUopdXi5pgxwJs3iC1Rggd4uXJIiY9HsWLFRGci9FMq4aNS4QYRNlBanVk9EXYqlUgmwnSVCqM1GlE+LEzGzZvAkCFIKlEC27VaKMxWZSQRvjELTdl8HhNx+b32ROjauTN+2bgR4swZtvjWr2dL08uLq/FrNPzo3dxYq7fhfvzqq69QrVo1m28yKSkJly9fxuHDh5GYmIht27bh+vXriP3tN3bPvI8Zawtr12bJNPzY2LJlC5ydnQURoVKlSu93qWzcyK4fIBMx48/Gf0pgPnjA1oG/P6eOrFsHLFuG4V9/jSoZXfLnz3M88NQpZoBbyGft27M35PBhICQE8vPnyK/V4trp0+whKVTIqkA74uOZHW1ees5kMsHOzo5ZmhbcucOkOrOwPXv2LEqXLi3b2dmhdOnS0lddusjjlUpLupL47JNP8ODqVU5TKl6cST/jx7NVI8ssoGNiAJMJC+rUwfKQEPm9FkViIqd2meHt7S2n5lxbMHcuh1fS51BbsGFDWv1YG7hx4waM73JbC8ELMRcrxorKhg2s+F67xkq10ch5mytWWIVbhBDYtGkTwsLCZAcHB3Tu3FlAlq2FrAW//cZzT4MGOOPggNEjRgjIMl6/fg2j0Qij0SgqVKggx8XEcP8gYuWjY0dg3z5EPn4MrVYLiwfg1q1bKFasWCqByc7ODnq9Ho6OjmK0SoW1KhV8fX2Ft7e3cHNzg6OjI/R6PXQ6HVQqFYgISqUSB4nwTKlEeQcHKJVKqFQqFCxYUB4xYoT4dedOiFy5uL/ZmNcGDRokHB0dkT9/fqlDhw7YsmWLzaLsAPDzN9/gsUKBTq1aSR+wTua/U2ASUTEXF5eEVO0pJoa134MHObC8cye8PD3laIUC+4ng5+AgtlhilnZ2wDff4L6nJ7qVK4fk9u1hCg7G81698FnevHI+nQ4jDAZoiFCbCOeI8F358vJbIiwrUkTGyZM8aaxdizgibB05EntGj8aPX3wh9rVti2RiMlGsmQh0V6tFDiLUyZ9f3qZWIzQ4WERXrMh062fPOMG5Tx8gb17W+CZN4gFuMLCbLCYmlek1bNiwTJPY0qVLxdy5c+Hp6Qmj0SgsOY9EvFbhASLc79jxfR3BNl6+5FXT/4LYYceOHZErVy5x5syZLDu3TQjBrqdbt9j9ZcN987HRvHnz/5ZLNj2+/hro2RNDv/oKfYsW5X1mNyt27+bJGeC+m5CAqKgoLF26FF9++aX4pm9fsa5uXdHy00+xj4hjmjEx7EIcO5bjXxYWa4aJLnfu3NKPGRePTkzM5NJ8+fIlJnXsiONeXmjapAkCAgJEzZo10xSs5GRmd1+5woJAqczkJenTpw++L1xYQmholmkKALjt6VbKCA8Pl4YOHcqDQQi2uKpWtS2IZJkF3TtyjFcsX45cOXJIuHePFYQrV1iRvnmTDYD+/Vk56dKFK3kZDDw9lyzJ812BAmzZ9+rF69zOmsVpNRcv8ridPRsX167FCAcHcTNnTlZwTp5kw+L2bX6HKSlInDsXqz79FOcVCvFGpwNkGdKKFRiiVOLonj28gPPixaxMaTTMJ6hTh+Og9+8jh7+/tGrVKhw+fBgODg6iW7du8rVr11LTlZ4+fYpbt26xJR0VxTHnLBivQggkJydDTJvGLFuw61qSJCZUjR7N79hC6sqAc+fOQa/X4+A7FBUrXLyIqKlTkSdPHsnPz0+ceXe4598pMAGQk5PTgWnTpnHnTUjggbFwISfM5suH0aNHpxZUf+3ujpdOThji5SVj925EHziAowoF9sydy/RlIsS0aoVaej0eOTvj2ZUr2GFnh0QimCZMAK5dw40GDbCSCMlOTvybs2fxlghvg4MBf38crVdPVFYo8JB4HU5L3FIQ4VCuXDIePIDcrBny+vpiWJMmMubPZ8E7dy67X9es4UnJsujqmTPcIXr1YnIQgIWdOqFCuXJWb1Cn08HNzU1u1aqVADi+M3bsWBw7dgyJiYn41MVFnmsuHi1JEoYPHy4NGzZMPMyqNJ4Z8fHxvHp827a2K/l/RAwfPhx6vR4jR478fWZwTAwLytGjOd5RvDhXevmTCyH8p0g/AFvqGdzbM9u1E3EqFbvfevTgKivpS4VVqgSsW4fixYvLLi4uokaNGqhRowaqV6+O4OBguLu7y5AkjpfVrs0x+8BAnl5CQzPF57p27YpatWpZv/83bzjVIGNfXbiQl9UD5y3a29tzIXDewVYZAJhzpVG9OgskMwOyT58+vBxedDRbkNOn23YbP31qZWH+/PPPcHV1hentW7awsqr2wwdzGsdvv7ESN2oUF4Rv356FT/HieGlvj+8rVmSFr0MHdrX26cNuz/Hj2R0bEcGVfLZsYfeuvT0L1PPnWRF49IhXkDlxgmPLc+fyNYcO5Xe6fz8iSpfGaY1GoHdvZpvu2QNTeDie+Pjgew8P+ZBCgaVubsKyqhOuXQMmTcKO/Pnl+vXrC9y5w89n0SI2SITgZzNqFNCyJVbly4fvc+cWlezthZXnzxYkiZWMLIqxA+ACCVWrWivq8fFMzClZMkteRUxMDHx9fcUXNlZMson1661WV+rXrx/s7e0xevRoOQul/d8rMImoqIuLS7yFcYbnz3lQJCQAx49DlmV8r9WKOCLEKBQ4o9OJQkSY07Ahoi9cwEOFArdr18b14cPRys9P2Ol0CNVq8TA4mF1FOXLwo6hXD1i9GlfDwlCDCLJOx/vv3IEzEc4OGgT5k09wmwjXzYIyykz4uUiEqxoNhnbpIuPtW+DSJXR0dJSfFSzIGrfJxPUjW7Viay4mhl1dGd0Mb94AsowkrRbdihYFLlzA4SFDROXKlWW1Wo39GeJHqahdG/NbtUL+/PllIQQaNmwoGY1GERISIhNRpkIC8fHxWLJkCapXry4pFAp89tlnYkl4uEjq3j3TqWNiYpCJrfw/YPfu3SAibLfkqmUFIXjC3rmTO/mYMTxp9u/PRKB27fj/UaP+cJveh08//fQ/Y2EWLVpU6h0UJLUPDpby588vhYaGmkJCQkxOTk5oQ4TbRiNiVCpMDA2VqlSpIlWrVs1Uq1YtaXb+/HL30qUlIsK8DASUGTNmoG7evBIsK4z4+zPze8kSVmxLlmRLdfRoVhCTk3Hp4kXY29tnTquIjrauUnPqFAuKdJNply5dpO+Dg9n7c/Ikl34UgtNBDh9mpmW7dlwZS5axvGpVfFqzJs+I5rgmjh7NnMpy+zYrYukQ4uYmHxw/ni24jBP3y5dsda1YwcqwVstx2tatef+tWxwfjIwEXr5EnoAA8dO7Fsa2sGXDw1lYPXrEVnq6xeffixcv8PbyZeh0ulRyUZs2bUBEKFKkiDxlyhTx8N49nou6deN0EDP27NmTtowXwMI4Y6xPCOycNg0ttVqcL1xYoHx5FobXr2ed35qczFawraUOU1K4lGj//mn7Ro1iUo4QWXq7hBD45JNPpPz583+40t23Lysl6WCusCSICMOGDUOGtUf/vQITADk5Oe1LtTLLlGG3ZvXqnJ8ky1hRqRJmeXiIPioVjmg0eO3tjSQitFOpMJ84VzKGCAkKBd40bw53pRLPiRAREIArly9jZWCgGF+ypNy5c2cQEdb16ydj/37g3Dk0btwYJ4kgabV4ljcvzhNht6+v+FqpRJQ5djmUCOXs7PCqVSvA3R2RjRqhHhGWzJjBLobbt7mSSXo3wA8/2HbxABgxfDgqVaqE9cWKyfuIUKVKFWzPmxdyhqW/AHCnrFcPyU+ewN7eHpbKIZZFqr29veXq1avLaYcno0iRIrKPj4/UuXNndOzYEURchH5EYKBITkqCEAJHjhzBgAEDQERwdnbGhg+pzZgBJpMJX331FXLnzi0ZDAaMGTMmcydPSuJBt2gRa8vdurErr0MHfmaPHvE9LlrEz2z5cp5M/oIye/8lluzUqVOxaPhwzJ4yBfPnz8eiRYuwav58dM6VC5O1Wsxo2hSju3bF1wMG4HDJkvgtOBj9+/fH3IYNMbVdOwwfPhxxMTEsbGQZKFAA4wcOxNzAQK5wA7DQKF+ehebgwWnLZe3cyeSWy5eBQoXQyWAQO5cutc6hE4Lry1q8HEWKMDkOYCV5+HBcuXIFm1UqxE2ejMTERMTExODNmzeIvngRsYsW4c2LFzB17Mjl9pKT8dTFBROKFJFw6VIaWWnwYLa+nj1Lu/ajR9Zs1Ph43Pf0xDx3d2HF5l65ktmtx46xgD11isNDlqLwDRqwknf3rlWhhLp160pdLEn9GfHqFbtC0wuoRYtYaNnbf3jt6K++AgoVQs2aNaWOHTsiJiYGdnZ2OJq+ilhgILt0AS6jZ54jUlJSYDAYcOrUKbbwOna0GasNDw+X6tSpIwtZ5jE7Zw4rtF268Ji9dSuz8Lx1i8NR6Z83wNb+wYPcBxYv5tj4rVu2yxamw5YtW+Do6Gh73Utb2LeP+50NAbxq1SoQEQICAoSHh4fctWtXmGsH/7sFJhEVd3Nzi09KSmICTceO7MpwcwNiYpAyZgx2KZW45+iIIwoFIsqWRbJajeWlS+Nbf3+x3cUFR1xc8Ja40EEYEYrq9cjr6ioTEbz0ehQ3GoVCoYCeCLHlywPbt0OWZZShtIWpm2u1SFKrEUeENSoVEojwKRHuqVT4rnt3nuSDgxE7ZAi0Wi2cnJyEq50djuTNK2KnTMGLFy+waNEiFCxYUB4TEiIf6toV6YvNCyFw9uxZ+Pj4pC7Oe+HChbQ4SUQEl45K74pYvDg1PvPmzZtM62++fPkSSqUyNSl53LhxwtvbO9UVkZiYiGrVqomI+/exXaVC4XSxUSLCJ598grx584KI0KJFC9nm4sAZcP78edSuXVs4OjqK0NBQecGCBbhsKUv2+jUrCiNHsltqwgR2X/Xrx6SujHlYUVE82Q4dmkb4CQj4S8rr/adcsiYTpxdYJszkZKBYMZwsUgTVq1e3vvEbN9g1+OYNp5IULsz7a9ZMq7TzxRcY1rWrXKdOHf58+za7Y6dM4UnwwgV2IaaHEMDhw2hXrBg2eHoK+Plxf1i9mifqzZu5j9++zd4fc7k8PHjA7l2TCZZ6zAqFAiqVCiqVCu2VSlwhXthdQQR/hQLbiVCSCHZ2dhhMhLNE0Go06KpUoq1ajd5aLera28PZ2Rk1HB2xVauFl5eXaOrmJqbmzi1MJ0/Cx8tL7lO7tix16cIM4cmTM+dPDhuWOaXk4EFm0755A8ybh5/GjEHevHmtlcUbN9gaFoJrUKdH167AyJEwxcZi+fLlqFWrlhQeHi517dpVOn36NIdQbCExEcuWLUNQUJA0btw4FCxYkKXX0aPc5jNn0pSUOnWs3k/Lli2lJk2asNDq0yfTqRcsWACDwYDfbBWBePSI31XFijyOV6xgJcIiPBMTmQxmYQGfPcseiDVrOF7ZqpVtKzQDTCYTcubMKUaOHPneYwGwBy8kJHM/TAcPDw957dq1WLlyJWrUqCE1adJEwr9dYAIgJyenI3PnzhWIjQU+/ZQf9KtX3AGOH0ezoCBhIEKoVotezs5yLycnydvdXV5FvKxXFZ0Ov5pdqNMUClRwcxPJycl49eoVB7nN1TSaBgaKUQUKyJ/UrSsm29mJkno9Etu0wfXy5fGpnR36qlTYkDev/CogADNUKkSYhem2PHkkAIh7/BirvLxESZ0Ob16/xo2ZM/Gti4twMK+K7u/vL5cqVQql1Wp8TQSNRoN8+fKJ0qVLw9nZOVVQ5c6dG5G2knjXr2fhIQQLDp0u6yW8zPD39wcRoWvXrlCpVMjEADTjxZgxOPL991i+fLm1Zgrgzp07cHFxEY0aNcpcsgzsuh0wYAD8/f0le3t7tG3bVjp+5AiEhd3Xv3+acOzZk+M8Fy++e5myHTt4gt679/eVvvpIMAvM/0alH8si0JLE72HIEODmTWZkV63KY8mSbzluHLvGIiJ4YiPiSe/wYSsLok+fPnK9evVYuOXJY71c1549WaYDdejQAS1atJBS639WqcIu2/XruS6onR23o2PHTK73gIAAadM7CmwLIZCQkIDkH3/EvXLlUCggQIqJicHLly/x7Pp1pOTIgUdr1+LpN98gMSgIl9aswW+TJuF55crYv3kzYn188CkRxLBhMOXMiWru7vKK0qXlLNMpvvgi61QFWQaqVMGz/v1RymAQWL6c+/Hdu9z3GzRA3OvXiIuLw/3797Fv3z4cPXoU2+bNw8Thw4WXl5c4r1ZjcqNGmDhxImrUqCGMRqPIkyePWLBgAd5axk7Pnqmx3gsXLsDFxUWEhITIEyZMEOaHxqkv6XH7tlWsevv27fD09JTx+eeslKfDhQsXYDAYYDAY3r8w/K1brPiMHMmK1syZnPc4aRK76AF2p+/cyQrc78gbX7hwITw9PWU5KxdwRjx69N4Sof3790fhwoUznvDfLzCJqKyXl1d8yuXLzOAqVYoH1r59QL9+WFu3Lr43GMRBIoyxrB6iUKC5i4t822DAax8f7ClWDNWJMMnDAyOImKH24gXHFPfvB44fx113d1TS6dBdq8XtEiUQd+MGYmJiUM3FRRwoUwbttFp8Xby4kHQ6xNvb4wIRUlQqTPPwkLFpE5KVSlQtUEDE58rFQfxatfDkwQMMGDAAN2/ehIuLC3LkyCHVr1cPyT4+2Lx4Mdq0aQOlmTpPRJlrRNrClStcyUOp5JhK69ZcDQVgFl06ssVvv/0Gg8EAnU6Hrl27Zn3OmzeZ9JEFzp49CyJC8eLFhWWJspiYGISHh0v29vYoV6yYvGXMGCSPGcOTbo8eLCi7deMB8vTpu+t5WpCUxBr9wIGZ69z27Jn1UkYfCbIsw2QyoVGjRm//MwLz6VN2c+7YwQKqUyfg9Ws88PHBZScndlvVrMnuxsuX2SqRZWbMHj7MSpmfH6c8PH4MmEz4/PPP5WlFivBvMr6nmzet14RMB5uFuBMSmMxCxNuYMTZ/mydPHtOSJUusdx4/nmb5psPKpUtxU6Ph8mwZ0bs3E5q++YbdyHXqAM2awVSxIqYoFJyTeuMGIiIioNVqMy8oALDSWreu7RVC0iEqKgrN1Wogf36ktGyJWFdXtK5aVXJ2doZarYZGo4HBYICPj4/k6ekpn9LpRPfcueU5c+awcpNu0jeZTOjcuTOMRiP0ej06d+4sb+jbFwubNhW1a9eWfH19BREhX758ckKnTvwebI25lSuZt2FGcnIyDPb2iClVyqoSTlxcHNzc3OQRI0bIarU6TUh/CKKi2Crv25ef84gRbJgoFKxk/I6SnPHx8XB1dcWCjII/K7x4wa7g9xgTiYmJMBqN4rx13W6bY0j9/iWm/zkAcMLFxeX2zrt3wxqtWUNUsSLR2rVEwcFECQlUt3Fj2rpzp6ISEU1PSJCJSAWAPi1QQHnpzBlq+uwZKfz9sZ1IkfDyJYUT0ejISKKBA4nKlePV4AsUoCdJSTQjOZmOTZwoB/Trp9LpdFS2RAm69+aNYv2LF2JmWJjScPas4jURfa5QUFGVip5oNPTb69fKTW3aiIpCKFfmyKHQ795NNGUK0bhxZOfgQFOmTKHu3bsLSZIUDx8+VBER0YQJ1EirpUYrVtCKFSuIiCggIEDetm2b0t7eXlGgQAGqXLmyePv2rcLX15f69u2raN+uHdnNnEn09i3RL78Q/fwz0YMHRJMmET19yivQz57Nq7Q/e0Y0ZQoVOHeOyqlUdI2Ifvzxx6wfcmAg0aNHfB5f30xfFy9enA4cOEDNmzcnby8vGtq1K5396Sf09/ZW1v/0U3IKClLS1at87WbNiDw9idzcft+LjooiatCAfz9kCJHBYP19xYpEev3vO+fvwOHDh6ly5cqkUCgAwEBEUX/axf5KjB1LdPEiUadORIsXE23cSGQy0ebGjWnz2bO0xsuLpCVLSJIkIiKCoyNJ9+8TqVRksrcnKSaGsGULpSQnU/Cnn5LqzRuKcXWlJteu0clLlyg6KIjo0SMCQEIIQkwMlf3pJ9ofEkImk4n3mb+7fv06OTs7E0kSkVJJNGYM0U8/Ed27R/TZZ0R58hDVqUM0bBjRnTtEy5YR6XRERKRSqRSWNqbCxYWoQIFMtywpFNTH11fao1ar6cABosqViVQ89GjmTKKXL3mbMIH3TZ5Msfny0VcnTtCA2rWJiCgnEWk0Gho2bBgVKFCA1Go1qVQqUqlUpI+Pp1JRUXRm506r/SqVitRqdeoWFRVFLSWJ1ri4iMGbNys3yDKqmkyqL8ePpzAXF1K2bEmkUBARceMGDqRSAwcqUsfgnj1EGg1RzpykVqvp1atXInfu3DRz5kzloV69FOvi4+WowEBlgQIFVG3atKEuXbrQ/v37lfY9exI9ecLPMyOKFSPasiX1o1arpc8qVJDXvnih6ppu7FeoUEEuWrQoNW/eXDVp0iQyGAx04sQJ2rdvn/j666+VGo2GFNz2zHBz4/lVkohWriQymYguXeL3FRhI5Oho+3c2MG3aNGE0GtGlSxfVB/3AZCIaPpwoR453HmZnZ0eBgYHiyJEjqmLFir37nFlJ0n/qplKphvTo0SMJ5cqxv7xDBzb3N24Ebt5EY09PqQkRPDQaFHRzkwMcHeWCGg1+JcJdIgxTq3HJ1xebiODh5ibQpAlX32nUCCCCrNViNBFW2dtjea1a4joRIoiwoVkz7FcosIwIL728IHQ6oH9/XB47FmNUKrQmwpaxY7Grc2ck5MqFHzt3xlGDQUQTiblqNdTmWIqrqyv0en1a/uGpU2nEBjN27tyJ8PBwycvLSzYYDCAijBw5EqGhoTIRYe3AgexiyZePLbGNGzNVwACAV69eoXLOnFI3pRJarRYXiLDc25sZxpUrc+woKipz8eJjx2y7mJKT2UKcMQNi0CAMVauxkghbPDwgb9nCFPd0ha9/N1JS0grHZ1WcICqK41l/IjZt2gR3d/dD+Af094+67dzJ7s7p05kw1b49EB2NUaNGgcyhAa1WC0sSup2dHezt7XFbocAEtdqS3A5nZ2e4Ozpio1qNCUolnhKhvJubCPD2Fj4+PsLX11f4+fmJHP7+4qROh/w5c8q5cuWSQ0JC5JCQEDlPnjxy3pAQodfrEWtnx2SPN2/YJbx1K/fLWbPYbXfnDnsqHjxgD8qjRwgNDTUtylhpR5Js5lkuXrwYefPmNUGS2Jpq1Ypjq48fc0zN3p5dhj17shVUqxae/fQTQjUaq/PY2dkhKChItrCL8+TJYwoJCTGVCQw09XV3l81LcUn+/v6Sn5+f5OvrK/n4+MheXl5yJ6NR9nJ2Fl9ptegQFiafSE/imTeP3ZQmE9+nmSWP0aOtSSpFiljF+Dp27CgpFAqULl1akurX5+PT4ZZKhSuWlJt3YeVKq+d2tm9fDDUaU83ROnXqyAEBAeL169fYtm0b7O3t0b9/f+j1eri5uQmFQgGFQoFGjRpljtEIwfPbpk3sZapXj+fqvXuZfPShblUAhw8fhl6vx+4PXRP4xAkmhX5gXnnHjh3RuHHj9Pdgcwz9/YP4d25EVCIwMDAW1aoxndvNjWMc/foBe/Zgf9GiuElcuCCaCFc9PCCIMJm4UPsv6cgsdYoUkdC3L7t0c+cGiJAcEID6QUFyIwcHuWKePHKbqlVhIIJGrU51meqIUMbBQbRp0wY5cuSQNBoN7gwezB0+OhoJXl6INV/jdrNmOHP4MBJr1cKrxo0REhIi7OzscNVSeeTZM3YNZYjPPX78GCtXrkRAQIBMZC6pFRmJSwYDdlgC85Y0mzt3OF4gSdi9ezcqVaok8uTJIxkMBrRo0UJ68eIFXrx4gbCwMNGyZUsm1fTowdesXZufoyRxTtS9exyI792bSRgnTrD78/vv2a3avTvHTw8cwJ0TJ2Cn02FqxmWE/hfcucMTQvv2745pTprEbpY/EZs3b/5PCsyX58+nsTlfvGC3bGQkVrVqhQaW2NKHIDaWY5vNm6NESIhp3ZgxPOEHBjIDNf0ktWKFdU6nEMwXcHfH5cuXEabVYo0lXibLPJ6PHePC3umLeL96xf3vxAmM8PKStgwdat2mY8c4jzMDFixYgHz58kmIiODznTnDVXNGjOCQRf783LfTxeyejRiB00oll2SMjcXhw4dhNBpt1z5ev57bZQuyzIxiDw+YzpyBnZ1d1vG/qChm/+/ezczirPp4umf7+PFjlA8Pl5s0aZK20+xK/S5vXrlRtWryexe/btjQirAU17kzyiuVkGUZDRs2lH19fcVjc5WmMWPGCH9/f1G4cGFp/vz5qb+JiYmBvb09Llpqyz56xITMS5f4+U6alJl/8OJFlhkCGXHx4kU4ODhg4sSJH3Q8ADYgLHnuH4D169fDx8cnvXT9zwhMtU6nS3h2/z5rpg8fcvxuxgzgt9+Q3L07LhAhiXiprmve3hBE+JEIcSoVbhMvGn0gd248XLkSspMTEwssGifADC69PlUgnTp1Cs+ePUN4eLh8KShIiI0bMXXqVGGnUmFLaCiC9Hr0JcIso1EMypdPKkeEkUTYu3cvxzfu3GFG3ObNSLxyBR5GI6xIC23bMrvQjJSUlNSyUbly5RL29vZ4duECcOUK1nl6iiiNBjh4EImJiRgwYABatmyJva6uGNSoEYgIXl5emDRpEtKvwXnmzBkQETqbiQGpiI1lenhkJK9yvmIFksLCIBQKZqY2acJEkFu3ABsxnH79+sHJyQmZ1i79UAjBydutWtm0km3id2im/wv+qwLzlkqFyJMnM91vRGAgVvv48Pu1VTKsR4+0+OTz58wdMFt4VgsL37/PMakZM9giiolhFqi5XimKFePUB0t9UgC9e/eWq1atyhOVyWTt7ciiCtRqJyf5TqlSTJyxEJmSkmzmDV6sVAntgoMlrF/PqWjR0Vxv2c6OJ1RJYoFpLhJy4MABODk5iZphYTI2bwZq1MAPlSujWbNmtnP+xoyxHWs9doxrXickpAq50NBQafLkydYmjyzz90uXMkHKZOJpWa/n9qXv61WqsCWcDkleXuiv0wmTycTncXICNmzAq1evEBgYKBkMBlSpUkXat2+fzebjl1/Scj0fPcJVDw8UKVJEatq0qfD29rYqepI3b145K6HVpE4dMdzTU2D8eH6evXtzf8jKwjt+nMsovofId/fuXbi4uKBfxlVq3oWTJ1nx+B1ITk62KvuH/4rABECurq6HDn35JRN/HB2ZGNKhA1fEaNsW3xoM4jgR5hLhmTlHUqvRYBsR7pvzJpOJ4E8ER3t7NAgKks65u6NC+fKwEFlu7dyJAvny4XpwMI4tXAgnJyfh6+sr3vTowfVhlyyBnJCA2Lx5UcpgQM6cOcXV8HAkKxSoQISTP/3EL658eavqOTG1auEAEW7dupX2tmbPtirzZnHDEvHSQnXt7UWypyfw66/Y0q8fvO3t8cknn8BgMCA0NFRu3769tKpaNbE+Rw4xf/78VE1YCIGbN29CfvUK1377DQOIcP+nn5jAER6eth5l8eKswefMiXgnJ9QmwkNX1w9Kmk5MTAQR4dj/Urj92TP2Dnz99YfVh42L47b+yQtOmwXmYfwD+vrH3G4YjSAivEi35iMAjBs3DmXLlBGYPJkr5lgmcQvq1WPL7MIFVv6WLEmdyIOCgkw/p2fGCsHW57ffcr9Xqzmp32Rib0UGgbx//35OmE9IYAvRYoFZlqrK0FYgHelnwwausPP0KQt0iwtyyxaejAFEeXriuIODgBB8/l9+Ybegk1Nan5NlID4eK1asgIODA3r16iWnpmadOoWvSpbE1YCAzKkfAFum6Vb8wNGjTHKxEJjSYezYsShQoICMJUvY9Xz3LpfAu3aNn+nAgVyJ6bffWPiPH89j1dLOI0esiHxXrlxB1zJlJHsiPP7qK/YIZWDynj9/Hj179oRer7dKX0vFkSOcwgJgW+vW4iutVgwZMgRubm64n06Jv3nzJuzt7ZHJYj12DGjXDk39/KRfg4MFNm7MfI2ssHq1zVVNLHj+/Dl8fX1FixYtfl+9zjJlOOzwO+Hl5SWnUyz+OwJTqVQO6NG2bSI6d2bXTffuQLNmLMhq1cKxgABEU1ph9HNml6wgQqRSiRCVCoft7JD84AH27NmDXR06YKevr8iRIwc8PDzQu3dvqNVq5AkOxi4HBxGk12N0kyaS3KsXa02zZgFhYUiKi8PgJk3EWmdn+YBCISQiPCfC5fz5OTeyUydYVno4tmsX/N3cZAd7e+TSaBAxZQrkw4f51dy8ya5IcNyRiLBqxQqMrV9fukeECK0WCwMCZKjVkJ2dMd3PT0xTqTAyVy6IoUM5wfnIESA4GGL7dhz88Uc8K1wYX5Yrh3nE9W7btG4tjtnbsxX96BHHLqKj2Zpu3hwYMQIJK1bA2yyoD4aEfGhVfxgMBjH993bQbduYoTx9+oetCQiwFfyuEmUfCVu2bIG7u/sR/AP6+kfdbtyAL/dzcTOdgjJhwgSULl2aJ6Vnz3hS9vBgAWnBw4fsgbBMuomJwMOH6O7uLp/+8ktODfjqKxa4nTqxG27qVK78kz8/s25tICIiAg4ODtwHVq60/nLRIpul0fLkyWNatmwZf0hKYmXPxYXHWrNm3EY7OyAuDhu7dsXAwEBr61AIdnsKwcLTxwcL5s+HXq+HlfA3o1bNmlhav75A+/acNpPerdqjB1vLiYnsrZkyhT1Gluts2ZJatUf28oKDnR1e1q3LCoUkpZ0rJYX39e5tXf1rzhwgKCi1wACWLwdWroQQAtv0etGjfHk8evCAU3qyqgIGYObMmdDr9ejXrx/KlCkjFStWTPr888/lQzNnQgoKAgAM8fERbfLmhcFgYO9YOvTt21cuWbKkDCH4Pnv35tS+DRvwYuRIeGq1mfK/34v799nrkEWKWmhoqPigxRnS4+5dft7/gxcqPDw8LQ3nvyQwiSjMx8cnDi1a8KT722+c93PjBjBzJl60bYu6RPiCCKWIsMBgwCSNBm9UKnGqcGEM1mgQ2bAhF4x+9IjTHQDs2rVLNG7USArz9RVhKhWe7NrFWt7WrawlE/HAXLMGskqFyXo9jpsF8VsiEenggOFEXEbu0CEeMJMmwXTvHtzd3MRrnQ57ateGgghjiVjzBbjDjB0LPH8ONzc3Ua1SJZHUti2EpyeSiLC5UCF08feXMWoUxzoiIlBNo8E3jRuzpdiiBfDdd5BVKmxSKOBOhKH29sJfpcLiuXNTrdXU1eNlGYkjRkDkzg1cuICUtWuxcP781GLuCoUCo728RGq5s/egY8eOyJ8/v/ig3KiUFI63DhyYeSmj9yGrNQM/MrZu3Qp3d/ej+Af09Y+6mft527ZthaOjIxfEAPDdd9+hZMmS1i9v40a2WNq1437/9decFtCmDRM4+vYFmjXDSoNBnBkyhBP1L160rg7z8CGf5+BBrpOc3hIz49GjR3Cyt+cFxM3WS3x8PObMmYP533yDrZ99htmzZ2P+/PlYuHAhli1bBg8PD3np0qV8AoslXKMGC/kMpfsmT56M8PBw63t78oSFEwDcu4drnTtDr9djRxZ1lCtWrIjURPkJE1gBuH6dn8/AgUyaKVCAn1VSEiuw9evz8cHBrAi8fAls3YoB/frBxcUFU6ZMSSvF9ttvbBFHRKTeT0pKCvbt24dy5cpJYb6+YmbBgvIeT0+x3t9fXuznJxcuWFDeq1JBFC+epTKSHrNnz4bBYEDu3LnlYcOGYeLEiahevbrw8faWiqpUKJcnj7yJCF52dpg5c6aVRffmzRu42tvj+pgxQIUKnF9rrrsNAL169UKVKlV+/xJJsgzUqpXJk5WUlIQyZcrIBQoUkD4435J/yO/h0KHf3RQA6N27N8qVKyebi0L8pwSm0s7O7q3J3Z0Fmbc352BNncoCpF07LCteHCM9PWUTcaECmQidiFBUp8NbT09mUHXtyi6jGjVY46xfH7ObNxfPiXDZy4tdSOPHs+U6YwbifXzwa82aWD9+PCa7uyPR7N4d5ekpDqvVwkWnw+RhwwSePmVXS48eQHQ0tuTMiVFEeHPtGlIiI3GmQQO89vWFk4ODkPr0YYbbmDFI2LoVRIQdGg2SFQrsnTQJJ1QqrCxQANUrV5YwYwbw4AHkadMQTYQ1q1fzRPbLL3jw4AGaODlhc6FCyFiJJywsTFIplfi2WzdIn32GfUFB4hMi1M6RQ8yePRvOzs4wGAyYMGFCqoU7JkcOtqTfgeTkZFy4cAEmkwkODg7il19+eXePvHiR4zCbNtmMh74Xev2fXhwe4ARud3f34/gH9PWPuqXrF4MGDYKjoyOuXr2K77//HqWLFZNx9ixbSBERzEBv1IitJSImofz4I5OGnj1LtQp8fX2liRMn4syZMzhx4gSOHTuGo0eP4uDBg7jZuTPuN2iALVu24Ma4cXjWpQuu7NyJs2fP4syZMzh16hTWr1+P/ESIDQ3FkUOHMG3aNNjb2yN37txy3+Bg6YlGg4IFC8r58uWT8+bNK+fOnVsmIo5pCcETZPq8vObNuX+ZMXHixMzKwKZNvIIQgPN9+8Ld3h6LFi3K0u1XsmRJ+bvvvkvb8eoVP4sKFTgPesAAtjLt7DimevQoC00bMJlMcHFxEUQEZ2dnsWTYMHbrLliAhIQEbNiwAQ0bNpTMLFS5Q4cOWLNmDfp07ozjxYrh+88+w6JOnbCic2c8txAG0+VMZkRiYiIqVKggOzk52XbJAkiuWhWHGjbEb/nz43b6JP83b4CoKNwPDsYZnY4VXIshkA758+eXZn5AlR6bSEjgmLcQiI6OxsKFC5E/f345Z86cH1RRzApxcb+rCEJGPH/+HE5OTmLWrFkC/yWBCYBcXV33rJk1izvowoVMRPjyS6BsWaBNGyR1744FZuvvoF4vElxdUUipxMpJk1grvHUrbXHcnDkhnz6Ni7NmwcPeHoO+/DJN+7O4RGQZbi4uKOjqKs/U6+UeAQHSMy8vdCJeFzOSCHeI8IgIdhoNfPR63Far4WkwiJZEmFasWBrLLi4OYs8e5PXxEQmOjlyHcfFiYOhQpPTsiYtubiKZCLWI193crFajRpUqEvLnh3zoEFyUSrgQWRWvrlKlily5aFGRSk23QAjgp59w290d3QsVknc2bCgMRBg3bhxCQ0NlX19fqWvXrlYTSpkyZVBcpcLjxYsBIGNRYgBA5cqVTZY17QICAv6vve8Oj6rqvt7TSya9kYTQEgi9hF6kSBVBmkiTKl16U1SaIEgXkI5IFwSUDtJEQIqAVCF0QghJCEmA9My9Z31/7JnJpEFAfH9+OOt57hOYzEzu3Dn37Lb22kKhUGDgwIG5bzrWkVEffJB7HSi/sM47/IexZ88eeHl5ncC/YJ2/5oMjnaQkYN487KpYES2cnXHD3R2r/PyETWgiMpJJE1btz+bN+T74/XeOMO3g7u4uWzZ/eHh4wNPTE56envD29hbN3N1FPS8vQURwdnbGKKNRTDYY5GB3d9nLy0v29vaWPd3d5XImk/D19ZV9fX3lwMBASaFQwEZiySVd984775hXN23KadwNG7JqCh89ynV5y6Y+bdo0VK9ePeu6NJuB+HjsWb0aCURYt3z5c2tklStXlmfZC3enpvL7V6/OoiEtW/LjdmszPT0dn376KapWrSo3bNhQfPTRR6hVq5bw8PCQQ0ND5adPn+LU55/jskKBEW3aoHXr1pJer4efn5/UuXNnZGuiz8TOnazsRcT3U3o6O7Y3brAhv3LFVuPfu3cvXF1d5dq1a8u5KoZZsXYtR8cHD3KUduUKG/zQUKTv3YtWTk7YbE2B54LAwEBp27Ztz7uEeSIjLQ0JQUEYX726rNPpULhwYWn06NEvn94ND2fn6Tk10fzg0KFDqKDXA2+awVQoFAPXVaiQDq2WWyO++orp6uPHc7oEQBE/P1GOCIM1GowhwiSlEs0UCnxdooSEIUNY/SMyEoiIwEdvvy38lEpb60j9+vWxf9UqyFevAn/8gQcPHkCv10P+4w9WqSBCzMqVeJcIKUQ4RYRESyRbhQi9u3YFEWHmzJns2V24kCminJgIERkJnVqNq02bckqlcGHYFE4WLoTQ6/Fo7lx8166dPMZgEB5KJdycnYWzySSICA8ePEBkZCT69esn1apVSyYrK3fUKF74cXHcatOuHfDzz1jdqxe8PD3lUqVKyZOy9Wxlx40bN/C+j4/4mTJbcLLfwMHBwcLb2xsXLlywPWfYsGE5rVlSEp/DkCGvFlVaMWFClsG5/yT27t0LLy+vU/gXrPPXekyaxDUjq2TdtGkYP3w4AogQVKxY3hckNpbvqd9+y2RpWkhrOp0OP/74Y87XXL3KsmhCYMOGDQgKCuJdfMoUzgRZW4fWrOEMkV3UotFoYBvw26tXjvaAVg0bSs+Mxrxr7HfucHSclpa1PmvFyJGI7NQJTkYj1i9Z8kJCSaVKlSRb65TZzIShXbuyGvRu3YDWrSHLMjZs2AAvLy+5TJky8tSpUzFq1CjRtm1bady4cWLfvn3sOG/cCPzxB7aPHw9XV1d07Ngxc2xZ9s9y7Rq36wwcyBFl6dK8TzRtys5xvXqc9v3+e568dO8eHnh44BgRgjUaJBQrxiWrHTu4lzUpiYmGO3ZkRmVFinD2oEgR5oU8eADExWHmzJkiMDAw13RrYmIihgwZArVanZXE+AJY9bL79+8vubi4oKqHh9yvVauXeo8cOHQox7SZV0WKwQC8aQaTiEq97eqaDF9f9rhatOB0TMmSTDyYMQN769XDeyoV4okwkgiJJhNkpRJj9Hpc1WgQ26ULEhMTERAQIO9TKhH/9tsIDw/H9u3bMb5yZTmaeIKHl5eXvFupxEylEsd37oTs6gqzkxNWLV6Mc5YIM50IHYmwgAizLWngWUWLyli2jHud3N05/XvuHNC6NVICA+FqMgkRGMiTHTp14q9DrWZiwccfMyP0xg0k+vpiOxGixo1DWnAw7n7+Oc76+IjbRKjt4yNf8/DA0Zo1+SasVInZhfPm8eZkSde0bt1aWJ2Bi/kg88j37+O2pZ7p5uYmWls2A4AjTjc3NxgMBuzYsQO3bt3KKQZtbRepUYMN+N+NDMeOZer9/wC//PILPD09/8C/YJ2/1iMiIlca/9ChQ1GlSpW8Dcfvv2dOIwGYpKbXw5yQAIVCkdlTbI9NmzhzAmDTpk0oWrRo5qY7Zgyn4TIy2OBkky7LYjA3b87sEwwLAwoVQpv69c0j7EdCZYcQHDGtXImvvvoKNWvWzPLZ5IUL0c/JSX7k48Pp0xegfPny0o8jRrCkm9nMEVg2/Pnzz+hbpoxczNlZbNFqMW/u3Lxr+suXs5GLiMDTp0+hVqszszjR0Xztdu/m51Stytd/+XK+7kJw7+cvv7CRzKaTGhERgeDgYKlkwYLi0sGDWPntt6ItESb07Clw6hTXn9PTmTw3YQI7HZY5wZgzh9/TEqXFxsbCZDIht+jx3Llz0Ol0qFq1qrzFMnv0Rbh37x4mT54sAgMDhbOzs6hXr544ePAgf6agoOeSlp6LsLActeuXxo0bzLhOSbGKr7xxBlNhNBqfxPfvz6SCzz5j8kuTJpxO+vZbhA0ejOUqFcyWdOlwlQrxajXec3cXgUTo17s3ihQpIvv7+yM+NpZv4K1bOVK9excPZsyArNdj/65d2NW/P0oSwZcIS4nwExF6W4ylTNy+0vitt9Bbp8MmIiRpNBhUtKiM48c5jeLuDtSogV8PHULNmjUltVqNli1bSrYG8sWL+SbPZaSOLMsoWrSopFaroSSCXq1G+YIFsXvePIRfv87U7qtX+UsvW5bJD/v2cZri3j1cvnwZOp0OTk5OGDhwYP4WkCzDPH06kJ6OEydOwNXVVVSsWFGcP38eM2fOlN3d3cXy5cthNBoxffp0xNgTch494lRZ48bsqf5dpKf/T1KxVuzfvx+enp5n8C9Y56/1ePCAN4Vs46KWLFmC584WNJs5VWv/HSQl4bNPP8V5nQ4it8n1a9cyKQbcFF6kSJHMXiBZ5mkYnTpxNJMNGo0mq27r9u0cDaWlAXPmoGnTptLw5xlMgP/299/jx86dUatWLQEAV69exYgRI9De3x8lPT1F+oABWDxrVq4lh/T0dMyePRvDCxaUuhJh77p1bGBy6SscNWoUjEYjBg8eLF9Ztw6ieXP+RfbrkpbGUeLFi5ntH/fvI1ChQFr37kz82byZr83OncwKz/73Tp5kPd8uXdiAVqzI2Svw92gymUSPHj0k+5Tm0KFDERwcnFOwfOpUTmdv3MjXOCaGo9YGDYCzZzGiWTM5NDQ013XRunVrkWdvqh2ePn2K7777DpUrV5b0ej3Kli0rLVmyJOc1X7eOMxivgo4duY78Kjh1ijMeQrBwSuZ5vVkGEwC5u7tvT9doOEXq789ScR98wJHNs2dASgomaDSYS4RUhQKd9HpYSUBVCxTAaSL85OMjnm3ezHWPDRsyva2AAGakrl0LpKejU6dOktFoFOfKlsXKbt1sg6MrEaEnEaoSYUX16rIgwnQi7CVCy5YtBU6f5vqkhQHo7e2NJk2aIDIyknvazpzhL3v7dk4rPHzIKSp7dRQLYmNjMWnSJBgMBhQsWFBycXERzZo140WbmsoemiRl3mRDhiCjQgVoNBooiJBfT9AGO0GFqKgoFC9eHEQs8ffrr78CAFatWoUiRYrIFSpU4JV2+jSn7aZMybcs1QsxZAhnDv5HsBjMNy/CBHidPXrEpQtLmn38+PEw8Jgr4ebmJlxdXbMcLi4uYoheL4ZUrizZT6f38faWr7/zDrd+nDyZ2WN56xbX9yzP3bp1a6bBvHKFsx/HjvG9ptczUW/aNCbRLFiAo0SQS5XiemT16kys0Wr5tULkz2ACwPHjiPH0hJtKBXd3d6HX61GnTh3pprs7RqlUKG8ZE2ZtJUlMTMTUqVNRrlw5qaZOh9LFismn3npLPOnfP9e3v3jxImrUqCF7e3vjrFVAwQpJ4kyPVVlJlpko1K4dp6G/+ooNcPPmGOzlJX7t1cv2+Z6L8HAWBrCKRWzdikc7dqB5aKjs4uKSazQYGxuL4sWLC19fX3nFihV8bpLENb9Nm5ijYd/bnJiIo926iT+VSjzaujWL82NFuXLlpFatWom0XKQwMzIysGvXLrz33nuSXq9H4cKFpTFjxuQuXm+P0aMzhSjyi9u3ec982dplQgLbiO++y9Vpw5toMBUKRe+5lSunolw5rgGGhLDsVcWKzIAtUQKVAwKkWLUagghfEOFPg0FEEsFDr8d7CgW2jR+PRUOHQhQtyqo2LVqwQkfVqkBkJKQKFXC0eHHRmQgpNWoAej26VasmR1iiS2ExnL21Wly3qArdbd8eI4lQulQpTknOng0sWYLU6tXRs2dPUbx4cQm//MK9U5Z6K/r2tXmK+PbbPCOqffv2ISAgQAKAdu3aoW7duuLevXscoQYFwZyRgYSEBISFhSEmJgbfzp+PygYDhJPTy+u8fvZZDkGB2NjYzHSZBVevXoWHToeI4cNZNs+q7PK6IMuvJ1LNJ95ogwnwptyrFyvUREVh2MCBKFu2rPzXX3/hr7/+wrVr1xAWFoawsDBcv34dN2/eRHTfvljt7S0CAwPlyMhILF++HC4uLpnEsypVuH4OcBngp5/4e7t0CQfmzcNaJyceG/XJJ5yStQ5YvnSJN++MDODJE8jJyfAignTrFhv0AQPYGa5enVNvzZrhjJeXaFi3LnyIbJM+PD09ZftSQ6NGjaSQkBApSKHAPB8f/P7rr5mkux07YC5WDPe++AJDhw6VnZ2d4e/vL6lUKpQvU0aeNmWKkFxcnsvIXrJkCQwGA9q3by9FZx+MbL3G165xdqRwYXYOqlThZv2OHZlElZQEyDK+/PJLOceszNyQng7UqoWMjRtxfNw4NGnSBMWLF5cGqlQ4V6CAiH6O0EhGRga+//57BGu1kI3GzL7OgwdzqAetW7cOBoMBe7dv5+/y/ffZqBw8aGsFu3z5MogIjRs3BsDs399++81Wl/T29pY7deqEW7duvfBj2TB0KBMy8wshWBgmD0byc1G6NHM88sabZzCJqNgUjSZDFCjAeqf9+3MaQ5JY4LdfP4wLCYGnSoUezs5yS7Uaq0qXRpmAABARpgUGyr+r1TAR4ZJOh/3Vqom1ffsKIUlIe/AAD1u1QlGlEuXUanxsMAAWA3meCOFqNZLs6pcDFAo012rh7uICg16P40T4WqfDpuXLMWzYMLm2m5voqlZDRYQv9XqIO3cyDWRCArN77Y3klCncCJ4NBw8ehJ+fnwSwoSIiNCBCRno65s+Zk2Xws16vBxHB3d1d2IxYixasnZsfTJmSvyHNZ87gxwoVxCS1GhOGDn35fqznwSoS/rqi1Xzgja1h5oZevfDQywsN3377+fJJ0dFIv3oV3bt3l0wmE7RarW0gOYBMdaBRo7j956uv2FmsVg2H583DYA8POUf7w/79OeqX6enpUFhHPzVpwiUSO3k5RERgVvPmcn0PD0RrtUhdtQqJ+/ZhcrNmVraucHNzE0SEr7/+GiuWL8fTDh0y76XkZE4FP3sGmM3466+/UKdOHZmIsKxzZyb0pKfnqSaVkJCAGTNmwGQywdfXlx+8cycz4mvenB3k/fuZS7FmDQ9KIGIFoJiYHO/9+PFj6PX6PLkFN27cwMSJE9GtUiXxp1ot5iqVmGEh/3Xu3JmFC2bOZLWj3PRuAc4UTZqE0qVLy9ushkKS+HV257N27VoQESpVqpT19RkZXCesUIGN5uLF6N+lC4gzabY2GFtd8lWQns6lpecxerOf07p1eX/m3N6/WDHOgORS+sqGN9JgKr5WqxMFEd+kKhWnMzt35hTP0KG4U60a6vv5iYiAAEyziwp/IMLAihWRZDIh1WhE32rV0MfDA5EWY6NWqXBWoUA6EZ4RD6D+bfRo7FqwAEeI8BcR3C01zXRiofcMIjwgwn6FAoII14lQiheftH3dOqTfuQN50SIcUCpx3V7Tc9Uq9jjtkLxsGY506YLg4GDRp08fYdU4PHr0KDw8PGzW48axY3hKhBIWOb02bdpAoVAgCw3eHla1oqNHX6y3uGxZ7nME7fHNN+yhHjmCc+fOwWQy5U2JfxVcucKpuv8hfvnlF3h5eZ3Gv2CNv87j888/Fyey1S8hy5gxYABGlSsn4733srZo2CMlBfD0hEhMxNy5c4XRaMwpui8EZ0cMBmbjPnkCCIGdO3ciV6ZllSo5+uaSExPxIRFHYVZCy5UrWWZdLlu2DCaTCWuXL+cobdw4yKVK4erJk4hv1w43t2/HbftoKy6OU8CHDnGUatVptWLiRBypWFE0qV9f2FKodkhNTcX0ceMQUqKE1E+txoQCBeRPu3YVd4kQPn8+174qVuTPv3gx1y6FYCe+aVNm7F67xvM269XLFDWww5AhQ6SCBQvK9inv5cuXw9fXV9br9ahWrZq0tFMnsefHHxF39ixSbtyARqNBnDVSBDhDZo3y7ZGeztFhrVooX66cPH/+fH5861auV1qwZcsWGI1GfPDBByhUqFDuVshK0urWDddq10Y/Dw95StOmOPeyqdS8MGBA3mL29khL4xJcfiYXxcZmCvlPmZJftv6bZzABkKur66azjRoxqaZ4cS5klyzJRlOWcfH8eVwmQoZCgdFEWKrXCxAhrG5duLm5ievFiiGcSBR2dhZenp74fvJk8TkRBhHhaw8POUqnQ6ol7ZpGhHZ2/35ChO0W0k8CcS+mpFAgkkiY1WqsUygQNWIE92kVLgyoVIg4cwZ6tTqruIC1FcSC06dPg4jg4eQkNru5wdfS6Ozp6Qkiytx8/vgDMXfvQk0EZ2dnYa0RlClTRn5hI/GnnzKrGOAaQG64eNGmgpQDDx8y+3f1alZLsqBSpUrSJ5988voYOv/DyNKKN9Vg1qtcWRCRTeHHikmTJuGdqlUl9OnDEdjGjbmXBDp3tqmyHDhwAM7OzujUqRN/QZcucU0sMpIjFllmok6BAli/bh3c3d1h7bX09fWVfXx85DKennKgp6fs5eUle3l5yYU9PORuBoPYrFBkreHHxmb2OoIZmkSUQ6ADjx9zlmnPHq4PtmjBUUhaGtdY69fnEsOSJdwWMnUqs1LXrcP5ESPg6+vLUfCxY4iJisKNUqUwKCBA7qLRIEmhwNKJE8WzAQP4vTMyMLBkSfmToUNtFyo+Ph7nzp1D+G+/QX7/fc7kDByYeW9PmMAs1fh4Hk4dGmpb3+np6ahUqZLcoEEDKTw8HLVq1ZJdXFywevVq/pxJSbyvRUUBH36IG5Mnw9PTM+uXFBfHymf20n19+3I624KyZctmGszISJtaz+bNm2E0GrFx40Y8fPgQOp0O5hdpNgvBjn6FClx7nDs3M9X7qnj4kK/Xi+7769dfLJMpy2wcT5xg4/pye8mbaTCJqKtExF5t7dqcbrEWcsePR3pqKqYT90jGE2EzEWpUrSqaqdUII0JfjQZr167F9Vq1IGu1wIYN+FGpxMF338UtDw+xnQjxCgXMlkhymUplM8BmIhx96y0kN22Kj4hQWaVCHSIU1WptadHSRBiq1wsEBABKJdKfPIGLi4vYu3cvfy1paRylWVIEsbGxsAxJZc+walXgzh3cuXPHNqPQhsKFgQULMHjwYCYRgRm1Li4uyBFJ5IXTp5l4kVta4+TJnItSCFb7qF+fazzZNtYtW7bA3d0duZEBXhqyzNHAy0ro/U3s27fvjTSYcaGhGESELfXq4fhnn9k0fKdNm4aqVavyAnj4kB3PnTtzpiX//JPbGSy4ceMGChUqJNqVKCGlHT6cMxthNgP79uHtBg3kbUolTm/ahGPHjuHo0aM4fvw4HjVpgsvffYczZ87g4rZtiOrRA3c7d7am+cytWrWS2rRpI7Vo0UIa1Lmz/NmQIVCpVPD29hYmkwk/PK9cEBbG5/P4MY8MW7iQnbtWrXjzlCTuWfz6a+DaNcilSqGmRoNDZcrgsk4ndDodvvPyEt9//LEIv3kTMJsRFRWF/v37o0iRIpKnp6fs4eEh9Ho9unfvLhUrVkxotVqUc3WVjyqV6KlUokpAgLhvNGLCoEFi9erVOHPkCJLWrePMzoMHnKK1RKKpjx5hyZIlMFqISO3bt5ce2juyz54xex8AunbF9j59cmc2Jyby5+venSPqBw9srWXh4eEwGAy4Y+3tLFgQMJuxadMmm7G0wtvbW+zJy1nODiHYkerfn3kPixfzOnlVZvvcudxvmheiorgW/iID2L7938lOvbEGs+BjIlkQMVvW3Z29j6tXubCbkAA/Pz9pExEeWiLBRxoN/iIWZj9GxHU6Ij4qVMByZ2c5Xa8H1GpkEE81eWpH8Gmt12NQjRrSISL8ToSJFkKRIMLR0qXlkcWKybKPD1LPnEF60aIYS4T4ggXx9M4d3L59GyEhIXB3d8fp06exvF8/mN9/3/YtRUVFQalUZmWU3boF7NmDO3fuIDw8nFmO169nIfFIkmQz0iqV6sXeoT2sI22CgrJGlOHh3HxuRUoKM3pbt86zBiDLMjw9PeV27drJ1/MzgeRFOHXqf9pSAtgM5psnXGA2o7aPD1oTM7lFlSpIGDAA06pWRUsnJxzYvh0HDhzAr/v348jhw5CCg9mgWPHDD0wAsUPy7NmI0OsR7O0t50Xw6N6pkzis0Qgbm9a6dlq2ZO//t984ZbZ0qU2aceDAgejfvz/69OmDHj164CQRvrRkWbZt24ZBgwYhICAgV5ZmDkRGcp2xS5fM+/zRI06PfvklRyEbNqBn+/ZSQECAmDt3rshO5JkyZQqMRiPq1asnrV69GmfOnMG2bdugUChQvnx5LF+4EOnDh/OkjPR0xMTE4GTfvhjfowfefvttERwcbPb09JTfVipxTKPBtE8/FSdOnMBvu3cj2tUVIXo9ynl7y04GA77MbVxYmzaZDNJTp/Bhy5ZSN8vAhhz44AOe5GQvGQhg0aJFKFGiBBvZ334DJk/GokWLYDQac4hPNGrUSHqpkVr2WLiQ69enTnFpLK8MVl74448c6ywLJk3iz5gXGjTg6D429u8o/7yZBhMAzVGrn0p6PTfeBgdzL6IdGjZsiN+I8IgIf6nVIsNi+FKIMIMIh3/6CXdKlEDf0FDUfestMdbZWfzo7Y2dzs6QLK/zI8IvRBhAhCJEuGE0QliM5KAePbDA11dONhoxngixFtWejM2bUapoUfk7hQKVLDJyYUR4t0ABm9D5WLUak52chHWs2A8//ACdTpd1jM6YMUyAsKJXL2aH2eHZs2cvbyizY+VKTsktXZo5HmfMGDbMFy7wTbtixQvZtufPn0exYsUkZ2dnHnz9qli6FLnVlP5pvKlKPwcPHkTx4sUlIsLw4cMR4O2NUCI0JsJUYlnHz4jQhQgVifB+wYK8SW/cyHX2+PjMund4OK+NiAhIUVEYNGiQZDKZxE/Zxjtt2LBBGI1GhFmF1+vUYVEOIdho7djB/ceHDgFgh0uv1+N+NjIQrl/PIr4fGxsLIsLo0aPz9qZSUvh9R47kNpoPP+Rao4sLG8mQEP7doUM8Zi6P+u3u3bthNBpxzE7kYPny5SIoKEgQEdZ8/jkLMcyZk1kf+/137kPOtmGbzWYcGDsWgwoVkr28vGQfHx/Z398fPj4+EkJCcLJOHRQsWFBkkYZLSeF9zerY1q2L7iVKSDlmUx48yGo/16/nOt7q/Pnz3Iv90UfAqFEY2LevMJlM+MUua2DFpEmTRNWqVf+ep3rzJjNfFyzg6/vzz/kn6Fy+zOpE2RERwdFs9iHzspzJA/nyS55C8/fw5hrMVKJ0QQS0bcuLPpuayfDhw1GSCP2dnTHFYEBzInysVGJt/fpo6+4ur9ZqUdPLS/rcywsVifAnES4Qz9G8YyHzPLVEpE+IMEWlEu8TE3/2WeqYW4hwQaHAhooVkbZqFR56esLX11fu7O8vkrt3x40rV0BEWFupEhATg0ePHmHjxo1I7twZ7kRYuXIloqOjMXr0aPj7++e+qi5eZKq9JOUwWjExMTCwpNPfx8yZ7MVJEqdeV65kj/ElU6MVKlSQvL29kfKqXl7r1uyt/o9h0ZI9iX/B2n6dh5+fn81Rsz/Gjh0LhUKBNi1bYt3HH2PXBx9gb+vWSAwIYPLNkCFs6OLiWPHn+HGe39qtW5a07erVq4VRr8e4ESPY8bpyBbOGDZNrOzsL+ccf2UDOmsUpu48/5u2nbt0cvXdBQUHSsuzDxO/fzzIztnPnzlAqlTAYDEiy3zwTEzma7NqV2al79nDvaWIiG5EvvuBUdI0avKnKMhOCWrTgyLdcOV77T5/a6qiBgYHSjBkzsuT/GjRoIPv6+CD1xg2u3Vp0lwGwM3D6NAuq5IbERCYDWbI5M2fORIUKFSQkJ0N+9gyTPT3li/7+mX/v1KmsbVWzZuGjli2l9lYOAsD3au/ebKCstcxcsGXLFnQiwu9Go/D19RW5yvEBOHz4MLy8vF5famfxYiY9HT7M1ze7Q5Qdx45xxit7ANC6dQ6pRKSlcZrW1zfTqfj7eHMNZhTRdUEEfPRRrh7Mzp07sZgIn6vVOKlQYKfBIORDh1iowFr/3LYN5ytUQENnZzHbYjD/JMINC6nH2kKSbIkqJxMhVqVCBhFSLUcQEZycnKDT6RAcHCxWr1qF9LlzgZMnsX//fqhUKma1rV3LC+LePaB3b4RWrCiPGDECBQsWFBqNBiVKlMi6Sp494whPoWCx50mTOPqyzt4bOxYJEydirEbDefuBA9noTZnCTkS3bpmT4Vu04DrO+PFcb2jenFMYI0ey/FzjxuyFDxnCGr1EvIm87OQAC4gIq/5HknavC2+q+HpCQgI+/fRTTJo0yWYslUolAKBevXogoqwZgYwMrkWtW8eMz7p1eT3o9VzPbNeO19SECcwfOHAAZmdnxCgU+KRKFVnUqwfpyy8xx8tLnA4OZgP10Ue8Ls+dg7UEggYNmJyyYQNw/Tr6fvQRmjRpkvVGDgvjBnsLzGYzihUrJun1epQOChLRq1dzhNG+PdfAtm6FOSEBhw8fRu/evSU/FxeklirFkUmXLryms0/XEYKjzePHgZ9/Rpq3N7qXKiX6GwxIyxbt/LJ1K+YajQIDB+YUGdm5kw3y82psv/0GbN+OtEePULhwYTHFOvwaQNThwxig1YodW7cyOa9WLWQZtTdgAI7u3QuTycT6s40b8z1vxYUL3LuaCxISEhCs1aJRyZI50s72yMjIgMlkyhJVvxbcvMkGb9Mm1n5duzbvksuNG1kHiGdkcIbD3kGaPZtr0a8fb67BDCTyFUSQNRrAzy+zOG7BnTt30JMIMwICxB5nZ/GYCE/ff59vDFdX3A0KQnBgoFxYpcJAgwGDLUbRWrOULf9eS4SZRLhIhEc6Ha7odLhhSbNOIMKUSZPksPPncf34cQjrdPcePQBZxs6dO+Hi4sIn1L4914bOnAHWrMGePXtgMBhARPz7pCTeUMaO5eL2sGHMpDUYWFShSxf2Ok+cYLbfiROI3LkTNQwGJuScPcub019/8b8vX+aFevs213Zv3uTURkQEp9YiInhhxsbyz9272YA+fgxZpYLctu0rrThZlhESEiJnT2flCwMHshf+f4Ddu3e/kQbT+vnCw8NtNcArV66gTZs2NgNaqVIlOUs5wB7p6WzYqlVjI7FrF5NHoqJ4baWlAZKEBw8eoEyZMqJYsWJSXFwcDh8+DGdnZ5GF1RoezlFHWhoblt9+47Tm/Pl4VL06VqtUyDh5MlfFK+u5pO3Zg3cNBhGp0WC7Wo2jK1bAHB2NQ4cOoXv37pKzszM8PT3lxo0bix91Oqzu04ct2L597LD+9hs7jnY4fvw4WrVqJby8vGRPJycE+vmJ9Z6ePDz6zz95Tf7wA9I2bMB8hQL3c5nxienTM/Vvn4OnAwZgbWCgKFKkSI65jwsWLEAFV1fIJUowidHKRBeCDURKClZ17y6VMhgwr1EjpNu3V0hSrq0TYWFhqO/qKkcZDEjJJj6SG7p16ya98847/wxNXZa5vNOjB7e3fPaZTVXMhlOnOHq3qpdVr54ZtQ8dyi1MKSnM8Xj9eHMNJgC6QZQqiJjoEx3NX4bFg5RlGUW1WlwYPx4GnQ6/FC6Mj93cRGp0NDZ89hkKEOH34sVtBnKjJf2abmcwzZaf64i1ZCM0Gvyk09l0ZMNr1eJoLSiINwFrHdBS89m3bx88PDwyvw4hOIXy449AaiqmNmsmPlWrOWU0dCizfZcuZS/r9GnenJ4945t2/Xo2unbEmzt37sDZ2flVFkYmoqN581u/Hvdq1YK/v7+sI0LJEiXQ39VVDg0NlV4ob5ULpk2bBicnJ+SbdQdk9or+H8BiMN+8eZi5wMfHRxARfHx8xNOnT1GkSBHpgw8+kDdt2iRq1Kgh5xBWj49nFu0LiFgpKSlo27at5OHhIZ85cwYlS5aU7aMobNrELRZffAGMHo179+5hwIABCAwMlApoNPjAYMDttm15Q505k8kjlSuzgtewYawoNWgQRoSEyOMGD5atYgJW1Z933nlH/GbVJj16FFfKl0fFUqUyT/rnn3nzrVgRh3/5BU2bNhUuLi5WMQBp3759yMjIQNu2bTPlJ+Pi2JGrXx+oUgX39Xp8OmwY1w2fPOH3XbWKz/c5iImJwVtvvSX7OTnhgLu7SM4j0vPx8ZHvtW3L7F4PD35vIdgBNpuBggURNnEiKlWqJLu6uoqV1rTwpUscudth3759cHZ2FvO6dJHlfCrjHDt2DC4uLuJV7vmXwu3b/H0ePMjZL+vnkGVeH/HxfHTrxvV0SeLU/stKfb4c3myD+RPRn8KaLgK4AXbYMPZAzGa08POTHpQrh0KFCol4f39IRPiOCH8qFHhUpAhw5AhSTSYMd3YW5Ymwzc5YxlkIQklEiNRoIIgQ4+oKIsL3RPjhgw/k00ePQjIaOeW5aRPf4HYahceOHYMXEa5Ya3KVKvHlnzwZaNcOf/j4oIuzs8Bff+Uk1QwbxqnVzZuZ2ScEG9pKlWyqGHfv3v37BrNMGZypXRshISGCiHCiQgUR1rIlDMTEpw+rVJGdnJzESw92BQ/y9fLyyt+cuydPcgg5/C/xXzKY7733nrAnj6xYsSJLfdOmZmOPWbM4RfoCCCEwZcoU2Wg0onHjxihVqlRmmvXYMUR9/z0WN2iAUW5usk6nQ4MGDaTVq1fjyZMnaNWqlejUqZOEjAyOID7+mMsRSiVnWyx18VWrViEgIEAIIRAZGYnjx49nPQlZBiZORNLJk1nIRCl+fphWsaLwdHeXx+h0GNKhg3To0KEspLmrV6/CYDCwAk9CAqcQN22C+epVHDpwALX9/NCkdGkZdepwb+egQTw56Tkb+TfffAOj0YjWrVtLjRo1kst7ecnmcuUyDa4d3nrrLXGjUCF2HBMS2FB07MilmePHbfU9IQQ2bdoEJycnfPbZZ1zD7dzZ9j4LFy6E0WjEynnzOFLOL/EGQNOmTaV69er9b2jqksRMbGtryqhRHCx88AE7TklJrJ70utPEuePNNph+ev1iMxF/pIYNMz/22LFAiRJo9fbb2NSokXjvvfekO25uEMQKPr/16QN06oTjo0bBxWDA2NGj8WzECDxu3hwLVSrMIMIei/FcSISV1auLZI0GIy2CzvcUClTXatGPCEkKBad5lEo+D6tihV6PsC1b0JcIj728+GZ3deXnLF2K5Ph4KBSKnNJYQvACEoKPyEiOOq2IjeVazOrVCL97FyaTKf/LwYrERO7n/OMPNK5dG0SEJk2aiOPHj7Ont2sXrly5gvNnz0J+9gyBzs5YZ5UBe0kEBgZKvXv3fnGK59Ah1tn9P8KuXbv+MwbTClmWUbFiRZuhPH/+PMLCwkBEmDNnTtYnL1zIWrH5xLZt22z9hWFhYejSpQv26vWiukaDRo0aST/NnYvEXbuyvObUqVNwcnJCxu+/8zpctoxr9xMnskGYNAlYvBiyLMPZ2VlkF2Ow++OcsRECAQEBcoECBeDp6Sm7mkzo1q2bdPDgQcjNmvH72SEmJgaenp7yqFGjJFy8iIx583CjQwf07NTJlupt06iRuHr4MNCzJ2/oVarwPb1iBZdSskWaVgNsZaVKkoTKlSuLla6uIrviEQAsHzkSrYsVyzRWMTFcG9XrOerMhjNnzsDV1RX9unYVVmH9MWPGwMnJif/munUvXeYICwt7tX3l7yI8nL/r/fthawV6CUP/GvDmGkwi0hgMhqfpPj45L64sAxcv4pMOHZCsUuHHihVR0sVFxBcvjjUdO6KpRf5ullKJG/Xq8WgsImYFVq2K3ytVQkmtFt8RoYyV2KNSISgoCESEcuXKoU6dOlAR4XhICBugwEB+j88+43O4eBHlSpaUunbtKp7FxWFVs2ZShkbDUn49e0KaNQsuLi6YMGFC1q/szz/Zo0pKQkJCAj5o21aao9WiefPm0vbt23kGZXw80K8f4qdMQQGdLsvLf/31V6xcuRIrVqzAkiVLMGHCBG5aBphwNHw4G+IlS7Bq2TIQEQYPHsw3qNmclWgAAK1b45Kfn2jQoMEreZwnTpyATqfD5MmTxf79++Hv75+v2Zz/a+zcuRNeXl7H8S9Y26/5yBONGjUSlvUszJZmdqvx1Gq1yEIQOXDgpdjLycnJmDBhgo1kpCBCgqcnnlnX4sSJXBe1x+PHKOvuLicGBnJEC3DKzrpe5s7l+uOdO/gkKEjOVV0qJQW3PvkEw2vVEl5eXrLJZELXrl3lAwcOwBwVxY6i2cy12YQEW30sLi4OBQoUkNu0aSOf7dcPf3p7i6o6HXx8fOSeDRuKsytX8r0dEsIEmzFjuBSzezdHw/37s7P86afcXvLBB0BYGGrXri336NHDtuvLsowiRYqIvr16CXz5ZRZRCABI79ABX2o0OHDgAJdmjh3jyGv//hxzMK24du0aWnp4iDtGIzp06CDc3NwypSr//DM/GqpZ8PDhw9fHvs8Pzp/ngGLQIHZAjh7lvfR/b7TfaIPZokKFCs/Qp0+mwczmgS3p1g0yEW6o1fikcGHMVKnwp1YrrjZrBiURfvrpJy4wh4YCb72FjOBg9C1USCrr4YFDhw5BzJgBeelSPCtUCE0sG8m4ceOA06cxLSAACiKcIcKpVat4cf/+OxNuvvzS1jOmJMIMrRYRRFjj4yNQtSrQuDHiq1VDMBGW2zcanzzJC8di+MeNGycC/P3lNA8PvPfWW5LBYEDlypXlt99+W27TqpW8vGBBsZMIa2bPtrFSlUol/P39RUhIiFy6dGmZiNC9UyeuD548CdSti5BixeDk5CSICJs2bcr8+/v28SZgBxEfj5Vz58I7uyTXS2D//v0oXLiwZJ/yyzJ8WpaZ4Wcv7/U/hsVgHsO/YG2/5iNPXLx4EXv27IEsywgNDbUZy4sXL6JmzZpy1apVM7+kxET+jp7j8cfFxWHdunVo0aKFpNfr4efnJ/Xq1QsxMTFoHBoqrWjXLvPJssyHldxx7hxQsSJmlS8venbrlvlHmjXLQdLBoUNI9PBAGX9/IezIH4sWLUIPJyexSqVC165dpV9++YUnq1jbstq14024Y0cgIwPy778jxcUFDSpVkg0GA9yJsJwIXdzdpan16yNqwAB+bcmSLCx/8yY7q+Hh3B6yaBE32+/ZYzt/pKVxFmj8eJz87jvsUCqRYKeTeuHCBbi6uvKg6YMHucRif00PHsSiCRNQ0mRCaoUKWVtX5s3Lc7JHzPXreJcIrq6umZq6x49zf/pLRmlbt261TUf6x7BsGX+2K1dYlen8eXaMxo3j9qTevZn0tHXrS2U2/ibeXIPp6ur6w/z58/lGLlEi02jaMcf2TJuGJ0QY7+EhkpydIWu1OEyEhiaTbDKZRF9/f/zo6Snau7nJ3YOCEEmEL6tXl81ly3Lh+e5dpnevXcte6ZQpmSlSS62yaNGi8tERIzgd8/XXTA4oXhwftm4tlSpZUmpKhOgiRTCNSKxydeXF8f77ELKMy25umF6yJBsiIZjtu3Yt9u7di/fee08yGo3Yv38/GxJJQlxcHEaPHo169erBx8dHGAwG1CDCHp0OTYhn/BER7OW1evToIR1xd8ejcuWwbds2tGnTRhAROnXqJLZu3Zp1uaSnc+rLDvPmzUMhIiTq9bnWXF4GixYtwvbt20FEWetOZjOnsv7H6j722LlzJzw9Pf9TEaYVhQsXlq2RpizL2LZtm814rl+/PvOJ/fqx02WHxMRErFmzBlWqVJG1Wi0CAwOlLl264KpFr9SKPX37YpPJlDU1v3o1z6ANDWXm7ZUrOHToEFxcXDLHcllm3GaHLEkoYDTC7O7OxBhJwiejRuGQSoWMGzdY8WbHDj5fT0++l8+fh3ztGn765huMLlFCfqRQoK6np5jeo4d8+8gRhJcujeNqtcDs2dxiNXQoGz/7Hu/t23n6hXUKyxdfZLY8JCdzG87Nmzh37hyMRiN2DRki8P33vOnXq4ewH3+Em4uLsDFkz51j5ifAkeSIEcDu3ZgfGipaBgSIZ/bCCr/8krWVxAKz2YwhbdtKk/T6rD2WFy8yB+IlEBYWBj8/PzEqN0H3V0VqKpOnzp7lvfrRI762U6dmlp4A3ucCAriGGRzMe+qoUbl+5n8Ib67BNJlMj2xsPmttUKHIFD1OTcWBtm3Rv2BBeZ7JJE4T2QZAf2dhuX5FhN3vvotVAwbg83bt5K86dpQzTp1ir69bN26CNhozRaCrVcsx7SMoKEg+07kz9xk9eAB8/z3+unIFLdRqJIeG4ggRhEqFOCLcJ8IJd3fx4OBB4NEj/LZuHTZoNKjl5IQyRiN83dxQumRJ4e7kJAa/+y5ur17N71mwIDPJAL7xLfTwwMBAuXz58pBv3sRpoxGdPT1FicBAkZSUxIuwcmX82rOnXLZwYdnP19fWwP5pLiPEADAL9/ffbf8VQsDb2xvTp0/nze3vKArZgYhQsWLFzAcOHsyzlWDLli3o2rXrP67Gvm/fPnh6ep7Fv2Btv85j0PP0OS2YO3cuNmzYAFmWUdtS0yYijBkzBi4uLoixqu3s3Ans2IGoqCgsWrQINWvWlHQ6HQoWLCgNGzbsucOCnyxfjgFqddaez99+45mR77yTpX/R09NTPmRRAUK3blkijLt372LWrFmiVKlSslarxZdjxggkJgLvvgvZ1RVPiXDl7Flmzrdvz2u2ShWYo6Mxp2pV8YtKBV9fX3G1WDER3aULxJMnsDrbsT//jC+VSjzN5hTYsHkzK+qcO8eOwkcf4WD16khISMDDhw8xevRoHP3gA4jevVGpUiVp4MCBmVFaejqwahXkiAhcU6lwvV49fiw+ngca7N/Pad7Jk4GQEMiHD6NkyZJS586ds0Z60dFZFH2Sk5PRqFEjuZ27u2y2702Mjuba5UtEl1evXoWzszM+/PDDv3+/3b3LJEiAjeDgwXxOy5fn7O8Wgo3inTvsNIeHs8Niv9eOGvW/UAB7Mw0mERV1dXVNsaX1Pv6Yh0hbo8z164HvvsMdoxGxJhN2KxR4ajGWgliUXSKWvdvv5CREv37MSP3wQ26oNpm4BuHklGmI81CuqVGsmLzZujAAYMECfNqypXTRZJIFEYYQYZePD/pZWLd7FAr8oVQCjRvj7t27KKBWw6xUwqzRYAgRLrm6IvGzzziCbdyYCT5EXPvcvJk3gkGDEHHuHGKJcPfMGY6Ahw3jo3lz/ixmM0fCls1uwIABMhFhyJAheS+Xpk05tWzByZMn4ezsnOkNDxjwUoNbd+/ejTFjxuR43Loh63Q6aLVaXCDC7rfftoWX586dQ+vWrSV3d3fbc/9p/Prrr/D09LyIf8H6fp1HoUKFRFBQkHTvOenuxMREhIWFQaPRgIiFOEaOHIn69etn6dXsFBgoz3R2Fnq9HkFBQdKoUaPwvPfNgs8+w/tly0pZnLWUFBZByCbo37RpU9GrVy/e6QcORMyaNZgxY4YoXbq0bDAYUKpUKWnKlCmYP38+ChUqJCDLPAmECE9VKtG9Y0cJkybxXEohkNi2LUr7+8udgoLke0OHcjlgwwbWLzWbWay7Zk1g3jxUq1xZztWhXLUK5ooV8fXQoQgJCZH0ej2+8/QUywMChNYyeMFgMMDfz080cHaWNysUiLKb6GOPhcOGiQ8DAyWsXcsp03Xr2JD07MlRlcUhjoiIgF6vR4T9+zx+zOo20dGIjY1F+fLl5eDgYCnx2bOsxvHHH7NMe3kRhBCoW7eu3KpVq1czlunp/PcbN+Y06s8/c3YuLc32efLEkyc8cMKqBDR+PBtQ67AKgPvQs/Xa/wN4Yw1mlxYtWmTmKu7ezWSpEiG5e3csnDEDlUwm8WuXLrhJmaIEMrHk3XUipKpUeEqEryZMYMNUuTJHdEYj9zQlJHDj7BdfAO++y2kEy5d/7Ngx9OzZE98T4VJICGxG5f33sbpYMXl3jRp4VL48RhL3dq6xGOkbTk7iMyK0fe89odVq0UCphGwx3joifLd4MXDjBqQGDXA7OBhnQkNxo2FDfNW+Pdr5+krrFQqU0elQpXx5Mb5kSRlmM7e0fPYZp6+sTsOFC8w8vXkTkiTBzc0NHh4eeeu8Pn6cY4jryJEjrSQn6dNPP0XkyJHcE/UChIWFoXHjxjBZBt7aeuMAFClSRCYizJgxA+Hh4YiMjET37t2ztDVotVrUrFlTXrRoEYgI/nkomLxOHD9+HB4eHn/hX7C+X+eRnJyMPn36SE5OTpg7dy7u379vu8YGgwEqlQpKpRJGoxEmkwl+fn6oUKGCpFQqbd9Hz549MXv2bKxZtAixZcsiKvtQ6BdBCKB+fXz/1VcIDg7O3NUnT+Z0nSxz7dzSmrFv3z4olUrMnDlTNCxRQnbT6WxG0l5gwWw2o5FOh4wCBXjT3bYNEXPmYLBabYuKt2zZAmdnZ9G7d28p19aohAROuyYlASNH4mKDBgguVizzHNPSgAED8GmHDiJAr0fFihXlefPmsRE7fRqIjUV8fDyuXLkCvV6P9PR0/HrgAJJatmTDkR2yjEfHjyNIp0PE6tVcA711CzanOFt0W6xYMfOaNWuyvkdaGu7v24fAgABRq1YtFkA4dIhrqNbrvXfvC/WfM78egQEDBkienp5yvvsvJYm/r6goNnBeXvx3J0zIv6arLOc+3L51a5Y5HDAg6+MZGRwQ/HNchzfTYCoUirGjRo3KXNRmMyvieHpCthioCOIxWz1UKsRajGUGER4SCUGEWGJpOzMR2jk54dayZVzrCAvjAvSNGxy5Xb7MQsKPH/MN/vPP2NqmDYwGA9q0aSM1Ll9eFDUaodfrUbp0aXO7Zs1ELaUS54iwjAi3LOey1XIOKUolQtRqHCWW4TMRYZdKhZ+IcMrdHadUKswfMAADjEb5uMGA2YUKSVPc3MQFFxfx6ahRGE6Eg35+KE2EQ/YLbeNGptOXKcMMRICF0zt2xM5VqzCOCFf++CPvpTJ8eI4ht59++qlcr149efbs2ahevbpkMBhQr2RJeaOl1pUdZrMZ48aNg06nQ/v27eWlS5fiyy+/lN3d3cXBgwfRs2dPBAQEiO7du2d6sZ98AnngQNy4cQOXL1/GxIkTkZqaip07d9o27PD8DIz9m3hTxdftP5+7uzuqVq0q+VrS85s3b0ZKSkoWApZ1coglqhQ5hP379s25wb0IKSnApk2IssxcTEhI4PScZdQUAODHH/Hg998x6+uvbZFkUFCQFO3jg7QNG3K+Z2oqsHMnmpQqJW9t25Y/wLZtwKVLuK3RYMrHH4tu3brBaDRizZo1eUdNfftyn7PlPZ9+8w0WK5WIjY4GhEDyd9/hkMkkigcEiAsXLmS+LiGByUAWA96nTx9ZrVZnOs4ZGcCMGZwWvnUrswXl8mUgJATjTCYxvFgxGZ99xm1kH3zA2ZuaNTnatEz9qVSpkpS9xefypUv4S6nEmvLlMz/XtWt8DwOc3q1ePd+zIP/44w8YDIYXZwusw7JHjODPHxjIafXY2JefiSnLHJV+9FFWjVkhmBB0+3ZO7VkhMq/nP4M302CaTKbl87Ir869eDZQvb5O0k4lwXqUSzzQajFEqMdPyWCQRjhJhqUaD3y2PhRGhnF6Pg40ayeLRI9y+fRvb5s5FssGAzwcOFE+MRnw6ejQaNmyId7y8pAMqFf6aO5ep5KNHQ5jNiFm8GHVLl0ZjlQqyJf1qFW/PIMIlIrxDhPFE2K/RQCKe1flraChiiXCbCGalEt2Jez9/LV5cCCGYSn/gAB6ULAmtSoVixYqJ+d98g71EOKDXY9miRTi0YgXSnJ2RUrs2xKZNWcSIMzIy0KN2bZxXKNC2Th0xWa/H5nfeyblUbtzgm+45uHbtGlaMGYO7Gg1KBgWJ7IIEzZo1EyaTCd99953tThVC4P3337dFLAaLo2F70a5duUatS5YsQUhIiPxaZmzmA5s3b4anp+cB/AvW92s+bIiNjUXTpk1lNzc30apVKxiNRkyZMkW2d34SExNRuHBhsTkvssgPP7AD+TLYudOWHqxcuXKWtOyDBw8wd+5cUbZsWbmQVovHKhXmjxyZKXaxc2eWiSU2jBsHhIbim9mzERwczOvNYAAiIrBnzRp8plDgrdKl5SxGLjfEx2cVOc/IwKzAQPlKUBASS5VCFRcXuXmzZjkjr1u3Mg0UgGXLlqFo0aKZ6/qPP9hhdXfniOvYMVv0OH/+fBARPDw8BMLC2LBaZ9mmp3NP55gxwPTpaBAYaLbvgb5z5w7c3d0xoUsXNlLWNGx8fGZ0euNGluH0L0JycjL0ej1yiLI/eMDnHhbGDOk//uBzsx//9ioQglv4rK1D9ggP55rud99x3TM33L/PKdzs00v+Pt5Mg2k0Gr+dPHly1hBn3jxAp0OKJfV5lwgZGg0SFAq0IIIrEWIshilRqYSwGC+ryPpxIlxXKOCs18NoNCK4SBGpZo0a8CTCDYMBSzUaGI1GPCpdGslr13K9jwjQ6yGio/GYCA2IUNNiIBcRYQGxAMJByzn1VanwxNkZZiJ8YjmsxtVNp8MQpRJjmzUTk4mQfu0akJqKlMKFUSYoSPZUKjFt9GgpPT0dlStXlrUqFUa4uopHSiW+dHaWB7q5ye00GjxUKtG/e3cMGDAAI0aMQJMmTWRrA7nJZMKywECMJMLY99+XUbcup1TMZiZXZJv4khuEEJg8eTIqEmHp7NlZfleoUCHJNtk9G1JSUtCnTx+JiDBy5Eje4J49y9NIL1++HCWtDOL/AX744Qd4enruwb9gfb/mIwuEEFixYoVwcnJC7dq1hYeHh9y4cWM5Ib+9elevZrI684sFC2xTR7799lsULlhQTtPr0SIkRNbr9ShZsqQ0adIkJCYmclYnNTUznfjtt1nFuGfO5CgjLQ2QZaSnp8NoNOJmth7F+QaDuFu9+vPP6+BBjnAskCQJv/76Kyr4+GAM8RCGaePGySK3SG3ZsizqM7GxsdDpdFzyGDOG22Fu3sw1Gm/QoAF69OgBb52O+zZzG1Tw7BmwbRt+0OnEww4dgORkpKSkICgoSLRu3ZpPaMoUNsoAs3erVOHyVGjoS7eStG3bVnr//fc53dmzJ38H1apx7TA1la/V62CxZ2RkjhPMrTx08SKXl1atYuJPbrBGpnnpH7863kyDSUTNKlWqlNXli48HfH2xVa0WVnKPTDzrTyLCloIFkWxJ107T6cQJhUJsIh7ddZsI+5VKxPr44FGHDpCdnHgY6507WFqwoJyqViOaCOOKF8cFk0kcGT4cVzt3RkrhwkBQEI6MGoWrRGjg5CROlSyJdCJ86OkJyfK337f8nTJESFUoIBPhayKkWYz1zurV0c7ZmdPG7u7wIMKpU6eANWtwpEYNEBGeNmoE9OqFJ0+egIiwe948FNbpcECjEcJoBAoXRlpYGFYplQgICEBQUBA8PT3RqlUrkb1uuWDBAgQSIe3DD9kb9fJi7/wFm6YsyyhdujSICHEFCvANa8HTp0/h5+cnf/fdd3m+/tmzZxg6dChsVPl161g0Ihds3LgRRIS+fftKiYmJqFSpklyhQgU5NDRUqlKlilStWjWpRo0aUs2aNaXatWtLderUkerXry/VrVtXWr9+/Uvf2Zs2bYKnp+cv+Bes79d85Ipbt27Bw8MD33//PSpWrCj5+/uLx3mJntsjPZ3rSC/j3a9daxMSf/DgAQw6HT729ZUmTJiQO7N261agSBH+Wz4+HNk8ecLGYNWqHBmJcuXKSVPHjxewM5CfjxmD94sVk59LFNm6FfKAAThy5Ah69eolubi4oJGzs3xPp0NJIiRZJ6HkJu3YpEmOUV5dypaV/ypWjFm9T55wJFWqFEb16ye8vLzksmXLSgULFpSICJ+PHSs+IspkkuaBQm5uIvzrr4ESJbCqeXO5UmBgZkbg5s3MaO/xY06PbtvG5MX8wlLueFy2LGYZjQIXLzIjPy4u3yndl0Lt2tyelBdmz2ZyT2zsi6UYd+3iSPP1necbazANWq02/Un2vsA+fZBuMUSy5edji/F8SoQRRNhFhCNEmGUxXLeJx3elEqG/0Yj0BQtgI84MGoTpH30kZhHhY8vzkonwhVqNiUolXF1cEHbqFCa6u8sHWrUSXw4aJFsl9YaXKIEmRPhMqYTJyUng++8xSq0W4UT4UKvFW5aU7PtEKE6EE8S9jnuJ4EuEJcHBkDt2xCf166NEiRICd+7YeiS/Dw0VT4mQ+Pnn6KPX42irVsD06djcrx/ec3FBlD15R5IQ//PPaFm5stTFy0s+o9XCSafDQ60WaR06cGqpQAGu3+7e/dzVtGjRIuh0Onh6espzvvoKq77/HuaEBKuUljCZTDiZFyU/L+Sx2GVZRp8+feDn5yfu3LkDhUKBb775BrNmzcL06dMxdepUTJkyBZMmTcKECRMwbtw4jB07Fn5+fujUqdNLN11v3LgRnp6e+/AvWN+v+cgTLVu2NPezbF4FChSQ9mVrmcoTrVu/nEj+e+9lqTtNL1BAnptHAz4AjmQ2b2aDmZTE/y9fPlN2MhtmzZqFCkFBMpo1sz2Wnp6OohoN7hcuLJBNa9ZsNuPwwYMY07at5OLiAg8PD7lJkybi3MyZbIT27YO3t7e8Z/durge++25m3yW/OQ95SE+HEAJXzp7F8p49xTkifFy2bJYFfal0aQQ5OWH79u345ptvsHnzZsTGxkKsWYMVRJBfUHLw8/OT9u3bhxP79+NrtRqPundnB8RadomM5OzQpUvMXfj+++dHl5LEQ68vXeJ6pKsrkJ6O9PXrUVKtzpmWfV1ITOTo9cyZ57Nme/ViR2TFiszoOS/ExmY67X/DaO7evRsm7hF+Mw0mAPLw8DiZo/F+1Cgk6/XYYzGYksUQ/qxQ4IHF2KVYDOgCpRLpxMoeZy1p1DNE+LFJE4G33uJezEKFENakCdKIhci7qVSoRwRJoYAgwk9EmEiEXVotBBHg5IQZ48ahkUIBpSV6lIOCsMLyN5OIhHUKSoblsXTiftBWLi5w0euxxs1NLP7gA5h9fFjwnQhORPipXTsZ1aoBoaH4rndv7CUCDhzA/m+/RRmtFqVcXMQDIlwsXlw+sn07Vru5iWuFCqHX++8jUqHAxHLl5F1TpmB77drY/fPPkK1ki7AwZrZZF1zNmrnWKO7du2frzRs6dKhUqVIleYiHh3ioUMDD3R2enp7IQRB5HmSZWcnPGf5qFQUvUaKEyK9UV5cuXdCqVauXNpgbNmyAp6fnbvwL1vZrPvLE5MmTRfXq1WUAKF26tLQov+L3+/fnv44lSVxvtNvQYn188EGRIi/+jvr3Z/b7ihVMEsoDqampMOj1uJdtVNSgQYNQU6+H+ehRyJGR+O2339CzZ0/J2dkZPUwm+bFWC1u/5y+/cIuHhWzTqlUrYeuB/OEHbl2zks+2bEFagwYYOXKkXNXLS6xTq7HZ01Ms+vbbLPeALMvoQ4Td2Wt1p04BH38MXyLkRp6zR506deQPP/xQ9vX1FWPHjuVzsIq+z5rFhKry5TnFWaxY5qAGK8xmToOuWAE0asT3XdWqXBu2jGY7fvw4atWqJeVQ/nqdaNqUHafnQQh2Bp484dppbmSv3PDpp9zL+4po3Lgx+vAYuDfXYKpUqtG9e/fO6p4lJABduuC4i4u4T5mzLdMtxusWEX4kwipXV9Qnwh2LYY2wRHsSMXN1ABHaE4/0OmJhvF6yGLrPa9QQQ4gwXK0WiUol7hGJke+9J2SVCmnvvIMNRiPXMFUqyCoVYt3dMcZyLhcoc4RYIhHuKBSIsvxOKJWoXq2aHOHlxTTt0aMhNWyI8+vXIzg4WL7j7S1ABNnVFZu8vHDL1ZVTVaVLI7lSJYQtXQpZpwPKlEH9QoVQiwh1ieuWZcuWFbmSZw4f5k3C3tDt2cMMtQsXbFGBEAIff/wxfHx8stzdQpJwZM4ctGnTRvrss89eTtXg2TOujzxnw0hNTcXEiRMxYcIEbMnnWJ93330XXbt2fWmDuX79enh6eu7Cv2Btv+YjT/z8888IDAyUAKBZs2YYOnRo/lLZYWF515eyI1udEELgzp070Ov1yHMGpxW7dvF2NWLEC/9Mx8KFpXStNstjZrMZzs7OWOrnJ7ar1fByc5MbN27MI8DS03l9C8EG/caNLCphx48fh8lkyjSA+/ZxqvPKFWDJElzo0QP9iXDi3Xchb90KOY+oboVeL66MG5f5wI0bHEWdOQOlUvlcsQcAOH/+PIgIQUFBWb+b+/e5jlq6NJ/b1q1sZM6c4Trgrl0cgbm4cH3z+PFca88//fQTjEYj+vTpk3fL2d/Bw4fsjDx69GKOxL17LDQPcK08v9rFYWGs6vSKNdYpU6agYsWKMt5kg0lEtcuUKfMkyycXAvD2huzlhX3OzrZaprWeaRUtmGBnTK3H75afYZafaUR4aDFwZsoUPXhgMZ7dLdFrHBG8jEbsUCgQSYTplsc/IMJ5SyQZrFBgxtixtr8hWd5zLBGGKhR4RCRWqlSCiPBjYCDQvTvXESw3YWBgoPQgNBRJHTuirKcngoKCpMTc0hrDhiH8/HnodDqbtmyeMJs5hWPfHGxBamoqwuvUwf3QUMyfPx8dO3YUNh3d7BCCb9qXYOUByJrieo1o1aoV+vfv/9IGc82aNfDw8NiGf8Hafs1Hnrhx44ZtPNzw4cPRuHHj/F03SWJiRn6kEmfMYINkRYkSwMKFCAkJke1HjOXA7dvc+P7OO5wSzTbZJDumjRuHTkWLymazGdOnT0elSpUkJycn+Pr6isZvvy0ufPpppoj706cciaWnc7Rcp06uBJQsikMAG6UpU/j+XLgQh4oXF2Xd3UVQUJCk0Wjg4eEh/2qpa8qyjFWrVqG+SoVEqxD606ccFVruzaCgIJEXSc6Kc+fOQaVSYXUuk0ogBF+nd97hEWNEXLb56CMWLwGyOAHZsXHjRhiNxleeRJQv9O2b/2kpBw9m1l9/+ollRl8GdeuyeMtLIiYmBlp2tt5og1m6YMGCdkKLFgwdCvj4QF69GnPtDKawRJHfhITIc/V6xKlUuEbc6vGMWLbOTMxqjbBEmsmW1+0iQmUitCRClEIBsyVirUmEG5UqwazX4xIxoeeCxSBOUihwxxJNnrG8n3UgtXXmZgQxE+9acDBkInzfujXer1OHiQBarU0urr/RKK6tXo2eVavKy5RKbMxNPECScLhkSWG0qI4UKlRIuLm5oXnz5vKRI0eypn6E4L6wXAgRaWlpqFWrlqzX6+Hh7i6PMpnko+7unA7KC99++9wbM1d4eb2UalB+0bhxY4waNeqlCxr/xZSsJEnQaDSIjIzE2rVrUbRo0fy76L16cbP8i3D0KJN1rDh+HIiKwrRp00TZsmVzN9BpadyEf/ky1zIvX2YGaB6M6tTUVMwdORKd1GrbSLFBgwaJa/bPT0zkMsuOHZx2fecd3sjPnMkz8uldo4aY3ayZZKsTbtzIhpYIKF8eaTdu4L2iRVGsUCFERUVh7ty5wmg0Ijg42Ozs7Cz8/PzEwfbtBcaO5ejnm29YyceC8ePHi8qVK+fppGzcuBEmk0l8M2aMjPPnWbZyzRrOzAwbxmnZfv24tOHjwzKDEybklJ7LBevWrRNGoxF5tQ/JsoyTJ0+if//+0vz5819eiP3WLe5bT0/Pf31x/frM9pqkpJffU06d4ij1FVrRLOPM3miDWdDd3T0nfe3BA6BUKcDZGUtcXES0naFa5+IiY/NmbDIYMM5iDM9bft4hrhemWwxotMVgXrc8J5G4h9NsMaBxRKjj4SEEEWBJ36YTS+GFWwzhPZUK6XZG0mq4M4hrqWlEuEeEz7VaPCKCipjVe6pNG5itN/GZM/hNrRbnduzAwPbtpWgiaIj3watXr+Ldd99FiRIlpEoBAdJdIvTv3x8tW7YUn376KaZMmYKGDRsKnU6Hdu3aZS76sDC+8bPJ/f3yyy/w8PCAj4+PbEvPnD8PzJ/PN3w2Qe0s2LuX9XTzC7P5tWnT2qNOnTqYlG3OYX7wxrJkX3CNg4OD5RUrVuDevXvQ6XTItYUiN6xf/3y2oxXvvstpTIB7pS0b4s2bN2E0GpFDfUeW2ZhZSUV9+rBhfvyY1bgszfVJSUmYNm0aypQpIxkMBgwrUECO9fbG9evXUbFiRXm4XY+kDceO8To9cIBVenr04NJDairw+edsEH/8EVLp0vh1wgT8YjSK/Wo15NhYlvBbu5bJMo0a8T2RkIDEgADs1WoFTpwA5s3D9RMnsGrVKly4cIGv5erVQIcOTGR5//0sxuP8+fNwdnYWYdeuoV+7dpg3ZAiwcyeSx47F7OBg8ZVGg4gqVdh5+OILNpbffceO7v37XNbo1o0jq969+dp+8gmTenLrXbVgy5YtMBqN2LZtW5bHzWYzDhw4gO7du0suLi5wc3MThQsXFq80G3PhQu69fRkyzqBBXCYC2LGqWfPl/64QLB2Y3/qnBW5ubm92DZOIXHQ6XU7XMDmZL1hoKOZUq4afK1aU0ywkHUEEBARguq+vuGgxYlssxivd8vtjRLgdEoLHRPiVCE0LF8ZTy+/vEiHBkrZNIlYS2li1KjYQ4aYlFRtuMappRNhAhH2W93lmeSzNEm0KIszTaMQiLy+srFqVWyhq1cJOIpxXqdCqcGFRLzRUbqnVojIR4uLiEBcXh67vvisVUqkAsHHw9/fH7NmzsW7sWNxr0CDXxeDh4YGSJUvyyr12jcccZau5HD16FESEAgUKiFwlxLZu5SbsvG6AvXuzTHx/LqZP5w3nH0C1atWen+rLA28sS7ZmzeeqsLRt21bq0aMHAECv1+ORfc/j8xAbaxvSnCfMZjaY1rXWvn2WNorg4GB5drZeXgwdymOzrO87cKBNMi/14kVE+/mhWqlSklUEY9q0acI2zgrchK/T6dCoUaPcz+nzz3kLHDsW6NSJ1WqSkyF36YLTY8eie9OmUlWtFoEFCkg9e/aEq6urOHr0KBvb4sUztZYtjPVHjx7BVauFfPQoq/WcP8961IMHc1R79y4TbqpW5Wt26hTrrP78M8ScOdiuVOIQEZYZjRhNhEPlymGITicG16olRR4+nHfa+8ABlo7bv5+diTFj+O8Jwa0ZTZrkkLoEeNSe0WjEBotBMZvNOHjwILp37y6ZTCZ4eXnJ77zzjrCmomVZhkqlQkoeWto58NdfrBiWj57uLBCC9wTrWr10ifepV8Hp0xzZ5nO60uXLl63zP99og6lUKBS5s8w2bAA6dcKpQoWwuGpVWZQubathQqPBgKAgEUWZJB/ZciwiQiVLpGmtcXZSKrGMOJ37oV20ONpiaP+wGENhMZjJlud+rtFgr7s7oizPFZao9IRSiXC1GrFEGGwyCVmhgKRS4VciAbUaJYxGEd66NT4dMgRHKlbEJi8veHh4QJIkfPTRR+IzImyxpF01Gg0GDhzIF+DCBWbK5QKr1Nnhw4e5JcB+BifYW8+XyHlKCrNaGzbMvTdNlnmze1Hx/R80mOXLl89TPOF5eGMN5g8/cErzm29yNW5ff/01qlSpIgOAl5eXfCq/EyGEYKPwvLTZuXOZBjKXvz1lyhRRoUKFTM9twQJ+vt3mnL51Kzb17o3SpUtLBoMBrYoUkSMKF8aJvXuh1+szSTNr1gDdumHNmjXQ6/X4I7sMZHo6M0ObNmVJux07IJnNOHLkCPr06SO5urrCxcVFNG3aFFesETGAt99+Wyxs3FjG/fuZfYFxcZz+tPxtFxeXzL8nBEdJW7Ywga5wYS6vdO7M0XK9elzH3LMHOHgQD3ftQuLjx0hISIBer4fBYMDPP/+c9zVNSeGIvXLlTN7AmTOZcphWnD3LEe3+/baHJEmCj4+PmDp1qi2SNJlM8PT0lN955x1hr/lsDzc3N7E3F65DDgjBjkXPni9+bnbcvs2ynlYkJWURhnhpxMdzb3keQ7ftUbJkSWnw4MFvNukHAGk0mrRcWWbz5wMuLoh0dsaaihW5HUOh4I+uVOJo1arIoExCj0wsLHCJCFdcXRFLmexaawuIVbQ92WIgn1ieE2+JGs0WQzvF8txfFQpbrfIDT0/8QYQJSiX2KRSIDAjAW0ToaTDgXIUK6KTX4y9XVyEMBjR2dUXDunVl3LwJrFiB9UuWWHU9oVAoMKp9e3zh7y/69OmDZcuWIcnaQL5/P2+KuUCWZTRo0EAaq9WK8Q0byl9PmwZ7MsOff/4JIsKZM2eet64YMTGZEWr2lhCzmT12ax0iN2Rk/COpWCtKlSolLc/mEOQHFqWfN7OGefUqb7B//plDcGDnzp22YcHFihUzb3iZVNbs2ezN54XFizMJP++9xxGdHcLCwuDk5MRM1KNHWXA7MhKpqamYMWMGypYtK/VXqzHBx0f++uuvxR3rrNYffwS6dUO1smXlCRMm8GO//AJMnoyHFr3aLKS4s2fZUO7dC/nhQ/w1bRr2lC8vXC09mI0bNxb77QyLPfbMmYO7KhVHPPawq5OVLVtW/iaPew+//srp0uf0Hu7YsQMeHh4oU6aMdDdba0wW3L3LDu/YsVkdkIULudUkO27e5LTmb78BZjMuXrwIZ2dnODs7v9BI2mPq1KkwGAzo0aOHdMNumlEWXLvGMyzzK96eHRs38nWy4tIlViz6O7A6OHlMjQGApUuXwt3d3ep4vdkG08nJKSEit4tx9y5QtCgeqNUYXK+eFPfJJ0jRapFOhMNqtbilVMKPCAa1GpGW6O+xUilWU1ZGrP1hpqyM2ww7o2o1jGZLalYmwiEifGNJx/YjQitLBHfP8h5harXw9vISzYnwlVqNyi4uGK7TiSiNBo/GjGF69ZUruHHjBvR6PZRKZSZL9euveROwx7Jlz++NO30aydWro0ODBsLX1xd6vd72q7p16woPD4+XI8qkpAB6fSbz0AoheFPOiwX79desS/kPITg42PwqrL83XhpPljM3NbtN8s6dO3BycgIA1KxZU0yxU296Idav56gpL5w5k2lo7t7NuVYAFClSRKydOBFSu3ZYNmoUypYtKxmNRoSEhMjTpk0TkRs2cFuEPYQAlizBubp1UbZECY5Q7eT0ypUrJ0+2Ek7mzQNWrEDSqlX4eOBA2cPDA25ubuKXAgVw9kW9pEuWwHz0KAJ0upzC5FeucKYFQJs2baTe9pu9PUaMyN2YAYiMjMR7770nOTs7izwNLsDO6dy5bFS2bMmawRGCI7Nc0q+21/bsCYwfj8ObN8PDwyNfRjI7rly5grp168oGgwH169eXdu3alTnkOy2N20emTn3p97Xh4EFOZ1vx5EmeGbOXwvHjPK4xFyLQvXv3YDKZhF3L2pttMF1dXSP/ymuUzKxZOG5Jg2bYRYlpRDhnl1q9ZjF6qXapWWv6NrfD/jkxxLXMGCJctaRoJYvRjCaeiJJi934tNBrMdXOTk4l1ZAtYHk/TalFNp8O56dMhJIk3NLu0ore3N3Q6HXbu3MkP9O6dhW0HgFNZ2Yr4NsTFsQKIpSl74cKF0Ol0uHjxIu7fvw+FQoHj2dRQ8gVrOm706KwMxp49M/upskOIvG/u14DChQubcwha5AMW0s9+/AvW9Ws+smLPHva8p08H4uMhyzK0Wi3u3buHrl27omPHjvlnRD5+zFMx8krB16rFUe2RI0x+yQVfjh0r9iuVaKLVIiQkRP7qq69EFl3YHTtyT/GZzXjapw8Gq1RITkpi8oulDWHmzJmiUsmSEt57j8UP4uNx/fp1EBG2W43vb7+xxF9eePSIR45duIAyZcpIU6dOzepQPn3KZB5ZRvfu3aUOHTrk/j7t2+dY72azGXPnzrXq+crP7X988ICNZPv2WdnGVly+zMOVnwchgC+/xMkePfBOYODfSu/ExsaiR48e8PT0lP39/cW6yZMhPD1zThZ5WdgTfgA2mHmJr78sYmLYobKT2jObzQgICJD79etnv97fbIPp4eERlqcU28iRiDCZEG9n6Ow1Zq0qO0Mpa6vH84yl9Ui1e6/obO893PL/dCKsJu7H3ELMrC1ChEGW50kKBfwLFMBArRb7iXC9TRuud7Rvn0ML8vLly+jQoYMgIi7WJyWxR2ePmTOZdp4bPvqI++YsMJvNUKlUaN26Nfbt22dliL0ahADefpvrDdZUa1oabyjZU69mM28y+aC9vyr8/f3zL/Fmh//CtBIbkpM5tTdmDJCWhpCQEHnRokWYMWMGQkNDX66FYM6c3MctZWRwJiEtjbMKHTvmfI4QeNCkCd4lgj1xJwv272cSye3bOeugaWn4ydVV/PT++7wpRkQAZjNiP/sMF4mQfueO7TVCCBQoUEDYsg+yzE5kbnWyadO4zm8hrsydOxdBQUE57xFLH2TDhg2lEbmJK0RGsgqN3XlfvXoVFSpUkH18fGSbA5wXtm1jBuzx43nL3W3blm9G6KbWrcUBDw/x3DR6PiHLMpZPmIAi/v5SR6MR06ZNkzNeluhjj169svbCRkW93kzU118DRYsC4DYkq5RnNkGXN9tgenp6nsmr9oC7d7GpWDGRRJkSeUmWSPOW5ecDIoTYpVUl4haSh5bI1Po66++sRKBYy2vSiElCmygzbfuQCKeI21SEJQUr6XTYQ4Q4tRofarWIJkJHhQJNypcX6ytWlCMnT+bU1aRJ7JVbIkGARc0nTZqE1atXo0iRIiAirFu9mvVf7T3xuXNz71M7cYJZeXaqKv369UNAQABu3LiBtm3bir9lMO1RoQJvEABHtS4uWesHDx7kHXm+Jvj4+MivEi3v2LEDXl5ex/AvWNev+cgb6elAxYrYWrq03KVLFxw7dgyenp4vd+Hmzs09s3HrFkdGQuTNpB03DvKsWXB3cRG5ko3MZtY4dnVl2bfLl4G33uLN78oVYM8eLBw3TpzS6ZhIs3IlR7KffIISJhPOWQUDAFy6dAnu7u4YYD+UeNcuTpnan19KCguE2702PT0dTk5OyJHNWr4cwssLLi4usAoWZMHx41znBbNpBw0aJBsMBrRr1y7nnFF7xMVxxLV/f87SS3ZMnZq3o2yHJ0+ewNPTEz9/+SVnpxYv/nvTR2JjATc34OxZbNmyBT4+PnKPHj1evl8T4H0sW30bsgxERuLq1ato3bq1cHV1FXq9HgdfViDFHklJSL10Ca3LlROurq65tVC98QbzYJ6SaULgsUKBU5RZl3xqTYFaDJ61FvkXZdYg0y2jvy4VLiysA6YTLK9JJU7nWqPRd1QqnCAWPQi3GOAMy/OiibCdCLWIcFWtxrcWgz2wfHm5cKFC4q233pJx5gwQEsJpMm9vpq1b0i7p6ekYOHAgvLy85NKlS8uFCxcWRYsWFUTE6ixz52YxrHj7bRvV3YYbN/hxu/5Js9kMd3d38aNFtKBRo0bCz88v92v4svjrL/b0Dx1issP27VmjzH9i+kE2eHt7y7///ntmfSWf2LVrFzw9PU/gX7CuX/PxfFy+jB0ffoghRYvKqWfPQqVSZfe6n4+dO1nNJztWr2bnaeZMXuO5/X72bCAhAXXr1hUWliLDbGaFmLg4rmuNH88Zi7Q0YN8+yPv3I2zKFFwqUkS8o1LhslrN25paDfTqheuHDqGWVovEmBjcuHEDRYsWFQaDAZ06dco6+FyS+Byt0e0XX+SZBqxSpYo8duzYrAtYkvD7kSN5Z2imTUPklCno1auXpNfrUa5cOcmegZsroqPZeHzxRe5MdHukprJy0gu+r+joaJQtWzZTJCE6mrNY69e/GknnxAnOctntPxERETCZTGLXCxSZcsWWLVnSyrIsY8m8eUglgl6vx9tvvy2dOXMGc+fOFQaDAblG8/lAbGwstms04ld3d3Hdfu/MxJttMF1dXdcsfo5azEatVgginCRmwI5WKJBmiSK/JcJ3xMLrX9ilY62i6Kf1ehtLVrJ7/AmxEHsSZSoBWZ+TajHE1nrpcYsRtfZ73lKrhazVooFWi9tbtrDE1qNH3EBtveFlGefOnYOPj49cunRpkX3A78CBA4Ver8ennTtDnjmTHxQi542TkcG1ox9+yHJNrl69CiLCxYsXMWPGDCiVyhwNzH8bQ4dm9mQOHcobHsAeaXYCx2uGp6enIMuN9jL48MMPzTqd7gz+Bev6NR8vxN69ezHVZJJRogQ8TCZx/UVjleyRlMREouwR04EDfDx7ljPteeIEa9Fa/s7mzZvh4+PDA9MTEzNro9bsxKefQj5/Hr///jsGDBggeXh4wGQyQa1WY8uWLZBv3uRxfJUrI2L8eHQ3GHDJ1xdYvx63ihbFBb0e6TdvclniwAFOlUZE8H2zZQsTl5KSWIghezuKBatWrYKfn5/IHpXMr1pV3mIxmGlpabhw4QIWLlyIIYMHy1c0GhQzGFCmTBnRtWtXUa1aNWnp0qW5G1ch+PzKln3+NA97bNqUVXYwF/z555/w9vZGvXr15BzOwpw5LCqfu/HIHampHPXnUvaYP38+3N3dM5n7+X3LQ4dwZ9MmDBo0CCVLlpScnJzg4+UlYkJCIGVbV2fPnoWbmxtatGghdu7ciT/++APnz5/HlStXcOvWreeK2Y8aNQqVQ0NlkZHBWZGc/clvtsFUKpVfjhs3Ls+wpYyvr5Ss19vqjJ2Imav7KFNpJ0Wlwg6LAUyxS7dahQzmEPdaXrI854zldymWx/4wGPCNUmlrMYklwl7Le1vHhj0lwoeWyPWuWi1Gh4TIuHAh8wadM4e/FpMJiItD55AQNGvWLM+awOrVq0Upd3c8c3LiBfzsWWYq1IqpU5mmnwssvVcgImTpg3vdmDmTU2nWGsvly//E0NcsMJvNSE5OhlKpfOEkCHv07NkzhYgG4F+wrl/z8UJERERw43ZKCqY5O8vRNWq8XOQxdGiWFCYArjseP56ldg6AySEjR8J+5JYsy3BxcRHXli3j9KtlXqosyzh+/Dh2VK8uuphMcHNzE/Xr15e3b9+OqKgoaLVaJM+cyY7hlStAVBTOnj0LIsJ5C+Py4t69aKzVYt/KlSxa8N13XKOsWJGZmTVqsLEtWvS5GRBZluHs7CwuWpi+6enp2L17NxoSD6J3dnaGUqmEyWSCXq9HkxIlxNqAABQKDBROTk4IDQ2Vxo4dC4PBgClTpogYeyWey5c5pbx06XMVenJgxowcMzntYVX0yVX1yIqVK7lem58+y7Vr2bg+x6D7+/tLP/3004vfC3wNO3ToIFYRoYlGgwYNGkgLFizA9evXOV3600+5tqBdu3YNLVu2lAoUKCDc3Nzg4uICk8kEg8EAlUoFjUaTq8M8Y8YMVKpUiTeFSpV4382KN9tgEtGAHj165ClBUT04WL5HhK2WAdANibVj71mMXqzFeFa3/NueEGT9GUdZSUPpxHJ5Ry3/f0YEq2pQquV9rHXTeMvvfyLCWr0eC3v1gq+nZ+a8ykGDWO7Lqre4di2ubdiAZ0Q49PXXz72B58yZI8q4uspITma2qj1TLjmZiT55sFH379+fP6GCv4uYGPbgHz7kBZpXvfkfgEajyRxUnQ/06NEjhYj641+wrl/z8UIIIWAwGBAWFoZqpUqZTzVvzhJ0e/bk7+L9/HPWDTcjgx2l3bthP9QZycnsJGYnqVy8iJUBAeKTESNk+c8/ceLECXz88ce2FpBR5cvLd3IR8A4MDJTuVqvGggeTJ3ONHMBnn30m+/v7y9bP1qJFC7lT9hoZwHWyH3/kEWLVqrGTuWFDnio1VapUkRs3boz27dtLRqMRPj4+cvPmzcXSefNwdd8+mM1myLIMtVqN5gaD+NLfX16wYEGWkV/bt29HSEiI5OTkhEsXLnCkW6IEqwG9DMxmTtvmco8nJCRgyJAhspOTU/6E1U+cYOb9unV57zmyzKn1F0wNqlmzppgwYcILay+bNm2Ch4eHXKliRTm1RInc1ahKlHju+L/ckJKSgoiIiCx72507d3Dw4EFs2LABGo0GU6ZMkWy19Vmz7EmIb7zBbN2oUaM8XeFhw4ZhllKJ8VqtXIEICoUCjYiQ7O8PzJ1rq0VeJMLPlFX1x76NxH5iifWwEoiuWZ6TSJkiCKmW9xREiFEqRUciXNRqoSPiSRr163P0lZ7OTcghIejdu7coUKCAfIAIc729ecG99RbT4rNBlmUEBwfjEyJmH168mKnrGRfH3nP2eqYdnj59ijynj/xdJCTwIr9wgYWgnzzhBmQi9vD/B3iVCLNHjx7JRNQP/4J1/ZqPfMHafN+8eXMMHjxYxunTvEGePfvi2vOvv/J3bcWjRywkYP86IZjUtmRJTpLN99/jXpUqICJYjWS9evWErQXk3r2c/Z6//YatVauiUaNGnCEpUoRbWABkZGTA2dkZa9asQZEiRYSbm5s4b9/jB/C917Mn/33r+Rw9ylOC9u/niSJ2hu7+/fsoUqSI0Gg0aNmypbAnFKFVK1tPpg1LlrAjkQfeKlFC+qtWLRZRf1GtMjfcvMmtNHaIj4/H+PHjZZPJhJCQEOnq87SfsyMigs955Mgc4haYOpXTv/ngIIwdOxZly5aVn6dJHBERAa1WiyVLlgj51i1uccoNK1e+kpD6gwcPrNkz2cXFReh0Ovj4+AiDwQCj0QidToclS5Yg3VpOyCRNvfEGs0apUqWe5HXh5syZgwU+PnKCyYQAIni5u4srvXszMy4XoxhLhC8t6VT7VhP7kVxWg5pMmT2XVlGDA5Z/byVO/6ZptRis12NMt24CPXpgjkKBXdu3sxdrbYROTwf++gsKhQKzZ89G1KlTrNgTH89G7/p14ORJW89YRkaGLTrc9tVXbHC3buWFLgQ3N7+gL6tHjx7w8fHJv9C2PTIyOF139y6TNiIiOCXXuDGTJ3x8mMW4eTMTC6KiONLIj4rQa8Lly5fxsoLR/3WD2bVrV6l9+/YYNWoUGjZsyJbi6VNef5Uq5VS6scezZ1x7szooP/zAxBWjMdPofPst19vsU/Lt27PuKmwpz9zTh5LEdXD7jXzxYjxr1w46nS5HJkEIgSpVqshEhIoVK+buOMXFsb7r48eclrU3gFeucJvDqFEQhw9jxeLFwmQy4e2335ZzVRbLyMhpTEJC8laY2bIFH3p7S7vq1sXKpUvxzTff4Ndff30pBw/Tp7MgPHg81ZgxY4RF8OGV2qoAsPMwcSJH6/Z9lR988MLI0orU1FR4eXnJffv2lfP6PA0aNJDbt2/Pjs7PP/Melhs++ui5Osj2iIyMRJcuXVCwYEFJq9WiQoUK4quvvhIXL160XVchBG7cuIFJkyYJq/Pj4uIifEwmzGV97jfeYBbJdWKJBb///jvaajQQxEzWQs7OiK1RA6lLluDJgAH4UaPJMjPzGhEGqdWII55MYtWbtTeqiZb/f0eZYgdWIYQnlp9hROhNhM4FCkhbOnRgncuMDER7e6OUTpc5qPWdd4A1a9CvXz9oNBpcuHCBHw8L41StNd1y7pxtztujceOgJcoc8bVpE4scrFzJfWVffPFCTzAyMhJarRYPLCmsLBCCPd7YWFYPunGDN7uaNfmGMhiYwbh/P2tV/vUXEyl27eLX/h2q+mvC1atXbeo1+cV/3WB+8803KFeunLRhwwYUKVIkcwEJwWo5J08yUSKvdojRozMVhFav5vV45Aj/f/durhta1Z8WLuTnhoVxNGrBkCFD5Lfeeiv3xfvRR2zIYmI4srKwoAMCAqStS5cCPj64ffs2xo8fLwoVKiTUajV0Ol3OOZJJSUD58ll7R4cMybXx/t6VK/jN1RXnVSpsW7z4+ZKOQ4fydQKYlZ6b2EJyMhueZs3wcbVqkpOTE0qUKGGuUKGC5O7uLipVqiQ/VxoPLGVYq1Ytaberq+jfrJm5WbNmksFggJOTEwoXLvz3bz4hOBXfoQML5w8e/NLs9nv37sHLy0vu0KGDlN0pv3XrFgwGA2ziFL//zvtHbihYMIvYQF4YMmQIjEYjmjZtKm3cuBHx+UzjPn36FNevX8e9EydgZib3G28wFUajMSEPijAAoICvr1g2ZAhqE0GjUoGIoLbMzeuoUNjYr1ajOLFxY6myTgd3S/SYTNxWYms7sTz3mSUl+8RyWCPUIq6ucDcYxLMzZ3gMEJHlkgPHjh2DwWDIpO1//TWiLbR0m+CydYE5OwMeHlyfnDeP6fWpqUhwdkZlNzcZMTFsnHr3Btq0YUWUkiVzlR/LAUlCEW9v+ejAgRw5/PQTa23GxPAirVOHjXTz5ryxHT3KRt9sfqUUyf8aVkLIy6B79+4p/2WDefDgQRQoUEAKDw+HVqvNmX1ISmKnadSo3DfQ9eszRf0/+YQN3JMnmUo0p0/z2hGCJ2nkMuD85MmTcHV1FblGJiNGcO/gxYuc/hQCsiyjfPnyaNOggYSRI9GiRQvZyckJixYtwsmTJ2E0GpFFRUeW2dAOHZoZ6QqRQwREkiTMnz9fODk5oVmzZnL61asc4bZuzZ8jt5aluXM5qwKwwcn++X7/nUUIjhzJlTRjNpvRsmVL2WQyIbdWuSdPnqBDhw6Ss7OzmNCnD842aoRe3bujf//+uGKR0DQajUh9HaS6Z89Y+5eIo++5c5mMFRnJ4vEtWvC/q1Xj2vXOnbwHpabygO01a/D45k2MMRrlWcOHywgPBw4fhjkjA/VCQuRenTplXsBGjbIQwLIgLu6FutONGjWS/fz8RL50sF+MN9tgAiAXF5d1c3KynWzo3r27VLt2bVurQfHixeU4S5gffuYMHioUuGUxgg+cnQWcnPDXwIEwmUzwIx7yfFyjyZKatbaZ2Nc2n1r+/WWrVtis0UAEBvJoIq2Wa3iShDp16oh3331XhiwDI0dCTk1FYGCg1LlzZ0mWJK4XeXuzAZs5k+tAyclA+/a42aIFxhYqJLYR4dDs2Zzy6d6diQ4jR/ImZE0BCcELbedOruscP85platXOQVWsCCCdTrElirFEeqpUzxlJDGRN7l/QZT4dxAbGwu1Wv1Sr+nWrVsyEfXFv2BNv+YjX4iKioJer4csy9Dr9YjJja1pNvMm1rgxR4n2uHQpsxm+Vi0m/Tx4wJHl1q2cnShQ4Ll1PSEEPD09c29zOnIEKF3alnXZsWMHFAoFPDw8sODLLyVcvIiJEyciNDTUtnibNGkiVa9eHXv27IGcksJDy7M3+cfE8D0KntqzceNGBAcHC29v75xKPEeOsHxbrVo52rUsF5Fr+IsXZ0ZNaWns8G7alK+WqtWrV8NkMomBAwdKVpb8oUOH4O3tjbJly0pRUVHsxFqjWTuULFlSfiktYHtYs0NVqnDm6NYt3oOePePMwokT/PuwMN5HkpI4o3XvHpdbxozh9TF8uE396Wnlymis12NPw4ZCDgyEr4+PHKNWI6lNG76Ofn78nQwbxlE/wE7F0qW8ngwG3tvycNSbNGki6/V6WHvKXwPefINJRK1q1KiRJ/Fn48aNKFKkiKTRaNAut/lqjx7hXqFC2EkESaXiy9OiBW7VqYMPDQaIsDDErV+PbRajmmwxllZ1IDOxsk+K5fd9NRq4E2F32bIiecsWpAYGYuZXXyEtJASdiLBp0yauTZYqhQ5t2ogCBQoIqX179tASE4G2bVlpJBtT76uvvhIeRPhIreY0aZUqLB3VvTufc4ECbBgHDuT2lKQkrivNncte+fTp3LAcE4O927bBZDLhb0lZ/YsRFxf30gazc+fOyUTUB/+CNf2aj3xBCAEnJydcvHgR3t7e8onnTZyxpljPnctkMaamsiGJjeU6ekYGE4GmTuV0bWoqG5wXOGP9+vaVPqhdW+DhQ26bWLCAa1xlywJOTrZh1JMmTYJVrSV51izEFiwIvV6P5nb6sOfPn4fRaERxoxGVQ0PlRytX5vz7koRbe/eCiKDVauHn5yeNHTv2+fXEo0c5guzalSNraxRUqBB/3iZN2FlITwe6dHmpWhzAaUt/f385NDRU7tKli+zk5IQshnDixMxo1g7ffPONKF68+Mu3iV26xETBw4fZeT5/PisPokOHV9Z1PXXqFIoVKyZZeRfhVv1pa4Cwdi1zH6zTkxYu5Fa7hQsBlYoN6blzPIv3yBHmf3z+OSZPngx/d3dMnDhRODk5YU9+Gd3PR673kAJsaN4IKBQKk06nexwbG6tzdnbO8fuoqCgKCgoik8lEly9fJl9f36xPiI4mqluXkiIiSJOWRhqdjpQNGlBkqVK0YNEimtC8ORl27KAvZJkmE5GwvCyNiH4mogZEVIOIRrVtiyNHjyqqAWJUUpLyD1nGCCEU+4Sg00S0Xq2mdK2Wbjs7y2lms6J0iRLK6adOUfzKlVQlMJBIoSA6cYKoeHEik4no/n0iIYh8fSlp924K37CBTrm6UvkCBahqjRpEv/5KlJzMz711i2jYMKKBA4mcnYlUKiJv7zyvWfHixeX33nuPZs+erXod38G/Dc+ePSNXV1eKjo7O+X3ngXbt2qX89NNPXwCY+w+fngMOOPD/EZT/1yfwOgEgyWAwnF6zZk2uXoCfnx8lJCRQVFRU7ptngQJEixaR6ZNPaLK/v4jRaokKFCDP8eNptiyT+fRpgixTiyJFKMHykstEdIuIOioU5KdQUFuFgkJ37KDLjx+TLiWFFOnp9MjVVVFICDISUUMiipFl+vKdd+hMTIzqUkKCsuWff1K0VosqkZFE/foRffgh0bp1RJcuEV2+TCI9nS7HxtKcn3+m9374gaYFB0vdd+ygqsHBbFQ1GqKMDDaS6elEc+fy4wUKPNdYEhHFxMQo33vvvTfSWBIRubi4UN26dUWtWrUQHx+fr9cYjUYioqR/9MQccMCB//+QV+j5/+tBRGWcnJxScsysyy+ePgUKFMDTevV4GknJksDp0/D29kYztRoyEToQy+jZ1y2jiTDZrv1kl1KJe0T4mghBdu0nZoUCN4nwtkqFNCKc9vMTcz/6CEEqFa6fOMHyVLdvZ+nH6tevn+RMhLK+vvJvwcGc7795k4lAAwZwQ3h+1Dlygbu7u3huyu0NgCzLqFmzpuTq6oqJEyc+f4QSgC5duqQQ0SD8C9az43AcjuPfc/yfn8A/cej1+s9DQ0OTk1+lCRjgYvXIkZgeGChvCAgQ593dhVKpBBHBbGlNWZGtH/MREd5TKtFErcZKIsy01DGvEmGV5XnLiLCfCPWIkOTpid1EGO7iIlxcXITBYMDUEiUEWrQAUlMhSRIirlzBpLp15cZaLZ4ULswF8PXrmeRz/jwz9SZPfuXJ5i1atJCICCkpeQokvVH46aefEBISIplMJmzdujUH+zMpKQlhYWEoVqwYiGgd/gVr2XE4Dsfx7zneqBqmFQqFQuns7LypSpUqzfft22fUarUv9wZCEHXuTPeFINPmzeRGRH1btqTvdu6kOL0ermlpilQiciKuY4KIzESkI6JlRNTD8v94IvIloqdEpCeir0qWRKMmTRRp8+dTHScnipRl+r1AAdH/2jXlDicnGgdQkrc3Wul0OP/0qXLKs2ekNBgo4bvvqHlyMlF8PFHXrkSjRxO1bMk118GDiZSvllk3GAxkMBgov6nKNwUrVqygAQMGEBFRcHCwSE1NVTx69EhhNpvJyckJGRkZitTU1C4ANvwfn6oDDjjwL8IbVcO0AoBITEzsdPbs2ePt27dPTUtLe7k3UCqJzGYqFB5Of+h04kFoKI1MSKBCGg0d9vQkodHQE4WCwolIJjaOMhGlElEZInpERFtdXbFJpSIolaQgmPEzawAAC1lJREFUIhciGvXsGa1dvJgWEZFPcjK1T0sjg4sL0R9/UJCrK8YXKkTfffSRYoBer5w0bBgVvHGDqm/eTM3r1SN68IDoyROibdu4m7NECaKhQ1/ZWBIRubi4oGnTpq/8+v9f0bt3b0pPT6cLFy7QsGHDlDNmzFBcvHiR0tPT6cmTJ4oGDRokEpH0f32eDjjgwL8M/9ch7j95EJHO1dV1p7+/f/LixYsRGRmJuLg4HDt2DG3atEl7//33885FPnoEbNuGa15e2Fm0qJTm6oolKhXunzoFEEF2dcUCS00z2pKSTbW0lMiWVpN9RJhUoYJ8vmxZJBGhlZOTfGTJEqQSYd7cuUirUAEgAj77DLuKFRNbK1XKbISOjOR+NT8/pu5fusQ9b/v3v3IK1h79+vWDs7Mzcuhq/seQmJiImzdv4tixY9i8eTMWLFgAFxcXmYg64F+whh2H43Ac/57jjUzJZodCoWjk5uY2NC0trYEQQm0ymcJTUlIOpqenD4iMjFT4+fnl/sKmTSk2MpIeXruG8oCinV5PfZs0ofo7d9IvQtDbRGQkTskK4vYSBXH6NZ2IfmvQgERcHCbdukWF69fH+rVrlXqjkXa4ucFt2DBF3R07iMLDia5do1odO4rSpUsrVyxfTvTzz0QffUQUEUFkNPL/r1whKlOGqF07bjt5RTx79ox27txJH374If3xxx9UtWrVV36vfyskSaJHjx5RdHR0liMiIiI9IiIiIzIyUkRHR6vi4+N1QggyGAxPtFrtIyKKNpvNEUlJSfeEEEsBRP9ffxYHHHDg34P/hMHMCx4eHjtnz57domfPnrk/4fRpki9coHX9+1M3IoomTrt2VCqppRD0GRHdISJ3InKzvMRaz5xDRHqVippptSjUuLHCZccOIh8foocP6c9atTDu/n2x++xZFanVRL6+pFAoaLNOR+83aUK0aRNRWhqRmxtRnz7cYzlvHr/+b0AIQW5ubkhNTVVIkkRnz56lypUr/633/F8BACUmJtLDhw8pKirKZhAfPnwo379/Py0iIkKKjo5WxMbGapKSknQ6nS5Zr9fHqVSqKFmWI5OTk+9lZGREElEU8VdpPZ7hv3wTOOCAA/mG+v/6BP4v8eTJk1+OHj3aqGfPnvpcn1C9Oql27SJ3tRqPxoxR7L56lfx37qQZWi32ZGQoTsoyhRKRgdhQplr+rSSiAUTk6uZG6kKFFBQdTaTTEZnNRAoF+f/wg2JpUJDqym+/0fAFC+RBp06pqhJRYseORJ9/TmQwEF29SjRiBNHUqURVqvDr/yY6duwIHx8f+vPPP+no0aNUvnz5v/2efxeyLFNsbCxFRUVRVFQURUdHU1RUFFmMYMaDBw8QExOjSkhI0BORMBgMcRqN5hERRaenp99PTk4Ot0SCVkMYRUSxKSkpjhqkAw448FrxnzaYAP44fvx4BnEWNXeUKEEGSVLoN26kMpGRdFGWqW9qqqIeEf1KzIyNJKJYIipPnJKNIaKORNQ8JUWsuXePYpRKeq9DB+X7oaFU9sEDWvrtt6TSaknVvTu5vPMOJWu15BEcjJ6rVilIkoj++INo1Ciir74iql37b33GM2fO0NWrV2nTpk349ddfFadPnyYXFxdq0aLF33rfFyEtLc1m/CIiIigyMpIePnwoR0ZGpj98+FB6+PAhPXr0SJOYmKjT6XQpOp3usVqtjpZlOSI5OTk8IyMjgjINYDQRRQFwiAk44IAD/2f4T6dkFQqFs1arjUtNTdUo82KbAlRZqaS9xNGjnixCiEol9RSC1hHRZiJqSkSuxFFmABGFm0yk0evp0sKFVLRPH5rk6ytu3bypXEpE1zQaoi5d0PTOHYVy/356GBdH5cqVoxolS+KTqCiFi1ZLJf/8k/SsOPO3YDAYKD09nTp37iwPHz5c9XdTsImJibZI8OHDh9ZDfvDgQVpERIQUFRWlePTokS4tLU1tMBiearXaRwqF4kF6evodSzQYQ0wkthrDGADmv/1BHXDAAQf+YfzXI8xEk8mUFBER4V64cOHcn6RQUG0nJ2GWZeXvaWnUilgzzVeppGaC1WTrEhtTmYg0RLSaiAyBgaRxdqbq584RPXtGC5VKJWm1RBkZVMTbm+j7723MHX9/fzo7dSqtGzBAsQSgjUSkcnWlOnXqkIuLC02aNIkqVqz40p9v9erVpNVqMWvWLMXHH3+cp/wdAIqPj7elRa3HgwcPMsLDw9MfPHhgI8nIskxGozFerVbHKhSKyLS0tLspKSlWQ2g1gg+J6HFiYqLI62864IADDvz/hv+0wSQi0uv14bdv387bYBLRXm9vtP3wQwqfMUOYMzKURiKKkyRbtOlDbCX8Lc+fRURvR0dT2sOHtLNvX/pAraZTH35Iz4KCqMC1axTw5590afduUmi1pMzIILe7dyl42TK6FBAA31atFE+nTaNNmzZh8eLFOHLkiHLHjh2kUCho1qxZNGLEiHx9rgoVKoi7d+8q+/btS6GhobRv3z5bVHj//v10iyFETEyMOiEhQa9SqTL0en28Wq2OBhCZkpJyNy0t7b7lo9nXB589ffr0v5uWcMABB/6z+E+nZImIvL29T69fv75akyZN8nxO0aJFpT2lS6tL7tlD2zQa8jebqRJxZ7uG2OuwXkUFEfXUaKiXUimqpqcrKwQHS1dv3VIfNhjEEldXzI+JUX7o6SnGPn2qqGw2Kw7p9bilVisWGwxIyshQLF68WNGpUyfb346MjKTvv/+ebty4QXv27EF0dLRCqVRSXFwcRUVF2dKiUVFRFB4enhYeHp5x+/Ztxe3bt52JiPR6fYqFLRothHiQnJx821IftBrCh8T1wdR/4vo64IADDrwp+M9HmPmBn5+fcqmfn/jYw4OKxccraxBRC+J2kgflytHK69fhnZGhIOK0bHBICNWdOVNJHTrQjb/+UlOJEtRECGWTr78m6tKFjvTvr6Lp04mIqMOiRQrq0YM+VygUACguLo4uX75sXx+kiIiItLt370pxcXEmFxeXjIyMDJVarU43GAxxarU6ykKUuWNnCB9afj5KTU1N/j+5aA444IADbxj+8wZTCJEQFxf33OdMmjRJ2b59ezoWFCSqt2tHYeXLU+DgwaQgIpjN9OynnxSftWhBk4komYiuXr9O8k8/kcpSs4RCQcndu9NDvZ6k/v3pnL8/3R83jiIiI9PDf/wx48GcOSImJkYdHx+vV6vVGXq9Pk6tVkcBiEhKSrIawodElJyamnqdiB5KkvSSen8OOOCAAw78HfznDeazZ8/OXLlypTE9R1e3YcOGlJ6eTnFxcYoiwcFUeNAgogYNCFOmUEbhwtSoRQv6UKGg5AIFKEIIuhofT4137CAvN7fEI8WKKePj4/XqmTNthlBs2mRNjT6gzGjwIRFFSZLkSI064IADDvwL8Z83mJIkXT579mwyETkTMWP06dOnFBsbazsePXpEWq2WwsPDFXPmzJE2b96c/PDhQ+Xjx4/14J4I7TmAvkxL25WWlnYn1Wx+QDExThQTc5i4TTNKkqSU/9MP6oADDjjgwN/Cf570o1AoShPRX0FBQc/i4+PVz54906tUKrNOp3uq0WjiFArFI1mWoxITE0mW5QwiOk6siPfQcjik1RxwwAEH/gNwGEyFQkFEtYnLj7FE9BiAoz7ogAMOOOBAFvznDaYDDjjggAMO5Adv5ABpBxxwwAEHHHjdcBhMBxxwwAEHHMgHHAbTAQcccMABB/IBh8F0wAEHHHDAgXzAYTAdcMABBxxwIB9wGEwHHHDAAQccyAccBtMBBxxwwAEH8gGHwXTAAQcccMCBfMBhMB1wwAEHHHAgH3AYTAcccMABBxzIBxwG0wEHHHDAAQfyAYfBdMABBxxwwIF84P8BQ1MQHEOXRFQAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot tract geography\n", "f, axs = plt.subplots(1, 2, figsize=(8, 4))\n", "for i in range(2):\n", " ax = san_diego_tracts.plot(\n", " edgecolor=\"k\", facecolor=\"w\", ax=axs[i]\n", " )\n", " # Plot graph connections\n", " w_queen.plot(\n", " san_diego_tracts,\n", " ax=axs[i],\n", " edge_kws=dict(color=\"r\", linestyle=\":\", linewidth=1),\n", " node_kws=dict(marker=\"\"),\n", " )\n", " # Remove the axis\n", " axs[i].set_axis_off()\n", "axs[1].axis([-13040000, -13020000, 3850000, 3860000]);" ] }, { "cell_type": "markdown", "id": "following-macedonia", "metadata": {}, "source": [ "The weights object for San Diego tracts have the same attributes and methods as\n", "we encountered with our artificial layout above:" ] }, { "cell_type": "code", "execution_count": 22, "id": "dental-documentary", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "628\n", "1.018296888311899\n" ] } ], "source": [ "print(w_queen.n)\n", "print(w_queen.pct_nonzero)" ] }, { "cell_type": "markdown", "id": "scenic-going", "metadata": {}, "source": [ "First we have a larger number of spatial units. The spatial weights are\n", "also much sparser for the tracts than what we saw for our smaller toy\n", "grid. Moreover, the cardinalities have a radically different distribution:" ] }, { "cell_type": "code", "execution_count": 23, "id": "southwest-atlantic", "metadata": { "caption": "Cardinalities for the Queen contiguity graph among San Diego Tracts" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "s = pandas.Series(w_queen.cardinalities)\n", "s.plot.hist(bins=s.unique().shape[0]);" ] }, { "cell_type": "markdown", "id": "opposite-accommodation", "metadata": {}, "source": [ "As the minimum number of neighbors is 1, while there is one polygon with 29\n", "Queen neighbors. The most common number of neighbors is 6. For comparison, we\n", "can also plot the equivalent for rook weights of the same dataframe:" ] }, { "cell_type": "code", "execution_count": 24, "id": "professional-omega", "metadata": { "caption": "Cardinalities for the Rook contiguity graph among San Diego Tracts" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8722463385938578\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "w_rook = weights.contiguity.Rook.from_dataframe(san_diego_tracts)\n", "print(w_rook.pct_nonzero)\n", "s = pandas.Series(w_rook.cardinalities)\n", "s.plot.hist(bins=s.unique().shape[0]);" ] }, { "cell_type": "markdown", "id": "explicit-salad", "metadata": {}, "source": [ "The cardinality histogram shifts downward due to the increasing sparsity of the\n", "weights for the rook case relative to the Queen criterion. Conceptually, this makes sense: all Rook neighbors are also Queen neighbors, since Queen includes neighbors that share an edge; but, not all Queen neighbors are Rook neighbors, since some Queen neighbors only share a point on their boundaries in common. \n", "\n", "The example above shows how the notion of contiguity, although more\n", "straightforward in the case of a grid, can be naturally extended beyond the\n", "particular case of a regular lattice. The principle to keep in mind is that we\n", "consider contiguous (and hence call neighbors) observations which share part\n", "of their border coordinates. In the Queen case, a single point is enough to make\n", "the join. For Rook neighbors, we require a join to consist of one or more\n", "shared edges. This distinction is less relevant in the real world than\n", "it appears in the grid example above. In any case, there are some cases\n", "where this distinction can matter and it is useful to be familiar with the\n", "differences between the two approaches. " ] }, { "cell_type": "markdown", "id": "unknown-bridge", "metadata": {}, "source": [ "### Spatial Weights from surfaces\n", "\n", "Most often, we will use spatial weights as a way to connect features stored in rows of a geographic table. A more niche application is spatial weights derived from surfaces. Recalling from [Chapter 1](01_geo_thinking), the boundaries between what phenomena get stored as tables and what ones as surfaces is blurring. This means that analytics that were traditionally developed for tables are increasingly being used on surfaces. Here, we illustrate how one can build spatial weights from data stored in surfaces. As we will see later in the book this widens the range of analytics that we can apply to surface data.\n", "\n", "For the illustration, we will use a surface that contains population counts for the Sao Paulo region in Brazil:" ] }, { "cell_type": "code", "execution_count": 25, "id": "extraordinary-keeping", "metadata": {}, "outputs": [], "source": [ "sao_paulo = rioxarray.open_rasterio(\"../data/ghsl/ghsl_sao_paulo.tif\")" ] }, { "cell_type": "markdown", "id": "natural-filling", "metadata": {}, "source": [ "From version 2.4 onwards, `pysal` added support to build spatial weights from `xarray.DataArray` objects." ] }, { "cell_type": "code", "execution_count": 26, "id": "affiliated-shirt", "metadata": {}, "outputs": [], "source": [ "w_sao_paulo = weights.contiguity.Queen.from_xarray(sao_paulo)" ] }, { "cell_type": "markdown", "id": "blessed-tooth", "metadata": {}, "source": [ "Although the internals differ quite a bit, once built, the objects are a sparse version of the same object that is constructed from a geographic table. " ] }, { "cell_type": "code", "execution_count": 27, "id": "stopped-chess", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_sao_paulo" ] }, { "cell_type": "markdown", "id": "cross-syndication", "metadata": {}, "source": [ "## Distance Based Weights\n", "\n", "In addition to contiguity, we can also define neighbor relations as a function of\n", "the distance separating spatial observations. Usually, this means that a matrix expressing the distances between all pairs of observations are required. These are then provided to a **kernel** function which uses the proximity information to model proximity as a smooth function of distance. `pysal` implements a family of\n", "distance functions. Here we illustrate a selection beginning with the notion\n", "of *nearest neighbor* weights." ] }, { "cell_type": "markdown", "id": "dried-tamil", "metadata": {}, "source": [ "### K-Nearest Neighbor weights\n", "\n", "The first type of distance based weights defines the neighbor set of a\n", "particular observation as containing its nearest $k$ observations, where the\n", "user specifies the value of $k$. To illustrate this for the San Diego\n", "tracts we take $k=4$. This still leaves the issue of how to measure the distance\n", "between these polygon objects, however. To do so we develop a representative\n", "point for each of the polygons using the centroid. " ] }, { "cell_type": "code", "execution_count": 28, "id": "developing-guide", "metadata": {}, "outputs": [], "source": [ "wk4 = weights.distance.KNN.from_dataframe(san_diego_tracts, k=4)" ] }, { "cell_type": "markdown", "id": "sexual-lunch", "metadata": {}, "source": [ "The centroids are calculated from\n", "the spatial information stored in the `GeoDataFrame` as we have seen before. Since we are dealing with\n", "polygons in this case, `pysal` uses inter-centroid distances to determine the\n", "$k$ nearest observations to each polygon. \n", "\n", "The k-nearest neighbor weights displays no island problem, that is *everyone* has at least one neighbor:" ] }, { "cell_type": "code", "execution_count": 29, "id": "radio-fourth", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wk4.islands" ] }, { "cell_type": "markdown", "id": "interstate-magazine", "metadata": {}, "source": [ "This is the same for the contiguity case above but, in the case of k-nearest neighbor weights, this is by construction. Examination of the cardinality histogram for the k-nearest neighbor weights shows another built-in feature:" ] }, { "cell_type": "code", "execution_count": 30, "id": "listed-strain", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(4, 628)]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wk4.histogram" ] }, { "cell_type": "markdown", "id": "material-island", "metadata": {}, "source": [ "Everyone has the same number of neighbors. In some cases, this is not an issue but a desired feature. In\n", "other contexts, however, this characteristic of k-nearest neighbor weights can be undesirable.\n", "In such situations, we can turn to other types of distance-based weights.\n", "\n", "### Kernel weights\n", "\n", "The k-nearest neighbor rule assigns binary values to the weights for neighboring observations.\n", "`pysal` also supports continuously valued weights to reflect Tobler's first law\n", "{cite}`Tobler1970computer` in a more direct way: observations that are close to a unit have larger\n", "weights than more distant observations.\n", "\n", "Kernel weights are one of the most commonly-used kinds of distance weights. They\n", "reflect the case where similarity/spatial proximity is assumed or expected to\n", "decay with distance. The essence of kernel weights is that the weight between\n", "observations $i$ and $j$ is based on their distance, but it is further modulated by\n", "a kernel function with certain properties. `pysal` implements several kernels.\n", "All of them share the properties of distance decay (thus encoding Tobler's First \n", "Law), but may decay at different rates with respect to distance.\n", "\n", "As a computational note, it is worth mentioning that many of these distance-based decay functions require more resources than the contiguity weights or K-nearest neighbor weights discussed above. This is because the contiguity & k-nearest neighbor structures embed simple assumptions about how shapes relate in space, while kernel functions relax several of those assumptions. Thus, they provide more flexibility at the expense of computation.\n", "\n", "The simplest way to compute Kernel weights in `pysal` involves a single function\n", "call:" ] }, { "cell_type": "code", "execution_count": 31, "id": "varied-roommate", "metadata": {}, "outputs": [], "source": [ "w_kernel = weights.distance.Kernel.from_dataframe(gdf)" ] }, { "cell_type": "markdown", "id": "coastal-hopkins", "metadata": {}, "source": [ "Like k-nearest neighbor weights, the Kernel weights are based on distances between observations. By default, if the input data is an areal unit, we use a central representative point (like the centroid) for that polygon.\n", "The value of the weights will be a function of two main options for\n", "kernel weights: choice of kernel function; and the bandwidth. The\n", "former controls how distance between $i$ and $j$ is \"modulated\" to produce a\n", "the weight that goes in $w_{ij}$. In this respect, `pysal` offers a large number\n", "of functions that determine the shape of the distance\n", "decay function. The bandwidth specifies the distance from each focal unit over which\n", "the kernel function is applied. For observations separated by distances larger\n", "than the bandwidth, the weights are set to zero.\n", "\n", "The default values for kernels are to use a triangular kernel with a bandwidth distance\n", "equal to the maximum knn=2 distance\n", "for all observations. The latter implies a so-called fixed bandwidth where all\n", "observations use the same distance for the cut-off. We can inspect this from\n", "the generated `W` object:" ] }, { "cell_type": "code", "execution_count": 32, "id": "russian-landscape", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'triangular'" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_kernel.function" ] }, { "cell_type": "markdown", "id": "worldwide-white", "metadata": {}, "source": [ "for the kernel function, and:" ] }, { "cell_type": "code", "execution_count": 33, "id": "complex-poland", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1.0000001],\n", " [1.0000001],\n", " [1.0000001],\n", " [1.0000001],\n", " [1.0000001]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show the first five values of bandwidths\n", "w_kernel.bandwidth[0:5]" ] }, { "cell_type": "markdown", "id": "appropriate-rally", "metadata": {}, "source": [ "For the bandwidth applied to each observation.\n", "\n", "Although simple, a fixed bandwidth is not always the best choice. For example,\n", "in cases where the density of the observations varies over the study region,\n", "using the same threshold anywhere will result in regions with a high density\n", "of neighbors while others with observations very sparsely connected. In these\n", "situations, an *adaptive* bandwidth -one which varies by observation and its\n", "characteristics- can be preferred. \n", "\n", "Adaptive bandwidths are picked again using a K-nearest neighbor rule. A bandwidth for each observation is chosen such that, once the $k$-nearest observation is considered, all the remaining observations have zero weight. To illustrate it, we will use a subset of tracts in our San Diego dataset. First, visualising the centroids, we can see that they are not exactly regularly-spaced, although others do nearly fall into a regular spacing:" ] }, { "cell_type": "code", "execution_count": 34, "id": "controversial-disclaimer", "metadata": { "caption": "Centroids of some tracts in San Diego are (nearly) evenly spaced." }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create subset of tracts\n", "sub_30 = san_diego_tracts.query(\"sub_30 == True\")\n", "# Plot polygons\n", "ax = sub_30.plot(facecolor=\"w\", edgecolor=\"k\")\n", "# Create and plot centroids\n", "sub_30.head(30).centroid.plot(color=\"r\", ax=ax)\n", "# Remove axis\n", "ax.set_axis_off();" ] }, { "cell_type": "markdown", "id": "previous-budapest", "metadata": {}, "source": [ "If we now build a weights object with adaptive bandwidth (`fixed=False`), the values for bandwith differ:" ] }, { "cell_type": "code", "execution_count": 35, "id": "happy-valley", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[7065.74020822],\n", " [3577.22591841],\n", " [2989.74807871],\n", " [2891.46196945],\n", " [3965.08354232]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Build weights with adaptive bandwidth\n", "w_adaptive = weights.distance.Kernel.from_dataframe(\n", " sub_30, fixed=False, k=15\n", ")\n", "# Print first five bandwidth values\n", "w_adaptive.bandwidth[:5]" ] }, { "cell_type": "markdown", "id": "worthy-religion", "metadata": {}, "source": [ "And, we can visualize what these kernels look like on the map, too, by focusing on an individual unit and showing how the distance decay attenuates the weight by grabbing the corresponding row of the full kernel matrix:" ] }, { "cell_type": "code", "execution_count": 36, "id": "solved-stamp", "metadata": { "caption": "A Gaussian kernel centered on two different tracts." }, "outputs": [ { "data": { "image/png": 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nn38+OmPGjIqM0Gtf+9oD0ai9hPv374+86U1vmr158+ZmETF9fX0C8Kc//Sm5YsWKPYUhp0mTJuWqPW9lZJJMdM0BXotNJTUKG/cUBlOADwM3hSRfCRm12WqzlaFJJrrGAf8HOB9rs6cDhzg5y0pQfDmZ6Hp1flRuxOKrs/qFL3xhx2te85qWHTt2xHO5nBOLxdzTTjst09XVtcNPuQUGise9+eabx+7bty+6fv36J5uamszUqVPPSafTFYdEtLW1uYXPN9xww9SlS5ce+v3vf//cxo0b4y996UvPLOggIiP6JlJOJR97ugh4Ddb5TAywjAHO7Hf4LsLjk8lE189T6c4nQtRBCQm12WqzG5lkomsG8EZsuqiBbHYLcA525n8x2wjHWX0l8BbguyHIDgxfY1bPPvvs3htvvHFHX1+fJBIJt6+vT2688cYdCxYs6PVSzrhx43LJZDL3m9/8pg3glltuGbdkyZLDY8eOdSdPnpz57ne/OxognU7LoUOHnJ6ensj48eP7mpqazJ133jlqx44dNc9MTaVSkWnTpmUAbr755uP50F7+8penVq5cOaGvrw+AXbt2RQBaW1tzPT09OrN0hJFMdE1PJro6gQ3Aw8CngPdj40HfArwOeDXwEuBFnOqogjWGYdEC/CyZ6BoTog5KSKjNVpvdaCQTXclkousdyUTXXcBm4F+ADwLvAd6GTRH118DLgEuA8zjVUQXw9DdSIf+ZTHSdH6J83/H9h/fDH/5wTCKRcD/2sY/tSCQS7o9+9CNfHoLf+c53Nt1www3T5s6dO//xxx9PFHoCbrvttk1f//rXJ86dO3f+hRdeOG/btm3R6667bv+6detazz777LNuu+22sbNnz645Ru+GG27Y+ZnPfGba+eefPy+XOzFq9KEPfWjPtGnTMvPmzVtw5plnzr/lllvGArz97W/f+6pXveoMDdYfGSQTXRcnE10/x87k/wIwv4bmqk6v4xGnAz9IJro0D3MDojZbbXYjkEx0zUgmuv4d2AncAiyjNtvrDr2LbzQDP08muk4LUQdf8T111erVq1s6Ojoy06dPz27bti26adOm+OWXX360Yk0bCE2BMnxIJrouA24ErvCw2e3YYaiw+UYq3fn3YSuh1Iba7GBQuz08SCa6TsfOon873oZC7sQm6w+TB4BlqXRnOmQ9PMf3npOlS5ceN3LTp0/PTp8+XWdSKsOeZKJrMvBv2CEir9lBfTir700mutan0p0rw1ZECQ612cpIJJnoagY+CdyA90n7U4TvqAJcDHwTG3I2otD4G0WpgGSiS5KJruuAJ/HHUQWop7fif0omusKMoVUURamJZKLrcmylqRvxp7rUUz60WS1vTia6Fg292/CiGmfVdV037Ji6EUv+2oYZ+6IMQDLRlQD+iH1zHe2jqNk+tl0p44H/G7YSSk2ozfYZtdv1SzLR9SVgNaUns3pFn49tV8PHw1bAa6pxVjfs2bOnXY2f97iuK3v27GnHziRX6oB8T+oFyUTXZ4GHsLP4/WQdwZdaHYoPJxNdvlUyUnxHbbaPqN2uP5KJrtnJRNcH8zP8P+KzuMPYfKv1xBuTia566vSomYpjVrPZ7HU7d+781s6dO89Gwwi8xgU2ZLPZ68JWpJFJJrpiwFJs0ue/IVjnMRWgrHLpwE4gC6MMrFIjarN9R+12yOTzWp+HtdmvARYGKH49sCRAeeXgYNMlfiJsRbyi4mwAijJSSSa6RgEfAD4KtIekxvPAtJBkD8atqXSnhgMoilI35Ed8rgb+ifDCp9YAl4YkezCeBeaOlMpW6qwqDU8y0bUAWIFNZRJGBZIC26i/EIACB4FJqXRnJmxFFEVpbJKJrknAO7GJ+2eErM4uYFLIOgzEolS687GwlfACTfqtNCzJRNcV2GGSpWHrkmc79eusjsZWcNFQAEVRQiGZ6DoL+Efg9fgzq79S9lC/jirYjDWPha2EF6izqjQc+eH+rwNvDVuXfoRZrq8c3pRMdOWAu1PpzporCCmKopRDMtHlAB8DPkd9+S0vABPCVmIQzkkmut4E/D6V7twftjK1oGEASkORTHQtBH6Av2lMquVu4PKwlRiErdght6PYFF6/BH6dSnduDVUrRVFGLMlE10TgO8Crw9alBPcDLwpbiUEoxNO6wL3Ar7B2e/1wi2VVZ1VpCPKJ7T+BrV5ST2/mxfwZeHHYSgzCc8CcEuvvB/4F+OlwM4CKotQn+d7UtwJfAcaGrM5A3AtcErYSg3APcFmJ9d3AvwP/MVxGydRZVUY0yURXKzapfSf1UcJ0MOq9Z/UZ4IxBtv8BeHsq3bkjIH0URRlh5Gf4X4WNTa23/KX9eRC4KGwlBmEgZ7XAs8CbU+nOBwLSp2rUWVVGHMlE13hsXtArgNcS7gz/ShjKsITNU8C8IfZ5AXhtKt15fwD6KIoyAsiPfC3DTuJ8I/WZvq8UQ73Ah005HSAZYEUq3fmdAPSpmnodDlWUinh95La5wNtzmBdHYnJhTsxwrGffFrYCQ1DOm+1pwOpkout9wDc1LEBRlFJcJ3eMB95iMK8ZG49P3O9k5oetUxXUa/aWSogD304mui4APlqvYQHas6oMW14fua0FeCnwfuAVhfU7naOrH4/ur5d0VJWwn/qNzQJbTvLsCvb/DfCBVLrzaZ/0URRlGHGd3BEFLsDmtb4W6yhxjNyjv2jatojhWRC4G1vlrx5ZTWWpGZ8Erk+lO//kkz5Vo86qMix4feS2duyP7tz8shA7/HKKeTOY9D2xnT3HJDc5WC09YaBJTPXAeuCcCo/JAbdjg/kf0J5WRWkMrpM7mrBlSM/H2o2FwHygqdT+D0b3Prgperie4z8Hop7Dtyp1VgusAv4V+FUq3dnnpULVos6qUpe8PnJbodbzK4FXYWdcRso9PiWZNffHdtdjCbyhqOdJVuuoreb2FuB3wAPALeq4KsrI4jq5YybWXr8KG3/aWu6xOcym/2naMtMIjl/6+UQ9p6+q1lktcBBrs+8DbkulO/d6oVQ1aMyqUle8PnLbeOBTwJuAidW2M8rEloxyY92HnL56HZ4ZiGTYCgyCW+PxM4F35ZdfYyt2KYoyjLlO7ogBH8D+rudW204Emb0gO/rPG2IH6zl9XykWYCcpxcNWxAdGYye8vRHYCdwRliLD7Q1GGcG8PnLbBGyu0fdRg6MKIIizKDv+gCeKBctCbK3pesTLntArPGxLUZQQuE7uiGAdmH+hBke1wFm59jOjRo7WrFiwjAIeCVuJAHjF0Lv4hzqrSl2Qj0n9NR5WlmomcsFEt/kxr9oLCMGmiKpHau1ZLeYbyURXPVakURSlDK6TOwT4T2x6QE8QZMKFfeMe9Kq9AGkEX+odyUTXJ/N5cAOnES6wMjz4KnaWqKcsyI4djvd4vaZD8bJntRn4ZTLR9T/JRNdLk4mukpMuFEWpW5YD7/C60elu6/kxI4e8btdnLqB+R8S85HPAQ8lE1xuSia7RQQrWmFWlXljmR6MxnHPHuE1PHnB6z/KjfZ/owAa0LwlbkX74MSHqb/PLkWSiawPwBHB7Kt35Bx9kKYriHcv8aFSQUedkx6x+JDas0g9GgI3ApLAV6YcfycDOA34I5JKJrieAp4E7ge+n0p1ZH+QB6qwqIfKVtu9PT7vmrx/tNf8HmOGXnPnZ0QfXxIfdS+9k7LB7PfUMexkG0J9WYHF+WZ5MdF2WSnfe66M8RVEq5I6OlUngb+7a3P5SkL/1S05HbtTpj0b3u8MsM8ASYAcwJWxFAiLCiVSSr8fOM/mSX8LUWVUC5Stt328C/gZ4J3BFsyDAESpIS1UpLUQvbjaRncMs7+psYA1QT+m3gko15QB3JBNd56XSnfsDkqkoSgnu6Fgp2Dyi7wDeALREHPNILie+lbF2kKlzcqPufzZ6qF5TQpUihi0QUE/OapBlFm5KJrruTqU7H/CjcXVWFd/5Stv3BVgE/F/gGmBcYZuIEMU8nbXbfUGQyBm59o3ro/uHk7MKtrc5S/38ToPMizod+A9sCjNFUQLmjo6VM4C3A2/FFmA5zujm7KHdR/zN1HRWdnT82ehwC11lCbAVH0cK65gYtpNhvh8lW+vlIaiMQL7S9v2xWAd1OYOU6RwbIbU7568uk9zEnPWGYN8za2c69VUdJegk/m9MJrq6UunORwOWqygNyR0dK6PA1Vib/VIGsJhT2zLj/HZWm3EWtbrRHUecbD31VA5FBJs/ul6c1aCfeLOB67AVCz1lOMWDKMOIr7R9P4GtDf8lhqgnPz4i4wbb7gUOMm2iGXZprMCWJ0yFrUSIfDhsBRSlEcgP9/8b8F1s9akBHZ0xiexZYDJ+6iOInJMd/YyfMnxiCXaiaD0QRvfMR5KJLs/lqrOqeMpdS26aeNeSm94/74ztvwAzq5xjmoXxPqsFwOm59uGWbBpsyMRjYSuRx88JVgPxmmSiqzkEuYrSEGy8+vrExquvv3rhhU/8PBLJlVU9yhEiAnv81m2a2zon8PEcpVZmARd53aiGASg1c9eSmxLYSVNvBV4JRBLNfcyYuu/BrdvHTxjqeGPjMn2n1UTPjxo5lBXj28QAn1gCPA9MC1uREBiFrZxyZ9iKKMpIYePV1zvYmvFvxc7kHuU4hrPOeWbvhsfOzIIM6RuYAPwHB5k2xW15bEfk6Hl+y/KYBcD9QNgTxMIKfHs94OlEK3VWlaq4a8lNpxi7/vuMHXP4gq3bx+0CGTT33GGXXQTgiAnSPDs36sFnoqn+MaC7gRw2JtNgexAH+0y/9f2/D7SAzXxQaf7AGLCJ8J3VsPo4XoU6q4pSMxuvvn4B1ma/mRL2JBbPjo83Ze7P9DYN6mRlXQ5TY0nscjknO/rIjkjJQbENwFhOtdOl7DZUb7MzwMVU7i8N2VETAGE5q68EPu5lg+qsKmVz15KbBJtT7RoGMHbFiOCMajv21KHDiUGd1f2uOeydloMz3W0b+8ypIaDb8TEbQQmqmTR1MdADtOe/Z4BHscZIsCE9gg3wL/48XIoh/Am4nROhSYXzSgE/CUspRRnubLz6+unAG4G3YBO6D8rkKXsiWzcN/l68Px17FmTItrwgaWIXxIwc6jt5RCzDEHMhPGYNtpe0khSLc4BHgPOL1j0C9GHtXMFOO/2WWUBLzRpb/Az13AZ8lhMOccFmu8DPvBamzqoyKHctuakJW6nkqvxS0SzHXM4Z8sd9yPXshzkkUZwFbW5s02Gnb3bR6qBjty8DHgTiwDllym8CHuJE3tUebAL9wQgjxrRankilO78VthKKMtzJD/Gfjw3NuooyHNRi+vqifUPts+dotKcq5apAkOYzs+0PbYgdLI6nDbo+/aXAM8A+bIdNuc+sI/2+j2foZ+gGvHPE/exZ3RukzVZnVTmJu5bcNB7rBL0o/3cJ0FZte+ljsSHTjmQNyWrbr4Zpbuu2p5yDYTqrcCIAPY19Q90DJLHO60AU61lOsq8cw2cSZSBxy4oy0th49fWt2Nr0Bbt9CbYCXlX0HEgOaVvS2Uigw8sz3NboBg4Wr3Kww/RB6nFGfskBm7HhY1ns9R7smGLKscde2mw/Oyx8Tjh5MuqsNjh3LbmpFdtzekV+medV25lMZLcxTsdQ+0nAN/0Y09T/vj8YpPx+JIC5+cUF1jJwj2mx41+OEcph4129osnDtvoT6D2gKMOVjVdfH8WGBV0B/BX2xdeTnkZjMOmjzdOH2s8JOHy9xUSn93NNnwNOD1SJE0SwQ/Wz8t9XM/A8hMnAXjie8aac/5OXzqqf6cXUWVX8Iz8x6jxOOKcvxluH5jjPbJrcTRlB+DGhJxeg7Wsz0XliKNSd7sMm368HCvFKOUobtZnAMaCZ8pxVr9+q/Zzgpc6qogzAxquvn411TK/A5kD1ZTTq+S2n3WOMc/lQ+7XFc7L/mC+PjZI4yNQxJv7cAcnMya/aTXjOan+WYifAzh5g+zZOOKvl9AR76az66eOps6p4w11Lbopjf9Dz88tF2CGLsX7L3rNv1NpMJlZW2o52h75jAd72gowdb5rX7ZFjc7E5TJcEJ31IJgF3Yx1SKbEcwT6oDjO08+jlVd2Gv069OqtKw5OPN53BCZu9CBsvOdNv2cfS8W17d48tKz/m+JbsuK0BlyqZm00+vza+dzY24f7cYKUPyXZsKFcpm72DE5Opypnz4aWzOuTIZg2os6pUTz7n6TuB92CH9EP5H29/YUzZVanGRGT0rpxJcyKVSCs+xyLNyLXt3eMcO5P6clQLDNazsQ4b4F8OXvasdqPOqqJ4Tt5BvQpbre1CvJsJXhFbuqdtBinrN97elJ0D5jAnYkfjIL52tU5xW8ZhJ5YOFtcfFoMVU0gBCytoyytndT3+Xit1VpXKuWvJTaOAv8MavEFTRfnN0XRskzFO2UM0YyNy7iWJE77p2rT7l5ztVfCNdhOfgu29HG5UUoXLS2Pid05FdVaVhmLj1ddHsCmlPkGwaZhOwRjM0SOJstPcRRyaXj6753gM+yMvtK7efyxWaf7oiogiZ0WN9GZl2JW0qtT59MpZPeBROwOhzqpSGXctuWke8FsqTCtVK5u2TFh9MNVyBrYXtDiJcq3xVL7P8IwgcyNGjuTEtPoty2OGTCtThFc9q/fhfw/0cEqzpSg1sfHq69uBnwIvCVLuvj2jH9y6aepUTtjYwu8uAlJ1BgECsNmCRCa7iaeejxw9f+i964pKe5y9cFY340PJ034EarPVWR3m3LXkpiXA/xJAHGp/sjlHQIZMTVUpJoBKSYJExrvNT++KpIMsBuAFlVwbL958NxFMr4/2rCoNwcarrz8N+DWVDQ17gus67nC12QAzcq2Hni9dzaqeqTSso1Zn9Sg2pVaixnaGIlCbPVxyMColuGvJTX8N/JEQHFUA8e9dOpDceRNNIuApAp5QSYqaWo3J3ry8U0rp+oA6q8qIZ+PV15+JHakI3FEFEDFhld/0hPFucxC2yGsqHWmsxS9zsRPQgsiUoGEAyuDkc6N+GhufGnQlj2KGXfBQMWPcpqqLHYRIJb/ZWoZpjmCdVc/y7g6BhgEoI5b8JKp3Al8ERoelh4/OaiBOcBPOGWIwRkKreV8NlT5nanmm38vgk728RJ1VZWDuWnLTVcC/E3B8ainEp0D3JmFz2rCnaFW1ggoGrf/xAhBFshhWF5m9gfYv1eZg60p9L66f7GINkgBjqCwNSyWJ+as1JlngKWxVnKDQnlVlRLLx6uvPAVYyeKWjYPDJyWuN5Q4fOBa7u2iVLzZbEDPKjR1NRfpa+m2r1Gb3X1/q80B/m7ApxcqlvYJ9oXpndbDiBH6gMatKae5aclMM+EXYevhNTJiVNpQ9M7VaojgkTezZlPQFnVy6uErVdmzJ1aHiix7I71N2SjCqf2A8SPApvdRZVUYq32TgqnTB4tNYWGvcbZPBU+55xmy37Z51kQOXBSGriOIqVC42ld9QOUyfxlZHHAfMGXzXk6jmheIegnVUQWNWlUEYHbYCI40pbuuOEMQWPzKmYnsxtw+y/93Y8ornUFmPejVvvncTTu5ZdVaVkcqYsBUoYliHbgFMc1viIYgttqUOEAc2DLL/fdhqhBdTmaNaaL8S7iO4of9iNAxAGZB6MnqYYW/2YLzbHIbh63/lFuXXPY7NjRfD5oAt9LgG0mNB8MNIxaizqoxU6spuD3daTbQDQ0BRssfp/+I/Lb90A89jHcwWIIMNo6rFeazkzB7FFpIII4ZXnVVlQHxNlN+IJIicHoLhK+XmC+VXpqpFzkDcS3iOKqizqoxANl59/WRCytYyUhFkQruJbe6RvlkBih3IPnXgfUnTcp9Gz2Bn/ftaOWwQRq6z2pPpEEDa490NN/N314eunQxchn3LjmCvvQvsxtYOfgbYM+mr3y/pYNy15KYLsTNJ6wjfPLzAXEdBxo828acOSiaoWe8Q3FBcuXKexA5XhUnD2YThQmblMie+YlXD/X9yty8ehe0hm4612RFsD9o+YCfwLLAtcs3aktdm49XXT8XGq4aZsWVE0pEbtfVRZ/+sAEXW2/1/CJtSMMxUXiPXWW2Pd5ueTMfsnkzHEuzQp2CdtOew3elb2uPd2SB18ppdH7p2Gjbm75L833Ox8S3lGKwDuz507WPYyTQPYevAP/eX+8/+OrDCF4UVZubadh109o9EZ1Www1IOJ7IPlOIo1hasze9XXI1sqL8U7e8CE/J/9+blRUrsX3ycm9/vUJnnpATP2MzKZZdibfYETrbZz8VXrDoWpnK1krt98WjgRVh7vQRb+WcU5dnsdO72xU9gbfYDWJv95LM/v+itwNeoPCG8z/iTwiXoMegZudbko7H9QYoM0lntyy+Fl6NSPIIdCduEfXmq1GaDPaccdi5MAvsCZjjZL+x/jClaBptn4TmBhwG0x7u7ge6eTMf3gfOA1wEfw3al9/VkOh4AfgL8T3u8e0tPpuNK4CvAMaA3//dJ4OH8sqE93t0b9HkA5H5wcRuwKHskvuDg+ukvAbkEG8dSLWOw5feOl+AzhiNjJu1bf2BXJZPAhz2BvsVOMImZIYQCBMFAgf2PcyLkYA9QKF/YS3Dxsf25JSS5yhDEV6zaC/wc+Hlm5bIZwGux9exfBJjMymVPAf+DtduPYe+7H2CHJzPY+2ozJ2z2o/EVq0IpyNGT6YgBC+iTC1p/M/YCyThLqS28KoGNGbwQeG9+XXbKko337LhvbstINCr1QBPOuQkT2ZOW3ISARAb5TBpfYl3/J9TM/N89eDcqVkkKRbAvrYERWsxqe7zbAI/2ZDqeAF6BdVZjwKX55Ss9mY5HsDOg+8dkFKet6OvJdGzAvmk8nP/7eHu8O+2lvrkfXNyCda4vLFrmARJtzTDmvC1rD66f3m5y3o74iNA6edYLFwJ/6tkzOg7kREwEMK7rNBvj+F3/dxD86SAM2rw7yKxRJtZ9SPq8jj0aiDCnpmWxzkOBndjesrDRmNVhQHzFqq3Av2ZWLuvAOqsCnAV8Mr9swvYmTup36CXAtYUvmZXLnuGEzS44sAe81LUn0xHFOqIFe30BtnJUEzHDkVfvey7xpzHRSMrzx2C0ZWLqJactfuauXQ/NiYtjsgWbbVwxuWzk8vCcWH+KAkjANk0Q54xs8qnHYweCsl1h26e7OXlOwaz83zCfJSM3DGAAPs/AbwbnD7C+mBh2eGoRtkIIQC7vBBcbw3Xt8e6yigrnfnDxJGzP0zlY43Y+1ugNmFIi0pxdPPaCTZsPPjFtd+5Ic6WpKgZFhOjkWS+82OScvxw+OOq8wnrHcY0xZrXrOktH2Bt84PFBM9y2bU84B4JyVsPkAU5OTj4xLEX6UW8xYcoAZFYuew3wvgE2zy6zmTPyy5uK2u3mZJv9SHzFqn3lNNaT6UgCZ2Pt9rnY58FCBstfHGFO+uUHDjU92vZAbFPC85jt1sk9L5l43ubV+5+YfsLJcAyO497bl4ldBBLWxBjvCeHxMzPXmng85un7zWCEaZ92cOrk163YNIZh6tU4RQF6Mh3TgI/60HSEE0ZreX6d25PpeBJ4uOmBUQ/EtjclOLkyxWlY5/RcquxpEodZo89+/uiRrePuO/bCGE9zVYoQP23O9o6tf5nV3Ztu7sivExGzVCR3dy4XuQwkUJPhl7Qw3O4JbnOQ6WXCfBvub2AmYdOfFGLIw0Kd1eHDV31qtzCz+vWFFZmVy7YAD2fPOHxP32X7o5x8j47G2uxzONHTVBnCqN7zD1+cPS2zuvm+5GVixNPc423T9i/tO9x036EtE48/D8ThklhT37q+3tgskEqrG9WKXz2rgZMgsiBipDcnppKqftUSpn0qFRO+Ceus1tOzxFcCd1Z7Mh0vBja1x7u3Y2fCZwPSwwEWAAucI85sTg4l8AwRWtpm7lsSH3307tRTUy7FiGdxASIkZ5y1+eBzj51x2HUjbUXrL49Eco/kcpEZIKXiXQbB9GDzeRZPhDH9Prv9vhe2+zUZLvAfYAxnQdRIKismGbTsgCl1bQthAeqsKqeQWblsNjAxvmLV2vyqHZTfg1orM4GZJpGbhA0P84XcaZmlR1+979HEH8bOdHodT1NNjT7zhfN7D7Y+nelpPR4TKMLCWFPf1mwmussYp8JYQZMGehjIZktJm53/bI5UeRqDIiH4TIIkprktD2+JHAmiLHS99WDO4+T/bRiMXGe1J9MxDvgx0NST6bge+D62zv0Hg9TDr/rIxcTb05ePPnvbmoPrZ3hqYMVhxtS52+7Z9tSsk5xtEc6PRHIZY+TPrhspOyFxJJJbL1JdAmPHcTdVc9xQBB3/ZGVKZIrb+sTWyGG/qzc9RniTmMDm5Xs4/7kQtJ/DzmIOMiNCf0ZAiYmRR2blMsFOfluaWbnsC8BnsRNefXMcB8B3m22azaKjr963te2nEzx1VkVomnjRczz/h3NM8XiUCDNiTVlcV/6czURfXO4pOhH3kWgsV9X1j8Wze6o5biiCHdM7wZzsqKNbIr7438WEWSwF7Khvf5stwEZOTLQKg0BtdtA9q1/jROD9bcA1hFMmLJCL7DRlfRniaW49dkmsKbO9rzc+tXi9CHEw54JxoezhrHosuRuK4zLNbTVbI4f9FhN2cPFp+aWY3YQTu5rDpj/aiE2FpNQf7+FEdpJPAn9NOHlDg7EJDjOMGNfrcAAnYuaOmrn3vkNbJpzyMuw45sUi5mljpKweVpEaageGbX08ZpxpCqOaVdC0YicH9qcQtxo0z2Nt9n1BCg3MWe3JdLwOeHO/1VcGJT8MxDG+5NgTITJx+q5ntz87fWqJbUkR84gxUs7kNAg3qXBJwuhZBWg10bPE4Brx1YGvR7Pq93DOEaxx+wvwNDb13BPAc6l0Z8Zn2UqVZFYumwN8qd/qhWHoEigxkyIjo71utn3OzlGHtpSeDhGJ5l7I9pUXDiBiapmFXY/2p2ocZOoYE3/2gGROD1uXEPDzpTGLTU31JNZ2P4W12RtT6U7fe3RK4buzmo9RfR642W9ZdYeYtqF3qo6W9iMLEJPFyCn/Q8dx57ku9xvjvGjwVgyEO4xQkrCcVUHGjDPN6/fKsXN8FVN/eOWsprBv+wfzba4Fvg5sS6U7NSZ1mJBZuWw+tkjDf2F7dRoKE3cPScYZ7XW7Tsw9OzbqaHffoZZTso44EbM0St/qbF/08qEG1f3qBKmFMI3aGdnk9gfiexvRWfWiU6UX2IItLJABdgGfAZ5NpTv7PGjfM3x1VnsyHRHg09ih/mY/ZVWCidUwjFIZvk3WEWF8clzPg6m9o0/JsypCSyTivsh1zWrXdQYzfjtFmFytDn19UX/e7MKI1s8zI9e2f6/ja0GekeCsHsQ6pT3YF4tRwBRsiM/ZRfvdnUp3bvFCQSVQ3gR8nDqy2XIsEliaJ9NkjuBT39HouTu37Xm4o2SKPCdilsacvof7emNngAz27JhSrfzeY3FfjFtYMasAU91EQ1XMKaISZzWNdUr3Y6tjFXIhT+fkYgCrU+nOJz3T0EN8c1Z7Mh1tWE/9MeDlfsmphty4vkh0d9x3OSI0gzF+/ZRHTzyQSe0dPeB2xzFLRXIP5XKRs0FOefCImB1Qi7MaOQbmMUqX3iz1mRL79t8Px3AAw5pB9u//mTLWg3UU+0c4yVHJZvvEjQmQFbcXWNNvH6foeCnaJoMszgDrDmBrijv99nP67Rcp+uwMsD5StL0WBnJW92JL6hWqDSWBqdgKK6PLaDfsRNpKBeQnU43GTuaoG0cVwNnZVLWDVikm5voWmtI89vCgWRREuCDW1LelLxM7hJFTwrzAHBWhxPrySB9JHBrCZve3BUOW8LRPN3MQTrHZA5VYHux7f0ra7F5y2SOSjRU2OoZ7XDk+LN7fPkNpG168bSDb7WAdvWdLrC/13SmxLVL0vfDZi46eUnb/MLYj4QDW/rZindJplDeBtm5tti/Oak+mQ4BbsTnzaik/6gu5iX0TCOjdQSLuEZOL+BIO0JTonTWkfOHCSCT3rOs6u42RxUAKpJBTtEYnR2bEoqbSEm1DEsnwcAQJIh0JAJsihx7cHjkSVCUwg52RXy33MHDaNYM1NjlOTjuWy/8tfC+sI/95HzZGKY0dFoph37jHU7r0X7no0P/w4v3AP2CrmtUVkopOw3AIKT/GvqfH5YqlO/jd6im0t5dv6kzc+Db8KY6ZEWnO7Mwdiw/YSSDCzFi8b6+bc1bnspFF2N9tMt/pUVMPs+tGEvF47rxa2ihFPBZZHUECyxBx0MmsXhXfFdTk7J3UZrPv5eRCLMUU2+iCzS7Y8IL9zBWtL7br+7AdCoXUg5PzSy3lgxvLWQXeDbzOp7Zrxh2d7TCYrHBqvKfXSMQ97JezKsLUUlkBSux3eiTinm4Me7DGbm0uFznNGKkpRYsIri8BFfU4UO4dtZ7dYMcL9jdd6X19DPti6TV1a/iUk8msXLYI+CIQ59RSqaEjCGRkM02m7Hjy3/36KBuf7OP3vznK699Uvgk2Tf71rAK0nnagO7Vp0qAjWiKMj0TdpU7ETWOHbndm+6Jp4zrnGcNukWqzd4yMEtnDjKFG+yJU19PaSnkjXJVQtx0Mfs163o2Na6tPHKJE8CVH6Cmi4rnU0HtVz+iJB8pO+SPCBBFGi7A4EsmdFonkajXKvlg+CfgHE7ChrcdUYX5db3VWhw9ZbBqxusU5GDtYzn7vfNtupozZzN+906YUXfGOPUwZs5l3vm13WXJMwvV1YknrtP1l2wAREiJMFeHMWDx7Xqwp81T1jmq4saXDmFqvml9zMPx4ltStzfblwdke7/4pNsXJBi/b7elxWXze8/T01P5sNTG3xwOVhsRp6vM1zUP7+IOTq/ktiBATqWloA/xzVkcyfvasVotfxrRuDZ9yMvEVq9YDF2KLttQlkoqVZfg/+akxTJ8RJZYfMI/FYPrMKDd+uryKym5LztcJntFEZpET79tXzbEitRbu8KlnVYKdEysj/jFRFuqseti2pwlri4d1hgtOPNs79F41tB8xc0eNSz3kp4xBGClVh4I8j1oD6/0w0n71rNbtkJJSkiwwJ2wlaqXj9Bif+PQY+vqgtVXo64NPfGoMs+eUF+5pml1fRz9EaBq34HlPO3IqkO1b0761XJogbXa99qz6kY2nsZzVnkxHDLgdj1I31TqsU5KA0iM5sVzWbxmTZ74wwYnkDvkt51R8SwEWqBMcsJV9vsbj1VlV/OILwKKwlRiQCqzCT390mJYWofMfx9DSIvz0x+UPcJmY8b1CV/OE1Ivi7Uee8VtOf2qqfjVYu340Wl/UoxPnx31atzbbrzfITwNDJKQvn1qHdcLEieV8/+eLw8yZCzY9JeJvrNUpcn1y+MMqChAQtZY1VWdV8ZzMymWvAj4Uth5e8f6PjOahDdN534faeWjDdD7wkdHlHxwzvuc1FKFp0sXPNUWaM7v8lhUQAdvsQKNvX8yJGffVMJx6VuvWZvvlrKbw8E2k1mGdAQjkxyWRYP73sXj2otnnPrfBcXJHAhFoqdsbu0KCNLS1XrPh5KzWY2+EMjAHwlZgUEz59/75FzYxcZJ9lk+cFGHRBU3li3FMIGXIxTEzplz2ZDbaemxrEPLAv9hSkWA7V0PoyfW9t70KNGa1Vtrj3V8ElgLbvGqzlmGdkgRVJckN7mcVjeUWdSx8bmusKbM9MKE+MMKHlOrRWdUJVg1OfMWqX2Mnxa4Zat/QCMiWignuJVwcpp526caWxMSedUHJVKqiFmfVL/vqxw+ibm22b4Hk7fHuNcD52DiomtM31TSsU4qAPKKgx7OdiHvWrLO7mxNtR5/yW5aPw/UjOWa1kZzVkdLz3hDEV6zaBiwD3kedp7HyFRPs1HYRxo8/b/PZyY5dvr8oBD1r30cCf7QGLG8oGi50y9d/QHu8e297vPsT2JQo6VraqmVYZwCCudlN8P2EIoybdubWyQH0sI4QyxeoBa9V1nAKA6hbw6eUJr5iVTa+YtW/A+cC68PW5yRGiLUphQiR9tN3XtI6Zf+DYeuilKTenFW/ekDr1mYH8g9oj3c/A1yKx3lXhwOmLxLKqLYIo6fN3brDXykj+OnhH43Us+p7JgzFH+IrVh0BXg78PGxdjhOUucmKrxWsBkIEGXv2thniuP6lOxzhMVY+UY8POr9sa+OFAfSnPd79KHAB8C9ByRwUn1J4FGNc6e473Fx2LWuviTVlL4o193oWN3wKQcX9jiwaqWe1bg2fMjTxFat2A38LLAeCnLg5AAF4Wi7bI3tjoTirACJMGjVrT1h5s5XS1KMd80unuu1gCLRruz3enWmPd38ceCW2JGt4BOBo7X941oTM/rZQ8xaOnnAwkLKyHhP0+3+Q8hqpZ7UejbxSAfEVq0x8xar/ws4/eCRMXSTn/++06eFRLzQ/Mmqp33IGo3XKft9mno+g/oUgbXY92rGG62AIJEVHf9rj3b/tyXQsBL4HvDQwwQacPfEnY0+17mZv0xjj8jj2n5Mj6vZKzL3UU3kSfvyH47h+Widf2jbBD7sMp5hVP14wdYKVMijxFauezqxcdgl2ZOx9gQp33O0Szz7X9GxTW9Pm+HrjkEVMzk3mjh67rOdyT2UFMOI2pAqO/0UJhjsB92bUowPXcDGroTirAO3x7p09mY4rgE9iiwj42ssb7U48FF/bPlWMnAWcdcoOOedeEzHPYH8HxQbLYDD5dQ6OmYCtzJUFjgGFRPwO4GLIUnCAMW2+nVCZpA+3tIatQ6UEXfc5YMNXj2EA6qwqQxJfsaoXeH9m5bJVwLeBdl8FOu5Wp633qAjzgKkYgb4TCT0l43Q7+6On2myT/5y32ybhjjYJdyKGHDlJS04y+TmVDkZcDDlcshhx6ZPQHZO+w83+1RPXmNVqCP2eKEHD2ezQnFWA9nh3DvhsT6bjHmwv62l+yIk+3bI2/lDyAkEGPl8jl9Bb0eWIAEOlJNhbSYNeYwy5wwfa5oWpQ5WE3rtRAY9gH9pNWKNmsD94t+izKfrcBDxetK7/PsULJb73YFPBFW+nxOeBkBKfn8dOfoxgX7oG+xspY3vh8/4y9FGGGfEVq/4ns3LZo8APgIt8EeK43U5bb5sIMwbaRVzpaLmroiqGDjBUJZnQc1Qf2TE2tHkOw4banhDbsQUwRnOyrS7Y71J2u7+NLmWzGWDdAeDPeGOzC9+PAVsZ2P4OZpcHs9/+zXGpkVCd1QLt8e67ejIdH8U6rJ4S2RHfEH8ouWhQR9UnRMiG6XXlspH1rhs5z6/2R0xZ1OrP4ilgHtDimS5D8xdgvsdt/iSV7rzW4zaVEUx8xapNmZXLXod9YHqLmL1OW2+TSM2liashVJtmDEeO7k6e7Vf7IyhmtVp6gAzg2zUuwd3Ykq1esiuV7rzC4zbrmrpwVnsyHQngH7xuVw5FdjStGjtZEN9rPZfChcdzWWcjcFK+VXHciBMxXt+8p3Bof7LmYgxKSbYCm4EFBOuoQoNVLVHqmn/yvknT54w69oII53jf9tC4vZH9bjay+ri9Lvh2jiHSkrlQBF/DqrJH4+txnRf5JmCEhAFUUd11H7ARGA/M9Vyh4Gk4m10XzirwVTx+05Gjzt7EryakBZniZbuVkMvETs8ea+ootS0S67s3Es9eJDLksFTVHNw9Zk7/dcbG1BZiovoPa0h+aRVhaAd/5Lym9z8PlxPGIAv0As9i38rPAGbklzBQZ1UJnczKZW8G3u5tqybrtPU+LIJ/ztoQZI82tfftbyvZkeA0ZzY2Tzk4xs8e3yPbx54SM2gMLoYj9qOUChcSEdMMtMgIcUaroHgYvzBvZDOwA5iGnadySVjK+UDD2ezQndWeTMdrgfd42mivpBK/mHhUXDnFWQsUIwNOGsv1xS4xrrM+2tR3DEyU44ZHjsfCmGKDZOw2NycGOwdJMDjGSBQbayIYKTibjpuTg4d72tqx+RFHYXsAW7HhEMkylN+LfRuFkx0kJ/9dsjkO9WVlc9E+UrS9eF+H0nE3cMJRFEAOZs2ju/q4QOBgv3aL2xsoTqjw2eVkB7QQw+PkcLf9Ib5jtgEHjGPAMTbuzuXEJD+n6HMMSGCrsN1DeE5qAT8mIjac4VOqJ7Ny2RxgpbetGpxRvQ+LY0JzVPNqDIh7LH5meuu4Hc2nHbxXIm6LMXIiLr0wCdfISTbbILjpWA4QYwRciRhXIsZ1otjX/YItF2OQXU9Oc/qONj+JtdEJrM1uwtrwoZQ/BOzE/p6L7cRx+3v0cNPRo+nYJk7Y1SK7LcX7lmezDZJx5ZlHdrQuMJgDnGyri9sbLA6/uMOkWOfCIj+PbzuSEbfdhQj2Il+SP89C5gThhD9T6AA6GztHYE6JcwkS7WDwgFCd1Z5MhwAf8rRRA4nfjv+LuBKu0bPKDNrz6OYi52SO+pOlJH24+W6QhdW3IOOxQyYDYlxnL8is6mWcimsNzRi/LIsgTTkx1Q7dvxh/YkYrQZ1VJWyuAzzNdCLNfXeLY7xNQ1WVIoNvNtnIlPS2cb6M1rk5ebrvaOLc6luQUQzh1BrjPG+MM7t6GaUalW0544zztM1+9IkxWTGJKg49AxszGv695S11O2vfL0Ktd9se7zbAS4AVwC4v2ow/mLzbORytA0cVnIgbWiWUg3tHB5GyqtEGnQRo5kS6sjBQZ1UJlfiKVf8A/BWwzpMGo7n1Es/Vhc0Wx4TmBKRTrS8EIMZzmz0MYsEuxo+JgOHScDY7VGcVoD3enW2Pd98MnI7Nt1p1Wb/ok633RZ9tucwz5WpEIiYUp8YYjh3uafN/gsLIiVmthA7g3hDl+/Gbbbi3dKU24itW/Q5b1ept1JLuJuI+67RkppcVIx8EIRYFOLR79FjfhfjQvTAMeiyaCTeNpIYBeEDozmqB9nj34fZ492ex8SX/SYUP0OjTLWvjj466WKifEHNx3FBuqFw28qQxThDGvxGdVbDxUs+GJFt7VpW6IL5ilRtfseq72NnVndj8v+XjuN1Oa+9oEUb7oF5ViIRj0ozhSDrVEmZ4UdUEccU8eKifj811GgbqrHpA3TirBdrj3bva493vARYCvy3nmMj2pvX5pP91VaYu0tznXyWSQcgcix8KQk5+clcjEsO+TIXRc+7HPd5whk/xjviKVcfiK1b9MzY+8D8o534Ssz+f9H/QuPigkagbSmeHcWUbxv/nl+v64o0H4a96IWMh4RR98OOearjRsLpzVgu0x7s3tMe7Xwm8EniiaNMO4NfHvxloum+0G0bS/6FIzto9D8ftDVqu6zqBdA+0jzpadcjGCGAucF8IcjUMQKlL4itW7Y6vWPVe4BzgV0WbcsB/Fe/rJDLrQ0r6PyhOc9+LnebMk0HLNUYCmd8wblzP5CDk1CmjsNWkRsLL+Ug4h4qoW2e1QHu8+7fAedj0VruB64ErgQ8Cmcj2pnWScWqY9e4fIoxxHDfw3tWghrJi0ZznLwjDLK7gcmBtgPIy2NQ0XpP1oU2lQYmvWPVkfMWqK4ErgPXA1+IrVi3Pf9+JmB6i7gVh6jgQIjhOLBfIyFQ/AnkWR2NZT7M4DEPOBtYELNOPETh1VuuR/CSs/8ROwnq+Pd5t2uPdXwMujj/YHnot50ERcm3T9gb644hEc8GEQ9RNdHConIXt7Q+CONZh9RrtWVU8J75i1e+BRcCdmZXLJP/9XCeRuU3E29RXniLGRFqPPSrRnB8vhiVxHBNE9hbwx2oPsz4GLgceClCexqx6wLBwVgu0x7sPtce7Hyz6vs5JR94IfDNEtQZl/Lmbu0fN2Btoz280lq0mH13FDFNf1Wu1k8AegjHY+7APf69pOMOnBEN8xapcfMWqVfEVq0z++x6Jue8DPoA/L141ExtzNNI0KXWuRNzgylWLmRCIGDHD6pnvI7MILkOAH6EXDWezh/2NG12+5kh0+Zp3A2/gRNWjuiHanL04aJmO4wZTr17MMPVXPWchNvG032wAX1L8NJzhU8Ijcs1aE7lm7b8Bi4HA40OHwom6F4r4MpFxQERoEwkkL7fabMt4YFMAch7Ezm/wmoaz2cPeWS0QXb7mx1inIYxJL3WFOKaM0nweyBmehs8vnS8FfoO9/9ZjjZSXdANLPG6zQMMZPiV8ItesfQxbxvjbIatSFzix3AG/ZYiYusqYEzIXYScCrgEeA1Z73H4afJtE2HA2e8Q4qwDR5Wu2AkuBfwlbl34EfZ2bghAi2rNaTBQ4EztMfw5wFO8MShYbpO9X7tyGM3xKfRC5Zu3RyDVr34ktLhBKqr8BCbhAgBNM5phhabPFvzCrlwBTsZO4W/F2MtRDwEwP2yum4Wx23aV7qpXo8jV9wMezt146H5s1IHREaHFi2f1uX9T/CiWWoGLBhqXh85HZwD3AZdiXpmIMdiJTqcX0++wUHdOLNaDN2GGrXL99TdHxpswFTkyqMth0LooSGpFr1n43d/viKUBX2LoUcOK5g+6x4OQZ1/9CLuJPx8lwybNaigS2BzSH7eXvj8sJm1vKhhfWFV/XTH6ZCWwuOj7Hyfa3v/0utb7YZhd/7672hIcrI85ZLWIVdeKsAjSNOfxcOohyeoAxEoiJrZ9aYRXht9aXAQ8D/VPzCDahfz0Ow/166F0UxXdWha1AMZGW3mPZVCBzVQFwXcd/YTrBqhRnYUMA+ncwgHVC6/GahZFeLVTq8Z/gFfvDVqCYpvajgQ1xBeWswrA0fEG42NMZXr2VDTekpNQldfWbcZqyQaWTAsDNOb7LExm2o2F+995ehp3AOlxoOJs9HJ2NIcneemkT8DIPmnoXNkD6E7U2JMEUlQLAzTqHg5DjR8+q31cpIEs9EXg6GFGeoHlWlVDJ3b5YsNUKa+Vb2N/fS2ptSMQENvJoDPsIpny15yM7QTzZfIxZLeBg0xAOl6qMDWezR5yzmr310nnYmX3XetBcKrp8zR6gZucvSGc1m40GErPqU/zTSGExwyczRcMZPqV+yN2+uA34JfA1D5pLR65Zuwdv0hgGZt+MkUB6lX3KBjCcY1aLmYEN4RoODLdCDDUzEp2NjcBHgb940FahI86DGyNAZ7UvGozzMTyzAQSp85nUYe7fEjTckJJSP0SuWXsYuAGb+q1WvPx9B+es5oIZDWPYTooNTO3LsakH652Gs9kjzlmNLl9josvX/BKbc3UFtcWuFq7PfwKjgTHYZMKTsbP8yia9LxlY/FMuGwnklz1M86wGyVjg8bCVKIOGe0tX6ovINWvXR65Z+yrgCmorFFCw2es5YbPHAZOAH1TSUO5YbF8NelSE6zpBpK0CfOhZDcB6BPygGQ4TzxvOZg+Hf0pVRJevyQI3Z2+99BfA96guhimSb6uQiuI42VsvzZbbiMnJs+ndyVJpMXzBuMH8tLV0X1lcAmzFDjHVK/p/VOqCyDVrf5+7ffEi4EvA9VU0Ec23kwN6ijfkbl9c9iRXY+jN7B01rwr51WGCcT6CrszlIUE6Z2cB92Jtd73ScDZ7xDqrBaLL17yQvfXSK4BvYCdMVXR4rfKNwd3/1LRjJd4NXww8AHwE+EKtck6WWfHUp+L8bYWccH35zzlsUvpsv++uiHke+6PJFS3Fueckv0zFzpAvS5ERRhR4gfp2VrWHXKkbItes7QXel7t98WPAN6ns/vTkmZZNNd9vspH+qYxuAL6KjUe/xws5BYyRSkO3inNvZvN/i212ruh7wXbnc32aHIjLyTa7OL+nA4wCFlSoi58EbaNmA8ew+a3rkYaz2SPeWQXby5q99dIV2GGhNwyy61exgf6/w/5gXw3cWq1cY8ge2THmvt6DrZeV2JyZ8c1b+ra+653pcprihDEpOI4fww6XFdYdN1K5bKQXm+i4jxPVj4o/Z1/+4A2B+YVfafv+TOzbagx7z0WKPhcWAZw+w1HscF3B0Y2W2DeK/f98H9iTFyOc/LYpResE6ACaesm5Revg1B99C/AjD067mMXYSX/nedyuVzSc4VPqn8g1a2/J3b54FNYuD8TvsHMUfocNz1qWu31xInLN2nLsaklyvdE1mb2jSuXc7EveeGdf6nNXldtDW0gCX3AYvw38pGjdcUeyLx3vw9rsDAPY7IW/+JIncxHOK2Of7874ZjvwIkrb3mIbLMdyzjGs3SzY3IJ9j+U/F/aPYPOZPpYXI0VL8feCHT8NG8aBOblYSvFcksLnrwOzyji1cjmNgXOv1gMNZ7MbwlkFiC5f42ZvvXQ51mnpn7B9L/D26PI1vwLI3nrpZ4DPAluzt14q0eVrSjl2V2HLXz6PLRMo/ZYzju0dZVKbJ+WwBqiPE4Yow4khqpuB/+bUChmFN97sjG/eUpFjWW9deB8+fO0WYEvIapQlP5noavdQ5gFswv1fAjuxD9R6Goa7B1u1peEMnzJs+Br2Rffd/dZngU8CX4pcs9bN3b74rdjf13NYu1zKWf008K/YeQx7OdVmT3H7nFHHnh9bsNV9RUuGE4nYH8fGwRac0WLn0wDZ5I13VuRYJrHeUb3w1q3v6gF+G7IaWwsfbh5ix2Siy6sk+b3Y4hR3Yp3V31Ff/5qnsFW3Gs5miwm2/HHoZG+9dCb2zW50ftUq4C3R5Wu2F+0TAV4aXb7m90Hrp4RLMtE1mtqSk6/HOqe/BO5PpTuPxzYnE13/j+ri8PzgeeAcYD7Qkkp3/iFkfRSlJLnbF8eBNZwoh7kZuCZyzdr7++33CuBP+XhVpYFIJro2UH7YQn+2c8Jm/zGV7jyeazWZ6HoTcEftGnpCFliC1fevU+nOb4asT6A0nLMKkL310tdjb8BPAf8cXb5GjZsCQDLRNRaodBZwoff0f1PpzgF7cJOJrjOwqdXCfis2wMtS6c67QtZDUcoid/vi07GdDL8A/i5yzdqewY9QGolkousJ7It3uTwC/BRrtx9LpTtLOkLJRFcU2ARMq1nJ2rkxle78fNhKhEVDOqsA2VsvPSO6fM0zYeuh1BfJRNc47BBhuXwrle4se+JeMtH1v8CVFSvmLV9KpTs/FrIOilIRudsXnwE8G7lmbWM+tJQBSSa6ngTKzd6wATg/le7sK7PtTjyeBF0F9wJLi0fqGo2GiVntjzqqygD0YkOkioP5+0/WKnzOAV+ssP2vEa6zug64MUT5ilIVkWvWqs1WBuIH2NjSwqhVf1td+GuA28t1VPN8EzsKm/BM28o4DLy1kR1VaOCeVUUJg2SiS4AnsJNGguYIcHEq3elFdTdFUZSGIJno+k8qT33pFW9JpTu/F5LsuqHhEssqSpjkY6P+LQzRwCvUUVUURamYMGw2wAp1VC3qrCpK8HyX2jIOVMpB4OWpdOd9AcpUFEUZEaTSnRuAPwYo0gDvSKU7h8ra1TCos6ooAZNPjXJLQOL2AS9JpTsfDEieoijKSCSo3lUXeFsq3fmdgOQNC9RZVZRw+HYAMg5iU1Q9FoAsRVGUkcyvgF0+yzDA21Ppztt8ljPsUGdVUUIgle58EvCzt/MQ8FepdOc6H2UoiqI0BPnZ+N/3Wcx71FEtjTqrihIeP/Gp3SPAlal05wM+ta8oitKI+GWzAT7QaFWpKkGdVUUJj5972NYjwKuAiUB7Kt15j4dtK4qiKHA/sNujtnZg02FNAZpS6c6wMg4MCzTPqqKESDLR9RRwZg1NpIH3At9NpTu1bLCiKIqPJBNd3wSuq7GZLwL/lEp3HvZApYagYStYKUqd8EPgH2s4/oOpdOetHumiKIqiDM4Pqc1Z/W4q3XmDV8o0ChoGoCjh8oMajv0pthSgoiiKEgx3AXuqPLYbuN5DXRoGdVYVJURS6c4ngA0VHrYWeBZ4V74ilqIoihIA+awAP67wsB3Ak8C1qXRnynutRj7qrCpK+NxRwb7fBJYCl6TSnft80kdRFEUZmEpGxO4FLgCWptKda33SZ8SjMauKEj4/Aj43xD5Z4Pqi8nvVDkMpiqIotXEPsBOYPMR+NwPvT6U7M/6rNLLRbACKUgckE13rgbMH2LwX+NtUuvPPAaqkKIqiDEAy0fUN4O8G2OwC7y3qXFBqRMMAFKU++N4A67PA69RRVRRFqSsGq2bVqY6qt6izqij1wa1AqaGiT6bSnXcHrIuiKIoyOGuAJ0qsvxP4UsC6jHjUWVWUOiCV7twJfKff6r8AXw1BHUVRFGUQ8plYuvqtPga8T7O0eI86q4pSP/wDsKXo+42pdGdfWMooiqIog/I94H+Lvn8jle7cMtDOSvWos6oodUIq3XkA+FtsCdUs8PNwNVIURVEGIt+D+lbgmfyqSvOvKmWizqqi1BGpdOejwJuAfYAOJSmKotQxqXTnQeBKbOL/nnC1Gbmos6oodUYq3Xkn8GaNe1IURal/UunOZ4BlwOGQVRmxaJ5VRVEURVEUpW7RnlVFURRFURSlblFnVVEURVEURalb1FlVFEVRFEVR6hZ1VhVFURRFUZS6RZ1VRVEURVEUpW5RZ1VRFEVRFEWpW9RZVRRFURRFUeoWdVYVRVEURVGUukWdVUVRFEVRFKVuUWdVURRFURRFqVvUWVUURVEURVHqFnVWFUVRFEVRlLpFnVVFURRFURSlbvn/AfNTFCSa11S1AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create full matrix version of weights\n", "full_matrix, ids = w_adaptive.full()\n", "# Set up figure with two subplots in a row\n", "f, ax = plt.subplots(\n", " 1, 2, figsize=(12, 6), subplot_kw=dict(aspect=\"equal\")\n", ")\n", "# Append weights for first polygon and plot on first subplot\n", "sub_30.assign(weight_0=full_matrix[0]).plot(\n", " \"weight_0\", cmap=\"plasma\", ax=ax[0]\n", ")\n", "# Append weights for 18th polygon and plot on first subplot\n", "sub_30.assign(weight_18=full_matrix[17]).plot(\n", " \"weight_18\", cmap=\"plasma\", ax=ax[1]\n", ")\n", "# Add centroid of focal tracts\n", "sub_30.iloc[[0], :].centroid.plot(\n", " ax=ax[0], marker=\"*\", color=\"k\", label=\"Focal Tract\"\n", ")\n", "sub_30.iloc[[17], :].centroid.plot(\n", " ax=ax[1], marker=\"*\", color=\"k\", label=\"Focal Tract\"\n", ")\n", "# Add titles\n", "ax[0].set_title(\"Kernel centered on first tract\")\n", "ax[1].set_title(\"Kernel centered on 18th tract\")\n", "# Remove axis\n", "[ax_.set_axis_off() for ax_ in ax]\n", "# Add legend\n", "[ax_.legend(loc=\"upper left\") for ax_ in ax];" ] }, { "cell_type": "markdown", "id": "certified-valentine", "metadata": {}, "source": [ "What the kernel looks like can be strongly affected by the structure of spatial proximity, so any part of the map can look quite different from any other part of the map. By imposing a clear distance decay over several of the neighbors of each observation,\n", "kernel weights incorporate Tobler's law explicitly. Often, this comes at the cost of\n", "increased memory requirements, as every single pair of observations within the\n", "bandwidth distance is considered:" ] }, { "cell_type": "code", "execution_count": 37, "id": "unlike-nickname", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "40.74074074074074" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_kernel.pct_nonzero" ] }, { "cell_type": "markdown", "id": "russian-terrorist", "metadata": {}, "source": [ "In many instances, this may be at odds with the nature of the spatial\n", "interactions at hand, which operate over a more limited range of distance. In\n", "these cases, expanding the neighborhood set beyond might lead us to consider\n", "interactions which either do not take place, or are inconsequential. Thus, for\n", "both substantive and computational reasons, it might make sense to further\n", "limit the range, keeping impacts to be hyper-local.\n", "\n", "### Distance bands and hybrid Weights\n", "\n", "In some contexts, it makes sense to draw a circle around each observation and\n", "consider as neighbors every other observation that falls within the circle.\n", "In the GIS terminology, this is akin to drawing a buffer around each point and\n", "performing a point-in-polygon operation that determines whether each of the\n", "other observations are within the buffer. If they are, they are assigned a\n", "weight of one in the spatial weights matrix, if not they receive a zero." ] }, { "cell_type": "code", "execution_count": 38, "id": "rotary-biography", "metadata": {}, "outputs": [], "source": [ "w_bdb = weights.distance.DistanceBand.from_dataframe(\n", " gdf, 1.5, binary=True\n", ")" ] }, { "cell_type": "markdown", "id": "northern-negotiation", "metadata": {}, "source": [ "This creates a binary distance weights where every other observation within\n", "a distance of 1.5 is considered neighbor.\n", "\n", "Distance band weights can also be continuously weighted. These could be seen as a kind of \"censored\" kernel, where the kernel function is applied only within a pre-specified distance. For example, let us calculate the DistanceBand weights that use inverse distance\n", "weights up to a certain threshold and then truncate the weights to zero for\n", "everyone else. For this example we will return to the small lattice example\n", "covered in the beginning:" ] }, { "cell_type": "code", "execution_count": 39, "id": "experimental-trading", "metadata": {}, "outputs": [], "source": [ "w_hy = weights.distance.DistanceBand.from_dataframe(\n", " gdf, 1.5, binary=False\n", ")" ] }, { "cell_type": "markdown", "id": "driving-viking", "metadata": {}, "source": [ "We apply a threshold of 1.5 for this illustration. `pysal` truncates continuous\n", "weights at this distance. It is important to keep in mind that the threshold\n", "distance must use the same units of distance as the units used to define the\n", "matrix.\n", "\n", "To see the difference, consider polygon 4, in the middle of the grid. The queen set of weights includes eight neighbors with a uniform weight of one:" ] }, { "cell_type": "code", "execution_count": 40, "id": "enclosed-yeast", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wq.weights[4]" ] }, { "cell_type": "markdown", "id": "allied-fluid", "metadata": {}, "source": [ "while the hybrid weights object modulates, giving less relevance to further observations (ie. in this case those which only share a point):" ] }, { "cell_type": "code", "execution_count": 41, "id": "greek-basketball", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.7071067811865475,\n", " 1.0,\n", " 0.7071067811865475,\n", " 1.0,\n", " 1.0,\n", " 0.7071067811865475,\n", " 1.0,\n", " 0.7071067811865475]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_hy.weights[4]" ] }, { "cell_type": "markdown", "id": "certified-notion", "metadata": {}, "source": [ "### Great Circle distances\n", "\n", "We must make one final curve before leaving the distance based weights. It is important that the\n", "calculation of distances between objects takes the curvature of the Earth's\n", "surface into account. This can be done before computing the spatial weights object, \n", "by transforming the coordinates of data points into a projected reference system. If \n", "this is not possible or convenient, an approximation that considers the\n", "curvature implicit in non-projected reference systems (e.g.\n", "longitude/latitude) can be a sufficient workaround. `pysal` provides such\n", "approximation as part of its functionality.\n", "\n", "To illustrate the relevance of ignoring this aspect altogether we will examine\n", "distance based weights for the case of counties in the state of Texas. First, let us compute\n", "a KNN-4 object that ignores the curvature of the Earth's surface (note how we use\n", "in this case the `from_shapefile` constructor to build the weights directly from a\n", "shapefile full of polygons):" ] }, { "cell_type": "code", "execution_count": 42, "id": "primary-brazil", "metadata": {}, "outputs": [], "source": [ "# ignore curvature of the earth\n", "knn4_bad = weights.distance.KNN.from_shapefile(\n", " \"../data/texas/texas.shp\", k=4\n", ")" ] }, { "cell_type": "markdown", "id": "strong-accused", "metadata": {}, "source": [ "Next, let us take curvature into account. To do this, we require the\n", "radius of the Earth expressed in a given metric. `pysal` provides this number\n", "in both miles and kilometers. For the sake of the example, we will use miles:" ] }, { "cell_type": "code", "execution_count": 43, "id": "forty-surface", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3958.755865744055" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "radius = geometry.sphere.RADIUS_EARTH_MILES\n", "radius" ] }, { "cell_type": "markdown", "id": "extensive-recommendation", "metadata": {}, "source": [ "With this measure at hand, we can pass it to the weights constructor (either\n", "straight from a shapefile or from a `GeoDataFrame`) and distances will be\n", "expressed in the units we have used for the radius, that is in miles in our\n", "case:" ] }, { "cell_type": "code", "execution_count": 44, "id": "stuck-exhaust", "metadata": {}, "outputs": [], "source": [ "knn4 = weights.distance.KNN.from_shapefile(\n", " \"../data/texas/texas.shp\", k=4, radius=radius\n", ")" ] }, { "cell_type": "markdown", "id": "japanese-provider", "metadata": {}, "source": [ "Comparing the resulting neighbor sets, we see that ignoring the curvature of the\n", "Earth's surface can create erroneous neighbor pairs. For example, the four *correct* nearest neighbors to observation 0 when accounting for the Earth's curvature are 6, 4, 5, and 3. However, observation 13 is *ever so slightly* closer when computing the straight line distance instead of the distance that accounts for curvature. " ] }, { "cell_type": "code", "execution_count": 45, "id": "indian-hostel", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{6: 1.0, 4: 1.0, 5: 1.0, 3: 1.0}" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn4[0]" ] }, { "cell_type": "code", "execution_count": 46, "id": "consolidated-repository", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{6: 1.0, 4: 1.0, 5: 1.0, 13: 1.0}" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn4_bad[0]" ] }, { "cell_type": "markdown", "id": "miniature-collective", "metadata": {}, "source": [ "## Block Weights \n", "\n", "A final type of spatial weight we examine here are block weights. In this case, \n", "it is membership in geographic\n", "a group that defines the neighbor relationships. Block weights connect every\n", "observation in a data set that belong to the same category in a provided list.[^regions]\n", "In essence, a block weight structure groups\n", "individual observations and considers all members of the group as \"near\" one another. This means that they then have a value of one for every pair of observations in the same group. Contrariwise, all members *not* in that group are considered disconnected from any observation within the group, and given a value of zero. \n", "This is done for every group, so the resulting matrix looks like \"blocks\" of 1s stacked on the diagonal (assuming that observations in the same group are near one another in the input data table), hence the \"block\" weights. \n", "\n", "To demonstrate this class of spatial weights, we will use the tract dataset for\n", "San Diego and focus on their county membership:" ] }, { "cell_type": "code", "execution_count": 47, "id": "extensive-columbus", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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GEOIDstatecountytract
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" ], "text/plain": [ " GEOID state county tract\n", "0 06073018300 06 073 018300\n", "1 06073018601 06 073 018601\n", "2 06073017601 06 073 017601\n", "3 06073019301 06 073 019301\n", "4 06073018700 06 073 018700" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "san_diego_tracts[[\"GEOID\", \"state\", \"county\", \"tract\"]].head()" ] }, { "cell_type": "markdown", "id": "particular-thinking", "metadata": {}, "source": [ "Every tract has a unique ID (`GEOID`) and a county ID, shared by all tracts in\n", "the same county. Since the entire region of San Diego is in California, the\n", "state ID is the same across the dataset.\n", "\n", "To build a block weights object, we do not even need spatial data beyond the\n", "list of memberships. In this case, we will use the county membership:" ] }, { "cell_type": "code", "execution_count": 48, "id": "australian-sword", "metadata": {}, "outputs": [], "source": [ "# NOTE: since this is a large dataset, it might take a while to process\n", "w_bl = weights.util.block_weights(\n", " san_diego_tracts[\"county\"].values,\n", " ids=san_diego_tracts[\"GEOID\"].values,\n", ")" ] }, { "cell_type": "markdown", "id": "billion-zimbabwe", "metadata": {}, "source": [ "As a check, let's consider the first two rows in the table above. If the block\n", "weights command has worked out correctly, both should be neighbors:" ] }, { "cell_type": "code", "execution_count": 49, "id": "pointed-wilson", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"06073000201\" in w_bl[\"06073000100\"]" ] }, { "cell_type": "markdown", "id": "maritime-fashion", "metadata": {}, "source": [ "We can use block weights as an intermediate step in more involved analyses\n", "of \"hybrid\" spatial relationships. Suppose for example, the researcher wanted to allow for\n", "queen neighbors within counties but not for tracts across different counties.\n", "In this case, tracts from different counties would not be considered neighbors. To create such\n", "as spatial weights matrix would require a combination of the queen and the block\n", "criteria, and `pysal` can implement that blending through one of the set operations shown in the next section." ] }, { "cell_type": "markdown", "id": "wired-monday", "metadata": {}, "source": [ "## Set Operations on Weights\n", "\n", "So far, we have seen different principles that guide how to build spatial\n", "weights matrices. In this section, we explore how we can create new matrices\n", "by *combining* different existing ones. This is useful in contexts where a\n", "single neighborhood rule is inapt or when guiding principles\n", "point to combinations of criteria.\n", "\n", "We will explore these ideas in the section by returning to the San Diego tracts.\n", "A number of ways exist to expand the basic criteria we have reviewed above and create\n", "hybrid weights. In this example, we will generate a combination of the original contiguity\n", "weights and the nearest neighbor weights. We will examine two different\n", "approaches that provide similar solutions, thus illustrating the value of set\n", "operations in `pysal`.\n", "\n" ] }, { "cell_type": "markdown", "id": "employed-comment", "metadata": {}, "source": [ "### Editing/connecting disconnected observations\n", "\n", "Imagine one of our tracts was an island and did not have any neighbors in the contiguity case. This can\n", "create issues in the spatial analytics that build on spatial weights, so it is good practice\n", "to amend the matrix before using it.\n", "The first approach we adopt is to find the nearest neighbor for the island observation\n", "and then add this pair of neighbors to extend the neighbor pairs from the\n", "original contiguity weight to obtain a fully connected set of weights. \n", "\n", "We will assume, for the sake of the example, that the disconnected observation was number 103. For us to reattach this tract, we can assign it to be \"connected\" to its nearest neighbor. Let's first extract our \"problem\" geometry:" ] }, { "cell_type": "code", "execution_count": 50, "id": "published-prison", "metadata": {}, "outputs": [], "source": [ "disconnected_tract = san_diego_tracts.iloc[[103]]" ] }, { "cell_type": "markdown", "id": "latter-hughes", "metadata": {}, "source": [ "As we have seen above, this tract *does* have neighbors:" ] }, { "cell_type": "code", "execution_count": 51, "id": "dominant-turkish", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{160: 1.0, 480: 1.0, 98: 1.0, 324: 1.0, 102: 1.0, 107: 1.0, 173: 1.0}" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen[103]" ] }, { "cell_type": "markdown", "id": "rocky-potato", "metadata": {}, "source": [ "But, for this example, we will assume it does not and thus we find ourselves in the position of having to create additional neighboring units. This approach does not only apply in the context of islands. Sometimes, the process we are interested in may require that we manually edit the weights to better reflect connections we want to capture.\n", "\n", "We will connect the observation to its nearest neighbor. To do this, we can construct the KNN graph as we did above, but set `k=1`, so observations are only assigned to their nearest neighbor:" ] }, { "cell_type": "code", "execution_count": 52, "id": "practical-conspiracy", "metadata": {}, "outputs": [], "source": [ "wk1 = weights.distance.KNN.from_dataframe(san_diego_tracts, k=1)" ] }, { "cell_type": "markdown", "id": "blessed-entrance", "metadata": {}, "source": [ "In this graph, all our observations are connected to one other observation by construction:" ] }, { "cell_type": "code", "execution_count": 53, "id": "wrong-nigeria", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(1, 628)]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wk1.histogram" ] }, { "cell_type": "markdown", "id": "technological-action", "metadata": {}, "source": [ "So is, of course, our tract of interest:" ] }, { "cell_type": "code", "execution_count": 54, "id": "characteristic-allowance", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[102]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wk1.neighbors[103]" ] }, { "cell_type": "markdown", "id": "social-bacteria", "metadata": {}, "source": [ "To connect it in our initial matrix, we need to create a copy of the `neighbors` dictionary and update the entry for `103`, including `102` as a neighbor. We copy the neighbors:" ] }, { "cell_type": "code", "execution_count": 55, "id": "combined-intersection", "metadata": {}, "outputs": [], "source": [ "neighbors = w_rook.neighbors.copy()" ] }, { "cell_type": "markdown", "id": "proprietary-delaware", "metadata": {}, "source": [ "and then we change the entry for the island observation to include its\n", "nearest neighbor (`102`) as well as update `102` to have `103` as a neighbor:" ] }, { "cell_type": "code", "execution_count": 56, "id": "responsible-determination", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{480: 1.0, 160: 1.0, 324: 1.0, 102: 1.0, 107: 1.0, 173: 1.0}" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "neighbors[103].append(102)\n", "neighbors[102].append(103)\n", "w_new = weights.W(neighbors)\n", "w_new[103]" ] }, { "cell_type": "markdown", "id": "smoking-shame", "metadata": {}, "source": [ "### Using the `union` of matrices\n", "\n", "A more elegant approach to the island problem makes use of `pysal`'s support for\n", "*set theoretic operations* on `pysal` weights. For example, we can construct the *union* of two weighting schemes, connecting any pair of observations if they are connected in *either* the Rook or if they are nearest neighbors:" ] }, { "cell_type": "code", "execution_count": 57, "id": "limited-simple", "metadata": {}, "outputs": [], "source": [ "w_fixed_sets = weights.set_operations.w_union(w_rook, wk1)" ] }, { "cell_type": "markdown", "id": "tired-therapy", "metadata": {}, "source": [ "It is important to mention that this approach is not exactly the same, at least\n", "in principle, as the one above. It could be that the nearest\n", "observation was not originally a Rook neighbor and, in this case, the resulting\n", "matrices would differ. This is a rare but theoretically possible situation." ] }, { "cell_type": "markdown", "id": "systematic-ready", "metadata": {}, "source": [ "## Visualizing weight set operations\n", "\n", "To further build the intuition behind different criteria, in this section \n", "we illustrate these concepts using the 32 states of Mexico. \n", "We compare the neighbor graphs that results from some of the \n", "criteria introduced to define neighbor relations. We first read in the data for Mexico:" ] }, { "cell_type": "code", "execution_count": 58, "id": "shared-youth", "metadata": {}, "outputs": [], "source": [ "mx = geopandas.read_file(\"../data/mexico/mexicojoin.shp\")" ] }, { "cell_type": "markdown", "id": "excess-warrant", "metadata": {}, "source": [ "We will contrast the look of the connectivity graphs built following several criteria so, to streamline things, let's build the weights objects first:\n", "\n", "- Queen contiguity" ] }, { "cell_type": "code", "execution_count": 59, "id": "after-season", "metadata": {}, "outputs": [], "source": [ "mx_queen = weights.contiguity.Queen.from_dataframe(mx)" ] }, { "cell_type": "markdown", "id": "burning-haiti", "metadata": {}, "source": [ "- $K$-NN with four nearest neighbors" ] }, { "cell_type": "code", "execution_count": 60, "id": "prescribed-referral", "metadata": {}, "outputs": [], "source": [ "mx_knn4 = weights.KNN.from_dataframe(mx, k=4)" ] }, { "cell_type": "markdown", "id": "motivated-minutes", "metadata": {}, "source": [ "- Block weights at the federal region level" ] }, { "cell_type": "code", "execution_count": 61, "id": "comparable-middle", "metadata": {}, "outputs": [], "source": [ "mx_bw = weights.util.block_weights(mx[\"INEGI2\"].values)" ] }, { "cell_type": "markdown", "id": "analyzed-copyright", "metadata": {}, "source": [ "- A combination of block and queen that connects contiguous neighbors _across_ regions" ] }, { "cell_type": "code", "execution_count": 62, "id": "lesbian-accessory", "metadata": {}, "outputs": [], "source": [ "mx_union = weights.set_operations.w_union(mx_bw, mx_queen)" ] }, { "cell_type": "markdown", "id": "legitimate-organ", "metadata": {}, "source": [ "With these at hand, we will build a figure that shows the connectivity graph of each weights object. For cases where we the federal regions are used to define blocks, we will color states based on the region they belong to." ] }, { "cell_type": "code", "execution_count": 63, "id": "egyptian-orleans", "metadata": { "caption": "The three graphs discussed above, shown side-by-side. Code generated for this figure is available on the web version of the book.", "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Set up figure and axis\n", "f, axs = plt.subplots(2, 2, figsize=(9, 9))\n", "\n", "# Contiguity\n", "ax = axs[0, 0]\n", "mx.plot(ax=ax)\n", "mx_queen.plot(\n", " mx,\n", " edge_kws=dict(linewidth=1, color=\"orangered\"),\n", " node_kws=dict(marker=\"*\"),\n", " ax=ax,\n", ")\n", "ax.set_axis_off()\n", "ax.set_title(\"Queen\")\n", "\n", "# KNN\n", "ax = axs[0, 1]\n", "mx.plot(ax=ax)\n", "mx_knn4.plot(\n", " mx,\n", " edge_kws=dict(linewidth=1, color=\"orangered\"),\n", " node_kws=dict(marker=\"*\"),\n", " ax=ax,\n", ")\n", "ax.set_axis_off()\n", "ax.set_title(\"$K$-NN 4\")\n", "\n", "# Block\n", "ax = axs[1, 0]\n", "mx.plot(column=\"INEGI2\", categorical=True, cmap=\"Pastel2\", ax=ax)\n", "mx_bw.plot(\n", " mx,\n", " edge_kws=dict(linewidth=1, color=\"orangered\"),\n", " node_kws=dict(marker=\"*\"),\n", " ax=ax,\n", ")\n", "ax.set_axis_off()\n", "ax.set_title(\"Block weights\")\n", "\n", "# Union\n", "ax = axs[1, 1]\n", "mx.plot(column=\"INEGI2\", categorical=True, cmap=\"Pastel2\", ax=ax)\n", "mx_union.plot(\n", " mx,\n", " edge_kws=dict(linewidth=1, color=\"orangered\"),\n", " node_kws=dict(marker=\"*\"),\n", " ax=ax,\n", ")\n", "ax.set_axis_off()\n", "ax.set_title(\"Queen + Block\")\n", "f.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "excess-sword", "metadata": {}, "source": [ "Queen and KNN graphs are relatively similar but, as one would expect, the KNN is sparser than Queen in areas with high density of irregular polygons (Queen will connect each to more than four), and denser in sparser areas with less but larger polygons (e.g. north-west). Focusing on the Queen and Block graphs, there are clear distinctions between the\n", "connectivity structures. The Block graph is visually denser in particular areas relative to the\n", "Queen graph and this is captured in their sparsity measures:" ] }, { "cell_type": "code", "execution_count": 64, "id": "editorial-jumping", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "19.140625" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mx_bw.pct_nonzero" ] }, { "cell_type": "code", "execution_count": 65, "id": "presidential-revolution", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13.4765625" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mx_queen.pct_nonzero" ] }, { "cell_type": "markdown", "id": "valuable-healthcare", "metadata": {}, "source": [ "The other distinguishing characteristic can be seen in the number of connected\n", "components in the different graphs. The Queen graph has a single connected\n", "component - which in graph theory terms, means for all pairs of states there is\n", "at least one path of edges that connects the two states. The Block graph has\n", "five connected components, one for each of the five regions. Moreover, each of\n", "these connected components is fully-connected, meaning there is an edge that\n", "directly connects each pair of member states. However, there are no edges\n", "between states belonging to different blocks (or components).\n", "\n", "\n", "As we will see in later chapters, certain spatial analytical techniques require\n", "a fully connected weights graph. In these cases, we could adopt the Queen\n", "definition since this satisfies the single connected component requirement.\n", "However, we may wish to use the Union weights graph as that provides a single\n", "connected component, but offers a blend of different types of connectivity\n", "intensities, with the intra-regional (block) linkages being very dense, while\n", "the inter-regional linkages are thinner but provide for the single connected\n", "component." ] }, { "cell_type": "markdown", "id": "economic-scene", "metadata": {}, "source": [ "## Use case: Boundary detection" ] }, { "cell_type": "markdown", "id": "widespread-fields", "metadata": {}, "source": [ "We close the chapter with an illustration of how weights can be useful by themselves in geographic data science. Note that the application displayed below involves some concepts and code that are a bit more advanced than in the rest of the chapter. If you are up for the challenge, we think the insights it enables are worth the effort!\n", "\n", "Spatial weights are ubiquitous in the analysis of spatial patterns in data, since they provide a direct method to represent spatial structure. \n", "However, spatial weights are also useful in their own right, such as when examining latent structures directly in the graphs themselves or when using them to conduct descriptive analysis. \n", "One clear use case that arises in the analysis of social data is to characterize latent *data discontinuities*. By *data discontinuity*, we mean a single border (or collection of borders) where data for a variable (or many variables) of interest change abruptly. \n", "These can be used in models of inequality {cite}`Lu2005bayesian,Fitzpatrick2010ecological,Dean2019frontiers` or used to adapt classic empirical outlier detection methods. \n", "Below, we'll show one model-free way to identify empirical boundaries in your data. " ] }, { "cell_type": "markdown", "id": "english-hunger", "metadata": {}, "source": [ "First, let's consider the median household income for our census tracts in San Diego, shown in the following figure." ] }, { "cell_type": "code", "execution_count": 66, "id": "former-hawaiian", "metadata": { "caption": "Median household incomes in San Diego.", "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(1, 2, figsize=(12, 4))\n", "san_diego_tracts.plot(\"median_hh_income\", ax=ax[0])\n", "ax[0].set_axis_off()\n", "san_diego_tracts[\"median_hh_income\"].plot.hist(ax=ax[1])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "united-cleaning", "metadata": {}, "source": [ "Now, we see some cases where there are very stark differences between neighboring areas, and some cases where there appear to be no difference between adjacent areas. Digging into this, we can examine the *distribution of differences* in neighboring areas using the adjacency list, a different representation of a spatial graph:" ] }, { "cell_type": "code", "execution_count": 67, "id": "national-table", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " focal neighbor weight\n", "0 0 1 1.0\n", "1 0 385 1.0\n", "2 0 4 1.0\n", "3 0 548 1.0\n", "4 0 27 1.0" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adjlist = w_rook.to_adjlist()\n", "adjlist.head()" ] }, { "cell_type": "markdown", "id": "sought-momentum", "metadata": {}, "source": [ "This provides us with a table featuring three columns. `Focal` is the column containing the \"origin\" of the link; `neighbor` is the column containing the \"destination\" of the link, or neighbor of the focal polygon; and `weight` contains how strong the link from `focal` to `neighbor` is. Since our weights are *symmetrical*, this table contains two entries per pair of neighbors, one for `(focal,neighbor)` and the other for `(neighbor,focal)`. \n", "\n", "Now we want to connect this table representing spatial structure with information on median household income. Using `pandas`, we can merge up the focal units' and neighboring units' median household incomes:" ] }, { "cell_type": "code", "execution_count": 68, "id": "exact-sculpture", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 3440 entries, 0 to 3439\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 focal 3440 non-null int64 \n", " 1 neighbor 3440 non-null int64 \n", " 2 weight 3440 non-null float64\n", " 3 median_hh_income_focal 3440 non-null float64\n", " 4 median_hh_income_neighbor 3440 non-null float64\n", "dtypes: float64(3), int64(2)\n", "memory usage: 134.5 KB\n" ] } ], "source": [ "adjlist_income = adjlist.merge(\n", " san_diego_tracts[[\"median_hh_income\"]],\n", " how=\"left\",\n", " left_on=\"focal\",\n", " right_index=True,\n", ").merge(\n", " san_diego_tracts[[\"median_hh_income\"]],\n", " how=\"left\",\n", " left_on=\"neighbor\",\n", " right_index=True,\n", " suffixes=(\"_focal\", \"_neighbor\"),\n", ")\n", "adjlist_income.info()" ] }, { "cell_type": "markdown", "id": "brutal-family", "metadata": {}, "source": [ "This operation brings together the income at both the focal observation and the neighbor observation. The difference between these two yields income differences between *adjacent* tracts:" ] }, { "cell_type": "code", "execution_count": 69, "id": "greek-hunger", "metadata": {}, "outputs": [], "source": [ "adjlist_income[\"diff\"] = (\n", " adjlist_income[\"median_hh_income_focal\"]\n", " - adjlist_income[\"median_hh_income_neighbor\"]\n", ")" ] }, { "cell_type": "markdown", "id": "sought-teens", "metadata": {}, "source": [ "With this information on difference we can now do a few things. First, we can compare whether or not this *distribution* is distinct from the distribution of non-neighboring tracts' differences in wealth. This will give us a hint at the extent to which income follows a spatial pattern. This is also discussed more in depth in the spatial inequality chapter, specifically in reference to the Spatial Gini. \n", "\n", "To do this, we can first compute the all-pairs differences in income using the `numpy.subtract` function. Some functions in `numpy` have special functionality; these `ufuncs` (short for \"universal functions\") often support special applications to your data. Here, we will use `numpy.subtract.outer` to take the difference over the \"outer cartesian product\" of two vectors." ] }, { "cell_type": "code", "execution_count": 70, "id": "weekly-creature", "metadata": {}, "outputs": [], "source": [ "all_pairs = numpy.subtract.outer(\n", " san_diego_tracts[\"median_hh_income\"].values,\n", " san_diego_tracts[\"median_hh_income\"].values,\n", ")" ] }, { "cell_type": "markdown", "id": "architectural-regulation", "metadata": {}, "source": [ "In practice, this results in an $N\\times N$ array that stores the subtraction of all of the combinations of the input vectors.\n", "\n", "Then, we need to filter out those cells of `all_pairs` that are neighbors. Fortunately, our weights matrix is *binary*. So, subtracting it from an $N \\times N$ matrix of $1$s will result in the *complement* of our original weights matrix:" ] }, { "cell_type": "code", "execution_count": 71, "id": "julian-dancing", "metadata": {}, "outputs": [], "source": [ "complement_wr = 1 - w_rook.sparse.toarray()" ] }, { "cell_type": "markdown", "id": "complicated-touch", "metadata": {}, "source": [ "Note `complement_wr` inserts a 0 where `w_rook` includes a 1, and vice versa. Using this complement, we can filter the `all_pairs` matrix to only consider the differences in median household income for tracts that are not neighboring: " ] }, { "cell_type": "code", "execution_count": 72, "id": "turkish-click", "metadata": {}, "outputs": [], "source": [ "non_neighboring_diffs = (complement_wr * all_pairs).flatten()" ] }, { "cell_type": "markdown", "id": "proved-cheat", "metadata": {}, "source": [ "Now, we can compare the two distributions of the difference in wealth:" ] }, { "cell_type": "code", "execution_count": 73, "id": "dangerous-oregon", "metadata": { "caption": "Diferences between median incomes among neighboring (and non-neighboring) tracts in San Diego." }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = plt.figure(figsize=(12, 3))\n", "plt.hist(\n", " non_neighboring_diffs,\n", " color=\"lightgrey\",\n", " edgecolor=\"k\",\n", " density=True,\n", " bins=50,\n", " label=\"Nonneighbors\",\n", ")\n", "plt.hist(\n", " adjlist_income[\"diff\"],\n", " color=\"salmon\",\n", " edgecolor=\"orangered\",\n", " linewidth=3,\n", " density=True,\n", " histtype=\"step\",\n", " bins=50,\n", " label=\"Neighbors\",\n", ")\n", "seaborn.despine()\n", "plt.ylabel(\"Density\")\n", "plt.xlabel(\"Dollar Differences ($)\")\n", "plt.legend();" ] }, { "cell_type": "markdown", "id": "internal-wallet", "metadata": {}, "source": [ "From this, we can see that the two distributions are distinct, with the distribution of difference in *non-neighboring* tracts being slightly more dispersed than that for *neighboring* tracts. Thus, on the whole, this means that neighboring tracts have more *smaller differences in wealth* than non-neighboring tracts. This is consistent with the behavior we will talk about in later chapters concerning *spatial autocorrelation*, the tendency for observations to be statistically more similar to nearby observations than they are to distant observations. " ] }, { "cell_type": "markdown", "id": "republican-observer", "metadata": {}, "source": [ "The adjacency table we have build can also help us find our *most extreme* observed differences in income, hinting at possible hard boundaries between the areas. Since our links are symmetric, we can then focus only on focal observations with *the most extreme* difference in wealth from their immediate neighbors, considering only on those where the *focal* is higher, since they each have an equivalent *negative* back-link." ] }, { "cell_type": "code", "execution_count": 74, "id": "architectural-reply", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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focalneighborweightmedian_hh_income_focalmedian_hh_income_neighbordiff
26054731631.0183929.037863.0146066.0
26094731571.0183929.064688.0119241.0
18863435101.0151797.038125.0113672.0
26104732381.0183929.074485.0109444.0
548891.0169821.066563.0103258.0
\n", "
" ], "text/plain": [ " focal neighbor weight median_hh_income_focal \\\n", "2605 473 163 1.0 183929.0 \n", "2609 473 157 1.0 183929.0 \n", "1886 343 510 1.0 151797.0 \n", "2610 473 238 1.0 183929.0 \n", "54 8 89 1.0 169821.0 \n", "\n", " median_hh_income_neighbor diff \n", "2605 37863.0 146066.0 \n", "2609 64688.0 119241.0 \n", "1886 38125.0 113672.0 \n", "2610 74485.0 109444.0 \n", "54 66563.0 103258.0 " ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "extremes = adjlist_income.sort_values(\"diff\", ascending=False).head()\n", "extremes" ] }, { "cell_type": "markdown", "id": "exciting-attraction", "metadata": {}, "source": [ "Thus, we see that observation $473$ appears often on the the `focal` side, suggesting it's quite distinct from its nearby polygons. " ] }, { "cell_type": "markdown", "id": "featured-louis", "metadata": {}, "source": [ "To verify whether these differences are truly significant, we can use a map randomization strategy. In this case, we shuffle values across the map and compute *new* `diff` columns. This time, `diff` represents the difference between random incomes, rather than the neighboring incomes we actually observed using our Rook contiguity matrix. Using many `diff` vectors, we can find the observed differences which tend to be much larger than those encountered in randomly-drawn maps of household income.\n", "\n", "To start, we can construct many random `diff` vectors:" ] }, { "cell_type": "code", "execution_count": 75, "id": "requested-queensland", "metadata": {}, "outputs": [], "source": [ "## NOTE: this cell runs a simulation and may take a bit longer\n", "## If you want it to run faster, decrease the number of shuffles\n", "## by setting a lower value in `n_simulations`\n", "\n", "# Set number or random shuffles\n", "n_simulations = 1000\n", "# Create an empty array to store results\n", "simulated_diffs = numpy.empty((len(adjlist), n_simulations))\n", "# Loop over each random draw\n", "for i in range(n_simulations):\n", " # Extract income values\n", " median_hh_focal = adjlist_income[\"median_hh_income_focal\"].values\n", " # Shuffle income values across locations\n", " random_income = (\n", " san_diego_tracts[[\"median_hh_income\"]]\n", " .sample(frac=1, replace=False)\n", " .reset_index()\n", " )\n", " # Join income to adjacency\n", " adjlist_random_income = adjlist.merge(\n", " random_income, left_on=\"focal\", right_index=True\n", " ).merge(\n", " random_income,\n", " left_on=\"neighbor\",\n", " right_index=True,\n", " suffixes=(\"_focal\", \"_neighbor\"),\n", " )\n", " # Store reslults from random draw\n", " simulated_diffs[:, i] = (\n", " adjlist_random_income[\"median_hh_income_focal\"]\n", " - adjlist_random_income[\"median_hh_income_neighbor\"]\n", " )" ] }, { "cell_type": "markdown", "id": "rocky-telescope", "metadata": {}, "source": [ "After running our simulation, we get many distributions of pairwise differences in household income. Below, we plot the shroud of all of the simulated differences, shown in black, and our observed differences, shown in red:" ] }, { "cell_type": "code", "execution_count": 76, "id": "mysterious-cream", "metadata": { "caption": "Differences between neighboring incomes for the observed map (orange) and maps arising from randomly-reshuffled maps (black) of tract median incomes. Code generated for this figure is available on the web version of the book.", "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Set up figure\n", "f = plt.figure(figsize=(12, 3))\n", "# Build background histogram for observed differences\n", "plt.hist(\n", " adjlist_income[\"diff\"],\n", " color=\"salmon\",\n", " bins=50,\n", " density=True,\n", " alpha=1,\n", " linewidth=4,\n", ")\n", "# Plot simulated, random differences\n", "[\n", " plt.hist(\n", " simulation,\n", " histtype=\"step\",\n", " color=\"k\",\n", " alpha=0.01,\n", " linewidth=1,\n", " bins=50,\n", " density=True,\n", " )\n", " for simulation in simulated_diffs.T\n", "]\n", "# Build histogram borderline for observed differences\n", "plt.hist(\n", " adjlist_income[\"diff\"],\n", " histtype=\"step\",\n", " edgecolor=\"orangered\",\n", " bins=50,\n", " density=True,\n", " linewidth=2,\n", ")\n", "# Style figure\n", "seaborn.despine()\n", "plt.ylabel(\"Density\")\n", "plt.xlabel(\"Dollar Differences ($)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "opponent-custom", "metadata": {}, "source": [ "Again, our random distribution is much more dispersed than our observed distribution of the differences between nearby tracts. Empirically, we can pool our simulations and construct and use their quantiles to summarize how unlikely any of our *observed* differences are if neighbors' household incomes were randomly assigned:" ] }, { "cell_type": "code", "execution_count": 77, "id": "wooden-screening", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3440000,)" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simulated_diffs.flatten().shape" ] }, { "cell_type": "code", "execution_count": 78, "id": "wound-behavior", "metadata": {}, "outputs": [], "source": [ "# Convert all simulated differences into a single vector\n", "pooled_diffs = simulated_diffs.flatten()\n", "# Calculate the 0.5th, 50th and 99.5th percentiles\n", "lower, median, upper = numpy.percentile(\n", " pooled_diffs, q=(0.5, 50, 99.5)\n", ")\n", "# Create a swith that is True if the value is \"extreme\"\n", "# (in the 0.5th percentile or/`|` in the 00.5th), False otherwise\n", "outside = (adjlist_income[\"diff\"] < lower) | (\n", " adjlist_income[\"diff\"] > upper\n", ")" ] }, { "cell_type": "markdown", "id": "brazilian-cradle", "metadata": {}, "source": [ "Despite the fact that that our observed differences are less dispersed on average, we can identify two boundaries in the data that are in the top 1% most extreme differences in neighboring household incomes across the map. These boundaries are shown in the table below:" ] }, { "cell_type": "code", "execution_count": 79, "id": "later-tampa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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focalneighborweightmedian_hh_income_focalmedian_hh_income_neighbordiff
8851574731.064688.0183929.0-119241.0
9151634731.037863.0183929.0-146066.0
26054731631.0183929.037863.0146066.0
26094731571.0183929.064688.0119241.0
\n", "
" ], "text/plain": [ " focal neighbor weight median_hh_income_focal \\\n", "885 157 473 1.0 64688.0 \n", "915 163 473 1.0 37863.0 \n", "2605 473 163 1.0 183929.0 \n", "2609 473 157 1.0 183929.0 \n", "\n", " median_hh_income_neighbor diff \n", "885 183929.0 -119241.0 \n", "915 183929.0 -146066.0 \n", "2605 37863.0 146066.0 \n", "2609 64688.0 119241.0 " ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adjlist_income[outside]" ] }, { "cell_type": "markdown", "id": "vocal-platform", "metadata": {}, "source": [ "Note that one of these, observation $473$, appears in both boundaries. This means that the observation is likely to be *outlying*, extremely unlike *all* of its neighbors. These kinds of generalized neighborhood comparisons are discussed in the subsequent chapter on local spatial autocorrelation. For now we can visualize this on a map, focusing on the two boundaries around observation $473$, shown also in the larger context of San Diego incomes:" ] }, { "cell_type": "code", "execution_count": 80, "id": "suspected-format", "metadata": { "caption": "The two most stark differences in median household income among San Diego tracts. Code generated for this figure is available on the web version of the book.", "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(1, 3, figsize=(18, 6))\n", "\n", "# Plot tracts\n", "for i in range(2):\n", " san_diego_tracts.plot(\"median_hh_income\", ax=ax[i])\n", "\n", "# Zoom 1\n", "first_focus = san_diego_tracts.iloc[[473, 157]]\n", "ax[0].plot(\n", " first_focus.centroid.x, first_focus.centroid.y, color=\"red\"\n", ")\n", "west, east, south, north = first_focus.buffer(1000).total_bounds\n", "ax[0].axis([west, south, east, north])\n", "ax[0].set_axis_off()\n", "\n", "# Zoom 2\n", "second_focus = san_diego_tracts.iloc[[473, 163]]\n", "ax[1].plot(\n", " second_focus.centroid.x, second_focus.centroid.y, color=\"red\"\n", ")\n", "ax[1].axis([west, south, east, north])\n", "ax[1].set_axis_off()\n", "\n", "# Context\n", "san_diego_tracts.plot(\n", " facecolor=\"k\", edgecolor=\"w\", linewidth=0.5, alpha=0.5, ax=ax[2]\n", ")\n", "contextily.add_basemap(ax[2], crs=san_diego_tracts.crs)\n", "area_of_focus = (\n", " pandas.concat((first_focus, second_focus))\n", " .buffer(12000)\n", " .total_bounds\n", ")\n", "ax[2].plot(\n", " first_focus.centroid.x, first_focus.centroid.y, color=\"red\"\n", ")\n", "ax[2].plot(\n", " second_focus.centroid.x, second_focus.centroid.y, color=\"red\"\n", ")\n", "west, east, south, north = area_of_focus\n", "ax[2].axis([west, south, east, north])\n", "ax[2].set_axis_off()" ] }, { "cell_type": "markdown", "id": "exceptional-intermediate", "metadata": {}, "source": [ "These are the starkest contrasts in the map, and result in the most distinctive divisions between adjacent tracts' household incomes. " ] }, { "cell_type": "markdown", "id": "paperback-answer", "metadata": {}, "source": [ "## Conclusion\n", "\n", "Spatial weights are central to how we *represent* spatial relationships in mathematical and computational environments. At their core, they are a \"geo-graph,\" or a network defined by the geographical relationships between observations. They form kind of a \"spatial index,\" in that they record which observations have a specific geographical relationship. Since spatial weights are fundamental to how spatial relationships are represented in geographic data science, we will use them again and again throughout the book. " ] }, { "cell_type": "markdown", "id": "cardiac-pressing", "metadata": {}, "source": [ "## Questions" ] }, { "cell_type": "markdown", "id": "graphic-sponsorship", "metadata": {}, "source": [ "1. Rook contiguity & Queen contiguity are two of three kinds of contiguity that are defined in terms of chess analogies. The third kind, *Bishop contiguity*, applies when two observations are considered connected when they share single vertices, but are considered *disconnected* if they share an edge. This means that observations that exhibit Queen contiguity are those that exhibit either Rook or Bishop contiguity. Using the Rook and Queen contiguity matrices we built for San Diego and the `Wsets.w_difference` function, are there any Bishop-contiguous observations in San Diego? \n", "\n", "2. Different kinds of spatial weights objects can result in very different kinds of graph structures. Considering the `cardinalities` of the Queen, Block, and the union of Queen & Block, \n", " 1. Which graph type has the highest average cardinality?\n", " 2. Which graph has more nonzero entries?\n", " 3. Why might this be the case?\n", "\n", "3. Graphs are considered \"connected\" when you can construct a path from any observation to every other observation. A \"disconnected\" graph has at least one node where there is no path from it to every other node. And, a \"connected component\" is a part of the graph that is connected internally, but is disconnected from another part of the graph. This is reported for every spatial weights object in its `w.n_components`. \n", "\n", " 1. How many components does the Queen Contiguity weights for San Diego have?\n", " 2. Using a K-nearest Neighbor Graph for San Diego tracts where $k=1$, how many connected components are there in this graph?\n", " 3. Increase $k$ by one until the `n_components` is 1. Make a plot of the relationship between $k$ and $n_components$. \n", " 4. What value of $k$ does `n_components` become 1?\n", " 5. How many non-zero links does this network have?\n", "\n", "4. Comparing their average cardinality and percentage of nonzero links, which graph in this chapter has the *most sparse* structure? That is, which graph is the most sparsely connected?\n", "\n", "5. In this chapter, we worked with regular *square* lattices using the `lat2W` function. In the same manner, the `hexLat2W` function can generate *hexagonal regular lattices*. For lattices of size (3,3), (6,6), and (9,9) for Rook & Queen `lat2W`, as well as for `hexLat2W`:\n", "\n", " 1. Examine the average cardinality. Does `lat2W` or `hexLat2W` have higher average cardinality? \n", " 2. Further, make a histogram of the cardinalities. Which type of lattice has higher variation in its number of neighbors? \n", " 3. Why is there no `rook=True` option in `hexLat2W`, as there is in `lat2W`?\n", "\n", "6. The *Voronoi diagram* is a common method to construct polygons from a point dataset. A Voronoi diagram is built up from *Voronoi cells*, each of which contains the area that is closer to its source point than any other source point in the diagram. Further, the Queen contiguity graph for a *Voronoi diagram* obeys a number of useful properties, since it is the *Delaunay Triangulation* of a set of points. \n", " 1. Using the following code, build and plot the Voronoi diagram for the *centroids* of Mexican states, with the states and their centroids overlayed:\n", " ```python\n", " from pysal.lib.weights.distance import get_points_array\n", " from pysal.lib.cg import voronoi_frames\n", " \n", " centroid_coordinates = get_points_array(mx.centroid)\n", " cells, centers = voronoi_frames(centroid_coordinates)\n", " \n", " ax = cells.plot(facecolor='none', edgecolor='k')\n", " mx.plot(ax=ax, edgecolor='red', facecolor='whitesmoke', alpha=.5)\n", " mx.centroid.plot(ax=ax,color='red', alpha=.5, markersize=10)\n", " ax.axis(mx.total_bounds[[0,2,1,3]])\n", " plt.show()\n", " ```\n", " 2. Using the `weights.Voronoi` function, build the Voronoi weights for the Mexico states data.\n", " 3. Compare the connections in the Voronoi and Queen weights for the Mexico states data. Which form is more connected? \n", " 4. Make a plot of the Queen contiguity and Voronoi contiguity graphs to compare them visually, like we did with the block weights & Queen weights. How do the two graphs compare in terms of the length of their links and how they connect the Mexican states?\n", " 5. Using `weights.set_operations`, find any links that are in the Voronoi contiguity graph, but not in the Queen contiguity graph. Alternatively, find any links that are in the Queen contiguity graph, but not the Voronoi contiguity graph. \n", "\n", "7. Interoperability is important for the Python scientific stack. Thanks to standardization around the `numpy` array and the `scipy.sparse` array data structures, it is simple and computationally-easy to convert objects from one representation to another:\n", " 1. Using `w.to_networkx()`, convert the Mexico Regions Queen+Block weights matrix to a `networkx` graph. Compute the Eigenvector Centrality of that new object using `networkx.eigenvector_centrality`\n", " 2. Using `w.sparse`, compute the number of connected components in the Mexico Regions Block weights matrix using the `connected_components` function in `scipy.sparse.csgraph`. \n", " 3. Using `w.sparse`, compute the all-pairs shortest path matrix in the Mexico Queen weights matrix using the `shortest_path` function in `scipy.sparse.csgraph`. \n", " \n", "8. While every node in a $k$-nearest neighbor graph has 5 neighbors, there is a conceptual difference between *in-degree* and *out-degree* of nodes in a graph. The *out-degree* of a node is the number of outgoing links from a node; for a K-Nearest Neighbor graph, this is $k$ for every variable. The *in-degree* of a node in a graph is the number of *incoming* links to that node; for a K-Nearest Neighbor graph, this is the number of other observations that pick the target as their nearest neighbor. The *in-degree* of a node in the K-Nearest Neighbor graph can provide a measure of *hubbiness*, or how central a node is to other nodes. \n", " 1. Using the San Diego Tracts data, build a $k=6$ nearest neighbor weight and call it `knn_6`. \n", " 2. Verify that the $k=6$ by taking the row sum over the weights matrix in `knn_6.sparse`.\n", " 3. Compute the in-degree of each observation by taking the *column sum* over the weights matrix in `knn_6.sparse`, and divide by 6, the out-degree for all observations. \n", " 4. Make a histogram of the in-degrees for the $k=6$ weights. How evenly-distributed is the distribution of in-degrees?\n", " 5. Make a new histogram of the in-degree standardized by the out-degree when $k=26$. Does hubbiness reduce when increasing the number of $k$-nearest neighbors?\n", "\n", "9. Sometimes, graphs are not simple to construct. For the `san_diego_neighborhoods` dataset:\n", " 1. Build the Queen contiguity weights, and plot the graph on top of the neighborhoods themselves. How many connected components does this Queen contiguity graph have? \n", " 2. Build the K-Nearest Neighbor graph for the default, $k=2$. How many connected components does this K-Nearest Neighbor graph have? \n", " 3. What is the smallest $k$ that you can find for the K-Nearest Neighbor graph to be fully-connected?\n", " 4. In graph theory, a link whose *removal* will increase the number of connected components in a graph is called a *bridge*. In the fully-connected KNN graph with the smallest $k$, how many bridges are there between the north and south components? *(hint: use the plotting functionality)*\n", " 5. What are the next two values of $k$ required for there to be an *additional* bridge at that $k$?" ] }, { "cell_type": "markdown", "id": "united-split", "metadata": {}, "source": [ "[^regions]: Usually, this list will have some relation to the spatial configuration of the data but, technically speaking, all one needs to create block weights is a list of memberships.\n", "\n", "## Next Steps\n", "\n", "For additional reading and further information on the topic of networks and spatial weights matrices, consider Chapter 3 of Anselin and Rey, *Modern Spatial Econometrics in Practice: A Guide to GeoDa, GeoDaSpace, and Pysal*. Further, for more general thinking on networks in geography, consider: \n", "\n", "Uitermark, Justus and Michiel van Meeteren. 2021. \"Geographcial Network Analysis.\" *Tijdschrift voor economische en sociale geografie* 112: 337-350. " ] } ], "metadata": { "jupytext": { "formats": "ipynb,md" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }