{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploratory Analysis of Spatial Data: Spatial Autocorrelation #\n", "\n", "\n", "In this notebook we introduce methods of _exploratory spatial data analysis_\n", "that are intended to complement geovizualization through formal univariate and\n", "multivariate statistical tests for spatial clustering.\n", "\n", "\n", "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import esda\n", "import pandas as pd\n", "import geopandas as gpd\n", "from geopandas import GeoDataFrame\n", "import libpysal as lps\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from shapely.geometry import Point\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our data set comes from the Berlin airbnb scrape taken in April 2018. This dataframe was constructed as part of the [GeoPython 2018 workshop](https://github.com/ljwolf/geopython) by [Levi Wolf](https://ljwolf.org) and [Serge Rey](https://sergerey.org). As part of the workshop a geopandas data frame was constructed with one of the columns reporting the median listing price of units in each neighborhood in Berlin:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "gdf = gpd.read_file('data/berlin-neighbourhoods.geojson')\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "bl_df = pd.read_csv('data/berlin-listings.csv')\n", "geometry = [Point(xy) for xy in zip(bl_df.longitude, bl_df.latitude)]\n", "crs = {'init': 'epsg:4326'} \n", "bl_gdf = GeoDataFrame(bl_df, crs=crs, geometry=geometry)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "neighbourhood_group\n", "Charlottenburg-Wilm. 58.556408\n", "Friedrichshain-Kreuzberg 55.492809\n", "Lichtenberg 44.584270\n", "Marzahn - Hellersdorf 54.246754\n", "Mitte 60.387890\n", "Neukölln 45.135948\n", "Pankow 60.282516\n", "Reinickendorf 43.682465\n", "Spandau 48.236561\n", "Steglitz - Zehlendorf 54.445683\n", "Tempelhof - Schöneberg 53.704407\n", "Treptow - Köpenick 51.222004\n", "Name: price, dtype: float32" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bl_gdf['price'] = bl_gdf['price'].astype('float32')\n", "sj_gdf = gpd.sjoin(gdf, bl_gdf, how='inner', op='intersects', lsuffix='left', rsuffix='right')\n", "median_price_gb = sj_gdf['price'].groupby([sj_gdf['neighbourhood_group']]).mean()\n", "median_price_gb" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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neighbourhoodneighbourhood_groupgeometrymedian_pri
0Blankenfelde/NiederschönhausenPankow(POLYGON ((13.411909 52.614871, 13.411826 52.6...60.282516
1HelmholtzplatzPankow(POLYGON ((13.414053 52.549288, 13.414222 52.5...60.282516
2Wiesbadener StraßeCharlottenburg-Wilm.(POLYGON ((13.307476 52.467882, 13.307434 52.4...58.556408
3Schmöckwitz/Karolinenhof/RauchfangswerderTreptow - Köpenick(POLYGON ((13.709727 52.396299, 13.709263 52.3...51.222004
4MüggelheimTreptow - Köpenick(POLYGON ((13.737622 52.408498, 13.737734 52.4...51.222004
5BiesdorfMarzahn - Hellersdorf(POLYGON ((13.566433 52.535103, 13.566974 52.5...54.246754
6Nord 1Reinickendorf(POLYGON ((13.336686 52.622651, 13.336632 52.6...43.682465
7West 5Reinickendorf(POLYGON ((13.281381 52.59958, 13.281575 52.59...43.682465
8Frankfurter Allee NordFriedrichshain-Kreuzberg(POLYGON ((13.453201 52.51682, 13.453212 52.51...55.492809
9BuchPankow(POLYGON ((13.464495 52.650553, 13.464566 52.6...60.282516
10KaulsdorfMarzahn - Hellersdorf(POLYGON ((13.621353 52.527041, 13.621956 52.5...54.246754
11NoneNone(POLYGON ((13.616591 52.58154, 13.614579 52.58...NaN
12NoneNone(POLYGON ((13.616681 52.57868, 13.607031 52.57...NaN
13nördliche LuisenstadtFriedrichshain-Kreuzberg(POLYGON ((13.444305 52.500656, 13.442658 52.5...55.492809
14Nord 2Reinickendorf(POLYGON ((13.306802 52.586062, 13.30667 52.58...43.682465
\n", "
" ], "text/plain": [ " neighbourhood neighbourhood_group \\\n", "0 Blankenfelde/Niederschönhausen Pankow \n", "1 Helmholtzplatz Pankow \n", "2 Wiesbadener Straße Charlottenburg-Wilm. \n", "3 Schmöckwitz/Karolinenhof/Rauchfangswerder Treptow - Köpenick \n", "4 Müggelheim Treptow - Köpenick \n", "5 Biesdorf Marzahn - Hellersdorf \n", "6 Nord 1 Reinickendorf \n", "7 West 5 Reinickendorf \n", "8 Frankfurter Allee Nord Friedrichshain-Kreuzberg \n", "9 Buch Pankow \n", "10 Kaulsdorf Marzahn - Hellersdorf \n", "11 None None \n", "12 None None \n", "13 nördliche Luisenstadt Friedrichshain-Kreuzberg \n", "14 Nord 2 Reinickendorf \n", "\n", " geometry median_pri \n", "0 (POLYGON ((13.411909 52.614871, 13.411826 52.6... 60.282516 \n", "1 (POLYGON ((13.414053 52.549288, 13.414222 52.5... 60.282516 \n", "2 (POLYGON ((13.307476 52.467882, 13.307434 52.4... 58.556408 \n", "3 (POLYGON ((13.709727 52.396299, 13.709263 52.3... 51.222004 \n", "4 (POLYGON ((13.737622 52.408498, 13.737734 52.4... 51.222004 \n", "5 (POLYGON ((13.566433 52.535103, 13.566974 52.5... 54.246754 \n", "6 (POLYGON ((13.336686 52.622651, 13.336632 52.6... 43.682465 \n", "7 (POLYGON ((13.281381 52.59958, 13.281575 52.59... 43.682465 \n", "8 (POLYGON ((13.453201 52.51682, 13.453212 52.51... 55.492809 \n", "9 (POLYGON ((13.464495 52.650553, 13.464566 52.6... 60.282516 \n", "10 (POLYGON ((13.621353 52.527041, 13.621956 52.5... 54.246754 \n", "11 (POLYGON ((13.616591 52.58154, 13.614579 52.58... NaN \n", "12 (POLYGON ((13.616681 52.57868, 13.607031 52.57... NaN \n", "13 (POLYGON ((13.444305 52.500656, 13.442658 52.5... 55.492809 \n", "14 (POLYGON ((13.306802 52.586062, 13.30667 52.58... 43.682465 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf = gdf.join(median_price_gb, on='neighbourhood_group')\n", "gdf.rename(columns={'price': 'median_pri'}, inplace=True)\n", "gdf.head(15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have an nan to first deal with:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.isnull(gdf['median_pri']).sum()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "gdf['median_pri'].fillna((gdf['median_pri'].mean()), inplace=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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