{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "A6iFUUQLNDlE" }, "source": [ "\n", " \n", " \n", " \n", "
\n", " Run in Google Colab\n", " \n", " View on Github\n", " \n", " View raw on Github\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "-DARAON8qoJR" }, "source": [ "# Module 12: Maps\n", "\n", "Let's draw some maps. 🗺🧐" ] }, { "cell_type": "markdown", "metadata": { "id": "d9E9W5lXqoJa" }, "source": [ "## A dotmap with Altair\n", "\n", "Let's start with altair. \n", "\n", "When your dataset is large, it is nice to enable something called \"json data transformer\" in altair. What it does is, instead of generating and holding the whole dataset in the memory, to transform the dataset and save into a temporary file. This makes the whole plotting process much more efficient. For more information, check out: https://altair-viz.github.io/user_guide/data_transformers.html" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 1171, "status": "ok", "timestamp": 1690232847801, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "2ycXU2IbqoJc", "outputId": "3f87e857-d4d1-43b4-cb40-a4099c2c2714" }, "outputs": [ { "data": { "text/plain": [ "DataTransformerRegistry.enable('json')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import altair as alt\n", "\n", "# saving data into a file rather than embedding into the chart\n", "alt.data_transformers.enable('json')" ] }, { "cell_type": "markdown", "metadata": { "id": "Vz-TNR-IqoJg" }, "source": [ "We need a dataset with geographical coordinates. This `zipcodes` dataset contains the location and zipcode of each zip code area." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "executionInfo": { "elapsed": 2397, "status": "ok", "timestamp": 1690232854409, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "qYOeI0eLqoJh", "outputId": "5547c589-e6fd-44f1-95d7-9cc955e209c2" }, "outputs": [ { "data": { "text/html": [ "
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zip_codelatitudelongitudecitystatecounty
00050140.922326-72.637078HoltsvilleNYSuffolk
10054440.922326-72.637078HoltsvilleNYSuffolk
20060118.165273-66.722583AdjuntasPRAdjuntas
30060218.393103-67.180953AguadaPRAguada
40060318.455913-67.145780AguadillaPRAguadilla
\n", "
" ], "text/plain": [ " zip_code latitude longitude city state county\n", "0 00501 40.922326 -72.637078 Holtsville NY Suffolk\n", "1 00544 40.922326 -72.637078 Holtsville NY Suffolk\n", "2 00601 18.165273 -66.722583 Adjuntas PR Adjuntas\n", "3 00602 18.393103 -67.180953 Aguada PR Aguada\n", "4 00603 18.455913 -67.145780 Aguadilla PR Aguadilla" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from vega_datasets import data\n", "\n", "zipcodes_url = data.zipcodes.url\n", "zipcodes = data.zipcodes()\n", "zipcodes.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "executionInfo": { "elapsed": 1517, "status": "ok", "timestamp": 1690232857459, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "EyzYgquoqoJj", "outputId": "388048a3-1be1-40dc-9f37-8d17cd398c12" }, "outputs": [ { "data": { "text/html": [ "
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zip_codelatitudelongitudecitystatecounty
00050140.922326-72.637078HoltsvilleNYSuffolk
10054440.922326-72.637078HoltsvilleNYSuffolk
20060118.165273-66.722583AdjuntasPRAdjuntas
30060218.393103-67.180953AguadaPRAguada
40060318.455913-67.145780AguadillaPRAguadilla
\n", "
" ], "text/plain": [ " zip_code latitude longitude city state county\n", "0 00501 40.922326 -72.637078 Holtsville NY Suffolk\n", "1 00544 40.922326 -72.637078 Holtsville NY Suffolk\n", "2 00601 18.165273 -66.722583 Adjuntas PR Adjuntas\n", "3 00602 18.393103 -67.180953 Aguada PR Aguada\n", "4 00603 18.455913 -67.145780 Aguadilla PR Aguadilla" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zipcodes = data.zipcodes(dtype={'zip_code': 'category'})\n", "zipcodes.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 3, "status": "ok", "timestamp": 1690232857814, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "VbSCHJVbqoJk", "outputId": "7f2a736f-11ad-46d9-b016-d76c000d01be" }, "outputs": [ { "data": { "text/plain": [ "CategoricalDtype(categories=['00501', '00544', '00601', '00602', '00603', '00604',\n", " '00605', '00606', '00610', '00611',\n", " ...\n", " '99919', '99921', '99922', '99923', '99925', '99926',\n", " '99927', '99928', '99929', '99950'],\n", ", ordered=False, categories_dtype=object)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zipcodes.zip_code.dtype" ] }, { "cell_type": "markdown", "metadata": { "id": "JS8nSE1YqoJn" }, "source": [ "Btw, you'll have fewer issues if you pass URL instead of a dataframe to `alt.Chart`." ] }, { "cell_type": "markdown", "metadata": { "id": "TPj8GsfMqoJo" }, "source": [ "### Let's draw it\n", "\n", "Now we have the dataset loaded and start drawing some plots. Let's say you don't know anything about map projections. What would you try with geographical data? Probably the simplest way is considering (longitude, latitude) as a Cartesian coordinate and directly plot them." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 368 }, "executionInfo": { "elapsed": 306, "status": "ok", "timestamp": 1690232859294, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "ldFAB0p_qoJq", "outputId": "30fed316-e3f1-4002-90e1-26974811ccc3" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(zipcodes_url).mark_circle().encode(\n", " x='longitude:Q',\n", " y='latitude:Q',\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "V4UTA0L3qoJr" }, "source": [ "Actually this itself is a map projection called [Equirectangular projection](https://en.wikipedia.org/wiki/Equirectangular_projection). This projection (or almost a *non-projection*) is super straight-forward and doesn't require any processing of the data. So, often it is used to just quickly explore geographical data. As you dig deeper, you still want to think about which map projection fits your need best. Don't just use equirectangular projection without any thoughts!\n", "\n", "Anyway, let's make it look slighly better by reducing the size of the circles and adjusting the aspect ratio. **Q: Can you adjust the width and height?**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 268 }, "executionInfo": { "elapsed": 307, "status": "ok", "timestamp": 1690232868969, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "CEMhFuGIqoJt", "outputId": "4eebb7f5-d423-47fb-a661-df0a300ec9f9" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "kd9NuhLlqoJu" }, "source": [ "But, a much better way to do this is explicitly specifying that they are lat, lng coordinates by using `longitude=` and `latitude=`, rather than `x=` and `y=`. If you do that, altair automatically adjust the aspect ratio. **Q: Can you try it?**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331 }, "executionInfo": { "elapsed": 308, "status": "ok", "timestamp": 1690232870219, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "qqXF2OQrqoJv", "outputId": "bd2f7d3d-fff2-4162-a037-848c464e40e6" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "QLhCkER6qoJx" }, "source": [ "Because the [American empire is far-reaching and complicated](https://www.youtube.com/watch?v=ASSOQDQvVLU), the information density of this map is very low (although interesting). Moreover, the US looks twisted because a default projection that is not focused on the US is used. \n", "\n", "A common projection for visualizing US data is [AlbersUSA](https://bl.ocks.org/mbostock/5545680), which uses [Albers (equal-area) projection](https://en.wikipedia.org/wiki/Albers_projection). This is a standard projection used in United States Geological Survey and the United States Census Bureau. Albers USA contains a composition of US main land, Alaska, and Hawaii.\n", "\n", "To use it, we call `project` method and specify which variables are `longitude` and `latitude`.\n", "\n", "**Q: use the `project` method to draw the map in the AlbersUsa projection.**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 433 }, "executionInfo": { "elapsed": 10, "status": "ok", "timestamp": 1690232871853, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "XqIx6087qoJx", "outputId": "371fdda2-849f-4f52-a365-c99c7a7fedbb" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "HgFp8NocqoJy" }, "source": [ "Now we're talking. 😎\n", "\n", "Let's visualize the large-scale zipcode patterns. We can use the fact that the zipcodes are hierarchically organized. That is, the first digit captures the largest area divisions and the other digits are about smaller geographical divisions.\n", "\n", "Altair provides some data transformation functionalities. One of them is extracting a substring from a variable." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 433 }, "executionInfo": { "elapsed": 7, "status": "ok", "timestamp": 1690232874556, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "wjHx0a39qoJz", "outputId": "3c2c2fa8-1c46-4f1a-d70c-013766e59f2b" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from altair.expr import datum, substring\n", "\n", "alt.Chart(zipcodes_url).mark_circle(size=2).transform_calculate(\n", " 'first_digit', substring(datum.zip_code, 0, 1)\n", ").encode(\n", " longitude='longitude:Q',\n", " latitude='latitude:Q',\n", " color='first_digit:N',\n", ").project(\n", " type='albersUsa'\n", ").properties(\n", " width=700,\n", " height=400,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "QeHehou1qoJ0" }, "source": [ "For each row (`datum`), you obtain the `zip_code` variable and get the substring (imagine Python list slicing), and then you call the result `first_digit`. Now, you can use this `first_digit` variable to color the circles. Also note that we specify `first_digit` as a *nominal* variable, not quantitative, to obtain a categorical colormap. But we can also play with it too.\n", "\n", "**Q: Why don't you extract the first two digits, name it as `two_digits`, and declare that as a quantitative variable? Any interesting patterns? What does it tell us about the history of US?**" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 431, "resources": { "http://localhost:8080/altair-data-420f3d354b7ce0e0779d651ffd5241c4.json": { "data": "CjwhRE9DVFlQRSBodG1sPgo8aHRtbCBsYW5nPWVuPgogIDxtZXRhIGNoYXJzZXQ9dXRmLTg+CiAgPG1ldGEgbmFtZT12aWV3cG9ydCBjb250ZW50PSJpbml0aWFsLXNjYWxlPTEsIG1pbmltdW0tc2NhbGU9MSwgd2lkdGg9ZGV2aWNlLXdpZHRoIj4KICA8dGl0bGU+RXJyb3IgNDA0IChOb3QgRm91bmQpISExPC90aXRsZT4KICA8c3R5bGU+CiAgICAqe21hcmdpbjowO3BhZGRpbmc6MH1odG1sLGNvZGV7Zm9udDoxNXB4LzIycHggYXJpYWwsc2Fucy1zZXJpZn1odG1se2JhY2tncm91bmQ6I2ZmZjtjb2xvcjojMjIyO3BhZGRpbmc6MTVweH1ib2R5e21hcmdpbjo3JSBhdXRvIDA7bWF4LXdpZHRoOjM5MHB4O21pbi1oZWlnaHQ6MTgwcHg7cGFkZGluZzozMHB4IDAgMTVweH0qID4gYm9keXtiYWNrZ3JvdW5kOnVybCgvL3d3dy5nb29nbGUuY29tL2ltYWdlcy9lcnJvcnMvcm9ib3QucG5nKSAxMDAlIDVweCBuby1yZXBlYXQ7cGFkZGluZy1yaWdodDoyMDVweH1we21hcmdpbjoxMXB4IDAgMjJweDtvdmVyZmxvdzpoaWRkZW59aW5ze2NvbG9yOiM3Nzc7dGV4dC1kZWNvcmF0aW9uOm5vbmV9YSBpbWd7Ym9yZGVyOjB9QG1lZGlhIHNjcmVlbiBhbmQgKG1heC13aWR0aDo3NzJweCl7Ym9keXtiYWNrZ3JvdW5kOm5vbmU7bWFyZ2luLXRvcDowO21heC13aWR0aDpub25lO3BhZGRpbmctcmlnaHQ6MH19I2xvZ297YmFja2dyb3VuZDp1cmwoLy93d3cuZ29vZ2xlLmNvbS9pbWFnZXMvbG9nb3MvZXJyb3JwYWdlL2Vycm9yX2xvZ28tMTUweDU0LnBuZykgbm8tcmVwZWF0O21hcmdpbi1sZWZ0Oi01cHh9QG1lZGlhIG9ubHkgc2NyZWVuIGFuZCAobWluLXJlc29sdXRpb246MTkyZHBpKXsjbG9nb3tiYWNrZ3JvdW5kOnVybCgvL3d3dy5nb29nbGUuY29tL2ltYWdlcy9sb2dvcy9lcnJvcnBhZ2UvZXJyb3JfbG9nby0xNTB4NTQtMngucG5nKSBuby1yZXBlYXQgMCUgMCUvMTAwJSAxMDAlOy1tb3otYm9yZGVyLWltYWdlOnVybCgvL3d3dy5nb29nbGUuY29tL2ltYWdlcy9sb2dvcy9lcnJvcnBhZ2UvZXJyb3JfbG9nby0xNTB4NTQtMngucG5nKSAwfX1AbWVkaWEgb25seSBzY3JlZW4gYW5kICgtd2Via2l0LW1pbi1kZXZpY2UtcGl4ZWwtcmF0aW86Mil7I2xvZ297YmFja2dyb3VuZDp1cmwoLy93d3cuZ29vZ2xlLmNvbS9pbWFnZXMvbG9nb3MvZXJyb3JwYWdlL2Vycm9yX2xvZ28tMTUweDU0LTJ4LnBuZykgbm8tcmVwZWF0Oy13ZWJraXQtYmFja2dyb3VuZC1zaXplOjEwMCUgMTAwJX19I2xvZ297ZGlzcGxheTppbmxpbmUtYmxvY2s7aGVpZ2h0OjU0cHg7d2lkdGg6MTUwcHh9CiAgPC9zdHlsZT4KICA8YSBocmVmPS8vd3d3Lmdvb2dsZS5jb20vPjxzcGFuIGlkPWxvZ28gYXJpYS1sYWJlbD1Hb29nbGU+PC9zcGFuPjwvYT4KICA8cD48Yj40MDQuPC9iPiA8aW5zPlRoYXTigJlzIGFuIGVycm9yLjwvaW5zPgogIDxwPiAgPGlucz5UaGF04oCZcyBhbGwgd2Uga25vdy48L2lucz4K", "headers": [ [ "content-length", "1449" ], [ "content-type", "text/html; charset=utf-8" ] ], "ok": false, "status": 404, "status_text": "" } } }, "executionInfo": { "elapsed": 368, "status": "ok", "timestamp": 1690232878782, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "X-FGT_ghqoJ1", "outputId": "46e6e02c-5cfc-448f-efe7-a5b887670cb9" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "ju0PurTgqoJ2" }, "source": [ "**Q: also try it with declaring the first two digits as a categorical variable**" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "executionInfo": { "elapsed": 343, "status": "ok", "timestamp": 1690232880830, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "IJD4cLCHrjhp", "outputId": "9fdc4eed-4016-44fe-c947-fcf3a35590ff" }, "outputs": [ { "data": { "text/html": [ "
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zip_codelatitudelongitudecitystatecounty
00050140.922326-72.637078HoltsvilleNYSuffolk
10054440.922326-72.637078HoltsvilleNYSuffolk
20060118.165273-66.722583AdjuntasPRAdjuntas
30060218.393103-67.180953AguadaPRAguada
40060318.455913-67.145780AguadillaPRAguadilla
.....................
420449992655.094325-131.566827MetlakatlaAKPrince Wales Ketchikan
420459992755.517921-132.003244Point BakerAKPrince Wales Ketchikan
420469992855.395359-131.675370Ward CoveAKKetchikan Gateway
420479992956.449893-132.364407WrangellAKWrangell Petersburg
420489995055.542007-131.432682KetchikanAKKetchikan Gateway
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42049 rows × 6 columns

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" ], "text/plain": [ " zip_code latitude longitude city state \\\n", "0 00501 40.922326 -72.637078 Holtsville NY \n", "1 00544 40.922326 -72.637078 Holtsville NY \n", "2 00601 18.165273 -66.722583 Adjuntas PR \n", "3 00602 18.393103 -67.180953 Aguada PR \n", "4 00603 18.455913 -67.145780 Aguadilla PR \n", "... ... ... ... ... ... \n", "42044 99926 55.094325 -131.566827 Metlakatla AK \n", "42045 99927 55.517921 -132.003244 Point Baker AK \n", "42046 99928 55.395359 -131.675370 Ward Cove AK \n", "42047 99929 56.449893 -132.364407 Wrangell AK \n", "42048 99950 55.542007 -131.432682 Ketchikan AK \n", "\n", " county \n", "0 Suffolk \n", "1 Suffolk \n", "2 Adjuntas \n", "3 Aguada \n", "4 Aguadilla \n", "... ... \n", "42044 Prince Wales Ketchikan \n", "42045 Prince Wales Ketchikan \n", "42046 Ketchikan Gateway \n", "42047 Wrangell Petersburg \n", "42048 Ketchikan Gateway \n", "\n", "[42049 rows x 6 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zipcodes" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 431, "resources": { "http://localhost:8080/altair-data-420f3d354b7ce0e0779d651ffd5241c4.json": { "data": "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", "headers": [ [ "content-length", "1449" ], [ "content-type", "text/html; charset=utf-8" ] ], "ok": false, "status": 404, "status_text": "" } } }, "executionInfo": { "elapsed": 677, "status": "ok", "timestamp": 1690232881911, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "1tiGYQJNqoJ2", "outputId": "c45b34f1-de72-40f0-e802-2d04991a89bf" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Implement\n", "\n", "# YOUR SOLUTION HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "SU4a0WzJqoJ3" }, "source": [ "Btw, you can always click \"view source\" or \"open in Vega Editor\" to look at the json object that **defines** this visualization. You can embed this json object on your webpage and easily put up an interactive visualization.\n", "\n", "**Q: Can you put a tooltip that displays the zipcode when you mouse-over?**" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 433 }, "executionInfo": { "elapsed": 9, "status": "ok", "timestamp": 1690232882918, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "lOv0DjHrqoJ4", "outputId": "4876bab4-c471-4b25-e21f-b066b5b590c5" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "executionInfo": { "elapsed": 7, "status": "ok", "timestamp": 1690232883207, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "mre6TyO5t_rZ", "outputId": "559d4e52-c99c-40e4-fce1-83de8464e8d6" }, "outputs": [ { "data": { "text/html": [ "
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zip_codelatitudelongitudecitystatecounty
00050140.922326-72.637078HoltsvilleNYSuffolk
10054440.922326-72.637078HoltsvilleNYSuffolk
20060118.165273-66.722583AdjuntasPRAdjuntas
30060218.393103-67.180953AguadaPRAguada
40060318.455913-67.145780AguadillaPRAguadilla
.....................
420449992655.094325-131.566827MetlakatlaAKPrince Wales Ketchikan
420459992755.517921-132.003244Point BakerAKPrince Wales Ketchikan
420469992855.395359-131.675370Ward CoveAKKetchikan Gateway
420479992956.449893-132.364407WrangellAKWrangell Petersburg
420489995055.542007-131.432682KetchikanAKKetchikan Gateway
\n", "

42049 rows × 6 columns

\n", "
" ], "text/plain": [ " zip_code latitude longitude city state \\\n", "0 00501 40.922326 -72.637078 Holtsville NY \n", "1 00544 40.922326 -72.637078 Holtsville NY \n", "2 00601 18.165273 -66.722583 Adjuntas PR \n", "3 00602 18.393103 -67.180953 Aguada PR \n", "4 00603 18.455913 -67.145780 Aguadilla PR \n", "... ... ... ... ... ... \n", "42044 99926 55.094325 -131.566827 Metlakatla AK \n", "42045 99927 55.517921 -132.003244 Point Baker AK \n", "42046 99928 55.395359 -131.675370 Ward Cove AK \n", "42047 99929 56.449893 -132.364407 Wrangell AK \n", "42048 99950 55.542007 -131.432682 Ketchikan AK \n", "\n", " county \n", "0 Suffolk \n", "1 Suffolk \n", "2 Adjuntas \n", "3 Aguada \n", "4 Aguadilla \n", "... ... \n", "42044 Prince Wales Ketchikan \n", "42045 Prince Wales Ketchikan \n", "42046 Ketchikan Gateway \n", "42047 Wrangell Petersburg \n", "42048 Ketchikan Gateway \n", "\n", "[42049 rows x 6 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zipcodes" ] }, { "cell_type": "markdown", "metadata": { "id": "Ilc45qeDqoJ5" }, "source": [ "## Choropleth\n", "\n", "Let's try some choropleth now. Vega datasets have US county / state boundary data (`us_10m`) and world country boundary data (`world-110m`). You can take a look at the boundaries on GitHub (they renders topoJSON files):\n", "\n", "- https://github.com/vega/vega-datasets/blob/main/data/us-10m.json\n", "- https://github.com/vega/vega-datasets/blob/main/data/world-110m.json\n", "\n", "If you click \"Raw\" then you can take a look at the actual file, which is hard to read.\n", "\n", "Essentially, each file is a large dictionary with the following keys." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 370, "status": "ok", "timestamp": 1690232886065, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "4omuucvZqoJ5", "outputId": "54d45d9b-7eb7-44bf-8a6b-fbc28c840771" }, "outputs": [ { "data": { "text/plain": [ "dict_keys(['type', 'transform', 'objects', 'arcs'])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "usmap = data.us_10m()\n", "usmap.keys()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "executionInfo": { "elapsed": 317, "status": "ok", "timestamp": 1690232886368, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "Ns1AOlGLqoJ6", "outputId": "e142821d-b9f7-4b0a-8c82-ddffb9d1e399" }, "outputs": [ { "data": { "text/plain": [ "'Topology'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "usmap['type']" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 8, "status": "ok", "timestamp": 1690232887374, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "iiLUWi-eqoJ6", "outputId": "655d2324-e2d2-470a-f99b-83f13d291f68" }, "outputs": [ { "data": { "text/plain": [ "{'scale': [0.003589294092944858, 0.0005371535195261037],\n", " 'translate': [-179.1473400003406, 17.67439566600018]}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "usmap['transform']" ] }, { "cell_type": "markdown", "metadata": { "id": "uU0dewNaqoJ7" }, "source": [ "This `transformation` is used to *quantize* the data and store the coordinates in integer (easier to store than float type numbers).\n", "\n", "https://github.com/topojson/topojson-specification#212-transforms" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 4, "status": "ok", "timestamp": 1690232887710, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "JkbKMqAfqoJ7", "outputId": "b93217f4-29f9-4291-b669-6b3f65e33500" }, "outputs": [ { "data": { "text/plain": [ "dict_keys(['counties', 'states', 'land'])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "usmap['objects'].keys()" ] }, { "cell_type": "markdown", "metadata": { "id": "xpDlwdPuqoJ8" }, "source": [ "This data contains not only county-level boundaries (objects) but also states and land boundaries." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 394, "status": "ok", "timestamp": 1690232937202, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "yUGjDhY5qoJ_", "outputId": "eaeb9c68-c1ed-4e9e-e1a3-92229c93f864" }, "outputs": [ { "data": { "text/plain": [ "('MultiPolygon', 'GeometryCollection', 'GeometryCollection')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "usmap['objects']['land']['type'], usmap['objects']['states']['type'], usmap['objects']['counties']['type']" ] }, { "cell_type": "markdown", "metadata": { "id": "JYlcwe0iqoJ_" }, "source": [ "`land` is a multipolygon (one object) and `states` and `counties` contains many geometrics (multipolygons) because there are many states (counties). We can look at a state as a set of arcs that define it. It's `id` captures the identity of the state and is the key to link to other datasets." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 3, "status": "ok", "timestamp": 1690232937472, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "osMJi24KqoKA", "outputId": "13195f5e-cfee-4291-d6aa-81fcad02ec26" }, "outputs": [ { "data": { "text/plain": [ "{'type': 'MultiPolygon',\n", " 'arcs': [[[10337]],\n", " [[10342]],\n", " [[10341]],\n", " [[10343]],\n", " [[10834, 10340]],\n", " [[10344]],\n", " [[10345]],\n", " [[10338]]],\n", " 'id': 15}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "state1 = usmap['objects']['states']['geometries'][1]\n", "state1" ] }, { "cell_type": "markdown", "metadata": { "id": "qktmc0T8qoKA" }, "source": [ "The `arcs` referred here is defined in `usmap['arcs']`." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 3, "status": "ok", "timestamp": 1690232938354, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "8S4lgQtTqoKA", "outputId": "c6c42b0e-bc3d-4f7c-c724-3d362b13cfb5" }, "outputs": [ { "data": { "text/plain": [ "[[[15739, 57220], [0, 0]],\n", " [[15739, 57220], [29, 62], [47, -273]],\n", " [[15815, 57009], [-6, -86]],\n", " [[15809, 56923], [0, 0]],\n", " [[15809, 56923], [-36, -8], [6, -210], [32, 178]],\n", " [[15811, 56883], [9, -194], [44, -176], [-29, -151], [-24, -319]],\n", " [[15811, 56043], [-12, -216], [26, -171]],\n", " [[15825, 55656], [-2, 1]],\n", " [[15823, 55657], [-19, 10], [26, -424], [-26, -52]],\n", " [[15804, 55191], [-30, -72], [-47, -344]]]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "usmap['arcs'][:10]" ] }, { "cell_type": "markdown", "metadata": { "id": "k5r3XGSTqoKB" }, "source": [ "It seems pretty daunting to work with this dataset, right? But fortunately people have already built tools to handle such data." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "executionInfo": { "elapsed": 3, "status": "ok", "timestamp": 1690232939346, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "Xq6w_c7fqoKC" }, "outputs": [], "source": [ "states = alt.topo_feature(data.us_10m.url, 'states')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 3, "status": "ok", "timestamp": 1690232939761, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "94sDpllZqoKC", "outputId": "55bb5c0e-d5d9-4e00-e4a9-10515f07fbd2" }, "outputs": [ { "data": { "text/plain": [ "UrlData({\n", " format: TopoDataFormat({\n", " feature: 'states',\n", " type: 'topojson'\n", " }),\n", " url: 'https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/us-10m.json'\n", "})" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "states" ] }, { "cell_type": "markdown", "metadata": { "id": "y_FDWolcqoKD" }, "source": [ "Can you find a mark for geographical shapes from here https://altair-viz.github.io/user_guide/marks/index.html# and draw the states?" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331 }, "executionInfo": { "elapsed": 327, "status": "ok", "timestamp": 1690232957050, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "oOLvExieqoKD", "outputId": "62cf18ce-f128-4f52-e60f-903ea8c930ef" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "LAVrvLESqoKD" }, "source": [ "And then project it using the `albersUsa`?" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331 }, "executionInfo": { "elapsed": 17, "status": "ok", "timestamp": 1690232957991, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "256_fw7hqoKE", "outputId": "164a356e-1b0f-4823-c0de-6e9056252773" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "sD5c6TroqoKE" }, "source": [ "Can you do the same thing with counties and draw county boundaries?" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331 }, "executionInfo": { "elapsed": 5, "status": "ok", "timestamp": 1690232958857, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "PucM5vM9qoKF", "outputId": "f4a74e7f-1cbd-4e30-c6c7-ff3a3f700700" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "JhiS_GwWqoKF" }, "source": [ "Let's load some county-level unemployment data." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "executionInfo": { "elapsed": 13, "status": "ok", "timestamp": 1690232959933, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "dsLXey0VqoKG", "outputId": "641cee21-733e-4d0c-80d6-16d66e31f345" }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " id rate\n", "0 1001 0.097\n", "1 1003 0.091\n", "2 1005 0.134\n", "3 1007 0.121\n", "4 1009 0.099" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unemp_data = data.unemployment(sep='\\t')\n", "unemp_data.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "jCQX5fJEqoKG" }, "source": [ "This dataset has unemployment rate. When? I don't know. We don't care about data provenance here because the goal is quickly trying out choropleth. But if you're working with a real dataset, you should be very sensitive about the provenance of your dataset. Make sure you understand where the data came from and how it was processed.\n", "\n", "Anyway, for each county specified with `id`. To combine two datasets, we use \"Lookup transform\" - https://vega.github.io/vega/docs/transforms/lookup/. Essentially, we use the `id` in the map data to look up (again) `id` field in the `unemp_data` and then bring in the `rate` variable. Then, we can use that `rate` variable to encode the color of the `geoshape` mark." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 431, "resources": { "http://localhost:8080/altair-data-141893a9f0c2b2c58be329ef58d66780.json": { "data": "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", "headers": [ [ "content-length", "1449" ], [ "content-type", "text/html; charset=utf-8" ] ], "ok": false, "status": 404, "status_text": "" } } }, "executionInfo": { "elapsed": 12, "status": "ok", "timestamp": 1690232960674, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "UknNEGG_qoKH", "outputId": "dcce3265-2bf7-46d3-97a4-17264b815d00" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(us_counties).mark_geoshape().project(\n", " type='albersUsa'\n", ").transform_lookup(\n", " lookup='id',\n", " from_=alt.LookupData(unemp_data, 'id', ['rate'])\n", ").encode(\n", " color='rate:Q'\n", ").properties(\n", " width=700,\n", " height=400\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "mMHYUbQsqoKH" }, "source": [ "There you have it, a nice choropleth map. 😎\n" ] }, { "cell_type": "markdown", "metadata": { "id": "gdInAvwdqoKI" }, "source": [ "## Raster visualization with datashader\n", "\n", "Although many geovisualizations use vector graphics, raster visualization is still useful especially when you deal with images and lots of datapoints. Datashader is a package that aggregates and visualizes a large amount of data very quickly. Given a *scene* (visualization boundary, resolution, etc.), it quickly aggregate the data and produce **pixels** and send them to you.\n", "\n", "To appreciate its power, we need a fairly large dataset. Let's use NYC taxi trip dataset on Kaggle: https://www.kaggle.com/kentonnlp/2014-new-york-city-taxi-trips You can download even bigger trip data from NYC open data website: https://opendata.cityofnewyork.us/data/\n", "\n", "Ah, and you want to install the datashader, bokeh, and holoviews first if you don't have them yet. \n", "\n", " pip install datashader bokeh holoviews jupyter-bokeh\n", "\n", "or\n", "\n", " conda install datashader bokeh holoviews jupyter-bokeh\n", " " ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "executionInfo": { "elapsed": 3016, "status": "ok", "timestamp": 1690233051824, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "Coy9hAAfqoKI" }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import datashader as ds\n", "from datashader import transfer_functions as tf\n", "from colorcet import fire" ] }, { "cell_type": "markdown", "metadata": { "id": "camMhr7pqoKI" }, "source": [ "Because the dataset is pretty big, let's use a small sample first. For this visualization, we only keep the dropoff location." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 345 }, "executionInfo": { "elapsed": 319, "status": "error", "timestamp": 1690233059618, "user": { "displayName": "Vincent Wong", "userId": "06927694896148305320" }, "user_tz": 240 }, "id": "0DxKvpjoqoKJ", "outputId": "b9b581ae-328f-42de-8bdf-aeba33b1ff65" }, "outputs": [ { "data": { "text/html": [ "
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dropoff_longitudedropoff_latitude
0-73.98222740.731790
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" ], "text/plain": [ " dropoff_longitude dropoff_latitude\n", "0 -73.982227 40.731790\n", "1 -73.960449 40.763995\n", "2 -73.986626 40.765217\n", "3 -73.979863 40.777050\n", "4 -73.984367 40.720524" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nyctaxi_small = pd.read_csv('~/Downloads/archive/nyc_taxi_data_2014.csv', nrows=10000,\n", " usecols=['dropoff_longitude', 'dropoff_latitude'])\n", "nyctaxi_small.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "bXyxUFG-qoKJ" }, "source": [ "Although the dataset is different, we can still follow the example here: https://datashader.org/getting_started/Introduction.html" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "L6ZCUMFAqoKJ", "outputId": "94c8f839-4197-4413-b19a-a867b964eb8f" }, "outputs": [ { "data": { "image/png": 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"text/html": [ "" ], "text/plain": [ "\n", "array([[4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278230783],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " ...,\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080]], dtype=uint32)\n", "Coordinates:\n", " * dropoff_longitude (dropoff_longitude) float64 -74.31 -74.18 ... -0.06197\n", " * dropoff_latitude (dropoff_latitude) float64 0.03423 0.1027 ... 40.97 41.04" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agg = ds.Canvas().points(nyctaxi_small, 'dropoff_longitude', 'dropoff_latitude')\n", "tf.set_background(tf.shade(agg, cmap=fire),\"black\")" ] }, { "cell_type": "markdown", "metadata": { "id": "OKsxrWfhqoKK" }, "source": [ "Why can't we see anything? Wait, do you see the small dots on the left top? Can that be New York City? Maybe we don't see anything because some people travel very far? or because the dataset has some missing data?\n", "\n", "**Q: Can you first check whether there are NaNs? Then drop them and draw the map again?**" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "id": "IczXK0shqoKK", "outputId": "11dc377d-ba70-4976-a9e3-daa71f7a8824" }, "outputs": [ { "data": { "text/plain": [ "dropoff_longitude 1\n", "dropoff_latitude 1\n", "dtype: int64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "id": "ac27UgIfqoKL", "outputId": "723f1494-65d5-4a7a-fdc7-1f03142643a2" }, "outputs": [ { "data": { "image/png": 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"text/html": [ "" ], "text/plain": [ "\n", "array([[4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278230783],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " ...,\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080]], dtype=uint32)\n", "Coordinates:\n", " * dropoff_longitude (dropoff_longitude) float64 -74.31 -74.18 ... -0.06197\n", " * dropoff_latitude (dropoff_latitude) float64 0.03423 0.1027 ... 40.97 41.04" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# drop the rows with NaN and then draw the map again.\n", "\n", "# YOUR SOLUTION HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "t-76U_7vqoKM" }, "source": [ "So it's not about the missing data.\n", "\n", "**Q: Can you identify the issue and draw the map like the following?**\n", "\n", "hint: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.between.html this method may be helpful." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "kZFc77QhqoKM", "outputId": "2003389f-aaa0-4b54-ca8e-67a82ee1d642" }, "outputs": [], "source": [ "# You can use multiple cells to figure out what's going on.\n", "\n", "# YOUR SOLUTION HERE" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "id": "WyBuABbSqoKN", "outputId": "a6b22583-78ae-4356-9ee0-44c1049031df" }, "outputs": [ { "data": { "image/png": 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", "text/html": [ "" ], "text/plain": [ "\n", "array([[4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " ...,\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080]], dtype=uint32)\n", "Coordinates:\n", " * dropoff_longitude (dropoff_longitude) float64 -74.09 -74.09 ... -73.71\n", " * dropoff_latitude (dropoff_latitude) float64 40.58 40.58 ... 40.98 40.98" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agg = ds.Canvas().points(nyctaxi_small_filtered, 'dropoff_longitude', 'dropoff_latitude')\n", "tf.set_background(tf.shade(agg, cmap=fire), \"black\")" ] }, { "cell_type": "markdown", "metadata": { "id": "nq6Px8I8qoKO" }, "source": [ "Do you see the black empty space at the center? That looks like the Central Park. This is cool, but it'll be awesome if we can explore the data interactively." ] }, { "cell_type": "markdown", "metadata": { "id": "FP4BKa0qqoKP" }, "source": [ "Ok, now let's get serious by loading the whole dataset. It may take some time. Apply the same data cleaning procedure." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "id": "-hKJ8Eo0qoKP" }, "outputs": [], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "DIp6mf1GqoKP" }, "source": [ "Can you feed the data directly to datashader to reproduce the static plot, this time with the full data?" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "id": "b3vV9m7RqoKQ", "outputId": "c7e39a6a-e484-4720-e8f9-4364a75b4a26" }, "outputs": [ { "data": { "image/png": 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"text/html": [ "" ], "text/plain": [ "\n", "array([[4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " ...,\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080],\n", " [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n", " 4278190080]], dtype=uint32)\n", "Coordinates:\n", " * dropoff_longitude (dropoff_longitude) float64 -74.1 -74.1 ... -73.7 -73.7\n", " * dropoff_latitude (dropoff_latitude) float64 40.5 40.5 40.5 ... 41.0 41.0" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "vk9lsI3RqoKQ" }, "source": [ "Wow, that's fast. Also it looks cool!\n", "\n", "Let's try the interactive version from here: https://datashader.org/getting_started/Introduction.html" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dropoff_longitudedropoff_latitude
0-73.98222740.731790
1-73.96044940.763995
2-73.98662640.765217
3-73.97986340.777050
4-73.98436740.720524
.........
14999994-74.00067540.725737
14999995-73.99128740.692535
14999996-73.77650540.740790
14999997-74.00595340.710922
14999998-73.97240740.747463
\n", "

14751421 rows × 2 columns

\n", "
" ], "text/plain": [ " dropoff_longitude dropoff_latitude\n", "0 -73.982227 40.731790\n", "1 -73.960449 40.763995\n", "2 -73.986626 40.765217\n", "3 -73.979863 40.777050\n", "4 -73.984367 40.720524\n", "... ... ...\n", "14999994 -74.000675 40.725737\n", "14999995 -73.991287 40.692535\n", "14999996 -73.776505 40.740790\n", "14999997 -74.005953 40.710922\n", "14999998 -73.972407 40.747463\n", "\n", "[14751421 rows x 2 columns]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nyctaxi_filtered" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We currently only have longitudes and latitudes. We need to conver them into a coordinate system that datashader understands. " ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/y9/6g21ty616b783y993xqz28hr0000gq/T/ipykernel_99414/1009409252.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " df['dropoff_x'], df['dropoff_y'] = lnglat_to_meters(df.dropoff_longitude, df.dropoff_latitude)\n", "/var/folders/y9/6g21ty616b783y993xqz28hr0000gq/T/ipykernel_99414/1009409252.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " df['dropoff_x'], df['dropoff_y'] = lnglat_to_meters(df.dropoff_longitude, df.dropoff_latitude)\n" ] } ], "source": [ "from datashader.utils import lnglat_to_meters\n", "\n", "df = nyctaxi_filtered\n", "df['dropoff_x'], df['dropoff_y'] = lnglat_to_meters(df.dropoff_longitude, df.dropoff_latitude)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can visualize the data interactively. See https://datashader.org/getting_started/Introduction.html" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "id": "I-BDGl86qoKR", "outputId": "be2ff1c4-1abe-4d7e-cda4-07928816f93a" }, "outputs": [ { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", " var py_version = '3.3.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", " var reloading = false;\n", " var Bokeh = root.Bokeh;\n", " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", "\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " 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