{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploratory Spatial Data Analysis (ESDA)\n", "\n", "> [`IPYNB`](../content/part1/04_esda.ipynb)\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import pysal as ps\n", "import pandas as pd\n", "import numpy as np\n", "from pysal.contrib.viz import mapping as maps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A well-used functionality in PySAL is the use of PySAL to conduct exploratory spatial data analysis. This notebook will provide an overview of ways to conduct exploratory spatial analysis in Python. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's read in some data:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = ps.pdio.read_files(\"../data/texas.shp\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NAMESTATE_NAMESTATE_FIPSCNTY_FIPSFIPSSTFIPSCOFIPSFIPSNOSOUTHHR60...BLK90GI59GI69GI79GI89FH60FH70FH80FH90geometry
0LipscombTexas4829548295482954829510.0...0.0318170.2869290.3782190.4070050.3730056.7245124.53.8353606.093580<pysal.cg.shapes.Polygon object at 0x7fd6da8ad...
1ShermanTexas4842148421484214842110.0...0.1399580.2889760.3593770.4154530.3780415.6657221.73.2537963.869407<pysal.cg.shapes.Polygon object at 0x7fd6da8ad...
2DallamTexas4811148111481114811110.0...2.0509060.3316670.3859960.3700370.3760157.5460497.29.47136614.231738<pysal.cg.shapes.Polygon object at 0x7fd6da8ad...
3HansfordTexas4819548195481954819510.0...0.0000000.2535270.3578130.3939380.3839247.5917864.75.5429867.125457<pysal.cg.shapes.Polygon object at 0x7fd6da8ad...
4OchiltreeTexas4835748357483574835710.0...0.0219110.2369980.3529400.3439490.3744615.1724144.04.7583929.159159<pysal.cg.shapes.Polygon object at 0x7fd6da8ad...
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5 rows × 70 columns

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" ], "text/plain": [ " NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS STFIPS COFIPS FIPSNO \\\n", "0 Lipscomb Texas 48 295 48295 48 295 48295 \n", "1 Sherman Texas 48 421 48421 48 421 48421 \n", "2 Dallam Texas 48 111 48111 48 111 48111 \n", "3 Hansford Texas 48 195 48195 48 195 48195 \n", "4 Ochiltree Texas 48 357 48357 48 357 48357 \n", "\n", " SOUTH HR60 ... BLK90 \\\n", "0 1 0.0 ... 0.031817 \n", "1 1 0.0 ... 0.139958 \n", "2 1 0.0 ... 2.050906 \n", "3 1 0.0 ... 0.000000 \n", "4 1 0.0 ... 0.021911 \n", "\n", " GI59 GI69 GI79 GI89 FH60 FH70 FH80 \\\n", "0 0.286929 0.378219 0.407005 0.373005 6.724512 4.5 3.835360 \n", "1 0.288976 0.359377 0.415453 0.378041 5.665722 1.7 3.253796 \n", "2 0.331667 0.385996 0.370037 0.376015 7.546049 7.2 9.471366 \n", "3 0.253527 0.357813 0.393938 0.383924 7.591786 4.7 5.542986 \n", "4 0.236998 0.352940 0.343949 0.374461 5.172414 4.0 4.758392 \n", "\n", " FH90 geometry \n", "0 6.093580 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "import geopandas as gpd\n", "shp_link = \"../data/texas.shp\"\n", "tx = gpd.read_file(shp_link)\n", "hr10 = ps.Quantiles(data.HR90, k=10)\n", "f, ax = plt.subplots(1, figsize=(9, 9))\n", "tx.assign(cl=hr10.yb).plot(column='cl', categorical=True, \\\n", " k=10, cmap='OrRd', linewidth=0.1, ax=ax, \\\n", " edgecolor='white', legend=True)\n", "ax.set_axis_off()\n", "plt.title(\"HR90 Deciles\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spatial Autocorrelation\n", "\n", "Visual inspection of the map pattern for HR90 deciles allows us to search for spatial structure. If the spatial distribution of the rates was random, then we should not see any clustering of similar values on the map. However, our visual system is drawn to the darker clusters in the south west as well as the east, and a concentration of the lighter hues (lower homicide rates) moving north to the pan handle.\n", "\n", "Our brains are very powerful pattern recognition machines. However, sometimes they can be too powerful and lead us to detect false positives, or patterns where there are no statistical patterns. This is a particular concern when dealing with visualization of irregular polygons of differning sizes and shapes.\n", "\n", "The concept of *spatial autocorrelation* relates to the combination of two types of similarity: spatial similarity and attribute similarity. Although there are many different measures of spatial autocorrelation, they all combine these two types of simmilarity into a summary measure.\n", "\n", "Let's use PySAL to generate these two types of similarity measures.\n", "\n", "### Spatial Similarity\n", "\n", "We have already encountered spatial weights in a previous notebook. In spatial autocorrelation analysis, the spatial weights are used to formalize the notion of spatial similarity. As we have seen there are many ways to define spatial weights, here we will use queen contiguity:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "data = ps.pdio.read_files(\"../data/texas.shp\")\n", "W = ps.queen_from_shapefile(\"../data/texas.shp\")\n", "W.transform = 'r'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Attribute Similarity\n", "\n", "So the spatial weight between counties $i$ and $j$ indicates if the two counties are neighbors (i.e., geographically similar). What we also need is a measure of attribute similarity to pair up with this concept of spatial similarity.\n", "The **spatial lag** is a derived variable that accomplishes this for us. For county $i$ the spatial lag is defined as:\n", "$$HR90Lag_i = \\sum_j w_{i,j} HR90_j$$\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "HR90Lag = ps.lag_spatial(W, data.HR90)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "HR90LagQ10 = ps.Quantiles(HR90Lag, k=10)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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MYRpOzUEEijRPRcqBClNB0Qs/ureLVeSNOXyoEa2fYpsO5BYXewd7ugfJNMgw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\naPfNCQIEHA4+4HDwANCji4P3+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L+NfsHTBwsBQcDk135riDc6ncjT2hbzYCl1UtMWabgj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\n4YOI5t3Wz3zmJ8vuvXdxr8ooR8/HRakBr4qdfFwNHfisip9eC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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(1, figsize=(9, 9))\n", "tx.assign(cl=HR90LagQ10.yb).plot(column='cl', categorical=True, \\\n", " k=10, cmap='OrRd', linewidth=0.1, ax=ax, \\\n", " edgecolor='white', legend=True)\n", "ax.set_axis_off()\n", "plt.title(\"HR90 Spatial Lag Deciles\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The decile map for the spatial lag tends to enhance the impression of value similarity in space. However, we still have the challenge of visually associating the value of the homicide rate in a county with the value of the spatial lag of rates for the county. The latter is a weighted average of homicide rates in the focal county's neighborhood.\n", "\n", "To complement the geovisualization of these associations we can turn to formal statistical measures of spatial autocorrelation." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "HR90 = data.HR90\n", "b,a = np.polyfit(HR90, HR90Lag, 1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(1, figsize=(9, 9))\n", "\n", "plt.plot(HR90, HR90Lag, '.', color='firebrick')\n", "\n", " # dashed vert at mean of the last year's PCI\n", "plt.vlines(HR90.mean(), HR90Lag.min(), HR90Lag.max(), linestyle='--')\n", " # dashed horizontal at mean of lagged PCI\n", "plt.hlines(HR90Lag.mean(), HR90.min(), HR90.max(), linestyle='--')\n", "\n", "# red line of best fit using global I as slope\n", "plt.plot(HR90, a + b*HR90, 'r')\n", "plt.title('Moran Scatterplot')\n", "plt.ylabel('Spatial Lag of HR90')\n", "plt.xlabel('HR90')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Global Spatial Autocorrelation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In PySAL, commonly-used analysis methods are very easy to access. For example, if we were interested in examining the spatial dependence in `HR90` we could quickly compute a Moran's $I$ statistic:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "I_HR90 = ps.Moran(data.HR90.values, W)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.085976640313889768, 0.012999999999999999)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "I_HR90.I, I_HR90.p_sim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thus, the $I$ statistic is $0.859$ for this data, and has a very small $p$ value. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.085976640313889505" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b # note I is same as the slope of the line in the scatterplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can visualize the distribution of simulated $I$ statistics using the stored collection of simulated statistics:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([-0.05640543, -0.03158917, 0.0277026 , 0.03998822, -0.01140814])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "I_HR90.sim[0:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A simple way to visualize this distribution is to make a KDEplot (like we've done before), and add a rug showing all of the simulated points, and a vertical line denoting the observed value of the statistic:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/serge/anaconda2/envs/gds-scipy16/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" ] }, { "data": { "text/plain": [ "(-0.15, 0.15)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.kdeplot(I_HR90.sim, shade=True)\n", "plt.vlines(I_HR90.sim, 0, 0.5)\n", "plt.vlines(I_HR90.I, 0, 10, 'r')\n", "plt.xlim([-0.15, 0.15])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead, if our $I$ statistic were close to our expected value, `I_HR90.EI`, our plot might look like this:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/serge/anaconda2/envs/gds-scipy16/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" ] }, { "data": { "text/plain": [ "(-0.15, 0.15)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.kdeplot(I_HR90.sim, shade=True)\n", "plt.vlines(I_HR90.sim, 0, 1)\n", "plt.vlines(I_HR90.EI+.01, 0, 10, 'r')\n", "plt.xlim([-0.15, 0.15])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result of applying Moran's I is that we conclude the map pattern is not spatially random, but instead there is a signficant spatial association in homicide rates in Texas counties in 1990.\n", "\n", "This result applies to the map as a whole, and is sometimes referred to as \"global spatial autocorrelation\". Next we turn to a local analysis where the attention shifts to detection of hot spots, cold spots and spatial outliers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Local Autocorrelation Statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the Global autocorrelation statistics, PySAL has many local autocorrelation statistics. Let's compute a local Moran statistic for the same data shown above:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "LMo_HR90 = ps.Moran_Local(data.HR90.values, W)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, instead of a single $I$ statistic, we have an *array* of local $I_i$ statistics, stored in the `.Is` attribute, and p-values from the simulation are in `p_sim`. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 1.12087323, 0.47485223, -1.22758423, 0.93868661, 0.68974296,\n", " 0.78503173, 0.71047515, 0.41060686, 0.00740368, 0.14866352]),\n", " array([ 0.013, 0.169, 0.037, 0.015, 0.002, 0.009, 0.053, 0.063,\n", " 0.489, 0.119]))" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LMo_HR90.Is[0:10], LMo_HR90.p_sim[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can adjust the number of permutations used to derive every *pseudo*-$p$ value by passing a different `permutations` argument:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "LMo_HR90 = ps.Moran_Local(data.HR90.values, W, permutations=9999)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the typical clustermap, a helpful visualization for LISA statistics is a Moran scatterplot with statistically significant LISA values highlighted. \n", "\n", "This is very simple, if we use the same strategy we used before:\n", "\n", "First, construct the spatial lag of the covariate:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Lag_HR90 = ps.lag_spatial(W, data.HR90.values)\n", "HR90 = data.HR90.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we want to plot the statistically-significant LISA values in a different color than the others. To do this, first find all of the statistically significant LISAs. Since the $p$-values are in the same order as the $I_i$ statistics, we can do this in the following way" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sigs = HR90[LMo_HR90.p_sim <= .001]\n", "W_sigs = Lag_HR90[LMo_HR90.p_sim <= .001]\n", "insigs = HR90[LMo_HR90.p_sim > .001]\n", "W_insigs = Lag_HR90[LMo_HR90.p_sim > .001]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, since we have a lot of points, we can plot the points with a statistically insignficant LISA value lighter using the `alpha` keyword. In addition, we would like to plot the statistically significant points in a dark red color. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "b,a = np.polyfit(HR90, Lag_HR90, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Matplotlib has a list of [named colors](http://matplotlib.org/examples/color/named_colors.html) and will interpret colors that are provided in hexadecimal strings:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8+fePTz65f1pTZqbTaQYHB9mz51EWLFjMyMgwPT0nxjVVFeLo0aPcccddHD9+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\nOsY3YdGiRcyde1LViaReSSE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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(sigs, W_sigs, '.', color='firebrick')\n", "plt.plot(insigs, W_insigs, '.k', alpha=.2)\n", " # dashed vert at mean of the last year's PCI\n", "plt.vlines(HR90.mean(), Lag_HR90.min(), Lag_HR90.max(), linestyle='--')\n", " # dashed horizontal at mean of lagged PCI\n", "plt.hlines(Lag_HR90.mean(), HR90.min(), HR90.max(), linestyle='--')\n", "\n", "# red line of best fit using global I as slope\n", "plt.plot(HR90, a + b*HR90, 'r')\n", "plt.text(s='$I = %.3f$' % I_HR90.I, x=50, y=15, fontsize=18)\n", "plt.title('Moran Scatterplot')\n", "plt.ylabel('Spatial Lag of HR90')\n", "plt.xlabel('HR90')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also make a LISA map of the data. " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sig = LMo_HR90.p_sim < 0.05" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "44" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sig.sum()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hotspots = LMo_HR90.q==1 * sig" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hotspots.sum()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "coldspots = LMo_HR90.q==3 * sig" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "17" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coldspots.sum()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "98 9.784698\n", "132 11.435106\n", "164 17.129154\n", "166 11.148272\n", "209 13.274924\n", "229 12.371338\n", "234 31.721863\n", "236 9.584971\n", "239 9.256549\n", "242 18.062652\n", "Name: HR90, dtype: float64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.HR90[hotspots]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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10 rows × 70 columns

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" ], "text/plain": [ " NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS STFIPS COFIPS \\\n", "98 Ellis Texas 48 139 48139 48 139 \n", "132 Hudspeth Texas 48 229 48229 48 229 \n", "164 Jeff Davis Texas 48 243 48243 48 243 \n", "166 Schleicher Texas 48 413 48413 48 413 \n", "209 Chambers Texas 48 071 48071 48 71 \n", "229 Frio Texas 48 163 48163 48 163 \n", "234 La Salle Texas 48 283 48283 48 283 \n", "236 Dimmit Texas 48 127 48127 48 127 \n", "239 Webb Texas 48 479 48479 48 479 \n", "242 Duval Texas 48 131 48131 48 131 \n", "\n", " FIPSNO SOUTH HR60 \\\n", "98 48139 1 9.217652 \n", "132 48229 1 9.971084 \n", "164 48243 1 0.000000 \n", "166 48413 1 0.000000 \n", "209 48071 1 3.211613 \n", "229 48163 1 3.296414 \n", "234 48283 1 0.000000 \n", "236 48127 1 0.000000 \n", "239 48479 1 2.057899 \n", "242 48131 1 2.487934 \n", "\n", " ... BLK90 GI59 \\\n", "98 ... 10.009746 0.325785 \n", "132 ... 0.514580 0.312484 \n", "164 ... 0.359712 0.316019 \n", "166 ... 0.903010 0.300170 \n", "209 ... 12.694146 0.299847 \n", "229 ... 1.358373 0.390980 \n", "234 ... 1.008755 0.421556 \n", "236 ... 0.575098 0.417976 \n", "239 ... 0.117083 0.382594 \n", "242 ... 0.092894 0.370217 \n", "\n", " GI69 GI79 GI89 FH60 FH70 FH80 FH90 \\\n", "98 0.365177 0.352516 0.372783 12.418831 10.5 9.076165 12.031635 \n", "132 0.373474 0.440944 0.476631 14.115899 7.7 8.959538 11.363636 \n", "164 0.367719 0.437014 0.399655 14.438503 10.1 5.970149 8.255159 \n", "166 0.387936 0.419192 0.419375 10.155148 9.8 7.222914 8.363636 \n", "209 0.374105 0.378431 0.364723 9.462037 9.2 8.568120 10.598911 \n", "229 0.463020 0.435098 0.473507 14.665445 9.4 11.842919 18.330362 \n", "234 0.482174 0.489173 0.492687 18.167702 14.1 13.052937 20.088626 \n", "236 0.452789 0.456840 0.479503 13.826043 10.1 10.944363 17.769080 \n", "239 0.443082 0.439100 0.461075 20.292824 15.5 17.419676 20.521271 \n", "242 0.427660 0.421041 0.458937 15.829478 13.2 12.803677 20.699881 \n", "\n", " geometry \n", "98 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import colors\n", "hmap = colors.ListedColormap(['grey', 'red'])\n", "f, ax = plt.subplots(1, figsize=(9, 9))\n", "tx.assign(cl=hotspots*1).plot(column='cl', categorical=True, \\\n", " k=2, cmap=hmap, linewidth=0.1, ax=ax, \\\n", " edgecolor='grey', legend=True)\n", "ax.set_axis_off()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0.000000\n", "3 0.000000\n", "4 3.651767\n", "5 0.000000\n", "13 5.669899\n", "19 3.480743\n", "21 3.675119\n", "32 2.211607\n", "33 4.718762\n", "48 5.509870\n", "51 0.000000\n", "62 3.677958\n", "69 0.000000\n", "81 0.000000\n", "87 3.699593\n", "140 8.125292\n", "233 5.304688\n", "Name: HR90, dtype: float64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.HR90[coldspots]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+Lpcr881vfvNfv/vuu4vXr19vbmtrcwAAWCwWBQBgYWEh/dFHH/27WCwmAwCk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\nIISQO3fufDw7O2s8w0tm1AOT4ZCOYm+Ac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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cmap = colors.ListedColormap(['grey', 'blue'])\n", "f, ax = plt.subplots(1, figsize=(9, 9))\n", "tx.assign(cl=coldspots*1).plot(column='cl', categorical=True, \\\n", " k=2, cmap=cmap, linewidth=0.1, ax=ax, \\\n", " edgecolor='black', legend=True)\n", "ax.set_axis_off()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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7Xiupt7fX2dPT8ytra2vswYMHtwHgb8p5Lqj6MBBBGhUxAvyF7fO/Gdn+oWy7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\nV+z1up1LVpY3NhRZSuWGwLLEMQwjsCwFc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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import colors\n", "hcmap = colors.ListedColormap(['grey', 'red','blue'])\n", "hotcold = hotspots*1 + coldspots*2\n", "f, ax = plt.subplots(1, figsize=(9, 9))\n", "tx.assign(cl=hotcold).plot(column='cl', categorical=True, \\\n", " k=2, cmap=hcmap,linewidth=0.1, ax=ax, \\\n", " edgecolor='black', legend=True)\n", "ax.set_axis_off()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/serge/anaconda2/envs/gds-scipy16/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.kdeplot(data.HR90)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/serge/anaconda2/envs/gds-scipy16/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AfUopC1CptS7MRyQBtNYvK6VOANuAl+f6hcFgqIRurX6RSAKz2cToaLR4LRDw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\nKoePjPDYfzxIR6oLS9PxrltD7yYfT+WfYbySAyD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.kdeplot(data.HR90)\n", "sns.kdeplot(data.HR80)\n", "sns.kdeplot(data.HR70)\n", "sns.kdeplot(data.HR60)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8.302494460285041" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.HR90.mean()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7.23234613355" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.HR90.median()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Exercises\n", "\n", "1. Repeat the global analysis for the years 1960, 70, 80 and compare the results to what we found in 1990.\n", "2. The local analysis can also be repeated for the other decades. How many counties are hot spots in each of the periods?\n", "3. The recent [Brexit vote](http://www.bbc.com/news/uk-politics-32810887) provides a timely example where local spatial autocorrelation analysis can provide interesting insights. One [local analysis of the vote to leave](https://gist.github.com/darribas/691ad184280590d1219ffcf9a1678030) has recently been repored. Extend this to do an analysis of the attribute `Pct_remain`. Do the hot spots for the leave vote concord with the cold spots for the remain vote?" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.1" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }