{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hands-on\n",
"## Spatial autocorrelation and Exploratory Spatial Data Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Spatial autocorrelation has to do with the degree to which the similarity in values between observations in a dataset is related to the similarity in locations of such observations. Not completely unlike the traditional correlation between two variables -which informs us about how the values in one variable change as a function of those in the other- and analogous to its time-series counterpart -which relates the value of a variable at a given point in time with those in previous periods-, spatial autocorrelation relates the value of the variable of interest in a given location, with values of the same variable in surrounding locations.\n",
"\n",
"A key idea in this context is that of spatial randomness: a situation in which the location of an observation gives no information whatsoever about its value. In other words, a variable is spatially random if it is distributed following no discernible pattern over space. Spatial autocorrelation can thus be formally defined as the \"absence of spatial randomness\", which gives room for two main classes of autocorrelation, similar to the traditional case: *positive* spatial autocorrelation, when similar values tend to group together in similar locations; and *negative* spatial autocorrelation, in cases where similar values tend to be dispersed and further apart from each other.\n",
"\n",
"In this session we will learn how to explore spatial autocorrelation in a given dataset, interrogating the data about its presence, nature, and strength. To do this, we will use a set of tools collectively known as Exploratory Spatial Data Analysis (ESDA), specifically designed for this purpose. The range of ESDA methods is very wide and spans from less sophisticated approaches like choropleths and general table querying, to more advanced and robust methodologies that include statistical inference and an explicit recognition of the geographical dimension of the data. The purpose of this session is to dip our toes into the latter group.\n",
"\n",
"ESDA techniques are usually divided into two main groups: tools to analyze *global*, and *local* spatial autocorrelation. The former consider the overall trend that the location of values follows, and makes possible statements about the degree of *clustering* in the dataset. *Do values generally follow a particular pattern in their geographical distribution*? *Are similar values closer to other similar values than we would expect from pure chance?* These are some of the questions that tools for global spatial autocorrelation allow to answer. We will practice with global spatial autocorrelation by using Moran's I statistic.\n",
"\n",
"Tools for *local* spatial autocorrelation instead focus on spatial instability: the departure of parts of a map from the general trend. The idea here is that, even though there is a given trend for the data in terms of the nature and strength of spatial association, some particular areas can diverege quite substantially from the general pattern. Regardless of the overall degree of concentration in the values, we can observe pockets of unusually high (low) values close to other high (low) values, in what we will call hot(cold)spots. Additionally, it is also possible to observe some high (low) values surrounded by low (high) values, and we will name these \"spatial outliers\". The main technique we will review in this session to explore local spatial autocorrelation is the Local Indicators of Spatial Association (LISA)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import pandas as pd\n",
"import esda\n",
"from pysal.lib import weights\n",
"from splot.esda import (\n",
" moran_scatterplot, lisa_cluster, plot_local_autocorrelation\n",
")\n",
"import geopandas as gpd\n",
"import numpy as np\n",
"import contextily as ctx\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data\n",
"\n",
"For this session, we will use the results of the 2016 referendum vote to leave the EU, at the local authority level. In particular, we will focus on the spatial distribution of the vote to Leave, which ended up winning. From a technical point of view, you will be working with polygons which have a value (the percentage of the electorate that voted to Leave the EU) attached to them.\n",
"\n",
"All the necessary data have been assembled for convenience in a single file that contains geographic information about each local authority in England, Wales and Scotland, as well as the vote attributes. The file is in the geospatial format [GeoPackage](http://www.geopackage.org/), which presents several advantages over the more traditional shapefile (chief among them, the need of a single file instead of several). The file is available as a download from the course website.\n",
"\n",
"````{margin}\n",
"```{admonition} Important\n",
"Make sure you are connected to the internet when you run this cell\n",
"```\n",
"````"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:577: RuntimeWarning: Sequential read of iterator was interrupted. Resetting iterator. This can negatively impact the performance.\n",
" for feature in features_lst:\n"
]
}
],
"source": [
"# Read the file in\n",
"br = gpd.read_file(\n",
" \"http://darribas.org/gds_course/content/data/brexit.gpkg\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"````{admonition} Alternative\n",
"Instead of reading the file directly off the web, it is possible to download it manually, store it on your computer, and read it locally. To do that, you can follow these steps:\n",
"1. Download the file by right-clicking on this link and saving the file\n",
"1. Place the file on the _same folder as the notebook_ where you intend to read it\n",
"1. Replace the code in the cell above by:\n",
"```python\n",
"br = gpd.read_file(\"brexit.gpkg\")\n",
"```\n",
"````\n",
"\n",
"Now let's index it on the local authority IDs, while keeping those as a column too:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Index: 380 entries, E06000001 to W06000024\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 objectid 380 non-null int64 \n",
" 1 lad16cd 380 non-null object \n",
" 2 lad16nm 380 non-null object \n",
" 3 Pct_Leave 380 non-null float64 \n",
" 4 geometry 380 non-null geometry\n",
"dtypes: float64(1), geometry(1), int64(1), object(2)\n",
"memory usage: 17.8+ KB\n"
]
}
],
"source": [
"# Index table on the LAD ID\n",
"br = br.set_index(\"lad16cd\", drop=False)\n",
"# Display summary\n",
"br.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preparing the data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's get a first view of the data:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot polygons\n",
"ax = br.plot(alpha=0.5, color='red');\n",
"# Add background map, expressing target CRS so the basemap can be\n",
"# reprojected (warped)\n",
"ctx.add_basemap(ax, crs=br.crs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Spatial weights matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As discused before, a spatial weights matrix is the way geographical space is formally encoded into a numerical form so it is easy for a computer (or a statistical method) to understand. We have seen already many of the conceptual ways in which we can define a spatial weights matrix, such as contiguity, distance-based, or block.\n",
"\n",
"For this example, we will show how to build a queen contiguity matrix, which considers two observations as neighbors if they share at least one point of their boundary. In other words, for a pair of local authorities in the dataset to be considered neighbours under this $W$, they will need to be sharing border or, in other words, \"touching\" each other to some degree.\n",
"\n",
"Technically speaking, we will approach building the contiguity matrix in the same way we did in Lab 5. We will begin with a `GeoDataFrame` and pass it on to the queen contiguity weights builder in `PySAL` (`ps.weights.Queen.from_dataframe`). We will also make sure our table of data is previously indexed on the local authority code, so the $W$ is also indexed on that form."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.6 s, sys: 76.4 ms, total: 2.68 s\n",
"Wall time: 2.68 s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n",
" There are 7 disconnected components.\n",
" There are 6 islands with ids: E06000046, E06000053, S12000013, S12000023, S12000027, W06000001.\n",
" warnings.warn(message)\n"
]
}
],
"source": [
"# Create the spatial weights matrix\n",
"%time w = weights.Queen.from_dataframe(br, idVariable=\"lad16cd\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, the `w` object we have just is of the same type of any other one we have created in the past. As such, we can inspect it in the same way. For example, we can check who is a neighbor of observation `E08000012`:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'E08000011': 1.0, 'E08000014': 1.0, 'E06000006': 1.0}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w['E08000012']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, the cell where we computed $W$ returned a warning on \"islands\". Remember these are islands not necessarily in the geographic sense (although some of them will be), but in the mathematical sense of the term: local authorities that are not sharing border with any other one and thus do not have any neighbors. We can inspect and map them to get a better sense of what we are dealing with:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = br.plot(color='k', figsize=(9, 9))\n",
"br.loc[w.islands, :].plot(color='red', ax=ax);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, all the islands are indeed \"real\" islands. These cases can create issues in the analysis and distort the results. There are several solutions to this situation such as connecting the islands to other observations through a different criterium (e.g. nearest neighbor), and then combining both spatial weights matrices. For convenience, we will remove them from the dataset because they are a small sample and their removal is likely not to have a large impact in the calculations.\n",
"\n",
"Technically, this amounts to a subsetting, very much like we saw in the first weeks of the course, although in this case we will use the `drop` command, which comes in very handy in these cases:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"br = br.drop(w.islands)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once we have the set of local authorities that are not an island, we need to re-calculate the weights matrix:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.08 s, sys: 58.4 ms, total: 2.14 s\n",
"Wall time: 2.15 s\n"
]
}
],
"source": [
"# Create the spatial weights matrix\n",
"# NOTE: this might take a few minutes as the geometries are\n",
"# are very detailed\n",
"%time w = weights.Queen.from_dataframe(br, idVariable=\"lad16cd\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And, finally, let us row-standardize it to make sure every row of the matrix sums up to one:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Row standardize the matrix\n",
"w.transform = 'R'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, because we have row-standardize them, the weight given to each of the three neighbors is 0.33 which, all together, sum up to one."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'E08000011': 0.3333333333333333,\n",
" 'E08000014': 0.3333333333333333,\n",
" 'E06000006': 0.3333333333333333}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w['E08000012']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Spatial lag\n",
"\n",
"Once we have the data and the spatial weights matrix ready, we can start by computing the spatial lag of the percentage of votes that went to leave the EU. Remember the spatial lag is the product of the spatial weights matrix and a given variable and that, if $W$ is row-standardized, the result amounts to the average value of the variable in the neighborhood of each observation.\n",
"\n",
"We can calculate the spatial lag for the variable `Pct_Leave` and store it directly in the main table with the following line of code:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"br['w_Pct_Leave'] = weights.lag_spatial(w, br['Pct_Leave'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us have a quick look at the resulting variable, as compared to the original one:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
lad16cd
\n",
"
Pct_Leave
\n",
"
w_Pct_Leave
\n",
"
\n",
"
\n",
"
lad16cd
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
E06000001
\n",
"
E06000001
\n",
"
69.57
\n",
"
59.640000
\n",
"
\n",
"
\n",
"
E06000002
\n",
"
E06000002
\n",
"
65.48
\n",
"
60.526667
\n",
"
\n",
"
\n",
"
E06000003
\n",
"
E06000003
\n",
"
66.19
\n",
"
60.376667
\n",
"
\n",
"
\n",
"
E06000004
\n",
"
E06000004
\n",
"
61.73
\n",
"
60.488000
\n",
"
\n",
"
\n",
"
E06000005
\n",
"
E06000005
\n",
"
56.18
\n",
"
57.430000
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" lad16cd Pct_Leave w_Pct_Leave\n",
"lad16cd \n",
"E06000001 E06000001 69.57 59.640000\n",
"E06000002 E06000002 65.48 60.526667\n",
"E06000003 E06000003 66.19 60.376667\n",
"E06000004 E06000004 61.73 60.488000\n",
"E06000005 E06000005 56.18 57.430000"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"br[['lad16cd', 'Pct_Leave', 'w_Pct_Leave']].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The way to interpret the spatial lag (`w_Pct_Leave`) for say the first observation is as follow: Hartlepool, where 69,6% of the electorate voted to leave is surrounded by neighbouring local authorities where, on average, almost 60% of the electorate also voted to leave the EU. For the purpose of illustration, we can in fact check this is correct by querying the spatial weights matrix to find out Hartepool's neighbors: "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['E06000004', 'E06000047']"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w.neighbors['E06000001']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then checking their values:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"lad16cd\n",
"E06000004 61.73\n",
"E06000047 57.55\n",
"Name: Pct_Leave, dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"neis = br.loc[w.neighbors['E06000001'], 'Pct_Leave']\n",
"neis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And the average value, which we saw in the spatial lag is 61.8, can be calculated as follows:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"59.64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"neis.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For some of the techniques we will be seeing below, it makes more sense to operate with the standardized version of a variable, rather than with the raw one. Standardizing means to substract the average value and divide by the standard deviation each observation of the column. This can be done easily with a bit of basic algebra in Python:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"br['Pct_Leave_std'] = (\n",
" br['Pct_Leave'] - br['Pct_Leave'].mean()\n",
") / br['Pct_Leave'].std()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, to be able to explore the spatial patterns of the standardized values, also called sometimes $z$ values, we need to create its spatial lag:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"br['w_Pct_Leave_std'] = weights.lag_spatial(w, br['Pct_Leave_std'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Global Spatial autocorrelation\n",
"\n",
"Global spatial autocorrelation relates to the overall geographical pattern present in the data. Statistics designed to measure this trend thus characterize a map in terms of its degree of clustering and summarize it. This summary can be visual or numerical. In this section, we will walk through an example of each of them: the Moran Plot, and Moran's I statistic of spatial autocorrelation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Moran Plot\n",
"\n",
"The moran plot is a way of visualizing a spatial dataset to explore the nature and strength of spatial autocorrelation. It is essentially a traditional scatter plot in which the variable of interest is displayed against its spatial lag. In order to be able to interpret values as above or below the mean, and their quantities in terms of standard deviations, the variable of interest is usually standardized by substracting its mean and dividing it by its standard deviation.\n",
"\n",
"Technically speaking, creating a Moran Plot is very similar to creating any other scatter plot in Python, provided we have standardized the variable and calculated its spatial lag beforehand:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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\n",
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