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kjordahl/GeoPandas grid track.ipynb

Created July 15, 2016 19:52
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GeoPandas grid track.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Demo of merging track points with a grid. The example grid is regular, but this will work with any arbitrary collection of polygons."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from geopandas import GeoSeries, GeoDataFrame\n",
"from geopandas import sjoin\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from shapely.geometry import Point, Polygon\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a regular grid of points"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x113dcb210>]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ExMCf+lHpEQ5CA8VBaBjhIDRQHIQGisPVChM+Kj3CQWigOAgNIxyEBoqD0EBxuFrB1QqI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\n+CIiM8GBLyIyE/4XnO2WeSSVCCYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113cfb910>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"N = 20\n",
"x = np.linspace(-10.0, 10.0, N+1)\n",
"y = np.linspace(-10.0, 10.0, N+1)\n",
"X, Y = np.meshgrid(x, y)\n",
"\n",
"plt.plot(X.ravel(), Y.ravel(), '.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a list of polygons representing the grid cells."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"I, J = np.meshgrid(range(N), range(N))\n",
"idx = zip(I.ravel(), J.ravel())\n",
"\n",
"upper_left = zip(X[:N, :N].ravel(), Y[:N, :N].ravel())\n",
"upper_right = zip(X[1:, :N].ravel(), Y[1:, :N].ravel())\n",
"lower_left = zip(X[:N, 1:].ravel(), Y[:N, 1:].ravel())\n",
"lower_right = zip(X[1:, 1:].ravel(), Y[1:, 1:].ravel())\n",
"points = zip(upper_left, lower_left, lower_right, upper_right)\n",
"cells = [Polygon(pts) for pts in points]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the cells in a `GeoSeries` and plot it."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x113d65690>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x113d6d710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gs = GeoSeries(cells)\n",
"gs.plot(color='gray')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate track line with a random walk until it leaves the domain."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tx, ty = [0.0, 0.0], [0.0, 0.0]\n",
"scale = 0.1\n",
"while True:\n",
" nx = tx[-1] + scale * np.random.normal()\n",
" ny = ty[-1] + scale * np.random.normal()\n",
" if nx < -10 or nx > 10 or ny < -10 or ny > 10:\n",
" break\n",
" tx.append(nx)\n",
" ty.append(ny)\n",
"t = GeoSeries([Point(x, y) for x, y in zip(tx, ty)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert our grid cells and the track to `GeoDataFrame` objects and plot them."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11af14f10>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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pu4ycEPJtSuk9QogbwLsARimlf615XsjIBYIG0nQyckrpvcf/xwghYQAvAPhr7Rghna7v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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113d6db90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"grid = GeoDataFrame({}, geometry=gs)\n",
"track = GeoDataFrame({}, geometry=t)\n",
"ax = grid.plot(color='gray')\n",
"track.plot(ax=ax, color='black')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's the spatial join, where we find the cell index for each point in the track.\n",
"\n",
"Points on the boundary of cells will return the index of all cells that they touch."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>geometry</th>\n",
" <th>index_right</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>POINT (0 0)</td>\n",
" <td>189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>POINT (0 0)</td>\n",
" <td>190</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>POINT (0 0)</td>\n",
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" <td>190</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>209</td>\n",
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" <td>POINT (-0.06116750550792893 0.08778656599356688)</td>\n",
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" <td>POINT (-0.3960097235648665 -0.6508290020255096)</td>\n",
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" <td>POINT (-0.6159593941226544 -0.7070961104629969)</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
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" <th>11047</th>\n",
" <td>POINT (0.04567327183286833 9.84502739087068)</td>\n",
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" <td>389</td>\n",
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" <td>390</td>\n",
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" <td>389</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>11063 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" geometry index_right\n",
"0 POINT (0 0) 189\n",
"0 POINT (0 0) 190\n",
"0 POINT (0 0) 209\n",
"0 POINT (0 0) 210\n",
"1 POINT (0 0) 189\n",
"1 POINT (0 0) 190\n",
"1 POINT (0 0) 209\n",
"1 POINT (0 0) 210\n",
"2 POINT (0.128081097932255 0.1855029175647382) 210\n",
"3 POINT (-0.002446857467254537 0.1627341412045903) 209\n",
"4 POINT (-0.06116750550792893 0.08778656599356688) 209\n",
"5 POINT (-0.01563912058913243 -0.001905538183850... 189\n",
"6 POINT (-0.03454186479423574 -0.07895009509970671) 189\n",
"7 POINT (0.05777227110220089 -0.121482708363661) 190\n",
"8 POINT (-0.04336074852723699 -0.07887971539965623) 189\n",
"9 POINT (-0.2040862385199124 -0.2587198007368925) 189\n",
"10 POINT (-0.2362151948996182 -0.3292472401642862) 189\n",
"11 POINT (-0.3572899130332071 -0.4419917125181131) 189\n",
"12 POINT (-0.4623907597270979 -0.4445057274771704) 189\n",
"13 POINT (-0.4159217142669286 -0.4284978718135353) 189\n",
"14 POINT (-0.5333037923901043 -0.4083595015494298) 189\n",
"15 POINT (-0.438691289969138 -0.5542132330852421) 189\n",
"16 POINT (-0.3852718180116098 -0.625782650303832) 189\n",
"17 POINT (-0.3522970001130649 -0.5985174006523056) 189\n",
"18 POINT (-0.3960097235648665 -0.6508290020255096) 189\n",
"19 POINT (-0.4606406711623388 -0.6803582089729925) 189\n",
"20 POINT (-0.568504826151878 -0.7221964199245303) 189\n",
"21 POINT (-0.5606302203280751 -0.74635001850909) 189\n",
"22 POINT (-0.6992612691859065 -0.6454553573844989) 189\n",
"23 POINT (-0.6159593941226544 -0.7070961104629969) 189\n",
"... ... ...\n",
"11027 POINT (-0.4305385110916736 9.076234368239961) 389\n",
"11028 POINT (-0.4537681559524029 9.161142986905665) 389\n",
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"11041 POINT (-0.2052381913188682 9.372774637079004) 389\n",
"11042 POINT (-0.0725611128992317 9.322555803362294) 389\n",
"11043 POINT (-0.1071371662459003 9.382525296034267) 389\n",
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"11045 POINT (-0.1521130755476637 9.536309382019757) 389\n",
"11046 POINT (-0.09021277761162953 9.623388679759447) 389\n",
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"11048 POINT (0.07352385919415433 9.871548063600704) 390\n",
"11049 POINT (-0.03412781031553851 9.73411378944385) 389\n",
"11050 POINT (0.05818398256509529 9.822144510541804) 390\n",
"11051 POINT (-0.01526728600292128 9.694190628712214) 389\n",
"11052 POINT (0.007312752895810504 9.798007971049278) 390\n",
"11053 POINT (0.0003221735375707304 9.773476537809671) 390\n",
"11054 POINT (-0.1529404673360687 9.778267263179336) 389\n",
"11055 POINT (0.01188548980026263 9.810784994069554) 390\n",
"11056 POINT (-0.02048703147162156 9.957768206221811) 389\n",
"\n",
"[11063 rows x 2 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sjoin(track, grid, how='left')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we aggregate these results to calculate the number of track points that intersected each cell."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"counts = sjoin(track, grid, how='inner').groupby('index_right').count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll insert the counts as a column in our grid object, filling with zeros for cells that are not intersected."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"grid['counts'] = counts\n",
"grid['counts'].fillna(0, inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can show the grid as a heatmap colored by the number of track points that touch each cell."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1302277d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = grid.plot(column='counts', cmap='Reds')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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