![\"drawing\"](\"figs/pysalGraphic.png\")
\n"
20 | ]
21 | },
22 | {
23 | "cell_type": "markdown",
24 | "metadata": {
25 | "slideshow": {
26 | "slide_type": "slide"
27 | }
28 | },
29 | "source": [
30 | "## Origins\n",
31 | "\n"
32 | ]
33 | },
34 | {
35 | "cell_type": "markdown",
36 | "metadata": {
37 | "slideshow": {
38 | "slide_type": "subslide"
39 | }
40 | },
41 | "source": [
42 | "## Motivation\n",
43 | "\n",
44 | "- Leverage Existing Tools\n",
45 | " - GeoDa/PySpace\n",
46 | " - STARS\n",
47 | "- Develop Core Library\n",
48 | " - spatial data analytical functions\n",
49 | " - enhanced specialization, modularity\n",
50 | " - fill void in Python scientific stack\n",
51 | "- Flexible Delivery System\n",
52 | " - interactive shells\n",
53 | " - GUI\n",
54 | " - Toolkits\n",
55 | " - webservices"
56 | ]
57 | },
58 | {
59 | "cell_type": "markdown",
60 | "metadata": {
61 | "slideshow": {
62 | "slide_type": "subslide"
63 | }
64 | },
65 | "source": [
66 | "## Release History\n",
67 | "\n"
68 | ]
69 | },
70 | {
71 | "cell_type": "markdown",
72 | "metadata": {
73 | "slideshow": {
74 | "slide_type": "slide"
75 | }
76 | },
77 | "source": [
78 | "## Use Case: shell\n",
79 | "\n"
80 | ]
81 | },
82 | {
83 | "cell_type": "markdown",
84 | "metadata": {
85 | "slideshow": {
86 | "slide_type": "subslide"
87 | }
88 | },
89 | "source": [
90 | "## Use Case: notebook\n",
91 | "\n"
92 | ]
93 | },
94 | {
95 | "cell_type": "markdown",
96 | "metadata": {
97 | "slideshow": {
98 | "slide_type": "subslide"
99 | }
100 | },
101 | "source": [
102 | "## Use Case: application\n",
103 | "\n"
104 | ]
105 | },
106 | {
107 | "cell_type": "markdown",
108 | "metadata": {
109 | "slideshow": {
110 | "slide_type": "subslide"
111 | }
112 | },
113 | "source": [
114 | "## Use Case: toolkit\n",
115 | "\n"
116 | ]
117 | },
118 | {
119 | "cell_type": "markdown",
120 | "metadata": {
121 | "slideshow": {
122 | "slide_type": "subslide"
123 | }
124 | },
125 | "source": [
126 | "## Use Case: cloud\n",
127 | "\n"
128 | ]
129 | },
130 | {
131 | "cell_type": "markdown",
132 | "metadata": {
133 | "slideshow": {
134 | "slide_type": "slide"
135 | }
136 | },
137 | "source": [
138 | "## Adoption\n",
139 | "\n",
140 | ""
141 | ]
142 | },
143 | {
144 | "cell_type": "markdown",
145 | "metadata": {
146 | "slideshow": {
147 | "slide_type": "subslide"
148 | }
149 | },
150 | "source": [
151 | "## Adoption\n",
152 | "\n",
153 | ""
154 | ]
155 | },
156 | {
157 | "cell_type": "markdown",
158 | "metadata": {
159 | "slideshow": {
160 | "slide_type": "subslide"
161 | }
162 | },
163 | "source": [
164 | "## Adoption\n",
165 | "\n",
166 | ""
167 | ]
168 | },
169 | {
170 | "cell_type": "markdown",
171 | "metadata": {
172 | "slideshow": {
173 | "slide_type": "subslide"
174 | }
175 | },
176 | "source": [
177 | "http://angrybirdsriogame.info/?d=How+To+Prepare+Your+Data+For+Machine+Learning+in+Python\n",
178 | "\n",
179 | ""
180 | ]
181 | },
182 | {
183 | "cell_type": "markdown",
184 | "metadata": {
185 | "slideshow": {
186 | "slide_type": "subslide"
187 | }
188 | },
189 | "source": [
190 | "## PySAL GitHub Stars\n",
191 | ""
192 | ]
193 | },
194 | {
195 | "cell_type": "markdown",
196 | "metadata": {
197 | "slideshow": {
198 | "slide_type": "slide"
199 | }
200 | },
201 | "source": [
202 | "## PySAL 2.0\n",
203 | ""
204 | ]
205 | },
206 | {
207 | "cell_type": "markdown",
208 | "metadata": {
209 | "slideshow": {
210 | "slide_type": "slide"
211 | }
212 | },
213 | "source": [
214 | "## Installation Options\n",
215 | "\n",
216 | ""
217 | ]
218 | }
219 | ],
220 | "metadata": {
221 | "celltoolbar": "Slideshow",
222 | "kernelspec": {
223 | "display_name": "Python 3",
224 | "language": "python",
225 | "name": "python3"
226 | },
227 | "language_info": {
228 | "codemirror_mode": {
229 | "name": "ipython",
230 | "version": 3
231 | },
232 | "file_extension": ".py",
233 | "mimetype": "text/x-python",
234 | "name": "python",
235 | "nbconvert_exporter": "python",
236 | "pygments_lexer": "ipython3",
237 | "version": "3.6.7"
238 | }
239 | },
240 | "nbformat": 4,
241 | "nbformat_minor": 2
242 | }
243 |
--------------------------------------------------------------------------------
/content/.ipynb_checkpoints/09_spatial_dynamics_analytics-checkpoint.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# Spatial dynamics analytics with [pysal/giddy](https://giddy.readthedocs.io/en/latest/index.html)\n",
8 | "\n",
9 | "* Dynamics of cross-sectional spatial autocorrelation \n",
10 | "* Modeling spatial dynamics with Markov-based methods\n",
11 | " * Classic Markov\n",
12 | " * Spatial Markov\n",
13 | " * LISA Markov"
14 | ]
15 | },
16 | {
17 | "cell_type": "code",
18 | "execution_count": null,
19 | "metadata": {},
20 | "outputs": [],
21 | "source": [
22 | "from pysal.lib import io, examples, weights"
23 | ]
24 | },
25 | {
26 | "cell_type": "code",
27 | "execution_count": null,
28 | "metadata": {},
29 | "outputs": [],
30 | "source": [
31 | "import matplotlib\n",
32 | "import numpy as np\n",
33 | "import geopandas as gpd\n",
34 | "import matplotlib.pyplot as plt\n",
35 | "%matplotlib inline\n",
36 | "import seaborn as sns\n",
37 | "import pandas as pd"
38 | ]
39 | },
40 | {
41 | "cell_type": "code",
42 | "execution_count": null,
43 | "metadata": {},
44 | "outputs": [],
45 | "source": [
46 | "pdUS_real = pd.read_csv(\"data/US_state_pci_constant09_1929_2009.csv\")\n",
47 | "data_table = gpd.read_file(examples.get_path('us48.shp'))\n",
48 | "complete_table = data_table.merge(pdUS_real,left_on='STATE_NAME',right_on='Name')\n",
49 | "complete_table.head()"
50 | ]
51 | },
52 | {
53 | "cell_type": "markdown",
54 | "metadata": {},
55 | "source": [
56 | "## Dynamics of Spatial Dependence"
57 | ]
58 | },
59 | {
60 | "cell_type": "code",
61 | "execution_count": null,
62 | "metadata": {},
63 | "outputs": [],
64 | "source": [
65 | "names = pdUS_real[\"Name\"].values\n",
66 | "years = range(1929,2010)\n",
67 | "pd_pci_real = pdUS_real[list(map(str,years))]\n",
68 | "pd_pci_real.index = names\n",
69 | "pd_pci_real.head()"
70 | ]
71 | },
72 | {
73 | "cell_type": "code",
74 | "execution_count": null,
75 | "metadata": {},
76 | "outputs": [],
77 | "source": [
78 | "pci_real = pd_pci_real.values.T\n",
79 | "pci_real.shape"
80 | ]
81 | },
82 | {
83 | "cell_type": "markdown",
84 | "metadata": {},
85 | "source": [
86 | "Prepare the spatial weight matrix - queen contiguity"
87 | ]
88 | },
89 | {
90 | "cell_type": "code",
91 | "execution_count": null,
92 | "metadata": {},
93 | "outputs": [],
94 | "source": [
95 | "w = io.open(examples.get_path(\"states48.gal\")).read()\n",
96 | "w.transform = 'R'"
97 | ]
98 | },
99 | {
100 | "cell_type": "code",
101 | "execution_count": null,
102 | "metadata": {},
103 | "outputs": [],
104 | "source": [
105 | "from pysal.explore import esda\n",
106 | "mits = [esda.moran.Moran(cs, w) for cs in pci_real]\n",
107 | "res = np.array([(mi.I, mi.EI, mi.seI_norm, mi.sim[974]) for mi in mits])\n",
108 | "fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (10,5) )\n",
109 | "ax.plot(years, res[:,0], label='Moran\\'s I')\n",
110 | "#plot(years, res[:,1], label='E[I]')\n",
111 | "ax.plot(years, res[:,1]+1.96*res[:,2], label='Upper bound',linestyle='dashed')\n",
112 | "ax.plot(years, res[:,1]-1.96*res[:,2], label='Lower bound',linestyle='dashed')\n",
113 | "ax.set_title(\"Global spatial autocorrelation for annual US per capita incomes\",fontdict={'fontsize':15})\n",
114 | "ax.set_xlim([1929,2009])\n",
115 | "ax.legend()"
116 | ]
117 | },
118 | {
119 | "cell_type": "markdown",
120 | "metadata": {},
121 | "source": [
122 | "## Markov-based methods \n",
123 | "* Role of space in shaping per capita income dynamics"
124 | ]
125 | },
126 | {
127 | "cell_type": "markdown",
128 | "metadata": {},
129 | "source": [
130 | "Spatial Markov - consider the impacts of regions' income levels on their neighbors in the following time period"
131 | ]
132 | },
133 | {
134 | "cell_type": "code",
135 | "execution_count": null,
136 | "metadata": {},
137 | "outputs": [],
138 | "source": [
139 | "mean = pci_real.mean(axis=1)\n",
140 | "mean.shape = (81,1)\n",
141 | "rpci_real = pci_real / mean"
142 | ]
143 | },
144 | {
145 | "cell_type": "markdown",
146 | "metadata": {},
147 | "source": [
148 | "Discretization"
149 | ]
150 | },
151 | {
152 | "cell_type": "code",
153 | "execution_count": null,
154 | "metadata": {},
155 | "outputs": [],
156 | "source": [
157 | "pooled_rpci_real = rpci_real.flatten()\n",
158 | "sns.kdeplot(pooled_rpci_real,shade=True)\n"
159 | ]
160 | },
161 | {
162 | "cell_type": "code",
163 | "execution_count": null,
164 | "metadata": {},
165 | "outputs": [],
166 | "source": [
167 | "pooled_n = len(pooled_rpci_real)\n",
168 | "pooled_rpci_real.sort()\n",
169 | "plt.axvline(pooled_rpci_real[int(pooled_n * 0.2)],color=\"r\")"
170 | ]
171 | },
172 | {
173 | "cell_type": "code",
174 | "execution_count": null,
175 | "metadata": {},
176 | "outputs": [],
177 | "source": [
178 | "sns.kdeplot(pooled_rpci_real,shade=True)\n",
179 | "plt.axvline(pooled_rpci_real[int(pooled_n * 0.2)],color=\"r\")\n",
180 | "plt.axvline(pooled_rpci_real[int(pooled_n * 0.4)],color=\"r\")\n",
181 | "plt.axvline(pooled_rpci_real[int(pooled_n * 0.6)],color=\"r\")\n",
182 | "plt.axvline(pooled_rpci_real[int(pooled_n * 0.8)],color=\"r\")"
183 | ]
184 | },
185 | {
186 | "cell_type": "code",
187 | "execution_count": null,
188 | "metadata": {},
189 | "outputs": [],
190 | "source": [
191 | "from pysal.explore import giddy\n",
192 | "smarkov = giddy.markov.Spatial_Markov(rpci_real.T, w, fixed = True, k = 5)"
193 | ]
194 | },
195 | {
196 | "cell_type": "code",
197 | "execution_count": null,
198 | "metadata": {},
199 | "outputs": [],
200 | "source": [
201 | "giddy.markov.Spatial_Markov?"
202 | ]
203 | },
204 | {
205 | "cell_type": "code",
206 | "execution_count": null,
207 | "metadata": {},
208 | "outputs": [],
209 | "source": [
210 | "smarkov.summary()"
211 | ]
212 | },
213 | {
214 | "cell_type": "markdown",
215 | "metadata": {},
216 | "source": [
217 | "Steady state distributions"
218 | ]
219 | },
220 | {
221 | "cell_type": "code",
222 | "execution_count": null,
223 | "metadata": {},
224 | "outputs": [],
225 | "source": [
226 | "smarkov.s"
227 | ]
228 | },
229 | {
230 | "cell_type": "code",
231 | "execution_count": null,
232 | "metadata": {},
233 | "outputs": [],
234 | "source": [
235 | "smarkov.S"
236 | ]
237 | },
238 | {
239 | "cell_type": "code",
240 | "execution_count": null,
241 | "metadata": {},
242 | "outputs": [],
243 | "source": [
244 | "smarkov.F"
245 | ]
246 | },
247 | {
248 | "cell_type": "markdown",
249 | "metadata": {},
250 | "source": [
251 | "LISA Markov - consider the joint transitions of regions' and neighbors' income levels\n",
252 | "\n",
253 | "* Markov state space={1(HH), 2(LH), 3(LL), 4(HL)}"
254 | ]
255 | },
256 | {
257 | "cell_type": "code",
258 | "execution_count": null,
259 | "metadata": {},
260 | "outputs": [],
261 | "source": [
262 | "giddy.markov.LISA_Markov?"
263 | ]
264 | },
265 | {
266 | "cell_type": "code",
267 | "execution_count": null,
268 | "metadata": {},
269 | "outputs": [],
270 | "source": [
271 | "lm = giddy.markov.LISA_Markov(pci_real.T, w)\n",
272 | "lm.classes"
273 | ]
274 | },
275 | {
276 | "cell_type": "code",
277 | "execution_count": null,
278 | "metadata": {},
279 | "outputs": [],
280 | "source": [
281 | "lm.p"
282 | ]
283 | },
284 | {
285 | "cell_type": "code",
286 | "execution_count": null,
287 | "metadata": {},
288 | "outputs": [],
289 | "source": [
290 | "lm.steady_state"
291 | ]
292 | },
293 | {
294 | "cell_type": "code",
295 | "execution_count": null,
296 | "metadata": {},
297 | "outputs": [],
298 | "source": [
299 | "giddy.ergodic.fmpt(lm.p)"
300 | ]
301 | },
302 | {
303 | "cell_type": "code",
304 | "execution_count": null,
305 | "metadata": {},
306 | "outputs": [],
307 | "source": [
308 | "lm.chi_2"
309 | ]
310 | },
311 | {
312 | "cell_type": "code",
313 | "execution_count": null,
314 | "metadata": {},
315 | "outputs": [],
316 | "source": []
317 | },
318 | {
319 | "cell_type": "code",
320 | "execution_count": null,
321 | "metadata": {},
322 | "outputs": [],
323 | "source": []
324 | },
325 | {
326 | "cell_type": "code",
327 | "execution_count": null,
328 | "metadata": {},
329 | "outputs": [],
330 | "source": []
331 | }
332 | ],
333 | "metadata": {
334 | "kernelspec": {
335 | "display_name": "Python 3",
336 | "language": "python",
337 | "name": "python3"
338 | },
339 | "language_info": {
340 | "codemirror_mode": {
341 | "name": "ipython",
342 | "version": 3
343 | },
344 | "file_extension": ".py",
345 | "mimetype": "text/x-python",
346 | "name": "python",
347 | "nbconvert_exporter": "python",
348 | "pygments_lexer": "ipython3",
349 | "version": "3.6.7"
350 | }
351 | },
352 | "nbformat": 4,
353 | "nbformat_minor": 2
354 | }
355 |
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/content/.ipynb_checkpoints/11_taz_example-checkpoint.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "### Weights operations"
8 | ]
9 | },
10 | {
11 | "cell_type": "code",
12 | "execution_count": null,
13 | "metadata": {},
14 | "outputs": [],
15 | "source": [
16 | "import numpy as np\n",
17 | "import libpysal as ps\n",
18 | "import random as rdm\n",
19 | "import geopandas as gpd\n",
20 | "from matplotlib.collections import LineCollection\n",
21 | "import matplotlib.pyplot as plt\n",
22 | "%matplotlib inline"
23 | ]
24 | },
25 | {
26 | "cell_type": "code",
27 | "execution_count": null,
28 | "metadata": {},
29 | "outputs": [],
30 | "source": [
31 | "shp = gpd.read_file(ps.examples.get_path(\"taz.shp\"))\n",
32 | "shp.head()"
33 | ]
34 | },
35 | {
36 | "cell_type": "code",
37 | "execution_count": null,
38 | "metadata": {},
39 | "outputs": [],
40 | "source": [
41 | "shp.plot(figsize=(15,15),color='white', edgecolor='grey')\n",
42 | "plt.axis('off')"
43 | ]
44 | },
45 | {
46 | "cell_type": "markdown",
47 | "metadata": {},
48 | "source": [
49 | "## County as unique values"
50 | ]
51 | },
52 | {
53 | "cell_type": "code",
54 | "execution_count": null,
55 | "metadata": {},
56 | "outputs": [],
57 | "source": [
58 | "shp[\"CNTY\"].describe()"
59 | ]
60 | },
61 | {
62 | "cell_type": "code",
63 | "execution_count": null,
64 | "metadata": {},
65 | "outputs": [],
66 | "source": [
67 | "fig, ax = plt.subplots(figsize=(15,15))\n",
68 | "ax.set_aspect('equal')\n",
69 | "shp.plot(ax=ax,color='white', edgecolor='black')\n",
70 | "shp.plot(column=\"CNTY\", ax=ax, categorical=True,cmap=\"Pastel1\",alpha=0.6)\n",
71 | "ax.set_axis_off()"
72 | ]
73 | },
74 | {
75 | "cell_type": "markdown",
76 | "metadata": {},
77 | "source": [
78 | "### Construct a Rook contiguity weight"
79 | ]
80 | },
81 | {
82 | "cell_type": "code",
83 | "execution_count": null,
84 | "metadata": {},
85 | "outputs": [],
86 | "source": [
87 | "wrook = ps.weights.Rook.from_dataframe(shp)"
88 | ]
89 | },
90 | {
91 | "cell_type": "code",
92 | "execution_count": null,
93 | "metadata": {},
94 | "outputs": [],
95 | "source": [
96 | "def w2line_graph(w, shp):\n",
97 | " segments = []\n",
98 | " centroids = shp.centroid.values\n",
99 | " for i in w.id2i:\n",
100 | " origin = np.array(centroids[i].coords)[0] \n",
101 | " for j in w.neighbors[i]:\n",
102 | " dest = np.array(centroids[j].coords)[0]\n",
103 | " ij = [i,j]\n",
104 | " ij.sort()\n",
105 | " segments.append([origin, dest])\n",
106 | "\n",
107 | " return segments "
108 | ]
109 | },
110 | {
111 | "cell_type": "code",
112 | "execution_count": null,
113 | "metadata": {},
114 | "outputs": [],
115 | "source": [
116 | "segs = w2line_graph(wrook, shp)\n",
117 | "fig, ax = plt.subplots(figsize=(15,15))\n",
118 | "ax.set_aspect('equal')\n",
119 | "shp.plot(ax=ax, color='white', edgecolor='grey')\n",
120 | "segs_plot = LineCollection(np.array(segs),colors=\"red\")\n",
121 | "segs_plot.set_linewidth(0.20)\n",
122 | "ax.add_collection(segs_plot)\n",
123 | "ax.set_axis_off()"
124 | ]
125 | },
126 | {
127 | "cell_type": "markdown",
128 | "metadata": {},
129 | "source": [
130 | "## Intersection weights"
131 | ]
132 | },
133 | {
134 | "cell_type": "code",
135 | "execution_count": null,
136 | "metadata": {},
137 | "outputs": [],
138 | "source": [
139 | "ps.weights.block_weights?"
140 | ]
141 | },
142 | {
143 | "cell_type": "code",
144 | "execution_count": null,
145 | "metadata": {},
146 | "outputs": [],
147 | "source": [
148 | "wb = ps.weights.block_weights(shp[\"CNTY\"])"
149 | ]
150 | },
151 | {
152 | "cell_type": "code",
153 | "execution_count": null,
154 | "metadata": {
155 | "scrolled": true
156 | },
157 | "outputs": [],
158 | "source": [
159 | "wint = ps.weights.w_intersection(wb, wrook)"
160 | ]
161 | },
162 | {
163 | "cell_type": "code",
164 | "execution_count": null,
165 | "metadata": {},
166 | "outputs": [],
167 | "source": [
168 | "segs = w2line_graph(wint, shp)\n",
169 | "fig, ax = plt.subplots(figsize=(15,15))\n",
170 | "ax.set_aspect('equal')\n",
171 | "shp.plot(ax=ax, color='white', edgecolor='grey')\n",
172 | "segs_plot = LineCollection(np.array(segs),colors=\"red\")\n",
173 | "segs_plot.set_linewidth(0.20)\n",
174 | "ax.add_collection(segs_plot)\n",
175 | "ax.set_axis_off()"
176 | ]
177 | },
178 | {
179 | "cell_type": "code",
180 | "execution_count": null,
181 | "metadata": {},
182 | "outputs": [],
183 | "source": [
184 | "fig, ax = plt.subplots(figsize=(15,15))\n",
185 | "ax.set_aspect('equal')\n",
186 | "shp.plot(ax=ax,color='white', edgecolor='black')\n",
187 | "shp.plot(column=\"CNTY\", ax=ax, categorical=True,cmap=\"Pastel1\",alpha=0.6)\n",
188 | "segs_plot = LineCollection(np.array(segs),colors=\"red\")\n",
189 | "segs_plot.set_linewidth(0.20)\n",
190 | "ax.add_collection(segs_plot)\n",
191 | "ax.set_axis_off()"
192 | ]
193 | },
194 | {
195 | "cell_type": "code",
196 | "execution_count": null,
197 | "metadata": {},
198 | "outputs": [],
199 | "source": []
200 | },
201 | {
202 | "cell_type": "code",
203 | "execution_count": null,
204 | "metadata": {},
205 | "outputs": [],
206 | "source": []
207 | },
208 | {
209 | "cell_type": "code",
210 | "execution_count": null,
211 | "metadata": {},
212 | "outputs": [],
213 | "source": []
214 | },
215 | {
216 | "cell_type": "code",
217 | "execution_count": null,
218 | "metadata": {},
219 | "outputs": [],
220 | "source": []
221 | }
222 | ],
223 | "metadata": {
224 | "anaconda-cloud": {},
225 | "kernelspec": {
226 | "display_name": "Python 3",
227 | "language": "python",
228 | "name": "python3"
229 | },
230 | "language_info": {
231 | "codemirror_mode": {
232 | "name": "ipython",
233 | "version": 3
234 | },
235 | "file_extension": ".py",
236 | "mimetype": "text/x-python",
237 | "name": "python",
238 | "nbconvert_exporter": "python",
239 | "pygments_lexer": "ipython3",
240 | "version": "3.6.6"
241 | }
242 | },
243 | "nbformat": 4,
244 | "nbformat_minor": 2
245 | }
246 |
--------------------------------------------------------------------------------
/content/.ipynb_checkpoints/18_gol-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# Conway's Game of Life with PySAL"
8 | ]
9 | },
10 | {
11 | "cell_type": "markdown",
12 | "metadata": {},
13 | "source": [
14 | "## Rules ##\n",
15 | "\n",
16 | " 1. A cell that is currently alive and that has two **or** three live neighbors stays alive\n",
17 | " 2. A cell that is currently dead with **exactly** three live neighbors comes alive \n",
18 | " 3. All other cells remain dead, or die due to loneliness (less than 2 neighbors) or overcrowding (more than 3 neighbors)"
19 | ]
20 | },
21 | {
22 | "cell_type": "code",
23 | "execution_count": null,
24 | "metadata": {},
25 | "outputs": [],
26 | "source": [
27 | "import libpysal as ps\n",
28 | "from scipy.stats import bernoulli\n",
29 | "%pylab inline"
30 | ]
31 | },
32 | {
33 | "cell_type": "code",
34 | "execution_count": null,
35 | "metadata": {},
36 | "outputs": [],
37 | "source": [
38 | "k = 8 # dimension of lattice"
39 | ]
40 | },
41 | {
42 | "cell_type": "code",
43 | "execution_count": null,
44 | "metadata": {},
45 | "outputs": [],
46 | "source": [
47 | "w = ps.weights.lat2W(k,k,rook=False)"
48 | ]
49 | },
50 | {
51 | "cell_type": "code",
52 | "execution_count": null,
53 | "metadata": {},
54 | "outputs": [],
55 | "source": [
56 | "w.n"
57 | ]
58 | },
59 | {
60 | "cell_type": "code",
61 | "execution_count": null,
62 | "metadata": {},
63 | "outputs": [],
64 | "source": [
65 | "w.neighbors[0]"
66 | ]
67 | },
68 | {
69 | "cell_type": "code",
70 | "execution_count": null,
71 | "metadata": {},
72 | "outputs": [],
73 | "source": [
74 | "w.neighbors[45]"
75 | ]
76 | },
77 | {
78 | "cell_type": "code",
79 | "execution_count": null,
80 | "metadata": {},
81 | "outputs": [],
82 | "source": [
83 | "w.weights[0]"
84 | ]
85 | },
86 | {
87 | "cell_type": "code",
88 | "execution_count": null,
89 | "metadata": {},
90 | "outputs": [],
91 | "source": [
92 | "y = bernoulli.rvs(0.45,size=w.n)"
93 | ]
94 | },
95 | {
96 | "cell_type": "code",
97 | "execution_count": null,
98 | "metadata": {},
99 | "outputs": [],
100 | "source": [
101 | "y"
102 | ]
103 | },
104 | {
105 | "cell_type": "code",
106 | "execution_count": null,
107 | "metadata": {},
108 | "outputs": [],
109 | "source": [
110 | "wy = ps.weights.lag_spatial(w,y)"
111 | ]
112 | },
113 | {
114 | "cell_type": "code",
115 | "execution_count": null,
116 | "metadata": {},
117 | "outputs": [],
118 | "source": [
119 | "wy"
120 | ]
121 | },
122 | {
123 | "cell_type": "markdown",
124 | "metadata": {},
125 | "source": [
126 | "## Rules ##\n",
127 | "\n",
128 | " 1. A cell that is currently alive and that has two **or** three live neighbors stays alive\n",
129 | " 2. A cell that is currently dead with **exactly** three live neighbors comes alive \n",
130 | " 3. All other cells remain dead, or die due to loneliness (less than 2 neighbors) or overcrowding (more than 3 neighbors)"
131 | ]
132 | },
133 | {
134 | "cell_type": "markdown",
135 | "metadata": {},
136 | "source": [
137 | "Rule 1: find live cells and count their neighbors"
138 | ]
139 | },
140 | {
141 | "cell_type": "code",
142 | "execution_count": null,
143 | "metadata": {},
144 | "outputs": [],
145 | "source": [
146 | "ywy = y*wy"
147 | ]
148 | },
149 | {
150 | "cell_type": "code",
151 | "execution_count": null,
152 | "metadata": {},
153 | "outputs": [],
154 | "source": [
155 | "lw23 = np.nonzero( (ywy==2) + (ywy==3) )"
156 | ]
157 | },
158 | {
159 | "cell_type": "code",
160 | "execution_count": null,
161 | "metadata": {},
162 | "outputs": [],
163 | "source": [
164 | "lw23"
165 | ]
166 | },
167 | {
168 | "cell_type": "markdown",
169 | "metadata": {},
170 | "source": [
171 | "Rule 2: find dead cells with exactly 3 neighbors"
172 | ]
173 | },
174 | {
175 | "cell_type": "code",
176 | "execution_count": null,
177 | "metadata": {},
178 | "outputs": [],
179 | "source": [
180 | "dw3 = (1-y) * wy"
181 | ]
182 | },
183 | {
184 | "cell_type": "code",
185 | "execution_count": null,
186 | "metadata": {},
187 | "outputs": [],
188 | "source": [
189 | "np.nonzero(dw3==3)"
190 | ]
191 | },
192 | {
193 | "cell_type": "markdown",
194 | "metadata": {},
195 | "source": [
196 | "Rules 1 and 2 give us the surviving cells"
197 | ]
198 | },
199 | {
200 | "cell_type": "code",
201 | "execution_count": null,
202 | "metadata": {},
203 | "outputs": [],
204 | "source": [
205 | "live_next = np.nonzero( (ywy==2) + (ywy==3) + (dw3==3) )"
206 | ]
207 | },
208 | {
209 | "cell_type": "code",
210 | "execution_count": null,
211 | "metadata": {},
212 | "outputs": [],
213 | "source": [
214 | "live_next"
215 | ]
216 | },
217 | {
218 | "cell_type": "code",
219 | "execution_count": null,
220 | "metadata": {},
221 | "outputs": [],
222 | "source": [
223 | "y[live_next]"
224 | ]
225 | },
226 | {
227 | "cell_type": "markdown",
228 | "metadata": {},
229 | "source": [
230 | "So we see that in the future, some dead cells will becoming alive. But what about live cells now that die in the next period?\n",
231 | "\n",
232 | "We know that they will be dead next period. Allocate an array with zeros for the next period and assign the live cells. Everyone else is dead."
233 | ]
234 | },
235 | {
236 | "cell_type": "code",
237 | "execution_count": null,
238 | "metadata": {},
239 | "outputs": [],
240 | "source": [
241 | "y1 = np.zeros_like(y)"
242 | ]
243 | },
244 | {
245 | "cell_type": "code",
246 | "execution_count": null,
247 | "metadata": {},
248 | "outputs": [],
249 | "source": [
250 | "y1[live_next] = 1"
251 | ]
252 | },
253 | {
254 | "cell_type": "code",
255 | "execution_count": null,
256 | "metadata": {},
257 | "outputs": [],
258 | "source": [
259 | "y1"
260 | ]
261 | },
262 | {
263 | "cell_type": "markdown",
264 | "metadata": {},
265 | "source": [
266 | "We would then iterate these steps for future generations. \n",
267 | "\n",
268 | "Let's place the process inside a function for reuse later"
269 | ]
270 | },
271 | {
272 | "cell_type": "code",
273 | "execution_count": null,
274 | "metadata": {},
275 | "outputs": [],
276 | "source": [
277 | "def generation(y,w):\n",
278 | " y1 = np.zeros_like(y)\n",
279 | " wy = ps.weights.lag_spatial(w,y)\n",
280 | " ywy = y * wy\n",
281 | " live_next = np.nonzero( ( ywy == 2 ) + ( ywy == 3 ) + ( ( 1-y ) * wy == 3 ) )\n",
282 | " y1[live_next] = 1\n",
283 | " return y1\n",
284 | " "
285 | ]
286 | },
287 | {
288 | "cell_type": "markdown",
289 | "metadata": {},
290 | "source": [
291 | "Try this out on some fresh data."
292 | ]
293 | },
294 | {
295 | "cell_type": "code",
296 | "execution_count": null,
297 | "metadata": {},
298 | "outputs": [],
299 | "source": [
300 | "y = bernoulli.rvs(0.45,size=w.n)"
301 | ]
302 | },
303 | {
304 | "cell_type": "code",
305 | "execution_count": null,
306 | "metadata": {},
307 | "outputs": [],
308 | "source": [
309 | "y1 = generation(y,w)"
310 | ]
311 | },
312 | {
313 | "cell_type": "code",
314 | "execution_count": null,
315 | "metadata": {},
316 | "outputs": [],
317 | "source": [
318 | "y1"
319 | ]
320 | },
321 | {
322 | "cell_type": "code",
323 | "execution_count": null,
324 | "metadata": {},
325 | "outputs": [],
326 | "source": [
327 | "y"
328 | ]
329 | },
330 | {
331 | "cell_type": "code",
332 | "execution_count": null,
333 | "metadata": {},
334 | "outputs": [],
335 | "source": [
336 | "y2 = generation(y1,w)"
337 | ]
338 | },
339 | {
340 | "cell_type": "code",
341 | "execution_count": null,
342 | "metadata": {},
343 | "outputs": [],
344 | "source": [
345 | "y2"
346 | ]
347 | },
348 | {
349 | "cell_type": "markdown",
350 | "metadata": {},
351 | "source": [
352 | "One interesting initial pattern is the so called R-pentomino.\n",
353 | "\n",
354 | "We will create one and then run a simulation to see how the solutions evolve."
355 | ]
356 | },
357 | {
358 | "cell_type": "code",
359 | "execution_count": null,
360 | "metadata": {},
361 | "outputs": [],
362 | "source": [
363 | "ngen=350\n",
364 | "k = 50\n",
365 | "w = ps.weights.lat2W(k, k, rook=False)\n",
366 | "#y = bernoulli.rvs(0.45,size=w.n)\n",
367 | "y = np.zeros((w.n,))\n",
368 | "#R-pentomino pattern http://conwaylife.com/w/index.php?title=R-pentomino\n",
369 | "ymat = np.zeros((k,k))\n",
370 | "mid = k//2\n",
371 | "ymat[mid,mid] = 1\n",
372 | "ymat[mid-1,mid ] = 1 # left\n",
373 | "ymat[mid,mid+1] = 1 # top\n",
374 | "ymat[mid+1, mid+1] = 1 # top right\n",
375 | "ymat[mid, mid-1] = 1 # bottom\n",
376 | "\n"
377 | ]
378 | },
379 | {
380 | "cell_type": "code",
381 | "execution_count": null,
382 | "metadata": {},
383 | "outputs": [],
384 | "source": [
385 | "\n",
386 | "imshow(ymat,cmap='Greys' )"
387 | ]
388 | },
389 | {
390 | "cell_type": "code",
391 | "execution_count": null,
392 | "metadata": {},
393 | "outputs": [],
394 | "source": [
395 | "y = ymat.reshape((k*k,1))"
396 | ]
397 | },
398 | {
399 | "cell_type": "code",
400 | "execution_count": null,
401 | "metadata": {},
402 | "outputs": [],
403 | "source": [
404 | "results = {}\n",
405 | "for i in range(ngen):\n",
406 | " y1 = generation(y,w)\n",
407 | " results[i] = y\n",
408 | " y = y1\n",
409 | " "
410 | ]
411 | },
412 | {
413 | "cell_type": "code",
414 | "execution_count": null,
415 | "metadata": {},
416 | "outputs": [],
417 | "source": [
418 | "ngen"
419 | ]
420 | },
421 | {
422 | "cell_type": "markdown",
423 | "metadata": {},
424 | "source": [
425 | "Create some graphs to look at both the the individual maps from every 10th generation and then plot the aggregate population size by generation."
426 | ]
427 | },
428 | {
429 | "cell_type": "code",
430 | "execution_count": null,
431 | "metadata": {},
432 | "outputs": [],
433 | "source": [
434 | "generations = np.zeros((ngen,))\n",
435 | "living = np.zeros_like(generations)\n",
436 | "keys = list(results.keys())\n",
437 | "keys.sort()\n",
438 | "for i in keys:\n",
439 | " generations[i] = i\n",
440 | " living[i] = results[i].sum()\n",
441 | " if not i%50:\n",
442 | " ymat = results[i]\n",
443 | " ymat.shape = (k,k)\n",
444 | " imshow(ymat,cmap='Greys', interpolation='nearest')\n",
445 | " title(\"Generation %d\"%i)\n",
446 | " show()\n",
447 | " "
448 | ]
449 | },
450 | {
451 | "cell_type": "code",
452 | "execution_count": null,
453 | "metadata": {},
454 | "outputs": [],
455 | "source": [
456 | "generations.shape"
457 | ]
458 | },
459 | {
460 | "cell_type": "code",
461 | "execution_count": null,
462 | "metadata": {},
463 | "outputs": [],
464 | "source": [
465 | "plot(generations,living)"
466 | ]
467 | },
468 | {
469 | "cell_type": "code",
470 | "execution_count": null,
471 | "metadata": {},
472 | "outputs": [],
473 | "source": [
474 | "ymat = results[ngen-1]"
475 | ]
476 | },
477 | {
478 | "cell_type": "code",
479 | "execution_count": null,
480 | "metadata": {},
481 | "outputs": [],
482 | "source": [
483 | "ymat.shape"
484 | ]
485 | },
486 | {
487 | "cell_type": "code",
488 | "execution_count": null,
489 | "metadata": {},
490 | "outputs": [],
491 | "source": [
492 | "ymat.shape=(50,50)"
493 | ]
494 | },
495 | {
496 | "cell_type": "code",
497 | "execution_count": null,
498 | "metadata": {},
499 | "outputs": [],
500 | "source": [
501 | "imshow(ymat, cmap='Greys', interpolation='nearest')\n",
502 | "title(\"Last Generation\")"
503 | ]
504 | },
505 | {
506 | "cell_type": "code",
507 | "execution_count": null,
508 | "metadata": {},
509 | "outputs": [],
510 | "source": [
511 | "ymat = results[0]"
512 | ]
513 | },
514 | {
515 | "cell_type": "code",
516 | "execution_count": null,
517 | "metadata": {},
518 | "outputs": [],
519 | "source": [
520 | "ymat.shape = (k,k)"
521 | ]
522 | },
523 | {
524 | "cell_type": "code",
525 | "execution_count": null,
526 | "metadata": {},
527 | "outputs": [],
528 | "source": [
529 | "imshow(ymat, cmap='Greys', interpolation='nearest')\n",
530 | "title('First Generation: R-pentomino')"
531 | ]
532 | },
533 | {
534 | "cell_type": "code",
535 | "execution_count": null,
536 | "metadata": {},
537 | "outputs": [],
538 | "source": [
539 | "ymat = results[ngen-2]\n",
540 | "ymat.shape=(50,50)\n",
541 | "imshow(ymat, cmap='Greys', interpolation='nearest')\n",
542 | "title(\"Penultimate Generation\")"
543 | ]
544 | },
545 | {
546 | "cell_type": "code",
547 | "execution_count": null,
548 | "metadata": {},
549 | "outputs": [],
550 | "source": [
551 | "ymat = results[ngen-1]\n",
552 | "ymat.shape=(50,50)\n",
553 | "imshow(ymat, cmap='Greys', interpolation='nearest')\n",
554 | "title(\"Final Generation\")"
555 | ]
556 | },
557 | {
558 | "cell_type": "code",
559 | "execution_count": null,
560 | "metadata": {},
561 | "outputs": [],
562 | "source": []
563 | },
564 | {
565 | "cell_type": "code",
566 | "execution_count": null,
567 | "metadata": {},
568 | "outputs": [],
569 | "source": []
570 | },
571 | {
572 | "cell_type": "code",
573 | "execution_count": null,
574 | "metadata": {},
575 | "outputs": [],
576 | "source": []
577 | },
578 | {
579 | "cell_type": "code",
580 | "execution_count": null,
581 | "metadata": {},
582 | "outputs": [],
583 | "source": []
584 | },
585 | {
586 | "cell_type": "code",
587 | "execution_count": null,
588 | "metadata": {},
589 | "outputs": [],
590 | "source": []
591 | }
592 | ],
593 | "metadata": {
594 | "anaconda-cloud": {},
595 | "kernelspec": {
596 | "display_name": "Python 3",
597 | "language": "python",
598 | "name": "python3"
599 | },
600 | "language_info": {
601 | "codemirror_mode": {
602 | "name": "ipython",
603 | "version": 3
604 | },
605 | "file_extension": ".py",
606 | "mimetype": "text/x-python",
607 | "name": "python",
608 | "nbconvert_exporter": "python",
609 | "pygments_lexer": "ipython3",
610 | "version": "3.6.6"
611 | }
612 | },
613 | "nbformat": 4,
614 | "nbformat_minor": 2
615 | }
616 |
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/content/00_introduction.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {
6 | "slideshow": {
7 | "slide_type": "slide"
8 | }
9 | },
10 | "source": [
11 | "# Geospatial Data Science with PySAL @FOSS4G2019\n",
12 | "\n",
13 | "Sergio Rey \n",
14 | "Stefanie Lumnitz \n",
15 | "Elijah Knaap \n",
16 | "Wei Kang \n",
17 | "\n",
18 | "\n",
19 | "\n"
20 | ]
21 | },
22 | {
23 | "cell_type": "markdown",
24 | "metadata": {
25 | "slideshow": {
26 | "slide_type": "slide"
27 | }
28 | },
29 | "source": [
30 | "## Origins\n",
31 | "\n"
32 | ]
33 | },
34 | {
35 | "cell_type": "markdown",
36 | "metadata": {
37 | "slideshow": {
38 | "slide_type": "subslide"
39 | }
40 | },
41 | "source": [
42 | "## Motivation\n",
43 | "\n",
44 | "- Leverage Existing Tools\n",
45 | " - GeoDa/PySpace\n",
46 | " - STARS\n",
47 | "- Develop Core Library\n",
48 | " - spatial data analytical functions\n",
49 | " - enhanced specialization, modularity\n",
50 | " - fill void in Python scientific stack\n",
51 | "- Flexible Delivery System\n",
52 | " - interactive shells\n",
53 | " - GUI\n",
54 | " - Toolkits\n",
55 | " - webservices"
56 | ]
57 | },
58 | {
59 | "cell_type": "markdown",
60 | "metadata": {
61 | "slideshow": {
62 | "slide_type": "subslide"
63 | }
64 | },
65 | "source": [
66 | "## Release History\n",
67 | "\n"
68 | ]
69 | },
70 | {
71 | "cell_type": "markdown",
72 | "metadata": {
73 | "slideshow": {
74 | "slide_type": "slide"
75 | }
76 | },
77 | "source": [
78 | "## Use Case: shell\n",
79 | "\n"
80 | ]
81 | },
82 | {
83 | "cell_type": "markdown",
84 | "metadata": {
85 | "slideshow": {
86 | "slide_type": "subslide"
87 | }
88 | },
89 | "source": [
90 | "## Use Case: notebook\n",
91 | "\n"
92 | ]
93 | },
94 | {
95 | "cell_type": "markdown",
96 | "metadata": {
97 | "slideshow": {
98 | "slide_type": "subslide"
99 | }
100 | },
101 | "source": [
102 | "## Use Case: application\n",
103 | "\n"
104 | ]
105 | },
106 | {
107 | "cell_type": "markdown",
108 | "metadata": {
109 | "slideshow": {
110 | "slide_type": "subslide"
111 | }
112 | },
113 | "source": [
114 | "## Use Case: toolkit\n",
115 | "\n"
116 | ]
117 | },
118 | {
119 | "cell_type": "markdown",
120 | "metadata": {
121 | "slideshow": {
122 | "slide_type": "subslide"
123 | }
124 | },
125 | "source": [
126 | "## Use Case: cloud\n",
127 | "\n"
128 | ]
129 | },
130 | {
131 | "cell_type": "markdown",
132 | "metadata": {
133 | "slideshow": {
134 | "slide_type": "slide"
135 | }
136 | },
137 | "source": [
138 | "## Adoption\n",
139 | "\n",
140 | ""
141 | ]
142 | },
143 | {
144 | "cell_type": "markdown",
145 | "metadata": {
146 | "slideshow": {
147 | "slide_type": "subslide"
148 | }
149 | },
150 | "source": [
151 | "## Adoption\n",
152 | "\n",
153 | ""
154 | ]
155 | },
156 | {
157 | "cell_type": "markdown",
158 | "metadata": {
159 | "slideshow": {
160 | "slide_type": "subslide"
161 | }
162 | },
163 | "source": [
164 | "## Adoption\n",
165 | "\n",
166 | ""
167 | ]
168 | },
169 | {
170 | "cell_type": "markdown",
171 | "metadata": {
172 | "slideshow": {
173 | "slide_type": "subslide"
174 | }
175 | },
176 | "source": [
177 | "http://angrybirdsriogame.info/?d=How+To+Prepare+Your+Data+For+Machine+Learning+in+Python\n",
178 | "\n",
179 | ""
180 | ]
181 | },
182 | {
183 | "cell_type": "markdown",
184 | "metadata": {
185 | "slideshow": {
186 | "slide_type": "subslide"
187 | }
188 | },
189 | "source": [
190 | "## PySAL GitHub Stars\n",
191 | ""
192 | ]
193 | },
194 | {
195 | "cell_type": "markdown",
196 | "metadata": {
197 | "slideshow": {
198 | "slide_type": "slide"
199 | }
200 | },
201 | "source": [
202 | "## PySAL 2.0\n",
203 | ""
204 | ]
205 | },
206 | {
207 | "cell_type": "markdown",
208 | "metadata": {
209 | "slideshow": {
210 | "slide_type": "slide"
211 | }
212 | },
213 | "source": [
214 | "## Installation Options\n",
215 | "\n",
216 | ""
217 | ]
218 | }
219 | ],
220 | "metadata": {
221 | "celltoolbar": "Slideshow",
222 | "kernelspec": {
223 | "display_name": "Python 3",
224 | "language": "python",
225 | "name": "python3"
226 | },
227 | "language_info": {
228 | "codemirror_mode": {
229 | "name": "ipython",
230 | "version": 3
231 | },
232 | "file_extension": ".py",
233 | "mimetype": "text/x-python",
234 | "name": "python",
235 | "nbconvert_exporter": "python",
236 | "pygments_lexer": "ipython3",
237 | "version": "3.7.3"
238 | }
239 | },
240 | "nbformat": 4,
241 | "nbformat_minor": 2
242 | }
243 |
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/content/data/CPI1913-2016.csv:
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1 | year,cpi
1913,9.9
1914,10
1915,10.1
1916,10.9
1917,12.8
1918,15.1
1919,17.3
1920,20
1921,17.9
1922,16.8
1923,17.1
1924,17.1
1925,17.5
1926,17.7
1927,17.4
1928,17.1
1929,17.1
1930,16.7
1931,15.2
1932,13.7
1933,13
1934,13.4
1935,13.7
1936,13.9
1937,14.4
1938,14.1
1939,13.9
1940,14
1941,14.7
1942,16.3
1943,17.3
1944,17.6
1945,18
1946,19.5
1947,22.3
1948,24.1
1949,23.8
1950,24.1
1951,26
1952,26.5
1953,26.7
1954,26.9
1955,26.8
1956,27.2
1957,28.1
1958,28.9
1959,29.1
1960,29.6
1961,29.9
1962,30.2
1963,30.6
1964,31
1965,31.5
1966,32.4
1967,33.4
1968,34.8
1969,36.7
1970,38.8
1971,40.5
1972,41.8
1973,44.4
1974,49.3
1975,53.8
1976,56.9
1977,60.6
1978,65.2
1979,72.6
1980,82.4
1981,90.9
1982,96.5
1983,99.6
1984,103.9
1985,107.6
1986,109.6
1987,113.6
1988,118.3
1989,124
1990,130.7
1991,136.2
1992,140.3
1993,144.5
1994,148.2
1995,152.4
1996,156.9
1997,160.5
1998,163
1999,166.6
2000,172.2
2001,177.1
2002,179.9
2003,184
2004,188.9
2005,195.3
2006,201.6
2007,207.342
2008,215.303
2009,214.537
2010,218.056
2011,224.939
2012,229.594
2013,232.957
2014,236.736
2015,237.017
2016,240.007
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/content/data/README.md:
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1 | # Data
2 |
3 | This tutorial makes use of a variety of data sources. Below is a brief description of each dataset as well as the links to the original source where the data was downloaded from. For convenience, we have repackaged the data and included them in the compressed file with the notebooks. You can download it [here](../../gds_scipy16.zip).
4 |
5 | ## Texas counties
6 |
7 | This includes Texas counties from the Census Bureau and a list of attached
8 | socio-economic variables. This is an extract of the national cover dataset
9 | `NAT` that is part of the example datasets shipped with `PySAL`.
10 |
11 | ## AirBnb listing for Austin (TX)
12 |
13 | This dataset contains information for [AirBnb](https://www.airbnb.com/) properties for the area of Austin (TX). It is originally provided by [Inside AirBnb](http://insideairbnb.com/). Same as the source, the dataset is released under a [CC0 1.0 Universal License](http://creativecommons.org/publicdomain/zero/1.0/). You can see a summary of the dataset [here](http://insideairbnb.com/austin/index.html).
14 |
15 | **Source**: [Inside AirBnb](http://insideairbnb.com/get-the-data.html)’s extract of AirBnb locations in Austin (TX).
16 |
17 | **Path**: `data/listings.csv.gz`
18 |
19 | ## Austin Zipcodes
20 |
21 | Boundaries for Zipcodes in Austin. The original source is provided by the City of Austin GIS Division.
22 |
23 | **Source**: open data from the city of Austin [[url]](https://data.austintexas.gov/Geodata/Zipcodes/23x8-agw7)
24 |
25 | **Path**: `data/Zipcodes.geojson`
26 |
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/content/data/participants2016.csv:
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1 | name,email,lon,lat
2 | Debra McKay,dmckay@iastate.edu,-93.6465,42.0266
3 | Alain Pholo Bala,apholo@uj.ac.za,27.9977,-26.1836
4 | Cory Yemen,yemenc@gmail.com,-74.4471,40.5015
5 | Johanna Manrique Hernandez,jemanriqueh@gmail.com,-74.0721,-4.711
6 | Sudhir Thakur,thakurs@csus.edu,-121.4944,38.5816
7 | David Patton,grazi1m@cmich.edu,-84.7752,43.5912
8 | Henry Renski,hrenski@umass.edu,-72.4969,42.3404
9 | Enza Maltese,enzamaltese@gmail.com,13.3614,38.1157
10 | Xian Bak,fangxian8663@gmail.com,-88.2073,40.1106
11 | Cynthia Goytia,cgoytia@utdt.edu,-96.797,32.7767
12 | Kenzo Asahi,kasahi@uc.cl,-70.6693,-33.4489
13 | Andrea Caragliu,10268614@polimi.it,9.1859,45.4654
14 | Daniel Broxterman,dbroxterman@fsu.edu,-84.2807,30.4383
15 | Nicholas Garcia,garcia.119@buckeyemail.osu.edu,-82.9988,39.9612
16 | Miriam Valdes,miriam.valdes@uadec.edu.mx,-101.7068,27.0587
17 | Hernan Ocampo Solarte,hocampo@uao.edu.co,-76.5358,3.4525
18 | Andres Vallone,andres.vallone@predoc.uam.es,-70.3975,-23.6509
19 | Jorge Alberto Orellana Aragón,jorgealbertoorellanaaragon@gmail.com,-51.2177,-30.0346
20 | Seva Rodnyansky,rodnyans@usc.edu,-118.2851,34.0224
21 |
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/content/data/participants2018.csv:
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1 | name,email,lat,lon,affiliation
2 | Alain Pholo Bala,apholo@uj.ac.za,-26.2614,27.9226,University of Johannesburg
3 | Annie Lee,annieslee777@gmail.com,40.5008,-74.4474,Rutgers University
4 | Yan Cen,cenyan0304@live.cn,43.8171,125.3235,Northeast Normal University
5 | Brian Asquith,basquith86@gmail.com,42.2900,-85.5981,W.E. Upjohn Institute
6 | Raul Silveira Neto,rau.silveira@uol.com.br,-8.0441,-34.8861,Federal University of Pernambuco
7 | Alin Halimatussadiah,alin.halimah@gmail.com,6.3628,106.8270,University of Indonesia
8 | Zhangliang Chen,zchen105@gmail.com,42.3770,-71.1167,Harvard Kennedy School
9 | André Chagas,achagas@usp.br,-23.5447, -46.62529,University of Sao Paulo
10 | Mitsuru Ota,ota@sk.tsukuba.ac.jp,36.1088,140.1037,University of Tsukuba
11 | Vikash Dangal,vdangal@agcenter.lsu.edu,30.4133,-91.1800,Louisiana State University
12 | Bryce Pludow,bapludow@gmail.com,34.4140,-119.8489,University of Santa Barbara
13 | Mark Folden,mfolden@nctcog.org,32.7503,-97.0678,North Central Texas Council of Governments
14 | Ziv Rubin,ziv.rubin@utoronto.ca,43.6629,-79.3957,University of Toronto
15 | Diego Firmino Costa da Silva,diegofirmino@gmail.com,-8.0175,-34.9492,Universidade Federal Rural de Pernambuco
16 | Toru Murayama,torumurayama0224@gmail.com,34.2305,135.1708,Wakayama College Japan
17 |
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1 | GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
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1 |
2 |