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jseabold/pysal_auto.ipynb

Created September 9, 2013 16:58
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Try to replicate some spatial autocorrelation statistics.
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pysal_auto.ipynb
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 179
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example from http://www.nku.edu/~longa/geomed/ppa/doc/globals/Globals.htm"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dta = pd.read_csv(\"https://gist.github.com/jseabold/6498405/raw/151423fa64807e3d6abe5701b68b254d84c6c1e1/hepatitis.csv\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 180
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dta"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>County</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" <th>Rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> Alameda</td>\n",
" <td> 195</td>\n",
" <td> 500</td>\n",
" <td> 14.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> Alpine</td>\n",
" <td> 318</td>\n",
" <td> 560</td>\n",
" <td> 0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> Amador</td>\n",
" <td> 265</td>\n",
" <td> 550</td>\n",
" <td> 12.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> Butte</td>\n",
" <td> 220</td>\n",
" <td> 630</td>\n",
" <td> 52.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> Calaveras</td>\n",
" <td> 280</td>\n",
" <td> 530</td>\n",
" <td> 22.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> Colusa</td>\n",
" <td> 195</td>\n",
" <td> 598</td>\n",
" <td> 23.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> Contra Costa</td>\n",
" <td> 192</td>\n",
" <td> 515</td>\n",
" <td> 12.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> Del Norte</td>\n",
" <td> 100</td>\n",
" <td> 790</td>\n",
" <td> 301.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> El Dorado</td>\n",
" <td> 260</td>\n",
" <td> 580</td>\n",
" <td> 32.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> Fresno</td>\n",
" <td> 320</td>\n",
" <td> 425</td>\n",
" <td> 53.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> Glenn</td>\n",
" <td> 180</td>\n",
" <td> 630</td>\n",
" <td> 35.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> Humboldt</td>\n",
" <td> 90</td>\n",
" <td> 705</td>\n",
" <td> 100.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> Imperial</td>\n",
" <td> 648</td>\n",
" <td> 56</td>\n",
" <td> 66.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> Inyo</td>\n",
" <td> 450</td>\n",
" <td> 403</td>\n",
" <td> 29.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> Kern</td>\n",
" <td> 396</td>\n",
" <td> 256</td>\n",
" <td> 41.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> Kings</td>\n",
" <td> 315</td>\n",
" <td> 380</td>\n",
" <td> 21.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> Lake</td>\n",
" <td> 155</td>\n",
" <td> 597</td>\n",
" <td> 39.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> Lassen</td>\n",
" <td> 270</td>\n",
" <td> 710</td>\n",
" <td> 59.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> Los Angeles</td>\n",
" <td> 436</td>\n",
" <td> 168</td>\n",
" <td> 21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> Madera</td>\n",
" <td> 315</td>\n",
" <td> 455</td>\n",
" <td> 45.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> Marin</td>\n",
" <td> 175</td>\n",
" <td> 510</td>\n",
" <td> 20.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> Mariposa</td>\n",
" <td> 305</td>\n",
" <td> 485</td>\n",
" <td> 10.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> Mendocino</td>\n",
" <td> 125</td>\n",
" <td> 602</td>\n",
" <td> 27.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> Merced</td>\n",
" <td> 285</td>\n",
" <td> 470</td>\n",
" <td> 16.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> Modoc</td>\n",
" <td> 265</td>\n",
" <td> 765</td>\n",
" <td> 59.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> Mono</td>\n",
" <td> 380</td>\n",
" <td> 515</td>\n",
" <td> 31.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> Monterey</td>\n",
" <td> 212</td>\n",
" <td> 415</td>\n",
" <td> 26.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> Napa</td>\n",
" <td> 185</td>\n",
" <td> 545</td>\n",
" <td> 23.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> Nevada</td>\n",
" <td> 255</td>\n",
" <td> 610</td>\n",
" <td> 13.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> Orange</td>\n",
" <td> 468</td>\n",
" <td> 112</td>\n",
" <td> 17.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> Placer</td>\n",
" <td> 270</td>\n",
" <td> 595</td>\n",
" <td> 50.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> Plumas</td>\n",
" <td> 272</td>\n",
" <td> 660</td>\n",
" <td> 34.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> Riverside</td>\n",
" <td> 600</td>\n",
" <td> 120</td>\n",
" <td> 46.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td> Sacramento</td>\n",
" <td> 235</td>\n",
" <td> 548</td>\n",
" <td> 43.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> San Benito</td>\n",
" <td> 220</td>\n",
" <td> 430</td>\n",
" <td> 25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> San Bernadino</td>\n",
" <td> 584</td>\n",
" <td> 216</td>\n",
" <td> 33.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> San Diego</td>\n",
" <td> 544</td>\n",
" <td> 52</td>\n",
" <td> 22.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> San Fransisco</td>\n",
" <td> 185</td>\n",
" <td> 503</td>\n",
" <td> 78.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> San Joaquin</td>\n",
" <td> 236</td>\n",
" <td> 520</td>\n",
" <td> 30.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td> San Luis Obispo</td>\n",
" <td> 272</td>\n",
" <td> 260</td>\n",
" <td> 11.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td> San Mateo</td>\n",
" <td> 190</td>\n",
" <td> 490</td>\n",
" <td> 26.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td> Santa Barbara</td>\n",
" <td> 300</td>\n",
" <td> 200</td>\n",
" <td> 24.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td> Santa Clara</td>\n",
" <td> 202</td>\n",
" <td> 475</td>\n",
" <td> 14.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td> Santa Cruz</td>\n",
" <td> 200</td>\n",
" <td> 450</td>\n",
" <td> 27.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td> Shasta</td>\n",
" <td> 197</td>\n",
" <td> 712</td>\n",
" <td> 197.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td> Sierra</td>\n",
" <td> 275</td>\n",
" <td> 630</td>\n",
" <td> 78.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td> Siskiyou</td>\n",
" <td> 180</td>\n",
" <td> 782</td>\n",
" <td> 75.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td> Solano</td>\n",
" <td> 192</td>\n",
" <td> 540</td>\n",
" <td> 23.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td> Sonoma</td>\n",
" <td> 170</td>\n",
" <td> 535</td>\n",
" <td> 24.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td> Stanislaus</td>\n",
" <td> 265</td>\n",
" <td> 491</td>\n",
" <td> 26.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td> Sutter</td>\n",
" <td> 210</td>\n",
" <td> 590</td>\n",
" <td> 32.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td> Tehama</td>\n",
" <td> 193</td>\n",
" <td> 680</td>\n",
" <td> 58.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td> Trinity</td>\n",
" <td> 140</td>\n",
" <td> 702</td>\n",
" <td> 75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td> Tulare</td>\n",
" <td> 365</td>\n",
" <td> 385</td>\n",
" <td> 30.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td> Tuolumne</td>\n",
" <td> 303</td>\n",
" <td> 515</td>\n",
" <td> 20.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td> Ventura</td>\n",
" <td> 372</td>\n",
" <td> 176</td>\n",
" <td> 16.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td> Yolo</td>\n",
" <td> 205</td>\n",
" <td> 570</td>\n",
" <td> 30.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td> Yuba</td>\n",
" <td> 228</td>\n",
" <td> 604</td>\n",
" <td> 79.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 181,
"text": [
" County X Y Rate\n",
"0 Alameda 195 500 14.4\n",
"1 Alpine 318 560 0.0\n",
"2 Amador 265 550 12.1\n",
"3 Butte 220 630 52.9\n",
"4 Calaveras 280 530 22.6\n",
"5 Colusa 195 598 23.8\n",
"6 Contra Costa 192 515 12.5\n",
"7 Del Norte 100 790 301.5\n",
"8 El Dorado 260 580 32.0\n",
"9 Fresno 320 425 53.9\n",
"10 Glenn 180 630 35.0\n",
"11 Humboldt 90 705 100.5\n",
"12 Imperial 648 56 66.3\n",
"13 Inyo 450 403 29.3\n",
"14 Kern 396 256 41.2\n",
"15 Kings 315 380 21.9\n",
"16 Lake 155 597 39.5\n",
"17 Lassen 270 710 59.2\n",
"18 Los Angeles 436 168 21.0\n",
"19 Madera 315 455 45.0\n",
"20 Marin 175 510 20.2\n",
"21 Mariposa 305 485 10.4\n",
"22 Mendocino 125 602 27.5\n",
"23 Merced 285 470 16.6\n",
"24 Modoc 265 765 59.8\n",
"25 Mono 380 515 31.6\n",
"26 Monterey 212 415 26.6\n",
"27 Napa 185 545 23.8\n",
"28 Nevada 255 610 13.8\n",
"29 Orange 468 112 17.3\n",
"30 Placer 270 595 50.8\n",
"31 Plumas 272 660 34.6\n",
"32 Riverside 600 120 46.5\n",
"33 Sacramento 235 548 43.1\n",
"34 San Benito 220 430 25.0\n",
"35 San Bernadino 584 216 33.7\n",
"36 San Diego 544 52 22.4\n",
"37 San Fransisco 185 503 78.2\n",
"38 San Joaquin 236 520 30.5\n",
"39 San Luis Obispo 272 260 11.8\n",
"40 San Mateo 190 490 26.6\n",
"41 Santa Barbara 300 200 24.4\n",
"42 Santa Clara 202 475 14.8\n",
"43 Santa Cruz 200 450 27.8\n",
"44 Shasta 197 712 197.5\n",
"45 Sierra 275 630 78.4\n",
"46 Siskiyou 180 782 75.9\n",
"47 Solano 192 540 23.6\n",
"48 Sonoma 170 535 24.6\n",
"49 Stanislaus 265 491 26.8\n",
"50 Sutter 210 590 32.6\n",
"51 Tehama 193 680 58.3\n",
"52 Trinity 140 702 75.0\n",
"53 Tulare 365 385 30.3\n",
"54 Tuolumne 303 515 20.7\n",
"55 Ventura 372 176 16.2\n",
"56 Yolo 205 570 30.6\n",
"57 Yuba 228 604 79.8"
]
}
],
"prompt_number": 181
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assume they are using Euclidean distance of these points."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy import spatial"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 182
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"W = spatial.distance.cdist(dta[[\"X\", \"Y\"]], dta[[\"X\", \"Y\"]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 183
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pysal"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 184
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"morans = []\n",
"gearys = []\n",
"for i in range(50, 251, 50):\n",
" incrW = W.copy()\n",
" # zero out observations not in increments\n",
" incrW[(incrW > i)] = 0.\n",
" # get inverse distance squared\n",
" invW = 1/incrW**2\n",
" invW[np.isinf(invW)] = 0\n",
" \n",
" # convert to pysal weights\n",
" Weights = pysal.weights.full2W(incrW)\n",
" if Weights.islands:\n",
" print \"Distance %d had islands.\" % i\n",
" morans.append(pysal.Moran(dta.Rate.values, Weights, transformation='O'))\n",
" gearys.append(pysal.Geary(dta.Rate.values, Weights, transformation='O'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"WARNING: there are 19 disconnected observations\n",
"Island ids: [7, 11, 12, 13, 14, 17, 18, 24, 25, 29, 32, 35, 36, 39, 41, 46, 52, 53, 55]\n",
"Distance 50 had islands.\n"
]
}
],
"prompt_number": 185
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for m in morans:\n",
" print m.I, m.EI, m.VI_norm, m.z_norm"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.087154370541 -0.0175438596491 0.00978953353049 1.05817705762\n",
"0.153129099135 -0.0175438596491 0.0020136343624 3.80342109226\n",
"0.137899510164 -0.0175438596491 0.000848323094094 5.33693062726\n",
"0.0692484969164 -0.0175438596491 0.000558927324912 3.67116419822\n",
"0.0286188042012 -0.0175438596491 0.000451622043023 2.17221744223\n"
]
}
],
"prompt_number": 186
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for g in gearys:\n",
" print g.C, g.EC, g.VC_norm, g.z_norm"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.196128720063 1.0 0.0378752769503 -4.13055685366\n",
"0.382786441472 1.0 0.0161432874978 -4.85779817252\n",
"0.432497164055 1.0 0.00936318071842 -5.86484222455\n",
"0.61026934039 1.0 0.0059026178373 -5.07273575029\n",
"0.827980766006 1.0 0.00397281092093 -2.72915411793\n"
]
}
],
"prompt_number": 187
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example from http://support.sas.com/kb/22/944.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from StringIO import StringIO"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 188
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dta = pd.read_table(StringIO(\"\"\"13.6 33.4 17.4906\\n 116.6 40.9 17.9709\\n 86.0 215.3 17.0232\n",
" 248.1 266.9 18.8413\\n 40.2 149.3 17.6422\\n 249.9 219.5 18.4011\n",
" 298.6 19.8 18.3800\\n 161.6 262.3 18.6537\\n 165.9 204.9 18.8376\n",
" 236.3 249.9 18.2338\\n 141.5 108.1 18.3977\\n 253.0 156.4 18.8378\n",
" 218.6 81.2 18.6493\\n 46.5 77.8 17.7590\\n 292.8 47.7 18.5304\n",
" 83.0 164.0 17.7159\\n 110.8 225.6 17.9139\\n 202.9 189.1 18.7072\n",
" 199.5 17.5 18.8718\\n 103.7 158.7 17.9594\\n 184.7 252.5 18.6570\n",
" 76.0 200.3 16.9481\\n 51.5 35.6 17.7746\\n 158.1 220.3 18.4775\n",
" 230.9 55.0 18.9772\\n 158.5 226.4 18.3128\\n 252.8 131.5 18.6633\n",
" 61.2 20.6 17.8255\\n 279.6 212.6 19.0338\\n 294.1 42.3 18.6408\n",
" 80.9 82.3 17.6355\\n 124.8 253.5 18.3031\\n 18.0 286.4 17.7586\n",
" 229.7 154.7 18.5950\\n 71.8 47.0 17.9481\\n 280.9 210.9 19.1626\n",
" 92.9 125.8 17.6257\\n 28.6 275.5 17.5359\\n 186.4 111.7 18.5684\n",
" 16.1 167.7 17.8476\\n 114.9 130.2 17.7519\\n 140.7 272.4 18.1968\n",
" 122.5 155.3 17.7771\\n 63.8 259.7 17.4883\\n 262.1 12.3 18.5385\n",
" 257.6 262.2 18.9482\\n 128.8 229.1 17.9638\\n 269.8 15.9 18.4247\n",
" 288.6 236.9 18.6137\\n 124.5 130.2 17.9114\\n 7.3 117.2 17.3567\n",
" 153.0 299.9 18.0288\\n 75.8 85.2 17.5313\\n 67.9 247.3 17.6824\n",
" 81.7 199.0 17.2191\\n 275.3 260.2 18.9168\\n 180.6 39.6 18.6395\n",
" 28.8 10.3 17.6430\\n 217.2 76.1 18.7481\\n 282.4 95.2 18.0833\n",
" 143.0 196.7 18.5825\\n 42.0 245.1 17.2660\\n 179.1 187.1 18.6990\n",
" 41.3 134.6 17.5906\\n 217.0 130.8 18.6883\\n 166.1 152.1 18.5032\n",
" 186.4 59.2 18.9922\\n 264.9 294.8 18.3716\\n 228.6 42.1 18.7740\n",
" 214.1 38.5 19.0145\\n 123.5 66.2 18.5217\\n 26.3 20.4 17.4753\n",
" 39.7 78.8 17.6835\\n 113.8 247.1 18.0232\\n 225.0 63.3 18.8826\n",
" 242.4 29.8 19.0110\\n 260.5 158.1 18.7582\\n 297.8 270.0 19.0336\n",
" 111.8 90.1 18.2008\\n 79.6 43.7 17.7467\\n 102.8 90.7 18.0879\n",
" 51.1 184.0 17.4158\\n 216.3 85.0 18.7803\\n 11.7 261.0 17.5346\n",
" 291.6 265.7 19.2310\\n 119.8 125.1 17.7093\\n 127.4 176.8 18.1153\n",
" 152.7 74.3 18.5789\\n 202.1 70.3 18.7343\\n 67.7 218.1 17.0247\n",
" 138.5 38.3 17.8701\\n 72.9 163.9 17.6970\\n 189.7 144.7 18.4584\n",
" 144.3 121.6 18.3561\\n 120.7 246.9 18.0552\\n 216.6 22.4 19.3410\"\"\"),\n",
" names=[\"Easting\", \"Northing\", \"daGSI\"],\n",
" sep=\" +\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 189
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dta[dta.columns[:3]].head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Easting</th>\n",
" <th>Northing</th>\n",
" <th>daGSI</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 13.6</td>\n",
" <td> 33.4</td>\n",
" <td> 17.4906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 116.6</td>\n",
" <td> 40.9</td>\n",
" <td> 17.9709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 86.0</td>\n",
" <td> 215.3</td>\n",
" <td> 17.0232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 248.1</td>\n",
" <td> 266.9</td>\n",
" <td> 18.8413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 40.2</td>\n",
" <td> 149.3</td>\n",
" <td> 17.6422</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 190,
"text": [
" Easting Northing daGSI\n",
"0 13.6 33.4 17.4906\n",
"1 116.6 40.9 17.9709\n",
"2 86.0 215.3 17.0232\n",
"3 248.1 266.9 18.8413\n",
"4 40.2 149.3 17.6422"
]
}
],
"prompt_number": 190
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"W = spatial.distance.cdist(dta[[\"Easting\", \"Northing\"]], dta[[\"Easting\", \"Northing\"]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 191
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Weights = pysal.weights.full2W(W)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 192
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"m = pysal.Moran(dta.daGSI.values, Weights, transformation=\"O\")\n",
"c = pysal.Geary(dta.daGSI.values, Weights, transformation=\"O\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 193
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print m.I, m.EI, m.z_norm\n",
"print c.C, c.EC, c.z_norm"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-0.211963774449 -0.0105263157895 -33.3968831853\n",
"1.23216336853 1.0 8.77001372913\n"
]
}
],
"prompt_number": 194
}
],
"metadata": {}
}
]
}
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