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Created June 7, 2021 16:10
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Untitled.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import libpysal as ps\n",
"import geopandas as gp\n",
"import numpy as np\n",
"\n",
"import sys\n",
"sys.path.append('/Users/Ziqi/Desktop/mgwr/')\n",
"from mgwr.gwr import GWR,MGWR \n",
"import matplotlib.pyplot as plt\n",
"from mgwr.sel_bw import Sel_BW "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#Load Georgia dataset and generate plot of Georgia counties (figure 1)\n",
"georgia_data = pd.read_csv(ps.examples.get_path('GData_utm.csv'))\n",
"georgia_shp = gp.read_file(ps.examples.get_path('G_utm.shp'))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#Prepare Georgia dataset inputs\n",
"g_y = georgia_data['PctBach'].values.reshape((-1,1))\n",
"g_X = georgia_data[['PctFB', 'PctBlack', 'PctRural']].values\n",
"u = georgia_data['X']\n",
"v = georgia_data['Y']\n",
"g_coords = list(zip(u,v))\n",
"\n",
"g_X = (g_X - g_X.mean(axis=0)) / g_X.std(axis=0)\n",
"\n",
"g_y = g_y.reshape((-1,1))\n",
"\n",
"g_y = (g_y - g_y.mean(axis=0)) / g_y.std(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"117.0\n"
]
}
],
"source": [
"#Calibrate GWR model\n",
"\n",
"gwr_selector = Sel_BW(g_coords, g_y, g_X)\n",
"gwr_bw = gwr_selector.search(bw_min=2)\n",
"print(gwr_bw)\n",
"gwr_results = GWR(g_coords, g_y, g_X, gwr_bw).fit()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<mgwr.gwr.GWRResults at 0x7f8b952dc6d0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gwr_results"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"===========================================================================\n",
"Model type Gaussian\n",
"Number of observations: 159\n",
"Number of covariates: 4\n",
"\n",
"Global Regression Results\n",
"---------------------------------------------------------------------------\n",
"Residual sum of squares: 71.793\n",
"Log-likelihood: -162.399\n",
"AIC: 332.798\n",
"AICc: 335.191\n",
"BIC: -713.887\n",
"R2: 0.548\n",
"Adj. R2: 0.540\n",
"\n",
"Variable Est. SE t(Est/SE) p-value\n",
"------------------------------- ---------- ---------- ---------- ----------\n",
"X0 0.000 0.054 0.000 1.000\n",
"X1 0.458 0.066 6.988 0.000\n",
"X2 -0.084 0.055 -1.525 0.127\n",
"X3 -0.374 0.065 -5.734 0.000\n",
"\n",
"Geographically Weighted Regression (GWR) Results\n",
"---------------------------------------------------------------------------\n",
"Spatial kernel: Adaptive bisquare\n",
"Bandwidth used: 117.000\n",
"\n",
"Diagnostic information\n",
"---------------------------------------------------------------------------\n",
"Residual sum of squares: 51.186\n",
"Effective number of parameters (trace(S)): 11.805\n",
"Degree of freedom (n - trace(S)): 147.195\n",
"Sigma estimate: 0.590\n",
"Log-likelihood: -135.503\n",
"AIC: 296.616\n",
"AICc: 299.051\n",
"BIC: 335.913\n",
"R2: 0.678\n",
"Adjusted R2: 0.652\n",
"Adj. alpha (95%): 0.017\n",
"Adj. critical t value (95%): 2.414\n",
"\n",
"Summary Statistics For GWR Parameter Estimates\n",
"---------------------------------------------------------------------------\n",
"Variable Mean STD Min Median Max\n",
"-------------------- ---------- ---------- ---------- ---------- ----------\n",
"X0 -0.004 0.180 -0.296 0.111 0.208\n",
"X1 0.477 0.234 0.123 0.556 0.741\n",
"X2 -0.043 0.083 -0.170 -0.053 0.100\n",
"X3 -0.328 0.060 -0.464 -0.308 -0.241\n",
"===========================================================================\n",
"\n"
]
}
],
"source": [
"gwr_results.summary()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" [-0.27323956, 0.15841475, 0.08679362, -0.35949224]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gwr_results.params"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" [0.0745143 , 0.08576682, 0.07230505, 0.08901723],\n",
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" [0.06554297, 0.08295969, 0.05989925, 0.08762616],\n",
" [0.07710598, 0.09126364, 0.0856913 , 0.08006184],\n",
" [0.06970041, 0.09015304, 0.06902599, 0.09168545],\n",
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" [0.07756448, 0.08230312, 0.08983603, 0.07989109]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gwr_results.bse"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"x_names = [\"Intercept\"] + ['PctFB', 'PctBlack', 'PctRural']"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"rslt_df = pd.DataFrame(np.hstack([georgia_data.AreaKey.values.reshape(-1,1),\n",
" gwr_results.params, \n",
" gwr_results.bse]))\n",
"\n",
"rslt_df.columns = [\"ID\"] + [\"param_\" + x for x in x_names] + [\"bse_\" + x for x in x_names]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>param_Intercept</th>\n",
" <th>param_PctFB</th>\n",
" <th>param_PctBlack</th>\n",
" <th>param_PctRural</th>\n",
" <th>bse_Intercept</th>\n",
" <th>bse_PctFB</th>\n",
" <th>bse_PctBlack</th>\n",
" <th>bse_PctRural</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>13001.0</td>\n",
" <td>-0.232046</td>\n",
" <td>0.228208</td>\n",
" <td>0.056974</td>\n",
" <td>-0.426495</td>\n",
" <td>0.075091</td>\n",
" <td>0.111697</td>\n",
" <td>0.087624</td>\n",
" <td>0.080841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>13003.0</td>\n",
" <td>-0.279224</td>\n",
" <td>0.165117</td>\n",
" <td>0.095165</td>\n",
" <td>-0.412263</td>\n",
" <td>0.074164</td>\n",
" <td>0.090157</td>\n",
" <td>0.085423</td>\n",
" <td>0.076427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>13005.0</td>\n",
" <td>-0.248944</td>\n",
" <td>0.204670</td>\n",
" <td>0.071212</td>\n",
" <td>-0.425736</td>\n",
" <td>0.074316</td>\n",
" <td>0.104136</td>\n",
" <td>0.086208</td>\n",
" <td>0.078969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>13007.0</td>\n",
" <td>-0.230368</td>\n",
" <td>0.152749</td>\n",
" <td>0.051038</td>\n",
" <td>-0.359387</td>\n",
" <td>0.078814</td>\n",
" <td>0.083033</td>\n",
" <td>0.092385</td>\n",
" <td>0.083184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>13009.0</td>\n",
" <td>0.190662</td>\n",
" <td>0.716275</td>\n",
" <td>-0.169202</td>\n",
" <td>-0.240918</td>\n",
" <td>0.068635</td>\n",
" <td>0.084626</td>\n",
" <td>0.075232</td>\n",
" <td>0.088053</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID param_Intercept param_PctFB param_PctBlack param_PctRural \\\n",
"0 13001.0 -0.232046 0.228208 0.056974 -0.426495 \n",
"1 13003.0 -0.279224 0.165117 0.095165 -0.412263 \n",
"2 13005.0 -0.248944 0.204670 0.071212 -0.425736 \n",
"3 13007.0 -0.230368 0.152749 0.051038 -0.359387 \n",
"4 13009.0 0.190662 0.716275 -0.169202 -0.240918 \n",
"\n",
" bse_Intercept bse_PctFB bse_PctBlack bse_PctRural \n",
"0 0.075091 0.111697 0.087624 0.080841 \n",
"1 0.074164 0.090157 0.085423 0.076427 \n",
"2 0.074316 0.104136 0.086208 0.078969 \n",
"3 0.078814 0.083033 0.092385 0.083184 \n",
"4 0.068635 0.084626 0.075232 0.088053 "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rslt_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"rslt_df.to_csv(\"mgwr_rslt.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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