Skip to content

Instantly share code, notes, and snippets.

@d-wasserman

d-wasserman/EDAStatsModels.ipynb

Last active February 21, 2018 15:27
Show Gist options
  • Save d-wasserman/9747e5225f4c4f4ec8c18777713e11b5 to your computer and use it in GitHub Desktop.
Save d-wasserman/9747e5225f4c4f4ec8c18777713e11b5 to your computer and use it in GitHub Desktop.
Download ZIP
Exploratory Data Analysis of Transit Ridership and Variables Relating to Demographics and Service Characteristics - This notebook demonstrates the use of pandas, statsmodels, and seaborn for the purpose of urban data analysis. This regression modeling script is an exploratory exercise used to identify possible determinants of P & R ridership.
Display the source blob
Display the rendered blob
Raw
EDAStatsModels.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# P & R Exploratory Data Analysis\n",
"This regression modeling script is an exploratory exercise used to determine the strength of various possible explanatory variables such as density, parking spaces, and other variables to park and ride ridership as part of an Access to Transit Study. None of these were a final model, just exploratory experiments. \n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#Import Libaries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import statsmodels.api as sm\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OBJECTID</th>\n",
" <th>NAME</th>\n",
" <th>INVENTORY_</th>\n",
" <th>REG_SPACES</th>\n",
" <th>lot_st</th>\n",
" <th>LU_Typology</th>\n",
" <th>Pop_2015_in_Driveshed</th>\n",
" <th>Employ_2015_in_Driveshed</th>\n",
" <th>Downtown_Bnd_Trn_Trips</th>\n",
" <th>SEATAC_Bnd_Peak_Trn_Trips</th>\n",
" <th>...</th>\n",
" <th>DT_OP</th>\n",
" <th>rte_dir_id</th>\n",
" <th>Uni_Wa_Bnd_Peak_Trn_Trips</th>\n",
" <th>UW_OP</th>\n",
" <th>KCM_ST_BDG_15</th>\n",
" <th>lot_val_master</th>\n",
" <th>lrt_BRDq2017</th>\n",
" <th>Num_PRs</th>\n",
" <th>sndr_brdq2017</th>\n",
" <th>OWNER</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>68</td>\n",
" <td>Eastgate P&amp;R</td>\n",
" <td>713.0</td>\n",
" <td>1614</td>\n",
" <td>713.0</td>\n",
" <td>3</td>\n",
" <td>10983.60</td>\n",
" <td>17404.20</td>\n",
" <td>16.386111</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.375000</td>\n",
" <td>(u'1kcmmarch2017:100162', 1)</td>\n",
" <td>6.039683</td>\n",
" <td>3.111111</td>\n",
" <td>3455</td>\n",
" <td>713.0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>WSDOT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>25</td>\n",
" <td>Northgate Transit Center P&amp;R</td>\n",
" <td>753.0</td>\n",
" <td>1502</td>\n",
" <td>753.0</td>\n",
" <td>4</td>\n",
" <td>25647.10</td>\n",
" <td>15424.40</td>\n",
" <td>21.583333</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>3.000000</td>\n",
" <td>(u'1kcmmarch2017:100177', 1)</td>\n",
" <td>20.678571</td>\n",
" <td>0.000000</td>\n",
" <td>5922</td>\n",
" <td>753.0</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>KC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>50</td>\n",
" <td>Federal Way Transit Center P&amp;R</td>\n",
" <td>877.0</td>\n",
" <td>1190</td>\n",
" <td>877.0</td>\n",
" <td>4</td>\n",
" <td>13753.70</td>\n",
" <td>8879.81</td>\n",
" <td>20.822222</td>\n",
" <td>5.666667</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>(u'1kcmmarch2017:100088', 1)</td>\n",
" <td>3.666667</td>\n",
" <td>0.000000</td>\n",
" <td>2393</td>\n",
" <td>877.0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>ST</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Angle Lake Station P&amp;R</td>\n",
" <td>891.0</td>\n",
" <td>1050</td>\n",
" <td>891.0</td>\n",
" <td>4</td>\n",
" <td>8894.45</td>\n",
" <td>12488.90</td>\n",
" <td>10.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>891.0</td>\n",
" <td>3390.0</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>ST</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>49</td>\n",
" <td>Issaquah Highlands P&amp;R</td>\n",
" <td>759.0</td>\n",
" <td>1010</td>\n",
" <td>759.0</td>\n",
" <td>2</td>\n",
" <td>3292.76</td>\n",
" <td>2580.19</td>\n",
" <td>10.136111</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.041667</td>\n",
" <td>(u'1kcmmarch2017:100241', 1)</td>\n",
" <td>2.095238</td>\n",
" <td>0.166667</td>\n",
" <td>1112</td>\n",
" <td>759.0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>KC</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" OBJECTID NAME INVENTORY_ REG_SPACES lot_st \\\n",
"0 68 Eastgate P&R 713.0 1614 713.0 \n",
"1 25 Northgate Transit Center P&R 753.0 1502 753.0 \n",
"2 50 Federal Way Transit Center P&R 877.0 1190 877.0 \n",
"3 1 Angle Lake Station P&R 891.0 1050 891.0 \n",
"4 49 Issaquah Highlands P&R 759.0 1010 759.0 \n",
"\n",
" LU_Typology Pop_2015_in_Driveshed Employ_2015_in_Driveshed \\\n",
"0 3 10983.60 17404.20 \n",
"1 4 25647.10 15424.40 \n",
"2 4 13753.70 8879.81 \n",
"3 4 8894.45 12488.90 \n",
"4 2 3292.76 2580.19 \n",
"\n",
" Downtown_Bnd_Trn_Trips SEATAC_Bnd_Peak_Trn_Trips ... DT_OP \\\n",
"0 16.386111 0.000000 ... 0.375000 \n",
"1 21.583333 0.000000 ... 3.000000 \n",
"2 20.822222 5.666667 ... 0.000000 \n",
"3 10.000000 0.000000 ... 0.000000 \n",
"4 10.136111 0.000000 ... 0.041667 \n",
"\n",
" rte_dir_id Uni_Wa_Bnd_Peak_Trn_Trips UW_OP \\\n",
"0 (u'1kcmmarch2017:100162', 1) 6.039683 3.111111 \n",
"1 (u'1kcmmarch2017:100177', 1) 20.678571 0.000000 \n",
"2 (u'1kcmmarch2017:100088', 1) 3.666667 0.000000 \n",
"3 NaN 0.000000 0.000000 \n",
"4 (u'1kcmmarch2017:100241', 1) 2.095238 0.166667 \n",
"\n",
" KCM_ST_BDG_15 lot_val_master lrt_BRDq2017 Num_PRs sndr_brdq2017 OWNER \n",
"0 3455 713.0 NaN 1 NaN WSDOT \n",
"1 5922 753.0 NaN 6 NaN KC \n",
"2 2393 877.0 NaN 1 NaN ST \n",
"3 0 891.0 3390.0 1 NaN ST \n",
"4 1112 759.0 NaN 1 NaN KC \n",
"\n",
"[5 rows x 29 columns]"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Set Seaborn Color Codes\n",
"sns.set(color_codes=True)\n",
"#Import data with relative paths\n",
"data_path=\"Data/ParkandRide_Master_List_Update_Regression.xlsx\"\n",
"sheet_name=\"PandasImport\"\n",
"prdf=pd.read_excel(data_path,sheet_name)\n",
"display (prdf.head())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pop_2015_in_Driveshed</th>\n",
" <th>Employ_2015_in_Driveshed</th>\n",
" <th>Total_Parking_Spaces</th>\n",
" <th>Downtown_Bnd_Trn_Trips</th>\n",
" <th>Uni_Wa_Bnd_Peak_Trn_Trips</th>\n",
" <th>SEATAC_Bnd_Peak_Trn_Trips</th>\n",
" <th>Bellevue_Bnd_Peak_Trn_Trips</th>\n",
" <th>LU_Typology</th>\n",
" <th>Q1_PandR_Utilization</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>10825.347398</td>\n",
" <td>7002.993308</td>\n",
" <td>223.277311</td>\n",
" <td>4.956071</td>\n",
" <td>1.845365</td>\n",
" <td>0.445378</td>\n",
" <td>1.313725</td>\n",
" <td>2.588235</td>\n",
" <td>0.586134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>5957.602136</td>\n",
" <td>8157.766873</td>\n",
" <td>325.533797</td>\n",
" <td>5.778180</td>\n",
" <td>3.946882</td>\n",
" <td>1.697055</td>\n",
" <td>3.145647</td>\n",
" <td>0.896431</td>\n",
" <td>0.379514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>85.370400</td>\n",
" <td>13.866100</td>\n",
" <td>10.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>6758.625000</td>\n",
" <td>1859.790000</td>\n",
" <td>25.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.285000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>10660.300000</td>\n",
" <td>5171.740000</td>\n",
" <td>53.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.560000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>15003.850000</td>\n",
" <td>8770.495000</td>\n",
" <td>338.500000</td>\n",
" <td>8.861111</td>\n",
" <td>2.095238</td>\n",
" <td>0.000000</td>\n",
" <td>1.166667</td>\n",
" <td>3.000000</td>\n",
" <td>0.960000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>32038.700000</td>\n",
" <td>45472.300000</td>\n",
" <td>1614.000000</td>\n",
" <td>21.583333</td>\n",
" <td>20.678571</td>\n",
" <td>11.000000</td>\n",
" <td>18.333333</td>\n",
" <td>4.000000</td>\n",
" <td>1.680000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pop_2015_in_Driveshed Employ_2015_in_Driveshed Total_Parking_Spaces \\\n",
"count 119.000000 119.000000 119.000000 \n",
"mean 10825.347398 7002.993308 223.277311 \n",
"std 5957.602136 8157.766873 325.533797 \n",
"min 85.370400 13.866100 10.000000 \n",
"25% 6758.625000 1859.790000 25.000000 \n",
"50% 10660.300000 5171.740000 53.000000 \n",
"75% 15003.850000 8770.495000 338.500000 \n",
"max 32038.700000 45472.300000 1614.000000 \n",
"\n",
" Downtown_Bnd_Trn_Trips Uni_Wa_Bnd_Peak_Trn_Trips \\\n",
"count 119.000000 119.000000 \n",
"mean 4.956071 1.845365 \n",
"std 5.778180 3.946882 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 3.000000 0.000000 \n",
"75% 8.861111 2.095238 \n",
"max 21.583333 20.678571 \n",
"\n",
" SEATAC_Bnd_Peak_Trn_Trips Bellevue_Bnd_Peak_Trn_Trips LU_Typology \\\n",
"count 119.000000 119.000000 119.000000 \n",
"mean 0.445378 1.313725 2.588235 \n",
"std 1.697055 3.145647 0.896431 \n",
"min 0.000000 0.000000 1.000000 \n",
"25% 0.000000 0.000000 2.000000 \n",
"50% 0.000000 0.000000 3.000000 \n",
"75% 0.000000 1.166667 3.000000 \n",
"max 11.000000 18.333333 4.000000 \n",
"\n",
" Q1_PandR_Utilization \n",
"count 119.000000 \n",
"mean 0.586134 \n",
"std 0.379514 \n",
"min 0.000000 \n",
"25% 0.285000 \n",
"50% 0.560000 \n",
"75% 0.960000 \n",
"max 1.680000 "
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Data Preparation\n",
"\n",
"#[Population_2015_Within_Driveshed, Employment_2015_Within_Driveshed,Total_Parkin_Spaces_At_PAndR,\n",
"#Downtown_Travel_Transit_Trips_At_Peak_Period,Uni_Washington_Travel_Transit_Trips_At_Peak_Period,\n",
"#SEATAC_Travel_Transit_Trips_At_Peak_Perod,Bellevue_Travel_Transit_Trips_At_Peak_Perod]\n",
"independent_features=[\"Pop_2015_in_Driveshed\",\"Employ_2015_in_Driveshed\",\"Total_Parking_Spaces\",\"Downtown_Bnd_Trn_Trips\",\n",
" \"Uni_Wa_Bnd_Peak_Trn_Trips\",\"SEATAC_Bnd_Peak_Trn_Trips\",\"Bellevue_Bnd_Peak_Trn_Trips\"]\n",
"categorical_independents = \"LU_Typology\"\n",
"#Quarter 1 Utilization of Park & Ride Facility\n",
"catgorical_names=[\"Rural\",\"Res\",\"Comm_Ind\",\"Sub_Downtown\"]\n",
"dependent_feature=\"Q1_PandR_Utilization\"\n",
"data_features= independent_features+[categorical_independents]+[dependent_feature]\n",
"regression_features= independent_features+catgorical_names+[dependent_feature]\n",
"\n",
"\n",
"regr_df = prdf[data_features].copy()\n",
"#Develop features\n",
"display(regr_df.describe())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pop_2015_in_Driveshed</th>\n",
" <th>Employ_2015_in_Driveshed</th>\n",
" <th>Total_Parking_Spaces</th>\n",
" <th>Downtown_Bnd_Trn_Trips</th>\n",
" <th>Uni_Wa_Bnd_Peak_Trn_Trips</th>\n",
" <th>SEATAC_Bnd_Peak_Trn_Trips</th>\n",
" <th>Bellevue_Bnd_Peak_Trn_Trips</th>\n",
" <th>LU_Typology</th>\n",
" <th>Q1_PandR_Utilization</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10983.60</td>\n",
" <td>17404.20</td>\n",
" <td>1614</td>\n",
" <td>16.386111</td>\n",
" <td>6.039683</td>\n",
" <td>0.000000</td>\n",
" <td>17.666667</td>\n",
" <td>3</td>\n",
" <td>1.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>25647.10</td>\n",
" <td>15424.40</td>\n",
" <td>1517</td>\n",
" <td>21.583333</td>\n",
" <td>20.678571</td>\n",
" <td>0.000000</td>\n",
" <td>4.666667</td>\n",
" <td>4</td>\n",
" <td>1.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>13753.70</td>\n",
" <td>8879.81</td>\n",
" <td>1190</td>\n",
" <td>20.822222</td>\n",
" <td>3.666667</td>\n",
" <td>5.666667</td>\n",
" <td>0.000000</td>\n",
" <td>4</td>\n",
" <td>0.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8894.45</td>\n",
" <td>12488.90</td>\n",
" <td>1120</td>\n",
" <td>10.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4</td>\n",
" <td>0.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3292.76</td>\n",
" <td>2580.19</td>\n",
" <td>1010</td>\n",
" <td>10.136111</td>\n",
" <td>2.095238</td>\n",
" <td>0.000000</td>\n",
" <td>4.666667</td>\n",
" <td>2</td>\n",
" <td>0.98</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pop_2015_in_Driveshed Employ_2015_in_Driveshed Total_Parking_Spaces \\\n",
"0 10983.60 17404.20 1614 \n",
"1 25647.10 15424.40 1517 \n",
"2 13753.70 8879.81 1190 \n",
"3 8894.45 12488.90 1120 \n",
"4 3292.76 2580.19 1010 \n",
"\n",
" Downtown_Bnd_Trn_Trips Uni_Wa_Bnd_Peak_Trn_Trips \\\n",
"0 16.386111 6.039683 \n",
"1 21.583333 20.678571 \n",
"2 20.822222 3.666667 \n",
"3 10.000000 0.000000 \n",
"4 10.136111 2.095238 \n",
"\n",
" SEATAC_Bnd_Peak_Trn_Trips Bellevue_Bnd_Peak_Trn_Trips LU_Typology \\\n",
"0 0.000000 17.666667 3 \n",
"1 0.000000 4.666667 4 \n",
"2 5.666667 0.000000 4 \n",
"3 0.000000 0.000000 4 \n",
"4 0.000000 4.666667 2 \n",
"\n",
" Q1_PandR_Utilization \n",
"0 1.01 \n",
"1 1.05 \n",
"2 0.99 \n",
"3 0.96 \n",
"4 0.98 "
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pop_2015_in_Driveshed</th>\n",
" <th>Employ_2015_in_Driveshed</th>\n",
" <th>Total_Parking_Spaces</th>\n",
" <th>Downtown_Bnd_Trn_Trips</th>\n",
" <th>Uni_Wa_Bnd_Peak_Trn_Trips</th>\n",
" <th>SEATAC_Bnd_Peak_Trn_Trips</th>\n",
" <th>Bellevue_Bnd_Peak_Trn_Trips</th>\n",
" <th>LU_Typology</th>\n",
" <th>Q1_PandR_Utilization</th>\n",
" <th>Rural</th>\n",
" <th>Res</th>\n",
" <th>Comm_Ind</th>\n",
" <th>Sub_Downtown</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10983.60</td>\n",
" <td>17404.20</td>\n",
" <td>1614</td>\n",
" <td>16.386111</td>\n",
" <td>6.039683</td>\n",
" <td>0.000000</td>\n",
" <td>17.666667</td>\n",
" <td>3</td>\n",
" <td>1.01</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>25647.10</td>\n",
" <td>15424.40</td>\n",
" <td>1517</td>\n",
" <td>21.583333</td>\n",
" <td>20.678571</td>\n",
" <td>0.000000</td>\n",
" <td>4.666667</td>\n",
" <td>4</td>\n",
" <td>1.05</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>13753.70</td>\n",
" <td>8879.81</td>\n",
" <td>1190</td>\n",
" <td>20.822222</td>\n",
" <td>3.666667</td>\n",
" <td>5.666667</td>\n",
" <td>0.000000</td>\n",
" <td>4</td>\n",
" <td>0.99</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8894.45</td>\n",
" <td>12488.90</td>\n",
" <td>1120</td>\n",
" <td>10.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4</td>\n",
" <td>0.96</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3292.76</td>\n",
" <td>2580.19</td>\n",
" <td>1010</td>\n",
" <td>10.136111</td>\n",
" <td>2.095238</td>\n",
" <td>0.000000</td>\n",
" <td>4.666667</td>\n",
" <td>2</td>\n",
" <td>0.98</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pop_2015_in_Driveshed Employ_2015_in_Driveshed Total_Parking_Spaces \\\n",
"0 10983.60 17404.20 1614 \n",
"1 25647.10 15424.40 1517 \n",
"2 13753.70 8879.81 1190 \n",
"3 8894.45 12488.90 1120 \n",
"4 3292.76 2580.19 1010 \n",
"\n",
" Downtown_Bnd_Trn_Trips Uni_Wa_Bnd_Peak_Trn_Trips \\\n",
"0 16.386111 6.039683 \n",
"1 21.583333 20.678571 \n",
"2 20.822222 3.666667 \n",
"3 10.000000 0.000000 \n",
"4 10.136111 2.095238 \n",
"\n",
" SEATAC_Bnd_Peak_Trn_Trips Bellevue_Bnd_Peak_Trn_Trips LU_Typology \\\n",
"0 0.000000 17.666667 3 \n",
"1 0.000000 4.666667 4 \n",
"2 5.666667 0.000000 4 \n",
"3 0.000000 0.000000 4 \n",
"4 0.000000 4.666667 2 \n",
"\n",
" Q1_PandR_Utilization Rural Res Comm_Ind Sub_Downtown \n",
"0 1.01 0 0 1 0 \n",
"1 1.05 0 0 0 1 \n",
"2 0.99 0 0 0 1 \n",
"3 0.96 0 0 0 1 \n",
"4 0.98 0 1 0 0 "
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Create Factorized Fields For Typology of Land uses surrounding P&R station\n",
"cat_factor_df=pd.get_dummies(regr_df[categorical_independents])\n",
"cat_factor_df.columns=catgorical_names\n",
"temp_pre_df=regr_df.copy()\n",
"display(regr_df.head())\n",
"regr_df=pd.merge(temp_pre_df,cat_factor_df,left_index=True,right_index=True)\n",
"display(regr_df.head())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating standarized column for field Pop_2015_in_Driveshed....\n",
"Creating standarized column for field Employ_2015_in_Driveshed....\n",
"Creating standarized column for field Total_Parking_Spaces....\n",
"Creating standarized column for field Downtown_Bnd_Trn_Trips....\n",
"Creating standarized column for field Uni_Wa_Bnd_Peak_Trn_Trips....\n",
"Creating standarized column for field SEATAC_Bnd_Peak_Trn_Trips....\n",
"Creating standarized column for field Bellevue_Bnd_Peak_Trn_Trips....\n",
"Creating standarized column for field Rural....\n",
"Creating standarized column for field Res....\n",
"Creating standarized column for field Comm_Ind....\n",
"Creating standarized column for field Sub_Downtown....\n",
"Creating standarized column for field Q1_PandR_Utilization....\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pop_2015_in_Driveshed</th>\n",
" <th>Employ_2015_in_Driveshed</th>\n",
" <th>Total_Parking_Spaces</th>\n",
" <th>Downtown_Bnd_Trn_Trips</th>\n",
" <th>Uni_Wa_Bnd_Peak_Trn_Trips</th>\n",
" <th>SEATAC_Bnd_Peak_Trn_Trips</th>\n",
" <th>Bellevue_Bnd_Peak_Trn_Trips</th>\n",
" <th>LU_Typology</th>\n",
" <th>Q1_PandR_Utilization</th>\n",
" <th>Rural</th>\n",
" <th>Res</th>\n",
" <th>Comm_Ind</th>\n",
" <th>Sub_Downtown</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.026675</td>\n",
" <td>1.280398</td>\n",
" <td>4.290194</td>\n",
" <td>1.986503</td>\n",
" <td>1.067185</td>\n",
" <td>-0.263552</td>\n",
" <td>5.220576</td>\n",
" <td>3</td>\n",
" <td>1.121587</td>\n",
" <td>-0.449467</td>\n",
" <td>-0.476240</td>\n",
" <td>0.927025</td>\n",
" <td>-0.350202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.498392</td>\n",
" <td>1.036683</td>\n",
" <td>3.990962</td>\n",
" <td>2.889763</td>\n",
" <td>4.791844</td>\n",
" <td>-0.263552</td>\n",
" <td>1.070406</td>\n",
" <td>4</td>\n",
" <td>1.227431</td>\n",
" <td>-0.449467</td>\n",
" <td>-0.476240</td>\n",
" <td>-1.078720</td>\n",
" <td>2.855494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.493610</td>\n",
" <td>0.231038</td>\n",
" <td>2.982211</td>\n",
" <td>2.757484</td>\n",
" <td>0.463405</td>\n",
" <td>3.089686</td>\n",
" <td>-0.419399</td>\n",
" <td>4</td>\n",
" <td>1.068665</td>\n",
" <td>-0.449467</td>\n",
" <td>-0.476240</td>\n",
" <td>-1.078720</td>\n",
" <td>2.855494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.325477</td>\n",
" <td>0.675320</td>\n",
" <td>2.766270</td>\n",
" <td>0.876618</td>\n",
" <td>-0.469527</td>\n",
" <td>-0.263552</td>\n",
" <td>-0.419399</td>\n",
" <td>4</td>\n",
" <td>0.989283</td>\n",
" <td>-0.449467</td>\n",
" <td>-0.476240</td>\n",
" <td>-1.078720</td>\n",
" <td>2.855494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.269712</td>\n",
" <td>-0.544451</td>\n",
" <td>2.426935</td>\n",
" <td>0.900274</td>\n",
" <td>0.063577</td>\n",
" <td>-0.263552</td>\n",
" <td>1.070406</td>\n",
" <td>2</td>\n",
" <td>1.042204</td>\n",
" <td>-0.449467</td>\n",
" <td>2.099784</td>\n",
" <td>-1.078720</td>\n",
" <td>-0.350202</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pop_2015_in_Driveshed Employ_2015_in_Driveshed Total_Parking_Spaces \\\n",
"0 0.026675 1.280398 4.290194 \n",
"1 2.498392 1.036683 3.990962 \n",
"2 0.493610 0.231038 2.982211 \n",
"3 -0.325477 0.675320 2.766270 \n",
"4 -1.269712 -0.544451 2.426935 \n",
"\n",
" Downtown_Bnd_Trn_Trips Uni_Wa_Bnd_Peak_Trn_Trips \\\n",
"0 1.986503 1.067185 \n",
"1 2.889763 4.791844 \n",
"2 2.757484 0.463405 \n",
"3 0.876618 -0.469527 \n",
"4 0.900274 0.063577 \n",
"\n",
" SEATAC_Bnd_Peak_Trn_Trips Bellevue_Bnd_Peak_Trn_Trips LU_Typology \\\n",
"0 -0.263552 5.220576 3 \n",
"1 -0.263552 1.070406 4 \n",
"2 3.089686 -0.419399 4 \n",
"3 -0.263552 -0.419399 4 \n",
"4 -0.263552 1.070406 2 \n",
"\n",
" Q1_PandR_Utilization Rural Res Comm_Ind Sub_Downtown \n",
"0 1.121587 -0.449467 -0.476240 0.927025 -0.350202 \n",
"1 1.227431 -0.449467 -0.476240 -1.078720 2.855494 \n",
"2 1.068665 -0.449467 -0.476240 -1.078720 2.855494 \n",
"3 0.989283 -0.449467 -0.476240 -1.078720 2.855494 \n",
"4 1.042204 -0.449467 2.099784 -1.078720 -0.350202 "
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Normalize/Center Variables by Z- Scores\n",
"for column in regression_features:\n",
" try:\n",
" print(\"Creating standarized column for field {0}....\".format(str(column)))\n",
" col_Standarized = column\n",
" regr_df[col_Standarized] = (regr_df[column] - regr_df[column].mean())/regr_df[column].std(ddof=0)\n",
" except Exception as e:\n",
" print(e.args[0])\n",
" pass\n",
"display(regr_df.head())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABo0AAAZICAYAAABAHonbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1cc23fc2a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Generate Pair Plot\n",
"pair_plot=sns.pairplot(regr_df[data_features],hue=\"LU_Typology\")\n",
"plt.show(pair_plot)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAGGCAYAAACE19M6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIM0P8DyH7bAI14QfUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1cc27fe4940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"##Develop Seaborn Correlation Matrix\n",
"#Compute Correlations\n",
"data_for_correlation_computation=regr_df[data_features].copy()\n",
"\n",
"corr_df=data_for_correlation_computation.corr()\n",
"#Generate Mask for Upper Triangle\n",
"mask= np.zeros_like(corr_df, dtype=np.bool)\n",
"mask[np.triu_indices_from(mask)] = True\n",
"#Generate correlation heat map\n",
"with sns.axes_style(\"ticks\"):\n",
" axplot=sns.heatmap(corr_df,mask=mask, annot=True, linewidths=.6,linecolor=\"white\",cmap=\"seismic\",vmin=-1,vmax=1)\n",
" plt.show(axplot)\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Developing regression plots with 95% confidence intervals for independent Pop_2015_in_Driveshed and dependent Q1_PandR_Utilization...\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAFcCAYAAACEFgYsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1cc28119588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Developing regression plots with 95% confidence intervals for independent Employ_2015_in_Driveshed and dependent Q1_PandR_Utilization...\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAFcCAYAAACEFgYsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnuZLf34AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1cc28bae710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Developing regression plots with 95% confidence intervals for independent Total_Parking_Spaces and dependent Q1_PandR_Utilization...\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAFcCAYAAACEFgYsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1cc28203c50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Developing regression plots with 95% confidence intervals for independent Downtown_Bnd_Trn_Trips and dependent Q1_PandR_Utilization...\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAFcCAYAAACEFgYsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1cc28267358>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Developing regression plots with 95% confidence intervals for independent Uni_Wa_Bnd_Peak_Trn_Trips and dependent Q1_PandR_Utilization...\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAFcCAYAAACEFgYsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEiOUcAkhJEb+PxFR+dhu7/FMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1cc282cd9e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Developing regression plots with 95% confidence intervals for independent SEATAC_Bnd_Peak_Trn_Trips and dependent Q1_PandR_Utilization...\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAFcCAYAAACEFgYsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niGzy/wF++MkVwhXiOgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1cc28339c18>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Developing regression plots with 95% confidence intervals for independent Bellevue_Bnd_Peak_Trn_Trips and dependent Q1_PandR_Utilization...\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAFcCAYAAACEFgYsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD1o7rY0gywlvAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1cc283ab160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def develop_regplots(data,dependent_feature,independent_features,hue=None):\n",
" \"\"\"Developed regression plots of dependent/independent features in a dataframe.\"\"\"\n",
" for feature in independent_features:\n",
" print(\"Developing regression plots with 95% confidence intervals for independent {0} and dependent {1}...\".format(\n",
" str(feature),dependent_feature))\n",
" g=sns.lmplot(x=feature,y=dependent_feature,data=data,hue=hue,size=5)\n",
" plt.show(g)\n",
"develop_regplots(regr_df,dependent_feature,independent_features)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"##Develop Multiple Regression Set up\n",
"\n",
"#Set Up Data for Regression\n",
"independent_features_with_typology=independent_features+catgorical_names\n",
"regr_df= regr_df[regression_features]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Q1_PandR_Utilization</td> <th> R-squared: </th> <td> 0.350</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.309</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 8.553</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 02 Oct 2017</td> <th> Prob (F-statistic):</th> <td>2.36e-08</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>07:41:40</td> <th> Log-Likelihood: </th> <td> -143.19</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 119</td> <th> AIC: </th> <td> 302.4</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 111</td> <th> BIC: </th> <td> 324.6</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 7</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td>-5.551e-17</td> <td> 0.077</td> <td>-7.26e-16</td> <td> 1.000</td> <td> -0.152</td> <td> 0.152</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Pop_2015_in_Driveshed</th> <td> -0.2564</td> <td> 0.089</td> <td> -2.879</td> <td> 0.005</td> <td> -0.433</td> <td> -0.080</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Employ_2015_in_Driveshed</th> <td> 0.1518</td> <td> 0.087</td> <td> 1.743</td> <td> 0.084</td> <td> -0.021</td> <td> 0.324</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Total_Parking_Spaces</th> <td> -0.0138</td> <td> 0.107</td> <td> -0.129</td> <td> 0.898</td> <td> -0.226</td> <td> 0.198</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Downtown_Bnd_Trn_Trips</th> <td> 0.3901</td> <td> 0.111</td> <td> 3.515</td> <td> 0.001</td> <td> 0.170</td> <td> 0.610</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Uni_Wa_Bnd_Peak_Trn_Trips</th> <td> 0.2517</td> <td> 0.099</td> <td> 2.534</td> <td> 0.013</td> <td> 0.055</td> <td> 0.449</td>\n",
"</tr>\n",
"<tr>\n",
" <th>SEATAC_Bnd_Peak_Trn_Trips</th> <td> 0.0425</td> <td> 0.086</td> <td> 0.494</td> <td> 0.622</td> <td> -0.128</td> <td> 0.213</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Bellevue_Bnd_Peak_Trn_Trips</th> <td> 0.0965</td> <td> 0.094</td> <td> 1.023</td> <td> 0.309</td> <td> -0.090</td> <td> 0.283</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>10.807</td> <th> Durbin-Watson: </th> <td> 1.893</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.005</td> <th> Jarque-Bera (JB): </th> <td> 11.510</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.616</td> <th> Prob(JB): </th> <td> 0.00317</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 3.896</td> <th> Cond. No. </th> <td> 2.86</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"================================================================================\n",
"Dep. Variable: Q1_PandR_Utilization R-squared: 0.350\n",
"Model: OLS Adj. R-squared: 0.309\n",
"Method: Least Squares F-statistic: 8.553\n",
"Date: Mon, 02 Oct 2017 Prob (F-statistic): 2.36e-08\n",
"Time: 07:41:40 Log-Likelihood: -143.19\n",
"No. Observations: 119 AIC: 302.4\n",
"Df Residuals: 111 BIC: 324.6\n",
"Df Model: 7 \n",
"Covariance Type: nonrobust \n",
"===============================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"-----------------------------------------------------------------------------------------------\n",
"const -5.551e-17 0.077 -7.26e-16 1.000 -0.152 0.152\n",
"Pop_2015_in_Driveshed -0.2564 0.089 -2.879 0.005 -0.433 -0.080\n",
"Employ_2015_in_Driveshed 0.1518 0.087 1.743 0.084 -0.021 0.324\n",
"Total_Parking_Spaces -0.0138 0.107 -0.129 0.898 -0.226 0.198\n",
"Downtown_Bnd_Trn_Trips 0.3901 0.111 3.515 0.001 0.170 0.610\n",
"Uni_Wa_Bnd_Peak_Trn_Trips 0.2517 0.099 2.534 0.013 0.055 0.449\n",
"SEATAC_Bnd_Peak_Trn_Trips 0.0425 0.086 0.494 0.622 -0.128 0.213\n",
"Bellevue_Bnd_Peak_Trn_Trips 0.0965 0.094 1.023 0.309 -0.090 0.283\n",
"==============================================================================\n",
"Omnibus: 10.807 Durbin-Watson: 1.893\n",
"Prob(Omnibus): 0.005 Jarque-Bera (JB): 11.510\n",
"Skew: 0.616 Prob(JB): 0.00317\n",
"Kurtosis: 3.896 Cond. No. 2.86\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Fit Simple OLS Model\n",
"X=regr_df[independent_features]\n",
"X=sm.add_constant(X)\n",
"y=regr_df[dependent_feature]\n",
"\n",
"OLSMod=sm.OLS(y,X).fit()\n",
"\n",
"OLSMod.summary()\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Q1_PandR_Utilization</td> <th> R-squared: </th> <td> 0.365</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.306</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 6.203</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 02 Oct 2017</td> <th> Prob (F-statistic):</th> <td>1.95e-07</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>07:41:40</td> <th> Log-Likelihood: </th> <td> -141.85</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 119</td> <th> AIC: </th> <td> 305.7</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 108</td> <th> BIC: </th> <td> 336.3</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 10</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td>-5.551e-17</td> <td> 0.077</td> <td>-7.24e-16</td> <td> 1.000</td> <td> -0.152</td> <td> 0.152</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Pop_2015_in_Driveshed</th> <td> -0.2983</td> <td> 0.112</td> <td> -2.659</td> <td> 0.009</td> <td> -0.521</td> <td> -0.076</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Employ_2015_in_Driveshed</th> <td> 0.1315</td> <td> 0.105</td> <td> 1.250</td> <td> 0.214</td> <td> -0.077</td> <td> 0.340</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Total_Parking_Spaces</th> <td> -0.0171</td> <td> 0.116</td> <td> -0.147</td> <td> 0.883</td> <td> -0.248</td> <td> 0.213</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Downtown_Bnd_Trn_Trips</th> <td> 0.4239</td> <td> 0.116</td> <td> 3.669</td> <td> 0.000</td> <td> 0.195</td> <td> 0.653</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Uni_Wa_Bnd_Peak_Trn_Trips</th> <td> 0.2335</td> <td> 0.102</td> <td> 2.291</td> <td> 0.024</td> <td> 0.031</td> <td> 0.436</td>\n",
"</tr>\n",
"<tr>\n",
" <th>SEATAC_Bnd_Peak_Trn_Trips</th> <td> 0.0401</td> <td> 0.088</td> <td> 0.458</td> <td> 0.648</td> <td> -0.134</td> <td> 0.214</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Bellevue_Bnd_Peak_Trn_Trips</th> <td> 0.1113</td> <td> 0.098</td> <td> 1.137</td> <td> 0.258</td> <td> -0.083</td> <td> 0.305</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Rural</th> <td> 0.0059</td> <td> 0.088</td> <td> 0.067</td> <td> 0.947</td> <td> -0.169</td> <td> 0.180</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Res</th> <td> -0.0772</td> <td> 0.067</td> <td> -1.153</td> <td> 0.252</td> <td> -0.210</td> <td> 0.056</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Comm_Ind</th> <td> 0.0701</td> <td> 0.058</td> <td> 1.208</td> <td> 0.230</td> <td> -0.045</td> <td> 0.185</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Sub_Downtown</th> <td> -0.0231</td> <td> 0.095</td> <td> -0.242</td> <td> 0.809</td> <td> -0.212</td> <td> 0.166</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 7.952</td> <th> Durbin-Watson: </th> <td> 1.899</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.019</td> <th> Jarque-Bera (JB): </th> <td> 7.892</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.508</td> <th> Prob(JB): </th> <td> 0.0193</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 3.749</td> <th> Cond. No. </th> <td>1.06e+16</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"================================================================================\n",
"Dep. Variable: Q1_PandR_Utilization R-squared: 0.365\n",
"Model: OLS Adj. R-squared: 0.306\n",
"Method: Least Squares F-statistic: 6.203\n",
"Date: Mon, 02 Oct 2017 Prob (F-statistic): 1.95e-07\n",
"Time: 07:41:40 Log-Likelihood: -141.85\n",
"No. Observations: 119 AIC: 305.7\n",
"Df Residuals: 108 BIC: 336.3\n",
"Df Model: 10 \n",
"Covariance Type: nonrobust \n",
"===============================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"-----------------------------------------------------------------------------------------------\n",
"const -5.551e-17 0.077 -7.24e-16 1.000 -0.152 0.152\n",
"Pop_2015_in_Driveshed -0.2983 0.112 -2.659 0.009 -0.521 -0.076\n",
"Employ_2015_in_Driveshed 0.1315 0.105 1.250 0.214 -0.077 0.340\n",
"Total_Parking_Spaces -0.0171 0.116 -0.147 0.883 -0.248 0.213\n",
"Downtown_Bnd_Trn_Trips 0.4239 0.116 3.669 0.000 0.195 0.653\n",
"Uni_Wa_Bnd_Peak_Trn_Trips 0.2335 0.102 2.291 0.024 0.031 0.436\n",
"SEATAC_Bnd_Peak_Trn_Trips 0.0401 0.088 0.458 0.648 -0.134 0.214\n",
"Bellevue_Bnd_Peak_Trn_Trips 0.1113 0.098 1.137 0.258 -0.083 0.305\n",
"Rural 0.0059 0.088 0.067 0.947 -0.169 0.180\n",
"Res -0.0772 0.067 -1.153 0.252 -0.210 0.056\n",
"Comm_Ind 0.0701 0.058 1.208 0.230 -0.045 0.185\n",
"Sub_Downtown -0.0231 0.095 -0.242 0.809 -0.212 0.166\n",
"==============================================================================\n",
"Omnibus: 7.952 Durbin-Watson: 1.899\n",
"Prob(Omnibus): 0.019 Jarque-Bera (JB): 7.892\n",
"Skew: 0.508 Prob(JB): 0.0193\n",
"Kurtosis: 3.749 Cond. No. 1.06e+16\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The smallest eigenvalue is 3.36e-30. This might indicate that there are\n",
"strong multicollinearity problems or that the design matrix is singular.\n",
"\"\"\""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fit OLS with Topology Dummies\n",
"X=regr_df[independent_features_with_typology]\n",
"X=sm.add_constant(X)\n",
"y=regr_df[dependent_feature]\n",
"\n",
"OLSModTopo=sm.OLS(y,X).fit()\n",
"\n",
"OLSModTopo.summary()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Robust linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Q1_PandR_Utilization</td> <th> No. Observations: </th> <td> 119</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>RLM</td> <th> Df Residuals: </th> <td> 111</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Df Model: </th> <td> 7</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Norm:</th> <td>HuberT</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Scale Est.:</th> <td>mad</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Cov Type:</th> <td>H1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 02 Oct 2017</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>07:41:40</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>12</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> -0.0267</td> <td> 0.073</td> <td> -0.365</td> <td> 0.715</td> <td> -0.170</td> <td> 0.117</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Pop_2015_in_Driveshed</th> <td> -0.2489</td> <td> 0.085</td> <td> -2.920</td> <td> 0.003</td> <td> -0.416</td> <td> -0.082</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Employ_2015_in_Driveshed</th> <td> 0.1395</td> <td> 0.083</td> <td> 1.675</td> <td> 0.094</td> <td> -0.024</td> <td> 0.303</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Total_Parking_Spaces</th> <td> -0.0036</td> <td> 0.102</td> <td> -0.035</td> <td> 0.972</td> <td> -0.204</td> <td> 0.197</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Downtown_Bnd_Trn_Trips</th> <td> 0.3746</td> <td> 0.106</td> <td> 3.529</td> <td> 0.000</td> <td> 0.167</td> <td> 0.583</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Uni_Wa_Bnd_Peak_Trn_Trips</th> <td> 0.2604</td> <td> 0.095</td> <td> 2.739</td> <td> 0.006</td> <td> 0.074</td> <td> 0.447</td>\n",
"</tr>\n",
"<tr>\n",
" <th>SEATAC_Bnd_Peak_Trn_Trips</th> <td> 0.0537</td> <td> 0.082</td> <td> 0.652</td> <td> 0.514</td> <td> -0.108</td> <td> 0.215</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Bellevue_Bnd_Peak_Trn_Trips</th> <td> 0.1186</td> <td> 0.090</td> <td> 1.314</td> <td> 0.189</td> <td> -0.058</td> <td> 0.296</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Robust linear Model Regression Results \n",
"================================================================================\n",
"Dep. Variable: Q1_PandR_Utilization No. Observations: 119\n",
"Model: RLM Df Residuals: 111\n",
"Method: IRLS Df Model: 7\n",
"Norm: HuberT \n",
"Scale Est.: mad \n",
"Cov Type: H1 \n",
"Date: Mon, 02 Oct 2017 \n",
"Time: 07:41:40 \n",
"No. Iterations: 12 \n",
"===============================================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-----------------------------------------------------------------------------------------------\n",
"const -0.0267 0.073 -0.365 0.715 -0.170 0.117\n",
"Pop_2015_in_Driveshed -0.2489 0.085 -2.920 0.003 -0.416 -0.082\n",
"Employ_2015_in_Driveshed 0.1395 0.083 1.675 0.094 -0.024 0.303\n",
"Total_Parking_Spaces -0.0036 0.102 -0.035 0.972 -0.204 0.197\n",
"Downtown_Bnd_Trn_Trips 0.3746 0.106 3.529 0.000 0.167 0.583\n",
"Uni_Wa_Bnd_Peak_Trn_Trips 0.2604 0.095 2.739 0.006 0.074 0.447\n",
"SEATAC_Bnd_Peak_Trn_Trips 0.0537 0.082 0.652 0.514 -0.108 0.215\n",
"Bellevue_Bnd_Peak_Trn_Trips 0.1186 0.090 1.314 0.189 -0.058 0.296\n",
"===============================================================================================\n",
"\n",
"If the model instance has been used for another fit with different fit\n",
"parameters, then the fit options might not be the correct ones anymore .\n",
"\"\"\""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fit Robust Regression \n",
"X=regr_df[independent_features]\n",
"X=sm.add_constant(X)\n",
"y=regr_df[dependent_feature]\n",
"\n",
"RLMMod=sm.RLM(y,X).fit()\n",
"RLMMod.summary()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Q1_PandR_Utilization</td> <th> R-squared: </th> <td> 0.367</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.318</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 7.524</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 02 Oct 2017</td> <th> Prob (F-statistic):</th> <td>3.91e-07</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>07:41:40</td> <th> Log-Likelihood: </th> <td> -121.82</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 99</td> <th> AIC: </th> <td> 259.6</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 91</td> <th> BIC: </th> <td> 280.4</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 7</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> -0.0131</td> <td> 0.092</td> <td> -0.142</td> <td> 0.887</td> <td> -0.197</td> <td> 0.170</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Pop_2015_in_Driveshed</th> <td> -0.2288</td> <td> 0.111</td> <td> -2.059</td> <td> 0.042</td> <td> -0.450</td> <td> -0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Employ_2015_in_Driveshed</th> <td> 0.1524</td> <td> 0.092</td> <td> 1.656</td> <td> 0.101</td> <td> -0.030</td> <td> 0.335</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Total_Parking_Spaces</th> <td> -0.0107</td> <td> 0.111</td> <td> -0.096</td> <td> 0.923</td> <td> -0.231</td> <td> 0.210</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Downtown_Bnd_Trn_Trips</th> <td> 0.3841</td> <td> 0.117</td> <td> 3.271</td> <td> 0.002</td> <td> 0.151</td> <td> 0.617</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Uni_Wa_Bnd_Peak_Trn_Trips</th> <td> 0.2469</td> <td> 0.105</td> <td> 2.356</td> <td> 0.021</td> <td> 0.039</td> <td> 0.455</td>\n",
"</tr>\n",
"<tr>\n",
" <th>SEATAC_Bnd_Peak_Trn_Trips</th> <td> 0.0456</td> <td> 0.089</td> <td> 0.512</td> <td> 0.610</td> <td> -0.131</td> <td> 0.223</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Bellevue_Bnd_Peak_Trn_Trips</th> <td> 0.1070</td> <td> 0.099</td> <td> 1.086</td> <td> 0.280</td> <td> -0.089</td> <td> 0.303</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>11.377</td> <th> Durbin-Watson: </th> <td> 1.835</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.003</td> <th> Jarque-Bera (JB): </th> <td> 12.539</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.675</td> <th> Prob(JB): </th> <td> 0.00189</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 4.104</td> <th> Cond. No. </th> <td> 2.69</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"================================================================================\n",
"Dep. Variable: Q1_PandR_Utilization R-squared: 0.367\n",
"Model: OLS Adj. R-squared: 0.318\n",
"Method: Least Squares F-statistic: 7.524\n",
"Date: Mon, 02 Oct 2017 Prob (F-statistic): 3.91e-07\n",
"Time: 07:41:40 Log-Likelihood: -121.82\n",
"No. Observations: 99 AIC: 259.6\n",
"Df Residuals: 91 BIC: 280.4\n",
"Df Model: 7 \n",
"Covariance Type: nonrobust \n",
"===============================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"-----------------------------------------------------------------------------------------------\n",
"const -0.0131 0.092 -0.142 0.887 -0.197 0.170\n",
"Pop_2015_in_Driveshed -0.2288 0.111 -2.059 0.042 -0.450 -0.008\n",
"Employ_2015_in_Driveshed 0.1524 0.092 1.656 0.101 -0.030 0.335\n",
"Total_Parking_Spaces -0.0107 0.111 -0.096 0.923 -0.231 0.210\n",
"Downtown_Bnd_Trn_Trips 0.3841 0.117 3.271 0.002 0.151 0.617\n",
"Uni_Wa_Bnd_Peak_Trn_Trips 0.2469 0.105 2.356 0.021 0.039 0.455\n",
"SEATAC_Bnd_Peak_Trn_Trips 0.0456 0.089 0.512 0.610 -0.131 0.223\n",
"Bellevue_Bnd_Peak_Trn_Trips 0.1070 0.099 1.086 0.280 -0.089 0.303\n",
"==============================================================================\n",
"Omnibus: 11.377 Durbin-Watson: 1.835\n",
"Prob(Omnibus): 0.003 Jarque-Bera (JB): 12.539\n",
"Skew: 0.675 Prob(JB): 0.00189\n",
"Kurtosis: 4.104 Cond. No. 2.69\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Fit Simple OLS Model without rural features. \n",
"X=regr_df[regr_df[\"Rural\"]<=0][independent_features]\n",
"X=sm.add_constant(X)\n",
"y=regr_df[regr_df[\"Rural\"]<=0][dependent_feature]\n",
"\n",
"OLSMod=sm.OLS(y,X).fit()\n",
"\n",
"OLSMod.summary()\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Q1_PandR_Utilization</td> <th> R-squared: </th> <td> 0.358</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.323</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 10.37</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 02 Oct 2017</td> <th> Prob (F-statistic):</th> <td>6.37e-08</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>07:41:40</td> <th> Log-Likelihood: </th> <td> -122.49</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 99</td> <th> AIC: </th> <td> 257.0</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 93</td> <th> BIC: </th> <td> 272.6</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 5</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> -0.0086</td> <td> 0.092</td> <td> -0.093</td> <td> 0.926</td> <td> -0.191</td> <td> 0.174</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Pop_2015_in_Driveshed</th> <td> -0.2535</td> <td> 0.108</td> <td> -2.352</td> <td> 0.021</td> <td> -0.468</td> <td> -0.039</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Employ_2015_in_Driveshed</th> <td> 0.1891</td> <td> 0.086</td> <td> 2.209</td> <td> 0.030</td> <td> 0.019</td> <td> 0.359</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Total_Parking_Spaces</th> <td> 0.0354</td> <td> 0.102</td> <td> 0.347</td> <td> 0.730</td> <td> -0.167</td> <td> 0.238</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Downtown_Bnd_Trn_Trips</th> <td> 0.3856</td> <td> 0.115</td> <td> 3.351</td> <td> 0.001</td> <td> 0.157</td> <td> 0.614</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Uni_Wa_Bnd_Peak_Trn_Trips</th> <td> 0.2566</td> <td> 0.099</td> <td> 2.591</td> <td> 0.011</td> <td> 0.060</td> <td> 0.453</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 9.441</td> <th> Durbin-Watson: </th> <td> 1.872</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.009</td> <th> Jarque-Bera (JB): </th> <td> 9.536</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.630</td> <th> Prob(JB): </th> <td> 0.00850</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 3.852</td> <th> Cond. No. </th> <td> 2.45</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"================================================================================\n",
"Dep. Variable: Q1_PandR_Utilization R-squared: 0.358\n",
"Model: OLS Adj. R-squared: 0.323\n",
"Method: Least Squares F-statistic: 10.37\n",
"Date: Mon, 02 Oct 2017 Prob (F-statistic): 6.37e-08\n",
"Time: 07:41:40 Log-Likelihood: -122.49\n",
"No. Observations: 99 AIC: 257.0\n",
"Df Residuals: 93 BIC: 272.6\n",
"Df Model: 5 \n",
"Covariance Type: nonrobust \n",
"=============================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"---------------------------------------------------------------------------------------------\n",
"const -0.0086 0.092 -0.093 0.926 -0.191 0.174\n",
"Pop_2015_in_Driveshed -0.2535 0.108 -2.352 0.021 -0.468 -0.039\n",
"Employ_2015_in_Driveshed 0.1891 0.086 2.209 0.030 0.019 0.359\n",
"Total_Parking_Spaces 0.0354 0.102 0.347 0.730 -0.167 0.238\n",
"Downtown_Bnd_Trn_Trips 0.3856 0.115 3.351 0.001 0.157 0.614\n",
"Uni_Wa_Bnd_Peak_Trn_Trips 0.2566 0.099 2.591 0.011 0.060 0.453\n",
"==============================================================================\n",
"Omnibus: 9.441 Durbin-Watson: 1.872\n",
"Prob(Omnibus): 0.009 Jarque-Bera (JB): 9.536\n",
"Skew: 0.630 Prob(JB): 0.00850\n",
"Kurtosis: 3.852 Cond. No. 2.45\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Fit Simple OLS Model without rural features - reduce to 5 variables with useful outcomes. \n",
"X=regr_df[regr_df[\"Rural\"]<=0][[\"Pop_2015_in_Driveshed\",\"Employ_2015_in_Driveshed\",\n",
" \"Total_Parking_Spaces\",\"Downtown_Bnd_Trn_Trips\",\"Uni_Wa_Bnd_Peak_Trn_Trips\"]]\n",
"X=sm.add_constant(X)\n",
"y=regr_df[regr_df[\"Rural\"]<=0][dependent_feature]\n",
"\n",
"OLSMod=sm.OLS(y,X).fit()\n",
"\n",
"OLSMod.summary()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Q1_PandR_Utilization</td> <th> R-squared: </th> <td> 0.357</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.330</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 13.05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 02 Oct 2017</td> <th> Prob (F-statistic):</th> <td>1.72e-08</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>07:41:40</td> <th> Log-Likelihood: </th> <td> -122.56</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 99</td> <th> AIC: </th> <td> 255.1</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 94</td> <th> BIC: </th> <td> 268.1</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 4</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> -0.0069</td> <td> 0.091</td> <td> -0.075</td> <td> 0.940</td> <td> -0.188</td> <td> 0.175</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Pop_2015_in_Driveshed</th> <td> -0.2613</td> <td> 0.105</td> <td> -2.490</td> <td> 0.015</td> <td> -0.470</td> <td> -0.053</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Employ_2015_in_Driveshed</th> <td> 0.1936</td> <td> 0.084</td> <td> 2.300</td> <td> 0.024</td> <td> 0.026</td> <td> 0.361</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Downtown_Bnd_Trn_Trips</th> <td> 0.4062</td> <td> 0.098</td> <td> 4.139</td> <td> 0.000</td> <td> 0.211</td> <td> 0.601</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Uni_Wa_Bnd_Peak_Trn_Trips</th> <td> 0.2590</td> <td> 0.098</td> <td> 2.632</td> <td> 0.010</td> <td> 0.064</td> <td> 0.454</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 8.898</td> <th> Durbin-Watson: </th> <td> 1.880</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.012</td> <th> Jarque-Bera (JB): </th> <td> 8.823</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.611</td> <th> Prob(JB): </th> <td> 0.0121</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 3.804</td> <th> Cond. No. </th> <td> 2.08</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"================================================================================\n",
"Dep. Variable: Q1_PandR_Utilization R-squared: 0.357\n",
"Model: OLS Adj. R-squared: 0.330\n",
"Method: Least Squares F-statistic: 13.05\n",
"Date: Mon, 02 Oct 2017 Prob (F-statistic): 1.72e-08\n",
"Time: 07:41:40 Log-Likelihood: -122.56\n",
"No. Observations: 99 AIC: 255.1\n",
"Df Residuals: 94 BIC: 268.1\n",
"Df Model: 4 \n",
"Covariance Type: nonrobust \n",
"=============================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"---------------------------------------------------------------------------------------------\n",
"const -0.0069 0.091 -0.075 0.940 -0.188 0.175\n",
"Pop_2015_in_Driveshed -0.2613 0.105 -2.490 0.015 -0.470 -0.053\n",
"Employ_2015_in_Driveshed 0.1936 0.084 2.300 0.024 0.026 0.361\n",
"Downtown_Bnd_Trn_Trips 0.4062 0.098 4.139 0.000 0.211 0.601\n",
"Uni_Wa_Bnd_Peak_Trn_Trips 0.2590 0.098 2.632 0.010 0.064 0.454\n",
"==============================================================================\n",
"Omnibus: 8.898 Durbin-Watson: 1.880\n",
"Prob(Omnibus): 0.012 Jarque-Bera (JB): 8.823\n",
"Skew: 0.611 Prob(JB): 0.0121\n",
"Kurtosis: 3.804 Cond. No. 2.08\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Fit Simple OLS Model without rural features - reduce to 4 variables with useful outcomes- remove parking control\n",
"X=regr_df[regr_df[\"Rural\"]<=0][[\"Pop_2015_in_Driveshed\",\"Employ_2015_in_Driveshed\"\n",
" ,\"Downtown_Bnd_Trn_Trips\",\"Uni_Wa_Bnd_Peak_Trn_Trips\"]]\n",
"X=sm.add_constant(X)\n",
"y=regr_df[regr_df[\"Rural\"]<=0][dependent_feature]\n",
"\n",
"OLSMod=sm.OLS(y,X).fit()\n",
"\n",
"OLSMod.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment