{"id":52240,"date":"2025-09-19T00:00:00","date_gmt":"2025-09-19T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/storing-smart-city-insights-in-a-smarter-database-a-griddb-epa-data-story\/"},"modified":"2025-09-19T00:00:00","modified_gmt":"2025-09-19T07:00:00","slug":"storing-smart-city-insights-in-a-smarter-database-a-griddb-epa-data-story","status":"publish","type":"post","link":"https:\/\/griddb.net\/en\/blog\/storing-smart-city-insights-in-a-smarter-database-a-griddb-epa-data-story\/","title":{"rendered":"Storing Smart City Insights in a Smarter Database: A GridDB + EPA Data Story"},"content":{"rendered":"
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Storing Smart City Insights in a Smarter Database: A GridDB + EPA Data Story\u00b6<\/a><\/h1>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Introduction\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Did you know that people living in walkable neighborhoods are healthier and less prone to cardiometabolic diseases? A study published in the popular scientific journal 'Nature' mentioned that walkability significantly improves health outcomes; with residents in walkable areas being less prone to chronic diseases. So, what makes a neighborhood walkable, and how does it impact our physical and mental well-being? Let\u2019s delve deeper into this topic.\n[Source: Nature, https:\/\/www.nature.com\/articles\/s41598-025-94192-x<\/a>]<\/p>\n

We use GridDB for this analysis. GridDB bridges the gap between SQL and NoSQL by providing users with a SQL interface that supports SQL-like syntax. In other words, when a user writes a SQL query, GridDB takes that query and translates it into NoSQL operations to scan the data. The key steps in SQL to NoSQL conversion are shown in the diagram below. Refer to the GridDB SQL Reference <\/a> to learn more about the SQL Interface.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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For this analysis, we use the Smart Location Database published by the U.S Environmental Protection Agency (EPA).The dataset has features that describe built environment characteristics across the U.S., like walkability, population density, land use mix, street connectivity, and employment accessibility for several census block groups. It generally helps measure how \u201cwalkable,\u201d \u201ccompact,\u201d or \u201ctransit-connected\u201d different neighborhoods are.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Importing Libraries\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[434]:<\/div>\n
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<\/span>from<\/span> <\/span>IPython.display<\/span> <\/span>import<\/span> Image<\/span>,<\/span> display<\/span>,<\/span> HTML<\/span>\nimport<\/span> <\/span>pandas<\/span> <\/span>as<\/span> <\/span>pd<\/span>\nimport<\/span> <\/span>base64<\/span>\nimport<\/span> <\/span>requests<\/span>\nimport<\/span> <\/span>os<\/span>\nimport<\/span> <\/span>json<\/span>\nimport<\/span> <\/span>http.client<\/span>\nimport<\/span> <\/span>matplotlib.pyplot<\/span> <\/span>as<\/span> <\/span>plt<\/span>\nimport<\/span> <\/span>seaborn<\/span> <\/span>as<\/span> <\/span>sns<\/span>\nimport<\/span> <\/span>plotly.express<\/span> <\/span>as<\/span> <\/span>px<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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About the Dataset\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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The data can be downloaded from  data.gov<\/a>. The URL provided in this page points to the '.csv' data file hosted on the EPA Commons website and available for public consumption. This URL is used in the section 'Loading the Dataset'. Below is a screenshot of the same.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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<\/span>Image<\/span>(<\/span>filename<\/span>=<\/span>'Dataset_Download.png'<\/span>,<\/span>width<\/span>=<\/span>800<\/span>,<\/span> height<\/span>=<\/span>600<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Loading the Dataset\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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<\/span># URL of the CSV file<\/span>\nurl<\/span> =<\/span> \"https:\/\/edg.epa.gov\/EPADataCommons\/public\/OA\/EPA_SmartLocationDatabase_V3_Jan_2021_Final.csv\"<\/span>\n\n# Load into a DataFrame<\/span>\nwalkability_data<\/span> =<\/span> pd<\/span>.<\/span>read_csv<\/span>(<\/span>url<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Subsetting the Data\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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<\/span># Step 1: Select necessary columns<\/span>\ncolumns_to_keep<\/span> =<\/span> [<\/span>\n    'OBJECTID'<\/span>,<\/span> 'GEOID10'<\/span>,<\/span> 'STATEFP'<\/span>,<\/span> 'COUNTYFP'<\/span>,<\/span> 'TRACTCE'<\/span>,<\/span> 'CBSA'<\/span>,<\/span>\n    'D5DRI'<\/span>,<\/span> 'D5DE'<\/span>,<\/span> 'D5DEI'<\/span>,<\/span> 'NatWalkInd'<\/span>,<\/span> 'Shape_Length'<\/span>,<\/span> 'Shape_Area'<\/span>\n]<\/span>\n\ndf_subset<\/span> =<\/span> walkability_data<\/span>[<\/span>columns_to_keep<\/span>]<\/span>\n\n# Filter for specific states<\/span>\nstates_to_filter<\/span> =<\/span> [<\/span>6<\/span>,<\/span> 48<\/span>,<\/span> 51<\/span>]<\/span>\n\nwalkability_subset<\/span> =<\/span> df_subset<\/span>[<\/span>df_subset<\/span>[<\/span>'STATEFP'<\/span>]<\/span>.<\/span>isin<\/span>(<\/span>states_to_filter<\/span>)]<\/span>\n\n# Filter out any rows with NaN or Infinite values in 'NatWalkInd'<\/span>\nwalkability_subset<\/span> =<\/span> walkability_subset<\/span>[<\/span>~<\/span>walkability_subset<\/span>[<\/span>'NatWalkInd'<\/span>]<\/span>.<\/span>isin<\/span>([<\/span>float<\/span>(<\/span>'inf'<\/span>),<\/span> -<\/span>float<\/span>(<\/span>'inf'<\/span>)])]<\/span>.<\/span>dropna<\/span>(<\/span>subset<\/span>=<\/span>[<\/span>'NatWalkInd'<\/span>])<\/span>\n\n# Drop rows with NaN values in any column<\/span>\nwalkability_subset<\/span> =<\/span> walkability_subset<\/span>.<\/span>dropna<\/span>()<\/span>\n\n# Filter by walkability score (e.g., NatWalkInd > 50)<\/span>\nwalkability_subset<\/span> =<\/span> walkability_subset<\/span>[<\/span>walkability_subset<\/span>[<\/span>'NatWalkInd'<\/span>]<\/span> ><\/span> 14.0<\/span>]<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Creating an Additional Dataset for State Mapping\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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<\/span># Data for the state_mapping container (all U.S. states)<\/span>\nstate_mapping_data<\/span> =<\/span> [<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"01\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Alabama\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"02\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Alaska\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"04\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Arizona\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"05\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Arkansas\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"06\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"California\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"08\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Colorado\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"09\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Connecticut\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"10\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Delaware\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"11\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"District of Columbia\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"12\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Florida\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"13\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Georgia\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"15\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Hawaii\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"16\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Idaho\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"17\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Illinois\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"18\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Indiana\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"19\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Iowa\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"20\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Kansas\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"21\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Kentucky\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"22\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Louisiana\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"23\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Maine\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"24\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Maryland\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"25\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Massachusetts\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"26\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Michigan\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"27\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Minnesota\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"28\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Mississippi\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"29\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Missouri\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"30\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Montana\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"31\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Nebraska\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"32\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Nevada\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"33\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"New Hampshire\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"34\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"New Jersey\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"35\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"New Mexico\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"36\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"New York\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"37\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"North Carolina\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"38\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"North Dakota\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"39\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Ohio\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"40\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Oklahoma\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"41\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Oregon\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"42\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Pennsylvania\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"44\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Rhode Island\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"45\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"South Carolina\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"46\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"South Dakota\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"47\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Tennessee\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"48\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Texas\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"49\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Utah\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"50\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Vermont\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"51\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Virginia\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"53\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Washington\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"54\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"West Virginia\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"55\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Wisconsin\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"56\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Wyoming\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"60\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"American Samoa\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"66\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Guam\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"69\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Northern Mariana Islands\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"72\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Puerto Rico\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"74\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"U.S. Virgin Islands\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"78\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Federated States of Micronesia\"<\/span>},<\/span>\n    {<\/span>\"STATEFP\"<\/span>:<\/span> \"82\"<\/span>,<\/span> \"StateName\"<\/span>:<\/span> \"Palau\"<\/span>}<\/span>\n]<\/span>\n\n# Convert the list of dictionaries into a DataFrame<\/span>\nstate_mapping_df<\/span> =<\/span> pd<\/span>.<\/span>DataFrame<\/span>(<\/span>state_mapping_data<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Explanation of Walkability Data Columns\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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<\/span>html_code<\/span> =<\/span> \"\"\"<\/span>\n<table border=\"1\"><\/span>\n  <thead><\/span>\n    <tr><\/span>\n      <th>Column Name<\/th><\/span>\n      <th>Description<\/th><\/span>\n    <\/tr><\/span>\n  <\/thead><\/span>\n  <tbody><\/span>\n    <tr><\/span>\n      <td>OBJECTID<\/td><\/span>\n      <td>A unique identifier for each row\/record (usually auto-generated by GIS software like ArcGIS).<\/td><\/span>\n    <\/tr><\/span>\n    <tr><\/span>\n      <td>GEOID10<\/td><\/span>\n      <td>2010 Census Tract identifier \u2014 a unique geographic code for a census tract.<\/td><\/span>\n    <\/tr><\/span>\n    <tr><\/span>\n      <td>STATEFP<\/td><\/span>\n      <td>State FIPS (Federal Information Processing Standards) code \u2014 uniquely identifies U.S. states.<\/td><\/span>\n    <\/tr><\/span>\n    <tr><\/span>\n      <td>COUNTYFP<\/td><\/span>\n      <td>County FIPS code \u2014 identifies counties within states.<\/td><\/span>\n    <\/tr><\/span>\n    <tr><\/span>\n      <td>TRACTCE<\/td><\/span>\n      <td>Census Tract Code \u2014 identifies individual census tracts within a county.<\/td><\/span>\n    <\/tr><\/span>\n    <tr><\/span>\n      <td>CBSA<\/td><\/span>\n      <td>Core-Based Statistical Area code \u2014 classifies urban areas (e.g., metros or micropolitan areas).<\/td><\/span>\n    <\/tr><\/span>\n    <tr><\/span>\n      <td>D5DRI<\/td><\/span>\n      <td>Distance to Retail Index \u2014 measures how far residents are from retail destinations.<\/td><\/span>\n    <\/tr><\/span>\n    <tr><\/span>\n      <td>D5DE<\/td><\/span>\n      <td>Diversity of Employment \u2014 an index measuring job diversity in a given area.<\/td><\/span>\n    <\/tr><\/span>\n    <tr><\/span>\n      <td>D5DEI<\/td><\/span>\n      <td>Employment Entropy Index \u2014 a statistical measure of employment mix (entropy means diversity).<\/td><\/span>\n    <\/tr><\/span>\n    <tr><\/span>\n      <td>NatWalkInd<\/td><\/span>\n      <td>National Walkability Index \u2014 score from 0 to 100 estimating how walkable an area is.<\/td><\/span>\n    <\/tr><\/span>\n    <tr><\/span>\n      <td>Shape_Length<\/td><\/span>\n      <td>Length of the geographic feature\u2019s shape (used in GIS, often in meters).<\/td><\/span>\n    <\/tr><\/span>\n    <tr><\/span>\n      <td>Shape_Area<\/td><\/span>\n      <td>Area of the geographic shape (used in GIS, often in square meters or feet).<\/td><\/span>\n    <\/tr><\/span>\n  <\/tbody><\/span>\n<\/table><\/span>\n\"\"\"<\/span>\n\ndisplay<\/span>(<\/span>HTML<\/span>(<\/span>html_code<\/span>))<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Column Name<\/th>\nDescription<\/th>\n<\/tr>\n<\/thead>\n
OBJECTID<\/td>\nA unique identifier for each row\/record (usually auto-generated by GIS software like ArcGIS).<\/td>\n<\/tr>\n
GEOID10<\/td>\n2010 Census Tract identifier \u2014 a unique geographic code for a census tract.<\/td>\n<\/tr>\n
STATEFP<\/td>\nState FIPS (Federal Information Processing Standards) code \u2014 uniquely identifies U.S. states.<\/td>\n<\/tr>\n
COUNTYFP<\/td>\nCounty FIPS code \u2014 identifies counties within states.<\/td>\n<\/tr>\n
TRACTCE<\/td>\nCensus Tract Code \u2014 identifies individual census tracts within a county.<\/td>\n<\/tr>\n
CBSA<\/td>\nCore-Based Statistical Area code \u2014 classifies urban areas (e.g., metros or micropolitan areas).<\/td>\n<\/tr>\n
D5DRI<\/td>\nDistance to Retail Index \u2014 measures how far residents are from retail destinations.<\/td>\n<\/tr>\n
D5DE<\/td>\nDiversity of Employment \u2014 an index measuring job diversity in a given area.<\/td>\n<\/tr>\n
D5DEI<\/td>\nEmployment Entropy Index \u2014 a statistical measure of employment mix (entropy means diversity).<\/td>\n<\/tr>\n
NatWalkInd<\/td>\nNational Walkability Index \u2014 score from 0 to 100 estimating how walkable an area is.<\/td>\n<\/tr>\n
Shape_Length<\/td>\nLength of the geographic feature\u2019s shape (used in GIS, often in meters).<\/td>\n<\/tr>\n
Shape_Area<\/td>\nArea of the geographic shape (used in GIS, often in square meters or feet).<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Further details are available in the official EPA website - https:\/\/www.epa.gov\/system\/files\/documents\/2023-10\/epa_sld_3.0_technicaldocumentationuserguide_may2021_0.pdf<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Connecting to GridDB\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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<\/span># Replace with your actual GridDB connection details<\/span>\nusername<\/span> =<\/span> \"XXXXXX\"<\/span> \npassword<\/span> =<\/span>   \"XXXXXXX\"<\/span>\ncredentials<\/span> =<\/span> f<\/span>\"<\/span>{<\/span>username<\/span>}<\/span>:<\/span>{<\/span>password<\/span>}<\/span>\"<\/span>\nencoded_credentials<\/span> =<\/span> base64<\/span>.<\/span>b64encode<\/span>(<\/span>credentials<\/span>.<\/span>encode<\/span>())<\/span>.<\/span>decode<\/span>()<\/span>\nprint<\/span>(<\/span>f<\/span>\"Encoded credentials: Basic <\/span>{<\/span>encoded_credentials<\/span>}<\/span>\"<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Encoded credentials: Basic TTAxa3dXMTRjdC1zdWJoYTpUaGF0aHB1cnVzaGFheWExMjMj\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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<\/span>#Construct an object to hold the request headers (ensure that you replace the XXX placeholder with the correct value that matches the credentials for your GridDB instance)<\/span>\n\nheader_obj<\/span> =<\/span> {<\/span>\n    \"Authorization\"<\/span>:<\/span> f<\/span>\"Basic <\/span>{<\/span>encoded_credentials<\/span>}<\/span>\"<\/span>,<\/span>  # Add encoded credentials here<\/span>\n    \"Content-Type\"<\/span>:<\/span> \"application\/json\"<\/span>,<\/span>  # Optional; depends on API requirements<\/span>\n    \"charset\"<\/span>:<\/span> \"UTF-8\"<\/span>,<\/span>\n    \"User-Agent\"<\/span>:<\/span>\"PostmanRuntime\/7.29.0\"<\/span>\n}<\/span>\n\n#Construct the base URL based on your GridDB cluster you'd like to connect to (ensure that you replace the placeholders in the URL below with the correct values that correspond to your GridDB instance)<\/span>\n#'https:\/\/[host]:[port]\/griddb\/v2\/[clustername]\/dbs\/[database_name]'<\/span>\nbase_url<\/span> =<\/span> 'https:\/\/[host]:[port]\/griddb\/v2\/[clustername]\/dbs\/[database_name]'<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Generating Container Structures for use in GridDB\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Functions to generate the container structures\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[9]:<\/div>\n
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<\/span>def<\/span> <\/span>map_dtype_to_griddb<\/span>(<\/span>dtype<\/span>):<\/span>\n    <\/span>\"\"\"<\/span>\n    Maps Pandas data types to GridDB data types.<\/span>\n    \"\"\"<\/span>\n    if<\/span> pd<\/span>.<\/span>api<\/span>.<\/span>types<\/span>.<\/span>is_integer_dtype<\/span>(<\/span>dtype<\/span>):<\/span>\n        return<\/span> \"INTEGER\"<\/span>\n    elif<\/span> pd<\/span>.<\/span>api<\/span>.<\/span>types<\/span>.<\/span>is_float_dtype<\/span>(<\/span>dtype<\/span>):<\/span>\n        return<\/span> \"DOUBLE\"<\/span>\n    elif<\/span> pd<\/span>.<\/span>api<\/span>.<\/span>types<\/span>.<\/span>is_bool_dtype<\/span>(<\/span>dtype<\/span>):<\/span>\n        return<\/span> \"BOOL\"<\/span>\n    elif<\/span> pd<\/span>.<\/span>api<\/span>.<\/span>types<\/span>.<\/span>is_datetime64_any_dtype<\/span>(<\/span>dtype<\/span>):<\/span>\n        return<\/span> \"LONG\"<\/span>  # GridDB stores timestamps as LONG<\/span>\n    else<\/span>:<\/span>\n        return<\/span> \"STRING\"<\/span>\n\ndef<\/span> <\/span>generate_griddb_data_obj<\/span>(<\/span>df<\/span>,<\/span> container_name<\/span>=<\/span>\"MyContainer\"<\/span>,<\/span> container_type<\/span>=<\/span>\"COLLECTION\"<\/span>,<\/span> rowkey<\/span>=<\/span>False<\/span>):<\/span>\n    <\/span>\"\"\"<\/span>\n    Generates a GridDB container data object for API request.<\/span>\n    \"\"\"<\/span>\n    columns<\/span> =<\/span> []<\/span>\n    for<\/span> col<\/span> in<\/span> df<\/span>.<\/span>columns<\/span>:<\/span>\n        griddb_type<\/span> =<\/span> map_dtype_to_griddb<\/span>(<\/span>df<\/span>[<\/span>col<\/span>]<\/span>.<\/span>dtype<\/span>)<\/span>\n        columns<\/span>.<\/span>append<\/span>({<\/span>\"name\"<\/span>:<\/span> col<\/span>,<\/span> \"type\"<\/span>:<\/span> griddb_type<\/span>})<\/span>\n    \n    data_obj<\/span> =<\/span> {<\/span>\n        \"container_name\"<\/span>:<\/span> container_name<\/span>,<\/span>\n        \"container_type\"<\/span>:<\/span> container_type<\/span>,<\/span>\n        \"rowkey\"<\/span>:<\/span> rowkey<\/span>,<\/span>\n        \"columns\"<\/span>:<\/span> columns<\/span>\n    }<\/span>\n    \n    return<\/span> data_obj<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Creating Containers in GridDB\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[65]:<\/div>\n
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<\/span>data_obj<\/span> =<\/span> generate_griddb_data_obj<\/span>(<\/span>walkability_subset<\/span>,<\/span> container_name<\/span>=<\/span>\"walkability_data\"<\/span>)<\/span>\n\n#Set up the GridDB WebAPI URL<\/span>\nurl<\/span> =<\/span> base_url<\/span> +<\/span> 'containers'<\/span>\n\n#Invoke the GridDB WebAPI with the headers and the request body<\/span>\nx<\/span> =<\/span> requests<\/span>.<\/span>post<\/span>(<\/span>url<\/span>,<\/span> json<\/span> =<\/span> data_obj<\/span>,<\/span> headers<\/span> =<\/span> header_obj<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[182]:<\/div>\n
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<\/span>data_obj_state_mapping<\/span> =<\/span> generate_griddb_data_obj<\/span>(<\/span>state_mapping_df<\/span>,<\/span> container_name<\/span>=<\/span>\"state_mapping_data\"<\/span>)<\/span>\n\n#Set up the GridDB WebAPI URL<\/span>\nurl<\/span> =<\/span> base_url<\/span> +<\/span> 'containers'<\/span>\n\n#Invoke the GridDB WebAPI with the headers and the request body<\/span>\nx<\/span> =<\/span> requests<\/span>.<\/span>post<\/span>(<\/span>url<\/span>,<\/span> json<\/span> =<\/span> data_obj_state_mapping<\/span>,<\/span> headers<\/span> =<\/span> header_obj<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Row-Registration (Loading the Containers)\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Row Registration in GridDB is analogous to inserting data in relational SQL databases. Refer to the  GridDB Data Model <\/a> to learn more about row registration and to know how GridDB processes transactions.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[66]:<\/div>\n
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<\/span># Parse the JSON string into a Python list of dictionaries<\/span>\nrequest_body_walkability_data<\/span> =<\/span> walkability_subset<\/span>.<\/span>to_json<\/span>(<\/span>orient<\/span>=<\/span>'values'<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[67]:<\/div>\n
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<\/span>#Setup the URL to be used to invoke the GridDB WebAPI to register rows in the container created previously<\/span>\nurl<\/span> =<\/span> base_url<\/span> +<\/span> 'containers\/walkability_data\/rows'<\/span>\n\n#Invoke the GridDB WebAPI using the request constructed<\/span>\nx<\/span> =<\/span> requests<\/span>.<\/span>put<\/span>(<\/span>url<\/span>,<\/span> data<\/span>=<\/span>request_body_walkability_data<\/span>,<\/span> headers<\/span>=<\/span>header_obj<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[194]:<\/div>\n
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<\/span># Parse the JSON string into a Python list of dictionaries<\/span>\nrequest_body_state_mapping<\/span> =<\/span> state_mapping_df<\/span>.<\/span>to_json<\/span>(<\/span>orient<\/span>=<\/span>'values'<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[195]:<\/div>\n
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<\/span>#Setup the URL to be used to invoke the GridDB WebAPI to register rows in the container created previously<\/span>\nurl<\/span> =<\/span> base_url<\/span> +<\/span> 'containers\/state_mapping_data\/rows'<\/span>\n\n#Invoke the GridDB WebAPI using the request constructed<\/span>\nx<\/span> =<\/span> requests<\/span>.<\/span>put<\/span>(<\/span>url<\/span>,<\/span> data<\/span>=<\/span>request_body_state_mapping<\/span>,<\/span> headers<\/span>=<\/span>header_obj<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Analysis using GridDB SQL\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Top 10 Metro Areas with High Walkability but Low Transit Access\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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GridDB supports type casting similar to explicit casting in relational SQL databases. It also supports standard aggregation operations like AVG, COUNT, and so on. Refer to  this resource <\/a> to learn more.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[117]:<\/div>\n
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<\/span>sql_query1<\/span> =<\/span> (<\/span>\"\"\"<\/span>\n                SELECT CAST(CBSA AS INT) AS CBSA, AVG(NatWalkInd) AS \"Average Walk Index\"<\/span>\n                FROM walkability_data<\/span>\n                GROUP BY CBSA<\/span>\n                ORDER BY 2 DESC<\/span>\n                LIMIT 10;<\/span>\n\"\"\"<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[118]:<\/div>\n
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<\/span>#Setup the URL to be used to invoke the GridDB WebAPI to retrieve data from the container<\/span>\nurl<\/span> =<\/span> base_url<\/span> +<\/span> 'sql'<\/span>\n\n# Construct the request body<\/span>\nrequest_body<\/span> =<\/span> json<\/span>.<\/span>dumps<\/span>([<\/span>\n    {<\/span>\n        \"type\"<\/span>:<\/span> \"sql-select\"<\/span>,<\/span>\n        \"stmt\"<\/span>:<\/span> sql_query1<\/span>\n    }<\/span>\n])<\/span>\n\n# Disable HTTP debugging<\/span>\nhttp<\/span>.<\/span>client<\/span>.<\/span>HTTPConnection<\/span>.<\/span>debuglevel<\/span> =<\/span> 0<\/span>\n\n#Invoke the GridDB WebAPI<\/span>\ndata_req1<\/span> =<\/span> requests<\/span>.<\/span>post<\/span>(<\/span>url<\/span>,<\/span> data<\/span>=<\/span>request_body<\/span>,<\/span> headers<\/span>=<\/span>header_obj<\/span>)<\/span>\n\n#Process the response received and construct a Pandas dataframe with the data from the response<\/span>\nmyJson<\/span> =<\/span> data_req1<\/span>.<\/span>json<\/span>()<\/span>\nhigh_wkblty_low_trnst_access<\/span> =<\/span> pd<\/span>.<\/span>DataFrame<\/span>(<\/span>myJson<\/span>[<\/span>0<\/span>][<\/span>\"results\"<\/span>],<\/span> columns<\/span>=<\/span>[<\/span>myJson<\/span>[<\/span>0<\/span>][<\/span>\"columns\"<\/span>][<\/span>0<\/span>][<\/span>\"name\"<\/span>],<\/span> myJson<\/span>[<\/span>0<\/span>][<\/span>\"columns\"<\/span>][<\/span>1<\/span>][<\/span>\"name\"<\/span>]])<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[132]:<\/div>\n
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<\/span># Plot<\/span>\nplt<\/span>.<\/span>figure<\/span>(<\/span>figsize<\/span>=<\/span>(<\/span>10<\/span>,<\/span> 6<\/span>))<\/span>\nbars<\/span> =<\/span> plt<\/span>.<\/span>bar<\/span>(<\/span>high_wkblty_low_trnst_access<\/span>[<\/span>\"CBSA\"<\/span>]<\/span>.<\/span>astype<\/span>(<\/span>str<\/span>),<\/span> high_wkblty_low_trnst_access<\/span>[<\/span>\"Average Walk Index\"<\/span>],<\/span> color<\/span>=<\/span>'seagreen'<\/span>)<\/span>\n\n# Set a tighter y-axis range to enhance differences<\/span>\nplt<\/span>.<\/span>ylim<\/span>(<\/span>16<\/span>,<\/span> 17<\/span>)<\/span>\n\n# Add labels and title<\/span>\nplt<\/span>.<\/span>xlabel<\/span>(<\/span>\"CBSA\"<\/span>)<\/span>\nplt<\/span>.<\/span>ylabel<\/span>(<\/span>\"Average Walkability Index\"<\/span>)<\/span>\nplt<\/span>.<\/span>title<\/span>(<\/span>\"Top CBSAs with Low Transit Access & High Walkability\"<\/span>)<\/span>\n\n# Add value labels on top of bars<\/span>\nfor<\/span> bar<\/span> in<\/span> bars<\/span>:<\/span>\n    yval<\/span> =<\/span> bar<\/span>.<\/span>get_height<\/span>()<\/span>\n    plt<\/span>.<\/span>text<\/span>(<\/span>bar<\/span>.<\/span>get_x<\/span>()<\/span> +<\/span> bar<\/span>.<\/span>get_width<\/span>()<\/span>\/<\/span>2<\/span>,<\/span> yval<\/span> +<\/span> 0.005<\/span>,<\/span> round<\/span>(<\/span>yval<\/span>,<\/span> 2<\/span>),<\/span> ha<\/span>=<\/span>'center'<\/span>,<\/span> va<\/span>=<\/span>'bottom'<\/span>)<\/span>\n\nplt<\/span>.<\/span>tight_layout<\/span>()<\/span>\nplt<\/span>.<\/span>show<\/span>()<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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 Insight(s): <\/b> The top CBSA having the highest walkability index is that of 43760 (New York-Newark-Jersey City, NY-NJ-PA). Being a large and highly urbanized metropolitan area in the U.S., it is not surprising that it has one of the highest walkability scores.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Average Walkability Index by State\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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GridDB supports Inner Join, Left Outer Join and Cross Join operations. Refer to  this resource <\/a> to learn more.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[428]:<\/div>\n
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<\/span>sql_query2<\/span> =<\/span> (<\/span>\"\"\"<\/span>\n                SELECT sm.StateName, ROUND(AVG(wd.NatWalkInd),3) AS \"Average Walkability Index\"<\/span>\n                FROM walkability_data wd<\/span>\n                JOIN state_mapping_data sm ON wd.STATEFP = CAST(sm.STATEFP AS INT)<\/span>\n                GROUP BY sm.StateName<\/span>\n                ORDER BY 2 DESC;<\/span>\n\n\"\"\"<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[429]:<\/div>\n
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<\/span>#Setup the URL to be used to invoke the GridDB WebAPI to retrieve data from the container<\/span>\nurl<\/span> =<\/span> base_url<\/span> +<\/span> 'sql'<\/span>\n\n# Construct the request body<\/span>\nrequest_body<\/span> =<\/span> json<\/span>.<\/span>dumps<\/span>([<\/span>\n    {<\/span>\n        \"type\"<\/span>:<\/span> \"sql-select\"<\/span>,<\/span>\n        \"stmt\"<\/span>:<\/span> sql_query2<\/span>\n    }<\/span>\n])<\/span>\n\n# Disable HTTP debugging<\/span>\nhttp<\/span>.<\/span>client<\/span>.<\/span>HTTPConnection<\/span>.<\/span>debuglevel<\/span> =<\/span> 0<\/span>\n\n#Invoke the GridDB WebAPI<\/span>\ndata_req2<\/span> =<\/span> requests<\/span>.<\/span>post<\/span>(<\/span>url<\/span>,<\/span> data<\/span>=<\/span>request_body<\/span>,<\/span> headers<\/span>=<\/span>header_obj<\/span>)<\/span>\n\n#Process the response received and construct a Pandas dataframe with the data from the response<\/span>\nmyJson<\/span> =<\/span> data_req2<\/span>.<\/span>json<\/span>()<\/span>\nwalkability_by_state<\/span> =<\/span> pd<\/span>.<\/span>DataFrame<\/span>(<\/span>myJson<\/span>[<\/span>0<\/span>][<\/span>\"results\"<\/span>],<\/span> columns<\/span>=<\/span>[<\/span>myJson<\/span>[<\/span>0<\/span>][<\/span>\"columns\"<\/span>][<\/span>0<\/span>][<\/span>\"name\"<\/span>],<\/span> myJson<\/span>[<\/span>0<\/span>][<\/span>\"columns\"<\/span>][<\/span>1<\/span>][<\/span>\"name\"<\/span>]])<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[430]:<\/div>\n
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<\/span># Create a figure and axis<\/span>\nfig<\/span>,<\/span> ax<\/span> =<\/span> plt<\/span>.<\/span>subplots<\/span>(<\/span>figsize<\/span>=<\/span>(<\/span>6<\/span>,<\/span> 2<\/span>))<\/span>\nax<\/span>.<\/span>axis<\/span>(<\/span>'off'<\/span>)<\/span>  # Hide axes<\/span>\n\n# Create the table<\/span>\ntable<\/span> =<\/span> ax<\/span>.<\/span>table<\/span>(<\/span>\n    cellText<\/span>=<\/span>walkability_by_state<\/span>.<\/span>values<\/span>,<\/span>\n    colLabels<\/span>=<\/span>walkability_by_state<\/span>.<\/span>columns<\/span>,<\/span>\n    cellLoc<\/span>=<\/span>'center'<\/span>,<\/span>\n    loc<\/span>=<\/span>'center'<\/span>\n)<\/span>\n\ntable<\/span>.<\/span>scale<\/span>(<\/span>1<\/span>,<\/span> 2<\/span>)<\/span>  # Optional: scale the table (width, height)<\/span>\ntable<\/span>.<\/span>auto_set_font_size<\/span>(<\/span>False<\/span>)<\/span>\ntable<\/span>.<\/span>set_fontsize<\/span>(<\/span>10<\/span>)<\/span>\n\nplt<\/span>.<\/span>title<\/span>(<\/span>\"Average Walkability Index by State\"<\/span>,<\/span> fontsize<\/span>=<\/span>12<\/span>,<\/span> pad<\/span>=<\/span>10<\/span>)<\/span>\nplt<\/span>.<\/span>show<\/span>()<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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 Insight(s): <\/b> <\/p>