Open Opportunity Data

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Why We Love It

See a city through the eyes of the people who live in its neighborhoods. We love how this map shows each neighborhood’s proximity to quality schools. It adds a second dimension of median home value to help identify areas with both affordable housing and access to quality schools. Those opportunities are represented by small, green diamond symbols. In a clean and clear way, this map tells the interrelated stories of income and access to education. It also provides tools for further inquiry of a nationwide dataset of indexes on School Proficiency, Job Proximity, Transportation Cost, Market Labor, and Median Home Value.

Why It Works

This map works because it uses color and symbol size to reveal the sometimes subtle, sometimes not so subtle, differences in neighborhoods. Red diamonds represent a low School Proficiency Index while green diamonds represent high. Diamond sizes signify Median Home Value for an area—an indication of how financially feasible it may be to move into that neighborhood. Educational bargains are found in neighborhoods with small, green diamonds as these are more affordable and have access to better schools. You can touch any neighborhood to see additional HUD Opportunity indexes.

Important Steps

View the open data available on the HUD eGIS Open Data site. Explore that data in ArcGIS. Then download the datasets from HUD and combine into a single layer so that an application can query all the relevant data from one source.

Join the layers using the analysis tools and publish the new map layer with all indexes included in the nationwide dataset at the geography level of your choosing (here we use Census tracts).

Think about which part of the relationship between access to quality schools and home value is most interesting. Where are the opportunities?

Create a new web map with the data. Symbolize the data in the web map using smart mapping to showcase the attributes that are important. Configure the pop-up window to incorporate more data in a nice format.

Create a web mapping application from the web map and configure it to the end-user’s need. We used Web AppBuilder for ArcGIS to create a custom application we thought necessary for dissemination of this information.

Requirements

Data

The HUD eGIS Open Data site hosts many Open Opportunity Datasets. Anyone can use open data sites to search by topic or location, download data in multiple formats, and view data on an interactive map and in a table. The specific datasets we used include:

  • Low Transportation Cost Index
  • Jobs Proximity Index
  • Environmental Health Hazard Index
  • School Proficiency Index
  • Location Affordability Index

Analysis

We used smart mapping to better understand the data, then set its symbols to highlight the interesting part. We viewed the distribution of the first variable, then the second. We set symbology for color and size in a way that calls attention to the need for lower-cost access to quality education.

Time

It took 30 minutes to collect the layers and join them into one finalized dataset. Another hour was spent exploring the data, finalizing symbols, and configuring the custom pop-up with text and a bar chart. It took just 10 minutes to configure the Web AppBuilder application because there was no custom code to write.

Attribute in the Data

Tips

Choose to investigate the relationship between two attributes in the data: school proficiency index and median home value. Opportunity is found where there is a high school proficiency index and low-to-moderate home values.

School Proficiency Index

Tips

Use the histogram in the Styling panel of the Map Viewer to see how the school proficiency index varies by neighborhood and determine the average value. Set the values at which dark green or dark red colors are applied in full.

Home Value Variation

Tips

Use the histogram in the Styling panel of the Map Viewer to see home value variation by neighborhood and average home value. Set the values at which large and small symbols are applied with everything proportionally sized in between.

Map Author

Patrick O’Brien

Patrick O’Brien

@PobrienEsri | LinkedIn

Patrick, a lifetime geographer, has been a Solution Engineer at Esri since 2015. Undergrad at Salisbury University and graduate school at University of Maryland, College Park, Patrick has tried to map just about everything.

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