Data Questions
Geographic Distribution

Where do the people helped/served live?

Where do people helped/served with different demographics live?

Where do people helped/served from your defined groups live?

Where do people helped/served with certain categories of legal problems live?

Where do people helped/served with different legal problems live?

Where do people served by staff live and where do people served by pro bono volunteers live?

Where do the people not helped/not served live?

Where do people not helped/not served with different demographics live?

Geographic Distribution

Geographic Distribution analyses show how people or problems or anything else of interest is distributed across service areas, which can be divided into smaller areas to reveal spatial patterns.  These patterns are opportunities to learn about the spatial dimensions of your organization and your clients. 

Example Data Question

Does the pattern of clients served vary throughout our service area?

Multiple Analyses Are Possible

  • What is the distribution of clients served throughout your service area?
  • Are the rates of service high in certain areas and low in other areas? Are those levels what you would expect? Are there any surprises in specific areas?

Data Sources

Intake data from your case management system, including 

  • Demographics about which you are curious
  • Open date and close date
  • Other case information that you might be able to use in other analyses, including problem code, other demographics, etc.
  • Exclude cases that were identified as errors or duplicates, but make sure to keep cases that ended up not being served

Example Analyses Steps

  1. GIS mapping using ArcGIS or other similar software.
  2. To look at these results in tabular format, you would need two create a “crosstab” (MS Excel calls them Pivot Tables) from variables in your in your case management system.  The rows of the resulting table would be the geography level – cities, for example.  The columns would be intakes and served.  So for each city in the table, you would know the number of intakes and the number of intakes served, from which you could calculate the percent of intakes served.  Directly from these data you could identify the high and low percent served cities in your service area.
  3. To map the results, you could start with that same crosstab/pivot table output, and join those data to their corresponding geography file to generate a map.  
  4. In this example, data are aggregated to the community level (the ward level within the city of Cleveland), and mapped for the service area over a 5-year period.  Darker shades represent higher percentages of all intakes that were served.
  5. You should note for maps based on percentages that small numbers might result in misleading percentages.  For example, a community with only 1 intake, which didn’t happen to be served, would show on the map as 0% served.  A 0% served finding might initially cause great concern, but such small numbers should be interpreted with great caution.  In this example, we noted communities where the total intakes were below 30.
Map 1