This document summarizes a presentation about using geographic information systems (GIS) and opportunity mapping for research and advocacy at the Kirwan Institute. It provides an overview of the Kirwan Institute's mission and work analyzing spatial trends related to opportunity, such as access to employment, education, healthcare, and more. It then describes the methodology of opportunity mapping, which identifies indicators of opportunity, calculates z-scores to measure relative performance, and develops an overall opportunity index to compare areas and identify gaps. Examples of opportunity maps for various regions are also presented to demonstrate how the technique can be used to analyze issues of inequity.

1. GIS in Professional Planning Practice CRP 608Winter ‘10 Class presentation February 04, 2010 SAMIR GAMBHIR Senior Research Associate Kirwan Institute for the Study of Race and Ethnicity

2. Overview Background Kirwan Institute Our work Using GIS for research and advocacy Opportunity Mapping Work in progress National Opportunity Model Web-based GIS

3. About Kirwan Institute Multidisciplinary applied research institute Our mission is to expand opportunity for all, especially for our most marginalized communities Founded in 2003 by john powell Opportunity Communities Program (1/3 of staff) Opening pathways to opportunity for marginalized communities through investments in people, places and supporting linkages Opportunity mapping 3

4. Maps: Powerful Visual Tools Maps are incredibly efficient compacting volumes of data ability to convey information in seconds tell a story or solve a problem Research has shown that people can solve problems faster with map based information, than by looking at charts, tables or graphs

5. Space and Social Equity Why are maps particularly effective in dealing with issues of equity? Regional, racial and social inequity often manifest as spatial inequity Maps are naturally the best tools to display this spatial phenomena Maps give us the opportunity to look at our entire regions or states Informing people about an issue at a scale they may not usually think of linking communities sharing similar problems

6. Using Maps for Advocacy In our work we see mapping as serving these primary advocacy goals Analysis Existing conditions, spatial trends, scenarios, optimization etc. Storytelling A narrative Combination

7. Analytical Examples Are minority businesses located in areas of economic opportunity? (Cleveland) Are hospital investments benefiting communities of color? (Columbus) Are marginalized communities disproportionately affected by foreclosure crisis? (Connecticut) Are job growth areas connected to transit? (Baltimore) What is the impact of stimulus money investment on job creation? (Florida)

8. MBE and Projected Job Change 2000-2030

9. Hospital Investments and African American nbhds:Columbus

10. Race and Foreclosure Crisis

11. Spatial Mismatch:Job Growth & PublicTransit in Baltimore

12. Stimulus investments and Job creation in Orlando MSA, Florida

13. Narratives Examples Subsidized housing policy is reinforcing segregation (Baltimore) Foreclosures in African American neighborhoods are due to subprime lending patterns (Cleveland) Vacant property problems are spreading, vacant property challenges are not just an inner city problem (Detroit) What if Montclair, NJ schools returned to neighborhood school system?

14. Conditions in Baltimore Subsidized housing opportunities in Baltimore are generally clustered in the region’s predominately African American neighborhoods

15. Subprime Lending, Race and Foreclosure(Note: Not one of our maps)

16. Subprime Lending, Race and Foreclosure(Note: Not one of our maps) Maps: Produced and adapted from Charles Bromley, SAGES Presidential Fellow, Case Western University

17. Looking at Issues Across Time and Space: The Growing Vacant Land Problem in Detroit

18. Montclair School District, NJ

19. Opportunity Mapping:Combining Analysis with a Strong Narrative Opportunity mapping is a research tool used to understand the dynamics of “opportunity” within metropolitan areas The purpose of opportunity mapping is to illustrate where opportunity rich communities exist (and assess who has access to these communities) Also, to understand what needs to be remedied in opportunity poor communities

20. Mapping Opportunity:why and How Inequality has a geographic footprint Maps can visually track the history and presence of discriminatory and exclusionary policies that spatially segregate people Identifying places with gaps in opportunity can help direct future investment and identify structures which impede access to opportunity

21. Opportunity Matters: Space, Place, and Life Outcomes “Opportunity” is a situation or condition that places individuals in a position to be more likely to succeed or excel. Opportunity structures are critical to opening pathways to success: High-quality education Healthy and safe environment Stable housing Sustainable employment Political empowerment Outlets for wealth-building Positive social networks

22. Which community would you choose? 22

23. Some people ride the “Up” escalator to reach opportunity. Others have to run up the “Down” escalator to get there.

24. Opportunity Mapping Model A refined model to depict spatial pattern of opportunity Identifying indicators as proxy for opportunity Supported by social science literature Data easily available Index based approach compresses multi-factors to an index Model is a good communications tool to work with communities

25. Opportunity Mapping Booklet

30. Methodology Identifying and selecting indicators of opportunity Identifying sources of data Compiling list of indicators (data matrix) Calculating Z scores Averaging these scores

31. Methodology:Indicator Categories Education Student/Teacher ratio? Test scores? Student mobility? Economic/Employment Indicators Unemployment rate? Proximity to employment? Job creation? Neighborhood Quality Median home values? Crime rate? Housing vacancy rate? Mobility/Transportation Indicators Mean commute time? Access to public transit? Health & Environmental Indicators Access to health care? Exposure to toxic waste? Proximity to parks or open space?

32. Methodology:Sources of Data Federal Organizations Census Bureau County Business Patterns (ZIP Code Data) Housing and Urban Development (HUD) Environmental Protection Agency (EPA) State and Local Governmental Organizations Regional planning agencies Education boards/school districts Transportation agencies County Auditor’s Office Other agencies (non-Profit and Private) Schoolmatters.org DataPlace.org ESRI Business Analyst Claritas

33. Methodology:Effect on Opportunity

34. Methodology:Calculating Z Scores Z Score – a statistical measure that quantifies the distance (measured in standard deviations) between data points and the mean Z Score = (Data point – Mean)/ Standard Deviation Allows data for a geography (e.g. census tract) to be measured based on their relative distance from the average for the entire region Raw z score performance Mean value is always “zero” – z score indicates distance from the mean Positive z score is always above the region’s mean, Negative z score is always below the region’s mean Indicators with negative effect on opportunity should have all the z scores adjusted to reflect this phenomena

35. Methodology:Calculating Opportunity using Z Scores Final “opportunity index” for each census tract is the average of z scores (including adjusted scores for direction) for all indicators by category Census tracts can be ranked Opportunity level is determined by sorting a region’s census tract z scores into ordered categories (very low, low, moderate, high, very high) Top 20% can be categorized as very high, bottom 20% - very low

36. Austin MSA, TX

37. Baltimore Opportunity and Subsidized Housing Subsidized housing opportunities in Baltimore are generally clustered in the region’s lowest opportunity neighborhoods

38. Detroit Opportunity and Race African American men are isolated from neighborhoods of opportunity in Detroit

39. Austin Opportunity and Linguistic Isolation Low opportunity neighborhoods have higher number of linguistically isolated households

40. Redlining: 1937 to 2009

41. Comp Opportunity and Race

42. Follow-up Need more research on methodology The model needs to be made more robust Critical analysis of all indicators e.g. job mismatch, park access issues

43. Work in progress Customizing data transfer procedures National Opportunity Mapping Web-based Opportunity mapping

44. Comparison

45. Web-based mapping Online interactive maps ArcGIS Server Baltimore Foreclosures(http://kirwan27:8399/BaltimoreForeclosure/mapviewer.jsf?width=261&height=438) Open source Austin Opportunity Mapping(http://www.gis.osu.edu/webgis-projects/opportunity/index.html)

46. Thank you! For questions, comments or for more information visit our website www.kirwaninstitute.org or e-mail me at Gambhir.2@osu.edu