CRP 608 Winter 10 Class presentation February 04, 2010 SAMIR GAMBHIR SAMIR GAMBHIR Senior Research Associate Kirwan Institute for the Study of Race and Ethnicity
Background Kirwan Institute Our work Using GIS for research and advocacy Opportunity Mapping Work in progress National Opportunity Model Web-based GIS
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
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
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
In our work we see mapping as serving these primary advocacy goals Analysis Existing conditions, spatial trends, scenarios, optimization etc. Storytellingt A narrative Combination
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? i (Connecticut) t) Are job growth areas connected to transit? (Baltimore) What is the impact of stimulus money investment on job creation? (Florida)
Recent Job Growth 98-02 and Public Transit in the Baltimore Region Percent Change in Jobs Job Loss 0-5 5-15 15-30 30-66.6
Subsidized housing policy is reinforcing segregation (Baltimore) Foreclosures in African American neighborhoods are due to subprime lending gp 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?
Subsidized housing opportunities in Baltimore are generally clustered in the region s predominately African American neighborhoods
Maps: Produced and adapted from Charles Bromley, SAGES Presidential Fellow, Case Western University
Growth of Vacant Housing in Detroit 1970-2000 (% Vacant Housing in 1970 and 2000) City of Detroit Highways Counties Legend: Prepared by: Kirwan Institute Source Data: U.S. Census Bureau W N % of Homes Vacant 0-3 3-10 10-15 15-20 20-57.6 S E % Vacant 1970 % Vacant 2000 8 0 8 16 Miles
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) Al t d t d h t d t b di d i Also, to understand what needs to be remedied in opportunity poor communities
Inequality has a geographic footprint Maps can visually track the history and presence of discriminatory and exclusionary policies that spatially segregate g peoplep Identifying places with gaps in opportunity can y gp g p pp y help direct future investment and identify structures which impede access to opportunity
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
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Some people ride the Up escalator to reach opportunity. Others have to run up the Down escalator to get there.
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
Identifying and selecting indicators of opportunity Identifying sources of data Compiling list of indicators (data matrix) Calculating Z scores Averaging these scores
Education Student/Teacher ratio? Test scores? Student mobility? Economic/Employment Indicators Unemployment rate? Proximity i 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?
Federal Organizations Census Bureau County Business Patterns (ZIP Code Data) Housing and Urban Development (HUD) Environmental Protection ti 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
INDICATORS DATA MATRIX EDUCATION DESCRIPTION Effect on opportunity Educational attainment for total population Percentage of population with college degree Positive School poverty for neighborhood schools Percentage of economically disadvantaged students Negative Teacher qualifications for neighborhood schools (or certified teachers) Percentage of Highly Qualified Teachers (HQT) Positive ENVIRONMENTAL & PUBLIC HEALTH Proximity i to toxic waste release sites Census tracts are ranked based on their distance from these facilities i Positive i Proximity to parks/open spaces Census tracts are ranked based on their distance from open spaces Negative Medically Underserved Areas Areas designated as MUA Positive
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
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
Subsidized housing opportunities in Baltimore are generally clustered in the region s lowest opportunity neighborhoods
African American men are isolated from neighborhoods of opportunity in Detroit
Low opportunity neighborhoods have higher number of linguistically isolated households
Need more research on methodology The model needs to be made more robust Critical analysis of all indicators e g job Critical analysis of all indicators e.g. job mismatch, park access issues
Customizing data transfer procedures National Opportunity Mapping Web-based based Opportunity mapping
Online interactive maps ArcGIS Server Baltimore Foreclosures (http://kirwan27:8399/baltimoreforeclosure/mapvie wer.jsf?width=261&height=438) Open source Austin Opportunity Mapping (http://www.gis.osu.edu/webgis- projects/opportunity/index.html)