1. Introduction to Opportunity Mapping OPPORTUNITY MAPPING WORKSHOP Nov. 30, 2007 Samir Gambhir GIS/Demographic Specialist

2. Presentation overview  SECTION I – Introduction  SECTION II – Methodology  SECTION III – Data and analysis  SECTION IV – Future possibilities

3. Section I introduction

4. The “community of opportunity” approach  Where you live is more important than what you live in… Housing -- in particular its location -- is the primary mechanism for accessing opportunity in our society Housing location determines  the quality of schools children attend,  the quality of public services they receive,  access to employment and transportation,  exposure to health risks,  access to health care, etc. For those living in high poverty neighborhoods, these factors can significantly inhibit life outcomes

5. Opportunity structures Housing ChildcareEmployment Education Health Transportation Effective Participation

6. framework  The “Communities of Opportunity” framework is a model of fair housing and community development  The model is based on the premises that Everyone should have fair access to the critical opportunity structures needed to succeed in life Affirmatively connecting people to opportunity creates positive, transformative change in communities

7. The web of opportunity  Opportunities in our society are geographically distributed (and often clustered) throughout metropolitan areas This creates “winner” and “loser” communities or “high” and “low” opportunity communities  Your location within this “web of opportunity” plays a decisive role in your life potential and outcomes Individual characteristics still matter… …but so does access to opportunity, such as good schools, health care, child care, and job networks

8. Opportunity mapping  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

9. background  Evolved out of neighborhood indicators project  One of the major applications at Kirwan Institute was Chicago MSA opportunity classification (in collaboration with Institute on Race and Poverty, University of Minnesota

10. background ( contd. )  Neighborhood Indicators Census 2000 data provided detailed neighborhood indicators Resulted in surge in neighborhood indicators based analysis Provided a snapshot of social and economic health of neighborhoods Shortcomings  Each indicator is analyzed and mapped separately  Overlay provides a complex view, hard to interpret

11. background ( contd. )  Opportunity mapping intended to provide a comprehensive view of any number of indicators

12. background ( contd. )  Resulted in a methodology that captures region wide opportunity distribution, in a comprehensive manner and it is reflective of today’s metropolitan characteristics Ignores Urban-Suburban dichotomy  Reflective of new trends: decline of the inner suburbs, exurbs, inner city gentrification  Reflective of the unique nature of each community: e.g. Austin, TX vs. Cleveland, OH

13. Section Ii methodology

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

15. Methodology: Identifying and Selecting Indicators of High and Low Opportunity  Established by input from Kirwan Institute and direction from the local steering committee  Based on certain factors Specific issues or concerns of the region Research literature validating the connection between indicator and opportunity  Central Requirement: Is there a clear connection between indicator and opportunity? E.g. Proximity to parks and Health related opportunity

16. 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

17. 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?

18. Methodology: effect on opportunity INDICATORS DATA MATRIX EDUCATIONDESCRIPTION Effect on opportunity Educational attainment for total populationPercentage of population with college degreePositive School poverty for neighborhood schoolsPercentage of economically disadvantaged studentsNegative Teacher qualifications for neighborhood schools (or certified teachers)Percentage of Highly Qualified Teachers (HQT)Positive ENVIRONMENTAL & PUBLIC HEALTH Proximity to toxic waste release sitesCensus tracts are ranked based on their distance from these facilitiesPositive Proximity to parks/Open spacesCensus tracts are ranked based on their distance from open spacesNegative Medically Underserved AreasAreas designated as MUAPositive  Examples Poverty vs Income Vacancy rate vs Home ownership rate

19. 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

20. 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)  Statistical measure  Grounded in Social Science research  Most intuitive but other measures can be used Example  Top 20% can be categorized as very high, bottom 20% - very low

21. Methodology: Averaging Z scores  Z score averages assume equal participation of all variables toward “Opportunity Index” calculations No basis to provide unequal weights  Issue of weighting should be considered carefully Need to have a strong rationale for weighting Theoretical support would be helpful Arbitrary weighting could skew the results

22. Examples of opportunity mapping

23. Austin MSA, TX

24. New orleans msa, la

25. Baltimore msa, md

26. Ohio education opportunity

27. Cleveland msa, oh

28. Ongoing opportunity mapping projects  Atlanta MSA, GA  State of Massachusetts  State of Connecticut

29. Section Iii data and analysis

30. Data sources  Census Data  Non-Census Data

31. Census 2000 overview  Information about 115.9 million housing units and 281.4 million people across the United States  Census 2000 geography, maps and data products are available  Website: www.census.govwww.census.gov

32. Geography hierarchy

33. Census 2000 Short Form and Long Form Short form Long form

34. Short form  100-percent characteristics: A limited number of questions were asked of every person and housing unit in the United States. Information is available on: Name Hispanic or Latino origin Household relationship Race Gender Tenure (whether the home is owned or rented) Age

35. long form For the U.S. as a whole, about one in six households received the long-form questionnaire.

36.  Additional questions were asked of a sample of persons and housing units. Data are provided on: Population Social Characteristics  Marital status  Place of birth, citizenship, and year of entry  School enrollment and educational attainment  Ancestry  Residence 5 years ago (migration)  Language spoken at home and ability to speak English  Veteran status  Disability  Grandparents as caregivers Economic Characteristics  Labor force status  Place of work and journey to work  Occupation, industry, and class of worker  Work status in 1999  Income in 1999 long form (contd.)

37. Housing Physical Characteristics  Units in structure  Year structure built  Number of rooms and number of bedrooms  Year moved into residence  Plumbing and kitchen facilities  Telephone service  Vehicles available  Heating fuel  Farm residence Financial Characteristics  Value of home or monthly rent paid  Utilities, mortgage, taxes, insurance, and fuel costs

38. Census 2010  For Census 2010 No long form questionnaire Short form questionnaire only  To all residents in the U.S.  Ask the same set of questions American Community Survey (ACS) to collect more detailed information  Will provide data every year rather than every 10 years  Sent to a small percentage of population on a rotating basis  No household will receive the survey more often than once every five years  It might take at least five years, and some data aggregation, to get Census tract or smaller geography level data

39. Available short form data  100% data or short-form information Summary File 1  Counts for detailed race, Hispanic or Latino groups, and American Indian/Alaska Native tribes  Tables repeat for major race groups alone, two or more races, Hispanic or Latino, White not Hispanic or Latino  Geography: block, census tract Summary File 2  36 Population tables at census tract (PCT) level  11 Housing tables (HCT) at census tract (HCT)

40. Available long form data  Sample data or long-form information Summary File 3  813 tables of data  Counts and cross tabulations of sample items (income, occupation, education, rent and value, vehicles available)  Lowest level of geography: block group Summary File 4  Tables repeated by race, Hispanic/ Latino, and American Indian and Alaska Native categories, and ancestry – 336 categories in all.

41. Census based maps  Fairly simple in calculations  Easy to display  Easy readability for the audience

42. Census data issues  Historical data hard to get Inconsistent categories Block group and census tract boundaries are regularly updated  Private data providers such as GeoLytics provide historical census data normalized to 2000 geographies Inconsistency in data categories are minimized but still exist

43. Non-census data  Data not available at census is gathered from other sources  Good news!! – It is available  Bad news!! – It might not be available at the geography of analysis (census tracts)  Data needs to be manipulated to represent census tracts

44. Non-census data ExampleS  School data Student poverty, test scores and teacher experience data might be available at school/District/County/State level  Transit data Transit route data might be available with the local Metropolitan Planning Organization (MPO) Bus-stops or train stations might be available as a point theme  Environmental data Toxic sites and toxic release data available at EPA as point data Parks and open spaces are available as shapefiles  Public health Hospital locations might be available  Main issue – How to represent this data at census tract level

45. Spatial techniques  Mapping software offers many techniques for data manipulation. Some of these methods used in our analysis are: Interpolation  Areal Interpolation Buffering

46. Interpolation  Technique to predict value at unknown locations based on values at known locations Example – Weather data  Areal interpolation - Transferring data from one geography to another based on the proportion of area overlapping the target area Data aggregation Example - Transferring jobs data at zip code level to census tracts

47. buffering  Buffering Creating a buffer of a specified radius around our data point Buffer distance decision should be research or knowledge based Captures proximity of events such as grocery stores, jobs etc.

48. Data issues and considerations  Missing data Input data average  Z score as zero  Macro level data Jurisdictions or school districts  When do we use ratio Grocery stores Jobs

49. Section Iv future possibilities

50. Future possibilities  Web-based mapping Currently used mainly to display information Provides tools to zoom to scale, identify and some analysis Can be developed to exchange live information  Google mash-up http://housingmaps.com http://wayfaring.com http://walkscore.com  Mapping blogs Could residents go on-line and show where impediments to opportunity are in their neighborhood, or share their experiences?  Semantic mapping Intelligence based Internet mapping