Slide 1

Introduction to Opportunity MappingOPPORTUNITY MAPPING WORKSHOPNov. 30, 2007

Samir GambhirGIS/Demographic Specialist

1Presentation overviewSECTION I IntroductionSECTION II MethodologySECTION III Data and analysisSECTION IV Future possibilities2Section Iintroduction3The community of opportunity approachWhere you live is more important than what you live inHousing -- in particular its location -- is the primary mechanism for accessing opportunity in our societyHousing 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 outcomes4Opportunity structuresFiscal PoliciesHousingChildcareEmploymentEducationHealthTransportationEffectiveParticipation5frameworkThe Communities of Opportunity framework is a model of fair housing and community developmentThe model is based on the premises that Everyone should have fair access to the critical opportunity structures needed to succeed in lifeAffirmatively connecting people to opportunity creates positive, transformative change in communities

6The web of opportunityOpportunities in our society are geographically distributed (and often clustered) throughout metropolitan areasThis creates winner and loser communities or high and low opportunity communitiesYour location within this web of opportunity plays a decisive role in your life potential and outcomesIndividual characteristics still matterbut so does access to opportunity, such as good schools, health care, child care, and job networks7Opportunity mappingOpportunity mapping is a research tool used to understand the dynamics of opportunity within metropolitan areasThe 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 communities8backgroundEvolved 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

9background (contd.)Neighborhood IndicatorsCensus 2000 data provided detailed neighborhood indicatorsResulted in surge in neighborhood indicators based analysisProvided a snapshot of social and economic health of neighborhoodsShortcomingsEach indicator is analyzed and mapped separatelyOverlay provides a complex view, hard to interpret

10background (contd.)Opportunity mapping intended to provide a comprehensive view of any number of indicators11background (contd.)Resulted in a methodology that captures region wide opportunity distribution, in a comprehensive manner and it is reflective of todays metropolitan characteristicsIgnores Urban-Suburban dichotomyReflective of new trends: decline of the inner suburbs, exurbs, inner city gentrificationReflective of the unique nature of each community: e.g. Austin, TX vs. Cleveland, OH12Section Iimethodology13MethodologyIdentifying and selecting indicators of opportunityIdentifying sources of dataCompiling list of indicators (data matrix)Calculating Z scoresAveraging these scores

14Methodology:Identifying and Selecting Indicators of High and Low OpportunityEstablished by input from Kirwan Institute and direction from the local steering committeeBased on certain factorsSpecific issues or concerns of the regionResearch literature validating the connection between indicator and opportunityCentral Requirement:Is there a clear connection between indicator and opportunity? E.g. Proximity to parks and Health related opportunity 15Methodology:Sources of DataFederal OrganizationsCensus BureauCounty Business Patterns (ZIP Code Data)Housing and Urban Development (HUD)Environmental Protection Agency (EPA)State and Local Governmental OrganizationsRegional planning agenciesEducation boards/school districtsTransportation agenciesCounty Auditors Office Other agencies (non-Profit and Private)Schoolmatters.orgDataPlace.orgESRI Business AnalystClaritas16Methodology:Indicator CategoriesEducationStudent/Teacher ratio? Test scores? Student mobility?Economic/Employment IndicatorsUnemployment rate? Proximity to employment? Job creation?Neighborhood QualityMedian home values? Crime rate? Housing vacancy rate?Mobility/Transportation IndicatorsMean commute time? Access to public transit?Health & Environmental IndicatorsAccess to health care? Exposure to toxic waste? Proximity to parks or open space?

17Methodology:effect on opportunityINDICATORS DATA MATRIXEDUCATIONDESCRIPTIONEffect on opportunityEducational attainment for total populationPercentage of population with college degreePositiveSchool poverty for neighborhood schoolsPercentage of economically disadvantaged studentsNegativeTeacher qualifications for neighborhood schools (or certified teachers)Percentage of Highly Qualified Teachers (HQT)PositiveENVIRONMENTAL & PUBLIC HEALTHProximity to toxic waste release sitesCensus tracts are ranked based on their distance from these facilitiesPositiveProximity to parks/Open spacesCensus tracts are ranked based on their distance from open spacesNegativeMedically Underserved AreasAreas designated as MUA PositiveExamplesPoverty vs IncomeVacancy rate vs Home ownership rate18Methodology:Calculating Z ScoresZ Score a statistical measure that quantifies the distance (measured in standard deviations) between data points and the meanZ Score = (Data point Mean)/ Standard DeviationAllows data for a geography (e.g. census tract) to be measured based on their relative distance from the average for the entire regionRaw z score performanceMean value is always zero z score indicates distance from the meanPositive z score is always above the regions mean, Negative z score is always below the regions meanIndicators with negative effect on opportunity should have all the z scores adjusted to reflect this phenomena19Methodology:Calculating Opportunity using Z ScoresFinal opportunity index for each census tract is the average of z scores (including adjusted scores for direction) for all indicators by categoryCensus tracts can be rankedOpportunity level is determined by sorting a regions census tract z scores into ordered categories (very low, low, moderate, high, very high)Statistical measureGrounded in Social Science researchMost intuitive but other measures can be usedExampleTop 20% can be categorized as very high, bottom 20% - very low20Methodology: Averaging Z scoresZ score averages assume equal participation of all variables toward Opportunity Index calculationsNo basis to provide unequal weightsIssue of weighting should be considered carefullyNeed to have a strong rationale for weightingTheoretical support would be helpfulArbitrary weighting could skew the results

21Examples of opportunity mapping22Austin MSA, TX

23New orleans msa, la

24Baltimore msa,md

25Ohioeducationopportunity

26Cleveland msa,oh

27Ongoing opportunity mapping projectsAtlanta MSA, GAState of MassachusettsState of Connecticut28Section Iiidata and analysis29Data sourcesCensus Data

Non-Census Data30Census 2000 overviewInformation about 115.9 million housing units and 281.4 million people across the United StatesCensus 2000 geography, maps and data products are availableWebsite: www.census.gov

31Geography hierarchy

32Census 2000Short Form and Long Form

Short form

Long form33Short form100-percent characteristics: A limited number of questions were asked of every person and housing unit in the United States. Information is available on:NameHispanic or Latino originHousehold relationshipRaceGenderTenure (whether the home is owned or rented)Age34long form

For the U.S. as a whole, about one in six households received the long-form questionnaire. 35Additional questions were asked of a sample of persons and housing units. Data are provided on:Population

Social CharacteristicsMarital statusPlace of birth, citizenship, and year of entrySchool enrollment and educational attainmentAncestryResidence 5 years ago (migration)Language spoken at home and ability to speak EnglishVeteran statusDisabilityGrandparents as caregiversEconomic CharacteristicsLabor force statusPlace of work and journey to workOccupation, industry, and class of workerWork status in 1999Income in 1999long form (contd.)36long form (contd.)HousingPhysical CharacteristicsUnits in structureYear structure builtNumber of rooms and number of bedroomsYear moved into residencePlumbing and kitchen facilitiesTelephone serviceVehicles availableHeating fuelFarm residence

Financial CharacteristicsValue of home or monthly rent paidUtilities, mortgage, taxes, insurance, and fuel costs37Census 2010For Census 2010No long form questionnaireShort form questionnaire onlyTo all residents in the U.S.Ask the same set of questions American Community Survey (ACS) to collect more detailed informationWill provide data every year rather than every 10 yearsSent to a small percentage of population on a rotating basisNo household will receive the survey more often than once every five yearsIt might take at least five years, and some data aggregation, to get Census tract or smaller geography level data 38Available short form data100% data or short-form informationSummary File 1Counts for detailed race, Hispanic or Latino groups, and American Indian/Alaska Native tribesTables repeat for major race groups alone, two or more races, Hispanic or Latino, White not Hispanic or LatinoGeography: block, census tractSummary File 236 Population tables at census tract (PCT) level11 Housing tables (HCT) at census tract (HCT)39Now that weve reviewed some of the changes to the questions, what about the data?

Census 2000 information is released on in a series of summary files. The summary files are basically a set of predefined tables for various levels of geography. Information from the short-form questions (100% data) is released first. Short-form information is included in the Redistricting Summary File, and Summary Files 1 and 2. Long form information is included in Summary Files 3 and 4.SPEAKERS NOTES: (Only if asked) The sample information takes longer to process. Special coding for write-in entries (place of work, ancestry, occupation) is required.

Available long form dataSample data or long-form information Summary File 3813 tables of data Counts and cross tabulations of sample items (income, occupation, education, rent and value, vehicles available)Lowest level of geography: block groupSummary File 4Tables repeated by race, Hispanic/ Latino, and American Indian and Alaska Native categories, and ancestry 336 categories in all.

40Census basedmapsFairly simple in calculationsEasy to displayEasy readability for the audience

41Census data issuesHistorical data hard to getInconsistent categoriesBlock group and census tract boundaries are regularly updatedPrivate data providers such as GeoLytics provide historical census data normalized to 2000 geographiesInconsistency in data categories are minimized but still exist42Non-census dataData not available at census is gathered from other sourcesGood news!! It is availableBad news!! It might not be available at the geography of analysis (census tracts)Data needs to be manipulated to represent census tracts43Non-census dataExampleSSchool dataStudent poverty, test scores and teacher experience data might be available at school/District/County/State levelTransit dataTransit route data might be available with the local Metropolitan Planning Organization (MPO)Bus-stops or train stations might be available as a point themeEnvironmental dataToxic sites and toxic release data available at EPA as point dataParks and open spaces are available as shapefilesPublic healthHospital locations might be available

Main issue How to represent this data at census tract level44Spatial techniques Mapping software offers many techniques for data manipulation. Some of these methods used in our analysis are:InterpolationAreal InterpolationBuffering45InterpolationTechnique to predict value at unknown locations based on values at known locationsExample Weather dataAreal interpolation - Transferring data from one geography to another based on the proportion of area overlapping the target areaData aggregationExample - Transferring jobs data at zip code level to census tracts46bufferingBufferingCreating a buffer of a specified radius around our data pointBuffer distance decision should be research or knowledge basedCaptures proximity of events such as grocery stores, jobs etc.47Data issues and considerationsMissing dataInput data averageZ score as zeroMacro level data Jurisdictions or school districtsWhen do we use ratioGrocery storesJobs48Section Ivfuture possibilities49Future possibilitiesWeb-based mappingCurrently used mainly to display informationProvides tools to zoom to scale, identify and some analysisCan be developed to exchange live informationGoogle mash-uphttp://housingmaps.comhttp://wayfaring.comhttp://walkscore.comMapping blogsCould residents go on-line and show where impediments to opportunity are in their neighborhood, or share their experiences? Semantic mappingIntelligence based Internet mapping50