Introduction to Opportunity Mapping

OPPORTUNITY MAPPING WORKSHOPNov. 30, 2007

Samir GambhirGIS/Demographic Specialist

Presentation overview

SECTION I – Introduction

SECTION II – Methodology

SECTION III – Data and analysis

SECTION IV – Future possibilities

Section I

introduction

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

Opportunity structures

Housing

Childcare Employment

Education

Health

Transportation

EffectiveParticipation

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

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

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

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

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

background (contd.)

Opportunity mapping intended to provide a comprehensive view of any number of indicators

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

Section Ii

methodology

Methodology

Identifying and selecting indicators of opportunity

Identifying sources of data Compiling list of indicators (data

matrix) Calculating Z scores Averaging these scores

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

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

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?

Methodology:effect on opportunity

INDICATORS DATA MATRIX

EDUCATION DESCRIPTIONEffect 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 to toxic waste release sites Census tracts are ranked based on their distance from these facilities Positive

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

     

Examples Poverty vs Income Vacancy rate vs Home ownership rate

Methodology:

Calculating Z Scores

Z Score – a statistical measure that quantifies the distance (measured in standard deviations) between data points and the meanZ 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

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

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

Examples of opportunity mapping

Austin MSA, TX

New orleans msa, la

Baltimore msa,md

Ohioeducationopportunity

Cleveland msa,oh

Ongoing opportunity mapping projects

Atlanta MSA, GA State of Massachusetts State of Connecticut

Section Iii

data and analysis

Data sources

Census Data

Non-Census Data

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.gov

Geography hierarchy

Census 2000Short Form and Long Form

Short form

Long form

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

long form

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

Additional 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 caregivers

Economic CharacteristicsLabor force statusPlace of work and journey to workOccupation, industry, and class of workerWork status in 1999Income in 1999

long form (contd.)

long form (contd.) Housing

Physical 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 costs

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

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)

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.

Census basedmaps

Fairly simple in calculations

Easy to display Easy readability

for the audience

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

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

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

Spatial techniques

Mapping software offers many techniques for data manipulation. Some of these methods used in our analysis are: Interpolation

Areal Interpolation Buffering

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

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.

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

Section Iv

future possibilities

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