introduction to opportunity mapping opportunity mapping workshop nov. 30, 2007 samir gambhir...
TRANSCRIPT
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Introduction to Opportunity Mapping
OPPORTUNITY MAPPING WORKSHOPNov. 30, 2007
Samir GambhirGIS/Demographic Specialist
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Presentation overview
SECTION I – Introduction
SECTION II – Methodology
SECTION III – Data and analysis
SECTION IV – Future possibilities
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Section I
introduction
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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
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Opportunity structures
Housing
Childcare Employment
Education
Health
Transportation
EffectiveParticipation
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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
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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
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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
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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
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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
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background (contd.)
Opportunity mapping intended to provide a comprehensive view of any number of indicators
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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
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Section Ii
methodology
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Methodology
Identifying and selecting indicators of opportunity
Identifying sources of data Compiling list of indicators (data
matrix) Calculating Z scores Averaging these scores
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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
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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
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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?
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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
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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
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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
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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
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Examples of opportunity mapping
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Austin MSA, TX
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New orleans msa, la
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Baltimore msa,md
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Ohioeducationopportunity
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Cleveland msa,oh
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Ongoing opportunity mapping projects
Atlanta MSA, GA State of Massachusetts State of Connecticut
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Section Iii
data and analysis
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Data sources
Census Data
Non-Census Data
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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
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Geography hierarchy
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Census 2000Short Form and Long Form
Short form
Long form
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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
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long form
For the U.S. as a whole, about one in six households received the long-form questionnaire.
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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.)
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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
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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
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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)
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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.
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Census basedmaps
Fairly simple in calculations
Easy to display Easy readability
for the audience
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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
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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
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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
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Spatial techniques
Mapping software offers many techniques for data manipulation. Some of these methods used in our analysis are: Interpolation
Areal Interpolation Buffering
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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
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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.
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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
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Section Iv
future possibilities
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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