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Introduction to Opportunity Mapping. OPPORTUNITY MAPPING WORKSHOP Nov. 30, 2007 Samir Gambhir GIS/Demographic Specialist. Presentation overview. SECTION I – Introduction SECTION II – Methodology SECTION III – Data and analysis SECTION IV – Future possibilities. Section I introduction.

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introduction to opportunity mapping

Introduction to Opportunity Mapping

OPPORTUNITY MAPPING WORKSHOP

Nov. 30, 2007

Samir Gambhir

GIS/Demographic Specialist

presentation overview
Presentation overview
  • SECTION I – Introduction
  • SECTION II – Methodology
  • SECTION III – Data and analysis
  • SECTION IV – Future possibilities
the community of opportunity approach
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

Fiscal Policies

Health

Childcare

Employment

Housing

Effective

Participation

Education

Transportation

Opportunity structures
framework
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
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
  • 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
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
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 contd1
background (contd.)
  • Opportunity mapping intended to provide a comprehensive view of any number of indicators
background contd2
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
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
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
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
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
Methodology:effect on opportunity
  • Examples
    • Poverty vs Income
    • Vacancy rate vs Home ownership rate
methodology calculating z scores
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
methodology calculating opportunity using z scores
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
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
ongoing opportunity mapping projects
Ongoing opportunity mapping projects
  • Atlanta MSA, GA
  • State of Massachusetts
  • State of Connecticut
data sources
Data sources
  • Census Data
  • Non-Census Data
census 2000 overview
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
short 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
long form

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

long form contd
long form (contd.)
  • Additional questions were asked of a sample of persons and housing units. Data are provided on:
    • Population
census 2010
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
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
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 based maps
Census basedmaps
  • Fairly simple in calculations
  • Easy to display
  • Easy readability for the audience
census data issues
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
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
Non-census dataExampleS
  • 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
Spatial techniques
  • Mapping software offers many techniques for data manipulation. Some of these methods used in our analysis are:
    • Interpolation
      • Areal Interpolation
    • Buffering
interpolation
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
  • 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
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
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