The geography of obesity mapping and modeling in king county
Download
1 / 53

The Geography of Obesity: Mapping and Modeling in King County - PowerPoint PPT Presentation


  • 222 Views
  • Uploaded on

The Geography of Obesity: Mapping and Modeling in King County. 3rd King County Overweight Prevention Initiative Forum: Activate Friday, October 14, 2005. Phil Hurvitz University of Washington College of Architecture and Urban Planning Urban Form Lab gis.washington.edu/phurvitz.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'The Geography of Obesity: Mapping and Modeling in King County' - colin


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
The geography of obesity mapping and modeling in king county l.jpg

The Geography of Obesity: Mapping and Modeling in King County

3rd King County Overweight Prevention Initiative Forum: Activate

Friday, October 14, 2005

Phil Hurvitz

University of Washington

College of Architecture and Urban Planning

Urban Form Lab

gis.washington.edu/phurvitz

Slide 1 (of 53)


Acknowledgements l.jpg
Acknowledgements

  • Apart from mine, much of this material is drawn from the work of:

    • Luc deMontigny (PhD student, UW-CAUP)

    • Lin Lin (PhD student, UW-CAUP)

    • Anne Vernez Moudon (Professor, UW-CAUP, director of the Urban Form Lab)

Slide 2 (of 53)


Outline l.jpg
Outline

  • Background: GIS, Epidemiology, the Built Environment, Spatial Scale & Unit of Analysis

  • Current Research from the Urban Form Lab

    • Walkable-Bikeable Communities Analyst (ArcView GIS Extension for Quantifying the Built Environment)

    • Fast Food Location Analysis

    • GIS-Based Spatial Sampling for SES, Behavior, and the Built Environment

    • Surface Modeling/Interpolation of Walkability Indices

Slide 3 (of 53)


Outline4 l.jpg
Outline

  • Background: GIS, Epidemiology, the Built Environment, Spatial Scale & Unit of Analysis

  • Current Research from the Urban Form Lab

    • Walkable-Bikeable Communities Analyst (ArcView GIS Extension for Quantifying the Built Environment)

    • Fast Food Location Analysis

    • GIS-Based Spatial Sampling for SES, Behavior, and the Built Environment

    • Surface Modeling/Interpolation of Walkability Indices

Slide 4 (of 53)


Background what is gis l.jpg
Background: What is GIS?

  • A computer-based method for

    • Capture,

    • Storage,

    • Manipulation,

    • Analysis, and

    • Display

      of spatially referenced data

Slide 5 (of 53)


Background what is gis6 l.jpg
Background: What is GIS?

  • Any object or phenomenon that is or can be placed on a map can be stored, managed, and analyzed in a GIS.

    • Built environment features (streets, buildings, bus routes, restaurants, schools)

    • Households (address points, parcel polygons)

    • Individuals (points or travel lines/polygons)

    • Ground surface elevation or slope

    • Movement of objects through time and/or space

Slide 6 (of 53)


Background why is gis important in epidemiology l.jpg
Background: Why is GIS Important in Epidemiology?

  • Epidemiology and public health are interested in population-wide effects

  • Population-wide effects can only be ascertained from individual-level measurements

  • GIS allows the measurement of individual characteristics within an explicitly spatial context

  • If location is an important factor in a public health issue, GIS should be incorporated as a data management and analysis tool

Slide 7 (of 53)


Background conceptual framework for social ecologic model l.jpg

Social/communityenvironmentalfactors

Institutional/policyenvironmentalfactors

Builtenvironmentalfactors

Intervention – behaviorchange/modification

Background: Conceptual Framework for Social Ecologic Model

  • Social ecologic model considers impacts of environment (institutional, physical, social, etc.) on behavior. (Stokols, 1992; Sallis and Owen 1997)

Person behavior

Inner personalenvironment

Personal/individualfactors

=

Outerenvironments

=

+

+

Slide 8 (of 53)


Background the built environment matters l.jpg
Background: The Built Environment Matters

  • Associations between income and built environment

  • Associations between walking/transit use and built environment

  • Confounder in studies of behavior and the built environment: Self-selection and causation

    • Do people move to walkable neighborhoods and then start walking, or do walkers search out walkable neighborhoods?

    • “We shape our buildings; thereafter they shape us.” -Sir Winston Churchill

Slide 9 (of 53)


Background comparing units of spatial data capture storage and analysis parcels l.jpg
Background: Comparing Units of Spatial Data Capture, Storage, and Analysis (Parcels)

  • Parcel-level data are inherently disaggregated

  • Variation at the household-unit population level is maintained and can be used for analytical purposes

Slide 10 (of 53)


Background comparing units of spatial data capture storage and analysis parcels11 l.jpg
Background: Comparing Units of Spatial Data Capture, Storage, and Analysis (Parcels)

Slide 11 (of 53)


Background comparing units of spatial data capture storage and analysis census tracts l.jpg
Background: Storage, and Analysis (Parcels)Comparing Units of Spatial Data Capture, Storage, and Analysis (Census Tracts)

  • Census data are inherently aggregated

  • Within-tract variation is lost as geometries become larger and more aggregated

Slide 12 (of 53)


Background unit of data capture analysis affects quantitative output l.jpg
Background: Unit of Data Capture & Analysis: Affects Quantitative Output

Rank order by value

Rank order by value

Slide 13 (of 53)


Outline14 l.jpg
Outline Quantitative Output

  • Background: GIS, Epidemiology, the Built Environment, Spatial Scale & Unit of Analysis

  • Current Research from the Urban Form Lab

    • Walkable-Bikeable Communities Analyst (ArcView GIS Extension for Quantifying the Built Environment)

    • Fast Food Location Analysis

    • GIS-Based Spatial Sampling for SES, Behavior, and the Built Environment

    • Surface Modeling/Interpolation of Walkability Indices

Slide 14 (of 53)


Gis software available to analyze environmental data the wbc analyst l.jpg
GIS Software Available to Analyze Environmental Data: the WBC Analyst

  • Commercial GIS software has a robust application programming interface

  • Allows the automation of measurement methods

Slide 15 (of 53)


The wbc analyst arcview gis extension l.jpg
The WBC Analyst ArcView GIS Extension WBC Analyst

  • Automates several measurement methods (inventories “what” and “where” of the built environment)

    • Buffer measures: built environment characteristics near the location of interest

      • Land use proportions

      • Count/length/area of features, e.g., groceries, restaurants, bus stops, streets, sidewalks

    • Proximity measures: airline and network distance from the location of interest to various other locations, land uses, etc

Slide 16 (of 53)


Wbc analyst proximity and buffer measures l.jpg
WBC Analyst: Proximity and Buffer Measures WBC Analyst

calculates over 200 different built environment variables within user-specified distance of location of interest

Slide 17 (of 53)


Wbc analyst what and where can explain the choice to walk l.jpg
WBC Analyst: “What” and “Where” Can Explain the Choice to Walk

  • Output of GIS coupled with telephone survey data

  • Using multinomial logit we were able to explain 35% of the variation in walking with only socio-demographic variables:

    • age

    • education

    • neighborhood social environment

    • attitude toward traffic and environmental quality

  • Adding environmental variables (presence of certain land uses within 1 mile of the home) obtained from the GIS increased the R2 to 47%

  • Extension is currently being used by researchers in public health, epidemiology, and transportation

Slide 18 (of 53)


Wbc analyst neighborhood centers ncs l.jpg
WBC Analyst: Neighborhood Centers (NCs) Choice to Walk

  • Generates “clusters” from locally aggregated land use parcels

Slide 19 (of 53)


Outline20 l.jpg
Outline Choice to Walk

  • Background: GIS, Epidemiology, the Built Environment, Spatial Scale & Unit of Analysis

  • Current Research from the Urban Form Lab

    • Walkable-Bikeable Communities Analyst (ArcView GIS Extension for Quantifying the Built Environment)

    • Fast Food Location Analysis

    • GIS-Based Spatial Sampling for SES, Behavior, and the Built Environment

    • Surface Modeling/Interpolation of Walkability Indices

Slide 20 (of 53)


Example application fast food location analysis l.jpg
Example Application: Fast Food Location Analysis Choice to Walk

  • Analysis of location of fast food restaurants

  • How do the densities and counts of these restaurants vary through space?

  • Where are they with respect to demographic variables?

Slide 21 (of 53)


Fast food location analysis l.jpg
Fast Food Location Analysis Choice to Walk

  • Fast food restaurant addresses are available free online (Qwest – dexonline.com)

  • Online telephone directories have regular structure (server-side script generated html) that can be extracted with customized client-side scripts

Slide 22 (of 53)


Fast food location analysis23 l.jpg
Fast Food Location Analysis Choice to Walk

Slide 23 (of 53)


Fast food location analysis24 l.jpg
Fast Food Location Analysis Choice to Walk

  • Asset mapping: address geocoding places fast food restaurants in spatial framework common with other regional data sets

Slide 24 (of 53)


Fast food location analysis25 l.jpg
Fast Food Location Analysis Choice to Walk

  • Analysis of locations

    • Kernel interpolation method

    • Calculates density of fast food restaurants at all locations across study area

    • Parameters are easily controlled

      • area (denominator)

      • classification (display) values

Slide 25 (of 53)


Fast food location analysis26 l.jpg
Fast Food Location Analysis Choice to Walk

  • Analysis of locations

    • Count of number of fast food restaurants within 1 mile for all locations

Slide 26 (of 53)


Fast food location analysis27 l.jpg
Fast Food Location Analysis Choice to Walk

  • Sociodemographic pattern?

  • Density of fast food restaurants appears higher in block groups with higher poverty levels

  • Pearson’s Product Momentcorrelation ρ= 0.23, p < 0.005

Slide 27 (of 53)


Fast food location analysis28 l.jpg
Fast Food Location Analysis Choice to Walk

  • Sociodemographic pattern?

  • Mean count of fast food restaurants higher in block groups with higher poverty levels

  • Pearson’s Product Momentcorrelation ρ= 0.25, p < 0.005

Slide 28 (of 53)


Outline29 l.jpg
Outline Choice to Walk

  • Background: GIS, Epidemiology, the Built Environment, Spatial Scale & Unit of Analysis

  • Current Research from the Urban Form Lab

    • Walkable-Bikeable Communities Analyst (ArcView GIS Extension for Quantifying the Built Environment)

    • Fast Food Location Analysis

    • GIS-Based Spatial Sampling for SES, Behavior, and the Built Environment

    • Surface Modeling/Interpolation of Walkability Indices

Slide 29 (of 53)


Gis based spatial sampling for ses behavior and the built environment l.jpg
GIS-Based Spatial Sampling for SES, Behavior, and the Built Environment

Context: Spatially-based population sampling is of benefit to inferential research using surveys

  • Ensures sufficient variation in and proper distribution of key variables in the sample(E.g., environmental characteristics such as residential density, proximity to activities, schools)

  • Ensures adequate occurrences of rare events in the sample(E.g., respondents belonging to racial minorities, those living close to public transit)

  • Controls for conditions of no interest(E.g., areas of low residential density)

Slide 30 (of 53)


Novel approach spatial sampling with gis method l.jpg
Novel approach: Spatial sampling with GIS Method Environment

  • Stratification criteria defining the population of interest is used to construct a spatial sample frame. Data can be taken from any GIS database, such as:

    Parcel data: e.g., land use, assessed property values

    Political data: e.g., urban growth boundary

    Environmental data: e.g., slope

    Census data: e.g., race

  • The spatial sample frame may be geographically continuous or discontinuous, yet it remains, for the purposes of statistical analysis, one spatial unit (or “neighborhood”)

  • Randomly select individual residential units (a proxy for households) from the spatial sample frame. Reverse address-look up can be used to generate a list of telephone numbers for the survey.

Slide 31 (of 53)


Gis based spatial sampling a demonstration of the approach l.jpg
GIS-Based Spatial Sampling: A Demonstration of the Approach Environment

Example of criteria for delimiting a sample frame of a population “At Risk” of obesity: Households that reside:

  • Farther than 1 mile from a Neighborhood Center cluster of grocery stores and restaurants

  • In a residential unit in the bottom 1/3 of assessed property value

  • In a census block for which less than 50% of the population is white

  • Within the King County Urban Growth Boundary

Slide 32 (of 53)


Gis based spatial sampling a demonstration of the approach33 l.jpg

> 1 mi from [rest + gro] Environment

< 50% white

combined sample frame(orange parcels)

lower 1/3 property value

GIS-Based Spatial Sampling: A Demonstration of the Approach

Slide 33 (of 53)


Spatial sampling l.jpg
Spatial Sampling Environment

  • Used to generate the sample for households surveyed in the WBC project.

  • Lee, C, Moudon, AV and Courbois, JP (in press). Built Environment and Behavior: Spatial Sampling Using Parcel, The Annals of Epidemiology

Slide 34 (of 53)


Outline35 l.jpg
Outline Environment

  • Background: GIS, Epidemiology, the Built Environment, Spatial Scale & Unit of Analysis

  • Current Research from the Urban Form Lab

    • Walkable-Bikeable Communities Analyst (ArcView GIS Extension for Quantifying the Built Environment)

    • Fast Food Location Analysis

    • GIS-Based Spatial Sampling for SES, Behavior, and the Built Environment

    • Surface Modeling/Interpolation of Walkability Indices

Slide 35 (of 53)


Surface modeling process l.jpg

y Environment

B

A

a

b

x

Surface Modeling Process

  • Measure sample locations

  • Create interpolated surface

  • Estimate values at non-sample locations

Slide 36 (of 53)


Application of surface modeling walkable bikeable communities wbc project l.jpg
Application of Surface Modeling: Walkable-Bikeable Communities (WBC) Project

  • Survey of 608 households (spatially sampled) for activity behavior & perception of environment (dependent variables)

  • Measured built environment characteristics with WBC Analyst Extension in the GIS (independent variables)

  • Multinomial logistic regression models developed to predict the probability of walking moderately (1-149 min/wk) or sufficiently (>= 150 min/wk) vs. not walking, based on built environment characteristics

  • Surface modeling to interpolate walkability values at non-sampled locations

Slide 37 (of 53)


Wbc project sample l.jpg
WBC Project Sample Communities (WBC) Project

Household locations randomly sampled from the GIS-derived sample frame

Slide 38 (of 53)


Slide39 l.jpg

Slide 39 (of 53) Communities (WBC) Project


Slide40 l.jpg

(Reported poor health) Communities (WBC) Project

Slide 40 (of 53)


Slide41 l.jpg

(Reported excellent health) Communities (WBC) Project

Slide 41 (of 53)


Slide42 l.jpg

(> $75,000) Communities (WBC) Project

Slide 42 (of 53)


Slide43 l.jpg

(< $25,000) Communities (WBC) Project

Slide 43 (of 53)


Likelihood of sufficient walking age l.jpg
Likelihood of Sufficient Walking--Age Communities (WBC) Project

Older Adult (>65 y)

Younger Adult (<35 y)

Slide 44 (of 53)


Likelihood of sufficient walking transit usage l.jpg
Likelihood of Sufficient Walking—Transit Usage Communities (WBC) Project

Transit User

Non-Transit User

Slide 45 (of 53)


Environmental intervention and assessment simulation l.jpg
Environmental Intervention and Assessment Simulation Communities (WBC) Project

  • Change values of environmental variables in regression model to predict new walking probability under different scenarios

  • ArcGIS Geostatistical Analyst generates “before” and “after” surface models

    Allows estimate of the impacts of environmental interventions

Slide 46 (of 53)


Slide47 l.jpg

Added 1 Grocery Store and 1 Grocery+Restaurant+Retail NC within 1 km of Home

Slide 47 (of 53)



Slide49 l.jpg

Slide 49 (of 53) 1km of Home



Slide51 l.jpg

Slide 51 (of 53) 1km of Home



Conclusion l.jpg
Conclusion 1km of Home

  • Parcel-level data and development of new methods are needed for precise spatial-epidemiologic modeling.

  • Use of geospatial analysis tools enhances the ability to understand the relationship between the built environment and health-related behaviors.

  • GIS provides a mix of quantitative analysis and output with telling visualizations

Slide 53 (of 53)


ad