GIS and the Built Environment: An Overview Phil Hurvitz UW-CAUP-Urban Form Lab GIS and the Geography of Obesity Workshop August 3, 2005

Overview Introduction to GIS and its role in epidemiology Comparing aggregated and individualistic data within GIS (parcel-level data) GIS data sets available to support built environment research in epidemiology Capturing environmental data in a GIS Example of 2 applications for GIS in public health

What is GIS? A method for  Capture,  Storage,  Manipulation,  Analysis, and  Display of spatially referenced data

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  Households  Individuals  Ground surface elevation or slope  Movement of objects through time and/or space

What is GIS? GIS stores  feature geometries: representation of anything that exists in space points (houses, bus stops) lines (roads, trails, walking pathways) polygons (parcels, blocks, census boundaries) surfaces (slope, elevation, continuous distance)  feature attributes: information about those objects house square footage, bus ridership, number of lanes, land use, population, health status

The Role of GIS 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

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

Comparing Units of Spatial Data Capture, 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 Census data are inherently aggregated Within-tract variation is lost as geometries become larger and more aggregated

Unit of Data Capture & Analysis Affects Quantitative Output

Data Sets Available for Representing & Quantifying the Built Environment Polygon data models  Census  Zoning, Comprehensive Plan, UGB  Parcels  Parks  Blocks  Neighborhood Centers

Data Sets Available for Representing & Quantifying the Built Environment Point data models  Crosswalks  Light signals  Bus stops  Households  Businesses  Groceries  Restaurants

Data Sets Available for Representing & Quantifying the Built Environment Line data models  Streets, highways  Bus lines  Bike lanes  Walking/cycling trails

GIS Software Available to Analyze Environmental Data Basic methods use analytical tools within the GIS, typically run within a graphical user interface

GIS Software Available to Analyze Environmental Data: Customization GIS has a robust application programming interface Allows the automation of measurement methods

Example Application: The WBC Analyst Automates several measurement methods  Buffer measures: built environment characteristics near the home location Land use proportions Count/length/area of features, e.g., groceries, restaurants, bus stops, streets, sidewalks  Proximity measures: airline and network distance from the home location to various other locations, land uses, etc

WBC Analyst: Proximity and Buffer Measures > 200 different land use metrics within 3 km of home location

WBC Analyst: Neighborhood Center Analysis Automates several measurement methods  Neighborhood Center (NC) measures: identifying and quantifying “clusters” of related land uses, e.g., cluster of [grocery + restaurant + tavern + theater] or [church + school] Buffer and proximity measures also calculated for NCs

WBC Analyst: Neighborhood Center Analysis

Example Application: Fast Food Location Analysis Analysis of location of fast food restaurants Where are they with respect to demographics? How do the densities of these restaurants vary through space?

Fast Food Location Analysis Fast food restaurant addresses are available online (Qwest – dexonline.com) Online telephone directories have regular structure that can be extracted with customized scripts

Fast Food Location Analysis

Asset mapping: address geocoding places fast food restaurants in common spatial framework

Fast Food Location Analysis Analysis of locations  Kernel interpolation method  Calculates density of fast food restaurants at all locations across study area  Parameters are easily controlled area classification values

Fast Food Location Analysis “Service areas” by allocation analysis network allocation costdistance allocation Voronoi allocation

Fast Food Location Analysis Sociodemographic pattern? Density of fast food restaurants may be higher in census tracts with greater poverty levels Pearson’s correlation = 0.49, p < 0.005