GIS and the Built Environment: An Overview

Phil HurvitzUW-CAUP-Urban Form Lab

GIS and the Geography of Obesity WorkshopAugust 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

0 200 400100 m[

property value< 100K

100-250K

250-500K

500K-1M

1-2M

> 2M

Wallingford Parcels

Comparing Units of Spatial Data Capture, Storage, and Analysis (Parcels)

property value< 100K

100-250K

250-500K

500K-1M

1-2M

> 2M

Wallingford Parcels

0 200 400100 m[ 0 50 10025 m[

property value< 100K

100-250K

250-500K

500K-1M

1-2M

> 2M

Wallingford 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

mean property value< 100K

100-250K

250-500K

500K-1M

1-2M

> 2M

Wallingford Census Tracts

0 200 400100 m[

Unit of Data Capture & Analysis Affects Quantitative Output

1 2 3 4 5 6 7 8

2500

0035

0000

4500

00

Tract Mean Parcel Value (n=8)

valu

e ($

)0 2000 4000 6000 8000

0.0

e+00

1.0

e+07

2.0

e+07

Individual Parcel Value (n=8875)

valu

e ($

)

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

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 allocationcostdistance 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 0 10 20 30 40 50

0.0

0.5

1.0

1.5

Demographics of fast food restaurant locations (n= 302 )

% below poverty level by tract

fast

food

rest

aura

nt d

ensi

ty