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