GIS and the Built Environment: An Overview Phil Hurvitz UW-CAUP-Urban Form Lab GIS and the...
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Transcript of GIS and the Built Environment: An Overview Phil Hurvitz UW-CAUP-Urban Form Lab GIS and the...
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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
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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
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What is GIS? A method for
Capture, Storage, Manipulation, Analysis, and Display
of spatially referenced data
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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
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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
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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
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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
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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
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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[
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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 ($
)
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Data Sets Available for Representing & Quantifying the Built Environment Polygon data models
Census Zoning, Comprehensive Plan, UGB Parcels Parks Blocks Neighborhood Centers
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Data Sets Available for Representing & Quantifying the Built Environment Point data models
Crosswalks Light signals Bus stops Households Businesses Groceries Restaurants
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Data Sets Available for Representing & Quantifying the Built Environment Line data models
Streets, highways Bus lines Bike lanes Walking/cycling trails
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GIS Software Available to Analyze Environmental Data Basic methods use analytical tools within the GIS,
typically run within a graphical user interface
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GIS Software Available to Analyze Environmental Data: Customization GIS has a robust application programming interface Allows the automation of measurement methods
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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
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WBC Analyst: Proximity and Buffer Measures
> 200 different land use metrics within 3 km of home location
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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
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WBC Analyst: Neighborhood Center Analysis
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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?
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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
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Fast Food Location Analysis
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Fast Food Location Analysis
Asset mapping: address geocoding places fast food restaurants in common spatial framework
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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
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Fast Food Location Analysis
“Service areas” by allocation analysis
network allocationcostdistance allocation Voronoi allocation
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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