the walkable-bikeable communities analyst extension for arcview 3.x © phil hurvitz, 2005 the...

The Walkable-Bikeable Communities Analyst Extension for ArcView 3.x

© Phil Hurvitz, 2005


Phil HurvitzUniversity of Washington

College of Architecture & Urban PlanningSeattle, WA, USA

[email protected]://gis.washington.edu/phurvitz

Twenty-Fifth Annual ESRI International User ConferenceJuly 25-29, 2005

San Diego Convention CenterSan Diego, CA, USA



wbc_analyst_esri_2005

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Overview

• Abstract

• Introduction/Background/Relevance

• Methods

• Results/Discussion

• Conclusions

• Acknowledgements

• References




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Overview

• Abstract


• Methods


• Conclusions


• References




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Abstract (1 of 3)

• Recent research in transportation, urban planning, and public health has focused on walkability and bikeability of the built environment.

• While a growing body of work is increasing the understanding of the relationship between the built environment and activity, more work needs to be done to operationalize and quantify “walkability” and “bikeability” using objectively measured values.




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Abstract (2 of 3)

• The Urban Form Laboratory at the University of Washington’s College of Architecture and Urban Planning (Seattle, USA) has developed an ArcView 3.x extension for quantifying objective measures of urban form that have been useful in modeling preferences for walking and cycling in different neighborhoods within the Seattle area.

• The WBC Analyst uses standard buffer and network analyses as well as some novel algorithms to generate these quantitative measures.




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Abstract (3 of 3)

• Output from the extension, when coupled with a telephone survey on socio-demographics, exercise, and activity levels, show promising results for the fields of urban planning, public health, and transportation.

• Using the combination of data from the telephone survey and environmental variables captured from the GIS, we were able to explain 47% of the variation in walking preference.




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Overview

• Abstract


• Methods


• Conclusions


• References




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Introduction/Background/Relevance

• Obesity is on the rise in the USA and many other places• In the USA, median BMI% has nearly doubled in a

decade

(CDC BRFSS 1990-2002)




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• Typically ascribed to lower levels of activity and greater consumption of energy-dense foods

• Obesity is associated with many other negative health issues (Aguilar-Salinas et al. 2001)




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• Walking is a good way to get moderate exercise• Not all locations in urban or rural environments are

suitable or safe for walking• Social-ecological approach (Stokols 1992) is becoming a

popular way to conceptualize the effects of environment on behavior (in this case health-related behavior)

• Evidence is mounting that the composition and configuration of the built environment may have detrimental effects on health (Sturm and Cohen 2004)

• The specific built environment elements beneficial or detrimental to health are not yet known with certainty




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• There is a need for obtaining objective measures of the built environment and their effect on health related behaviors

• Our study uses GIS and traditional survey methods to estimate the walkability of locations within the urban environment in the Seattle area

• We have developed an ArcView 3.x extension that collects and analyzes more than 200 variables related to the built environment for every location of interest




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Overview

• Abstract


• Methods


• Conclusions


• References




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Methods

• Data sources and preparation• Analytical components• Telephone survey• Statistical analysis




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Methods: Data sources and preparation

• Seattle and King County have a large and well-developed data bank; the project would have been impossible without such a collection

• Parcel layer is the most important because of land use encoding• Substantial effort was required to conflate a single parcel

layer containing all necessary data• Locations of interest

• Household locations geocoded from survey forms• Buffers around locations of interest stored as individual

shapefiles• Euclidean• Network




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Methods: Data sources and preparation

• Other data sources: typical/general urban GIS data• streets• blocks• sidewalks• crosswalks• intersections• traffic signals• bus stops• speed limits• traffic volume• slope (raster)




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Methods: Analytical components

• Land use proximity analysis• Land use buffer analysis• Neighborhood center analysis




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Analytical components: Land use proximity analysis

• Quantifies proximity to all individual land uses within buffer distance of location of interest

• Uses standard proximity tools within the GIS (polygon-in-polygon, network analysis)

• Automated within the Avenue API• faster processing than by hand• no user error• repeatable• compact output in a single table




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Analytical components: Land use proximity analysis




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Analytical components: Land use buffer analysis

• Quantifies amounts of features within buffer distance from location of interest• Land use classes (e.g., SF, MF, RET-SERV)

• count

• area

• Various other layer features (e.g., bike lane, sidewalk, bus stop, park, steep slope)

• count

• area

• length

• Same automation benefits as proximity analysis




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Analytical components: Land use buffer analysis




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Analytical components: Neighborhood center analysis

• Parcels with associated land uses frequently occur in clusters (e.g., shopping districts)

• Neighborhood Center (NC) analysis identifies clusters of land use and generates convex-hull polygons based on a combination of spatial and attribute properties

• Proximity and buffer measures are calculated for NCs as well• proximity to other land uses to each NC• inventory of features within buffer distance to each NC




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Analytical components: Neighborhood center analysis




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Methods: Telephone survey

• Extensive telephone survey (~25 min)• Spatially stratified random sample of able-bodied adults

in urbanized King County• Final sample of 608 subjects• Questions on:

• health status• sociodemographics• perception of various land uses in neighborhood• activity/exercise




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Methods: Statistical analysis

• Multinomial logit models• Dependent variable: sufficient walking• Independent variables

• Survey results• GIS data summaries

• Two models• Base model: survey results alone• Extended model: survey results coupled with

environmental variables




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Methods: Statistical analysis

• Significant environmental variables selected from:• art galleries/museums• offices• post offices• regional shopping centers• full-service restaurants• retail stores• schools• sports facilities• movie theaters• trails• parks

• grocery stores• fast food restaurants• pubs/bars/taverns• big box retail stores• banks, churches• neighborhood/community

shopping centers• convenience stores• day care centers• fitness centers,

medical/dental/hospital facilities• libraries• mixed use




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Overview

• Abstract


• Methods


• Conclusions


• References




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Results/Discussion

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




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Results/Discussion

• Land uses strongly associated with walking included frequently used destinations, e.g.,• banks• retail stores• grocery stores• restaurants• pubs (when singled out, this was the strongest

environmental predictor)• schools • NC: [grocery + retail + restaurant]• NC: [school + church]




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Results/Discussion

• Limitations:• Application developed specifically for Seattle/King

County data• Will need alteration to handle data from other areas

• Written for ArcView 3.x (Avenue); not the current flavor of choice among users

• Study frame of Seattle/King County reduces generalizability of results

• Will need to be repeated in other locales in order to characterize general patterns

• Self-selection of residents to neighborhoods reduces the ability to determine causation in all studies of location-behavior




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Overview

• Abstract


• Methods


• Conclusions


• References




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Conclusions

• “Three D’s” of activity emerge as drivers of walkability:• Destination• Distance• Density

• Use of detailed (parcel level GIS and individual responses) provides higher quality information than spatially aggregated data (e.g., census, neighborhood)

• Our work suggests more knowledge can be gained from taking similar approaches




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Overview

• Abstract


• Methods


• Conclusions


• References




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Acknowledgements

• US Centers for Disease Control, SIP18-01 under guidance of Tom Schmid

• Professor Anne Vernez Moudon, University of Washington College of Architecture and Urban Planning

• Professor Chanam Lee, Texas A&M (formerly Prof. Moudon’s student)




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Overview

• Abstract


• Methods


• Conclusions


• References




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References

• Aguilar-Salinas, C. A., C. Vazquez-Chavez, et al. (2001). "Obesity, diabetes, hypertension, and tobacco consumption in an urban adult Mexican population." Archives of Medical Research 32(5): 446-453.

• CDC (1990-2002). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia, U.S. Department of Health and Human Services, Centers for Disease Control and Prevention.

• Stokols, D. (1992). "Establishing and Maintaining Healthy Environments - toward a Social Ecology of Health Promotion." American Psychologist 47(1): 6-22.

• Sturm, R. and D. A. Cohen (2004). "Suburban sprawl and physical and mental health." Public Health 118(7): 488-496.




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Questions?

http://gis.washington.edu/phurvitz/wbc

[email protected]

the walkable-bikeable communities analyst extension for arcview 3.x © phil hurvitz, 2005 the...

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