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Urban Form Lab Research Phil Hurvitz, 2005Slide 1 (of 42) Research in the Urban Form Lab College of Architecture & Urban Planning University of Washington Phil Hurvitz 2 nd Year Doctoral Student Interdisciplinary PhD Program in Urban Design and Planning 2006.07.13 Slide 2 Urban Form Lab Research Phil Hurvitz, 2006Slide 2 (of 42) Overview Research Direction Research Project Examples Walkable-Bikeable Communities Project Pedestrian Safety Modeling Particulate Matter Measurement Physical Activity Measurement Slide 3 Urban Form Lab Research Phil Hurvitz, 2006Slide 3 (of 42) Overview Research Direction Research Project Examples Walkable-Bikeable Communities Project Pedestrian Safety Modeling Particulate Matter Measurement Physical Activity Measurement Slide 4 Urban Form Lab Research Phil Hurvitz, 2006Slide 4 (of 42) Research Direction Focus on Objective measures of environment Super-high resolution data Individual (person or household) as unit of analysis Analysis methods to handle objective measures and high- resolution data (research examples) Slide 5 Urban Form Lab Research Phil Hurvitz, 2006Slide 5 (of 42) Research Direction Focus on Objective measures of environment Built environment (through GIS) Other social and physical environment parameters e.g.: particulate matter demographics: socioeconomic position health: obesity, physical activity Super-high resolution data Analysis methods to handle objective measures and high- resolution data Slide 6 Urban Form Lab Research Phil Hurvitz, 2006Slide 6 (of 42) Research Direction Objective measures Scientific approach Central importance of measurements Repeatability Minimizing bias Slide 7 Urban Form Lab Research Phil Hurvitz, 2006Slide 7 (of 42) Research Direction Focus on Objective measures of environment Super-high resolution data Individual (person or household) as unit of analysis Analysis methods to handle objective measures and high- resolution data (research examples) Slide 8 Urban Form Lab Research Phil Hurvitz, 2006Slide 8 (of 42) Super-high resolution data Tax-lot parcel basis The most fundamental unit of urban structure: ownership, residence, neighborhood, land use, development, real estate Slide 9 Urban Form Lab Research Phil Hurvitz, 2006Slide 9 (of 42) Super-high resolution data Tax-lot parcel basis The most fundamental unit of urban structure: ownership, residence, neighborhood, land use, development, real estate Slide 10 Urban Form Lab Research Phil Hurvitz, 2006Slide 10 (of 42) Super-high resolution data Other urban features we commonly use in analysis: Streets Schools Intersections Parks Sidewalks Neighborhood centers retail-oriented food-oriented business/office-oriented Traffic signals Bus stops Street traffic volume Landform (slope) Slide 11 Urban Form Lab Research Phil Hurvitz, 2006Slide 11 (of 42) Research Direction Focus on Objective measures of environment Super-high resolution data Individual (person or household) as unit of analysis Analysis methods to handle objective measures and high- resolution data (research examples) Slide 12 Urban Form Lab Research Phil Hurvitz, 2006Slide 12 (of 42) Social-ecologic model Social ecologic model considers impacts of environment (institutional, physical, social, etc.) on behavior. (E.g., Stokols, 1992; Sallis and Owen 1997) Person (behavior) Personal/individual factors = = Social/community environmental factors Institutional/policy environmental factors Built environmental factors ++ Intervention behavior change/modification Inner personal environment Outer environments Slide 13 Urban Form Lab Research Phil Hurvitz, 2006Slide 13 (of 42) Super-high resolution data Individual and household Concentration on individual characteristics, e.g., Income Education Health status (weight, physical activity) Location, which is related to availability of Land use Schools Worksite Transportation features Food sources Utilitarian destinations (banks, retail, etc.) Slide 14 Urban Form Lab Research Phil Hurvitz, 2006Slide 14 (of 42) Individual (person or household) as unit of analysis Epidemiologic approach: individual basis ExposureOutcome Environment Built-up Urban features Neighborhood effects Specific land uses Demography Wealth Education Behavior Walking Bicycling Driving/Transit use Eating habits Slide 15 Urban Form Lab Research Phil Hurvitz, 2006Slide 15 (of 42) Research Direction Focus on Objective measures of environment Super-high resolution data Individual (person or household) as unit of analysis Analysis methods to handle objective measures and high- resolution data (research examples) Slide 16 Urban Form Lab Research Phil Hurvitz, 2006Slide 16 (of 42) Overview Research Direction Research Project Examples Walkable-Bikeable Communities Project Pedestrian Safety Modeling Particulate Matter Measurement Physical Activity Measurement Slide 17 Urban Form Lab Research Phil Hurvitz, 2006Slide 17 (of 42) Overview Research Direction Research Project Examples Walkable-Bikeable Communities Project Pedestrian Safety Modeling Particulate Matter Measurement Physical Activity Measurement Slide 18 Urban Form Lab Research Phil Hurvitz, 2006Slide 18 (of 42) Walkable-Bikeable Communities Project Research Question: Does the built environment have an effect on individual walking or bicycling behavior preference? Methods: Telephone survey Individual characteristics Measurement of environment with GIS Statistical analysis Age Income Race Gender Height Weight Exercise Perception of neighborhood Slide 19 Urban Form Lab Research Phil Hurvitz, 2006Slide 19 (of 42) Measurement of environment with GIS GIS applications developed for measurement of built environment in proximity of home location streets blocks sidewalks crosswalks intersections traffic signals bus stops speed limits traffic volume slope (raster) neighborhood centers Slide 20 Urban Form Lab Research Phil Hurvitz, 2006Slide 20 (of 42) Measurement of environment with GIS Custom GIS Application (WBC Analyst) automates the capture of over 200 built environment variables per household Slide 21 Urban Form Lab Research Phil Hurvitz, 2006Slide 21 (of 42) Walkable-Bikeable Communities Project Multivariate statistical analysis: Using only socio-demographic variables we were able to explain 35% of the variation in walking preference Adding environmental variables (presence of certain land uses within 1 mile of the home) obtained from the GIS increased the R 2 to 47% Conclusion: using spatially explicit data in analysis of the urban environment can help us understand the role of the physical environment on behavior Slide 22 Urban Form Lab Research Phil Hurvitz, 2006Slide 22 (of 42) Overview Research Direction Research Project Examples Walkable-Bikeable Communities Project Pedestrian Safety Modeling Particulate Matter Measurement Physical Activity Measurement Slide 23 Urban Form Lab Research Phil Hurvitz, 2006Slide 23 (of 42) Pedestrian Safety Modeling In 1999-2004, over 3000 auto + pedestrian/bicycle collisions occurred on State Highways in Washington State. Location, severity, and other details are recorded by Washington State Patrol and reported to Washington State Department of Transportation (WSDOT). WSDOT allocates funds to improve infrastructure based on need. Can spatial analysis help identify locations in greatest need of infrastructure improvement for safety? Slide 24 Urban Form Lab Research Phil Hurvitz, 2006Slide 24 (of 42) Pedestrian Safety Modeling Spatial measurements are based on milepost value Calculated for each point as a focus (moving window analysis) d3d3 d2d2 d1d1 measured distances b bandwidth fp2p2 p1p1 p3p3 collisions focal collision Slide 25 Urban Form Lab Research Phil Hurvitz, 2006Slide 25 (of 42) Pedestrian Safety Modeling Various statistics developed to describe spatial properties of collisions distance-frequency measures severity of injury mean inter-collision distance Slide 26 Urban Form Lab Research Phil Hurvitz, 2006Slide 26 (of 42) Pedestrian Safety Modeling Slide 27 Urban Form Lab Research Phil Hurvitz, 2006Slide 27 (of 42) Pedestrian Safety Modeling Slide 28 Urban Form Lab Research Phil Hurvitz, 2006Slide 28 (of 42) Overview Research Direction Research Project Examples Walkable-Bikeable Communities Project Pedestrian Safety Modeling Particulate Matter Measurement Physical Activity Measurement Slide 29 Urban Form Lab Research Phil Hurvitz, 2006Slide 29 (of 42) Particulate Matter Measurement Research Questions: 1.Is the International District affected by particulate matter (mainly from bus and truck traffic)? 2.Is air pollution dispersed evenly across space within this area? Slide 30 Urban Form Lab Research Phil Hurvitz, 2006Slide 30 (of 42) Particulate Matter Measurement Methods: Particle Soot Absorption Photometer (PSAP) measures fine particulate matter 1 measurement per second Mapping-grade GPS measures location to < 1 m precision 1 measurement per second All equipment mounted in a car for time-space measurement Measurements taken on weekdays at PM-peak period Slide 31 Urban Form Lab Research Phil Hurvitz, 2006Slide 31 (of 42) Particulate Matter Measurement Slide 32 Urban Form Lab Research Phil Hurvitz, 2006Slide 32 (of 42) Particulate Matter Measurement variation in air pollution over sampling period Slide 33 Urban Form Lab Research Phil Hurvitz, 2006Slide 33 (of 42) Particulate Matter Measurement variation in air pollution across space Slide 34 Urban Form Lab Research Phil Hurvitz, 2006Slide 34 (of 42) Particulate Matter Measurement variation in air pollution across space and time, aggregated to standardized sample locations Interstate 5 ?? Slide 35 Urban Form Lab Research Phil Hurvitz, 2006Slide 35 (of 42) Particulate Matter Measurement kriged surface ?? Slide 36 Urban Form Lab Research Phil Hurvitz, 2006Slide 36 (of 42) Overview Research Direction Research Project Examples Walkable-Bikeable Communities Project Pedestrian Safety Modeling Particulate Matter Measurement Physical Activity Measurement Slide 37 Urban Form Lab Research Phil Hurvitz, 2006Slide 37 (of 42) Physical Activity Measurement Research Questions: 1.Does a novel sensing device measure physical activity objectively? 2.Do people engage in different types and intensities of physical activity in different types of built environments? Slide 38 Urban Form Lab Research Phil Hurvitz, 2006Slide 38 (of 42) Physical Activity Measurement Methods: Subjects wear new MSB device Device measures 7 variables Multivariate data are processed to give common types of physical activity Subjects complete self-report diary of activity MSB data stream and self-report are compared Properties of built environment are associated with activity in real-time analysis Pilot study is rolling out at this time Slide 39 Urban Form Lab Research Phil Hurvitz, 2006Slide 39 (of 42) Physical Activity Measurement MSB equipment Slide 40 Urban Form Lab Research Phil Hurvitz, 2006Slide 40 (of 42) Physical Activity Measurement worn in free-living conditions Slide 41 Urban Form Lab Research Phil Hurvitz, 2006Slide 41 (of 42) Physical Activity Measurement pilot data: objectively measured physical activity in real-time/space Slide 42 Urban Form Lab Research Phil Hurvitz, 2006Slide 42 (of 42) Conclusion The Urban Form Lab is engaged in research for understanding the effect of place on human life Use of super high resolution data, new devices, and unique geospatial analysis allows us to see relationships in novel ways Understanding the effect of the built environment on human life has implications for urban design and urban policy: safer, healthier, more livable environments Slide 43 Urban Form Lab Research Phil Hurvitz, 2006 Questions URL: http://gis.washington/edu/phurvitz E-mail: phurvitz@u.washington.edu