A Behavioral Framework for Measuring Walkability and its Impact on Home Values and Residential Location Choices

详细信息   

• 作者：Foti ; Fletcher Scott ; Ph.D.
• 学历：Ph.D.
• 年：2014
• 关键词：Accessibility ; Amenities proximity ; Latent classes ; N
• 导师：Waddell,Paul
• 毕业院校：University of California
• Department：City & Regional Planning
• 专业：Transportation planning,Urban planning
• ISBN：9781321258912
• CBH：3640431
• Country：USA
• 语种：English
• FileSize：31012438
• Pages：131

文摘

Walking is underrepresented in large area models of urban behavior,largely due to difficulty in obtaining data and computational issues in representing land use at such a small scale. Recent advances in data availability,like the ubiquitous point-of-interest data collected by many private companies,as well as a worldwide dataset of local streets in OpenStreetMap,a standard format for obtaining transit schedules in GTFS,etc,provide the potential to build a scalable methodology to understand travel behavior at a pedestrian scale which can be applied wherever these datasets are available. This dissertation improves on similar indexes like WalkScore by estimating a model that represents the substitution of destinations around a location and between the modes of walking,automobile,and transit. This model is estimated using the San Francisco Bay Area portion of the 2012 California Household Travel Survey to capture observed transportation behavior,and accounts for the demographics included in the survey. These representations of travel behavior can then be used as right-hand side variables in other urban models: for instance,to create a residential location choice model where measures of accessibility and available demographics are used to understand why people choose to live where they do. This dissertation is organized into four topics,one for each of chapters 2-5. The first topic establishes a framework for measuring the network of destination opportunities in the city for each of the walking,transit,and auto transportation modes. Destinations in the form of parcels and buildings,businesses,population,and points of interest are tied to each network so that the distance from each location to every destination can be computed by mode. The use of a points-of-interest dataset as the set of public-facing destinations is novel in the context of a traditional travel demand destination model. This chapter also creates a case study model of trip generation for home-based walking trips is the 2012 California Household Travel Survey. This model finds that WalkScore is predictive of walking trips,that residential density and 4-way intersections have an additional but small impact,and that regional access by the transit network has a synergistic effect on walking,but regional access by auto has no impact when controlling for regional access by transit. The second topic engages with the question of the impact of accessibility to local amenities on home values. Although early research has found that the composite index WalkScore is positively correlated with home values,this dissertation unpacks the impact of each category of destination used in WalkScore (as well as several others) on home values. The model shows that some amenities are far more predictive of home values in the datasets used here;in particular,cafes and coffee shops tend to be the indicator of neighborhood-scale urban fabric that has the largest positive relationship with home values,where a one standard deviation increase in access to cafes is associated with a 15\% increase in home values. Although the previous topic provides some evidence that walkable amenities are related to increased home values with the datasets analyzed here,it does not prove that households are valuing walking to these amenities;it is equally plausible that households are capitalizing short driving trips into increased home values. The third topic thus creates a nested mode-destination model for each trip purpose (with destinations nested into modes) so that the logsums of the lower nest give an absolute measure of the accessibility by mode for each purpose for each location in the region. These logsums are then weighted by the number of trips made for each purpose,and segmented by income and weighted by the incomes of the people that live at each location in the city. The result is an index based only on empirically observed behavior (in this case,the primary dataset is the 2012 CHTS) which is an absolute measure of walking behavior,not just of walkability. The methodology from this chapter yields an index for all three modes,and all indexes are included in the hedonic model described above. The model shows that a one standard deviation change in the auto index has the largest impact on home values,but that the walking index is positive,statistically significant,and almost as large. Although part of the reason for this finding might be that these neighborhoods are undersupplied,where they exist they are clearly in high demand. (Abstract shortened by UMI.)