Synergistic neighborhood relationships with travel behavior: An analysis of travel in 30,000 US neighborhoods
Keywords:Neighborhood classification, travel behavior
AbstractA now substantial body of literature finds that land use and urban form have a statistically significant, albeit relatively modest, effect on travel behavior. Some scholars have suggested that various built-environment characteristics influence travel more in concert than when considered in isolation. Yet few previous studies have combined built-environment measures to create holistic descriptions of the overall character of neighborhoods, and fewer still have related these neighborhoods to residents’ travel decisions. To address this gap in the literature, we develop a typology of seven distinct neighborhood types by applying factor analysis and then cluster analysis to a set of 20 variables describing built-environment characteristics for most census tracts in the United States. We then include these neighborhood types in a set of multivariate regression models to estimate the effect of neighborhood type on the travel behavior of neighborhood residents, controlling for an array of personal and household characteristics. We find relatively little variation in the number of daily trips among neighborhood types, but there is substantial neighborhood variation in both person miles of travel and mode choice. Travel by residents of one particular neighborhood type is notably distinguished from all others by a very low number of miles traveled, little solo driving, and high transit use. However, this neighborhood type is found almost exclusively in just a few very large metropolitan areas, and its replicability is uncertain.
Blumenberg, E., B. D. Taylor, M. Smart, K. Ralph, M. Wander, and S. Brumbaugh. 2012. What’s Youth Got to Do with It? Exploring the Travel Behavior of Teens and Young Adults. Berkeley, CA: University of California Transportation Center. http://www.uctc.net/papers/UCTC-FR-2012-14.pdf.
Burgess, E. W. 1925. The growth of the city: An introduction to a research project. In The City, edited by Robert E. Park, Ernest W. Burgess, and Roderick D. McKenzie, 1–46. Chicago: The University of Chicago Press.
Cao, X., P. L. Mokhtarian, and S. L. Handy. 2009. Examining the impacts of residential self‐selection on travel behavior: A focus on empirical findings. Transport Reviews 29 (3): 359–95. doi:10.1080/01441640802539195.
Cattell, R. B. 1966. The scree test for the number of factors. Multivariate Behavioral Research 1(2): 245–76. doi:10.1207/s15327906mbr0102_10.
Cervero, R., and K. Kockelman. 1997. Travel demand and the 3Ds: density, diversity, and design.Transportation Research Part D: Transport and Environment 2(3): 199–219. doi:10.1016/S1361-9209(97)00009-6.
Chatman, D. G. 2013. Explaining the immigrant effect on auto use: The influences of neighborhoods and preferences. Transportation 41(3): 441–461. doi:10.1007/s11116-013-9475-4.
Chow, J. 1998. Differentiating urban neighborhoods: A multivariate structural model analysis. Social Work Research 22(3): 131–42. doi:10.1093/swr/22.3.131.
Desgraupes, B. 2013. Clustering Indices. Lab Modal’X, University Paris Oest. https://cran.r-project.org/web/packages/clusterCrit/vignettes/clusterCrit.pdf.
Desgraupes, B. 2014. clusterCrit: Clustering Indices. (Version R package version 1.2.4). http://CRAN.R-project.org/package=clusterCrit.
Ewing, R., and R. Cervero. 2010. Travel and the built environment. Journal of the American Planning Association 76(3): 265–94. doi:10.1080/01944361003766766.
Federal Highway Administration. 2011. 2009 National Household Travel Survey User’s Guide. Washington DC: United States Department of Transportation.
Handy, S., X. Cao, and P. Mokhtarian. 2005. Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transportation Research Part D: Transport and Environment 10(6): 427–44. doi:10.1016/j.trd.2005.05.002.
Hoyt, H. 1939. The Structure and Growth of Residential Neighborhoods in American Cities. Washington, DC: Federal Highway Administration. http://trid.trb.org/view.aspx?id=131170.
Kaiser, H. F. 1960. The application of electronic computers to factor analysis. Educational and Psychological Measurement 20: 141–51. doi:10.1177/001316446002000116.
Ledesman, R. D., and P. Valero-Mora. 2007. Determining the number of factors to retain in EFA: An easy-to-use computer program for carrying out parallel analysis. Practical Assessment, Research and Evaluation 12(2): 1–11.
Leigh, N. G., and S. Lee. 2005. Philadelphia’s space in between: Inner-ring suburb evolution. Opolis 1(1): 12–13. http://escholarship.org/uc/item/16t4c093.
Levine, J., A. Inam, and G.-W. Torng. 2005. A choice-based rationale for land use and transportation alternatives evidence from Boston and Atlanta. Journal of Planning Education and Research 24(3): 317–30. doi:10.1177/0739456X04267714.
Lin, J., and L. Long. 2008. What neighborhood are you in? Empirical findings of relationships between household travel and neighborhood characteristics. Transportation 35(6): 739–58. doi:10.1007/s11116-008-9167-7.
Li, Y.-S., and Y.-C. Chuang. 2009. Neighborhood effects on an individual’s health using neighborhood measurements developed by factor analysis and cluster analysis. Journal of Urban Health 86(1): 5–18. doi:10.1007/s11524-008-9306-7.
Mikelbank, B. A. 2011. Neighborhood déjà vu: Classification in metropolitan Cleveland, 1970-2000.Urban Geography 32(3): 317–33. doi:10.2747/0272-36126.96.36.1997.
Mokhtarian, P. L., and X. Cao. 2008. Examining the impacts of residential self-selection on travel behavior: A focus on methodologies. Transportation Research Part B: Methodological, A Tribute to the Career of Frank Koppelman 42(3): 204–28. doi:10.1016/j.trb.2007.07.006.
Müllner, D. 2013. Fastcluster: Fast hierarchical, agglomerative clustering routines for R and python.Journal of Statistical Software 53(9): 1–18.
Næss, P. 2014. Urban form, sustainability and health: The case of Greater Oslo. European Planning Studies 22(7): 1524–43. doi:10.1080/09654313.2013.797383.
Ramsey, K., and A. Bell. 2014. Smart Location Database, Version 2.0 User Guide. Washington DC: Environmental Protection Agency. http://www2.epa.gov/sites/production/files/2014-03/documents/sld_userguide.pdf.
Revelle, W. 2014. Psych: Procedures for Personality and Psychological Research. Evanston, IL: Northwestern University. http://CRAN.R-project.org/package=psych Version = 1.4.5.
Shay, E., and A. Khattak. 2007. Automobiles, trips, and neighborhood type: Comparing environmental measures. Transportation Research Record 2010 (-1): 73–82. doi:10.3141/2010-09.
Solon, G., S. J. Haider, and J. M. Wooldridge. 2015. What are we weighting for? Journal of Human Resources 50(2): 301–16. doi:10.3368/jhr.50.2.301.
Song, Y., and G.-J. Knaap. 2007. Quantitative classification of neighborhoods: The neighborhoods of new single-family homes in the Portland metropolitan area. Journal of Urban Design 12(1): 1–24. doi:10.1080/13574800601072640.
Song, Y., and R. G. Quercia. 2008. How are neighborhood design features valued across different neighborhood types? Journal of Housing and the Built Environment 23(4): 297–316. doi:10.1007/s10901-008-9122-0.
United States Census Bureau. 2010. Decennial Census. Washington DC: U.S. Census Bureau.
Vicino, T. J. 2008. The spatial transformation of first‐tier suburbs, 1970 to 2000: The case of metropolitan Baltimore. Housing Policy Debate 1(3): 479–518. doi:10.1080/10511482.2008.9521644.
Winship, C., and L. Radbill. 1994. Sampling weights and regression analysis. Sociological Methods and Research 23(2): 230–57. doi:10.1177/0049124194023002004.
Zhou, B., and K. Kockelman. 2008. Self-selection in home choice: Use of treatment effects in evaluating relationship between built environment and travel behavior. Transportation Research Record 2077(12): 54–61. doi:10.3141/2077-08.
How to Cite
Authors who publish with JTLU agree to the following terms: 1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution-Noncommercial License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. 2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. 3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.