A multi-dimensional multi-level approach to measuring the spatial structure of U.S. metropolitan areas
Arefeh Nasri
University of Maryland, College Park
Lei Zhang
University of Maryland, College Park
DOI: https://doi.org/10.5198/jtlu.2018.893
Keywords: Built environment, Spatial analysis, Land use, Metropolitan structure, Sprawl, Cluster analysis, Urban form.
Abstract
For many years, attempts to measure the urban structure and physical form of metropolitan areas have been focused on a limited set of attributes, mostly density and density gradients. However, the complex nature of the urban form requires the consideration of many other dimensions to provide a comprehensive measure that includes all aspects of the urban structure and growth pattern at different hierarchical levels. In this paper, a multi-dimensional method of measuring urban form and development patterns in urban areas of the United States is presented. The methodology presented here develops several variables and indices that contribute to the characterization and quantification of the overall physical form of urban areas at various hierarchical levels. Cluster analysis is performed to group metropolitan areas based on their urban form and land-use pattern. This allows for a better utilization of land-use transportation planning and policy analyses used by planners and researchers. This clustering of urban areas could eventually help policymakers and decision makers in the decision-making process to evaluate land-use transportation policies, identify similar patterns, and understand how similar policies implemented in urban areas with similar urban form structure would result in more efficient and successful planning in the future.References
Bento, A., Cropper, M., Mobarak, A., & Vinha, K. (2005). The effects of urban spatial structure on travel demand in the United States. The Review of Economics and Statistics, 87(3), 466–478.
Borgen, F. H., & Barnett, D. C. (1987). Applying cluster analysis in counseling psychology research. Journal of Counseling Psychology, 34(4), 456.
Cervero, R., & Murakami, J. Effects of built environments on vehicle miles traveled: Evidence from 370 US urbanized areas. Environment and Planning A 42(2), 400–418.
Ewing, R. (1994). Characteristics, causes, and effects of sprawl: A literature review. Environmental and Urban Issues, 21(2), 1–15.
Ewing, R. (1997). Is Los Angeles-style sprawl desirable? Journal of the American Planning Association, 63(1), 107–126.
Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association, 76(3), 265–294.
Fischer, M. M., & Getis, A. (Eds.). (2009). Handbook of applied spatial analysis: software tools, methods and applications. Berlin: Springer Science & Business Media.
Frenkel, A., & Ashkenazi, M. (2008). The integrated sprawl index: Measuring the urban landscape in Israel. The Annals of Regional Science, 42(1), 99–121.
Galster, G., Hanson, R., Ratcliffe, M. R., Wolman, H., Coleman, S., & Freihage, J. (2001). Wrestling sprawl to the ground: Defining and measuring an elusive concept. Housing Policy Debate, 12(4), 681–717.
Gordon P., Richardson, H., & Jun, M.-J. (1991). The commuting paradox: Evidence from the top twenty. Journal of the American Planning Association, 57(4), 416–420.
Gordon, P., Kumar, A., & Richardson, H. W. (1989). Congestion, changing metropolitan structure, and city size in the United States. International Regional Science Review 12(1), 45–56.
Grengs, J., Levine, J., Shen, W., & Shen, Q. (2010). Intermetropolitan comparison of transportation accessibility: Sorting out mobility and proximity in San Francisco and Washington, D.C. Journal of Planning Education and Research, 29(4), 427–443.
Holtzclaw, J., Clear, R., Dittmar, H., Goldstein, D., & Haas, P. (2002). Location efficiency: Neighborhood and socio-economic characteristics determine auto ownership and use-studies in Chicago, Los Angeles and San Francisco. Transportation Planning and Technology, 25(1), 1–27.
Ingram, G. (1998). Patterns of metropolitan development: What have we learned? Urban Studies, 35(7), 1019–1035.
Kaufman, L. R., & Rousseeuw, P. J. (1990) Finding groups in data: An introduction to cluster analysis. Hoboken, NJ: John Wiley & Sons Inc.
Kelly-Schwartz, A. C., Stockard, J., Doyle, S., & Schlossberg, M. (2004). Is sprawl unhealthy? A multilevel analysis of the relationship of metropolitan sprawl to the health of individuals. Journal of Planning Education and Research, 24(2), 184–196.
Malpezzi, S., & Guo, W. K. (2001). Measuring sprawl: Alternative measures of urban form in US metropolitan areas. Unpublished manuscript, Center for Urban Land Economics Research, University of Wisconsin, Madison.
McCann, B. A., & Ewing, R. (2003) Measuring the health effects of sprawl: A national analysis of physical activity, obesity and chronic disease. Smart Growth America Surface Transportation Policy Project. Retrieved from http://www.smartgrowthamerica.org/healthreportpr.html
Nasri, A., & Zhang, L. (2012). Impact of metropolitan-level built environment on travel behavior. Transportation Research Record, 2323(1), 75–79.
Nasri, A., & Zhang, L. (2014). Assessing the impact of metropolitan-level, county-level, and local-level built environment on travel behavior: Evidence from 19 US urban areas. Journal of Urban Planning and Development, 141(3), 04014031
Punj, G., & Stewart, D. W. (1983). Cluster analysis in marketing research: Review and suggestions for application. Journal of Marketing Research, 20(2), 134–148.
Schwanen, T., Dieleman, F. M., & Dijst, M. (2004). The impact of metropolitan structure on commute behavior in the Netherlands: A multilevel approach. Growth and Change, 35(3), 304–333.
Smith, J., & Saito, M. (2001). Creating land-use scenarios by cluster analysis for regional land use and transportation sketch planning. Journal of Transportation and Statistics, 4(1), 39–49.
Torrens, P. M., & Alberti, M. (2000). Measuring sprawl. Working Papers 27. Centre for Advanced Spatial Analysis, University College London. Retrieved from http://www.casa.ucl.ac.uk
Tsai, Y. H. (2005). Quantifying urban form: Compactness versus sprawl. Urban Studies, 42(1), 141–161.
Veneri, P. (2010). Urban polycentricity and the costs of commuting: Evidence from Italian metropolitan areas. Growth and Change, 41(3), 403–429.
Yang, J., French, S., Holt, J., & Zhang, X. (2012). Measuring the structure of US metropolitan areas, 1970–2000: Spatial statistical metrics and an application to commuting behavior. Journal of the American Planning Association, 78(2), 197–209.
