Influence of Different Spatial Aggregations on Variables Implemented in Macroscopic Road-Safety Modeling
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 4
Abstract
Spatially aggregated data are prone to the effects of the modifiable areal unit problem, especially when the phenomenon of interest is not used as a criterion to define the areas of analysis. Further, these conditions can result in statistical inconsistency. The purpose of this study is to assess the influence of different scales of analysis on the statistical variables employed in macroscopic modeling of road safety for the city of Fortaleza, Brazil. Two sets of spatial configurations were created with different aggregation criteria as a means of detecting changes in the averages, standard deviations, correlations, and spatial autocorrelations of the variables. The results show that the statistical variation is not only dependent on the change of scale, but also on the format of the variable produced by the aggregation process.
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Data Availability Statement
Some or all data, models, or code used during the study are available from the corresponding author by request: (1) spreadsheets with raw data of the variables in Table 1 for the two aggregation criteria (CRC and SPC) of each zoning analyzed; and (2) scatter and line plots of univariate and bivariate statistics (mean, standard deviation, correlation coefficient, and spatial autocorrelation) for all variables and zoning.
Acknowledgments
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) of Brasil under Finance Code 001, the National Council for Scientific and Technological Development (CNPq), and the Ceará Foundation for Scientific and Technological Development (FUNCAP). The authors acknowledge the financial support provided by these agencies.
References
Abdel-Aty, M., J. Lee, C. Siddiqui, and K. Choi. 2013. “Geographical unit based analysis in the context of transportation safety planning.” Transp. Res. Part A 49 (Mar): 62–75. https://doi.org/10.1016/j.tra.2013.01.030.
Aguero-Valverde, J., and P. P. Jovanis. 2006. “Spatial analysis of fatal and injury crashes in Pennsylvania.” Accid. Anal. Prev. 38 (3): 618–625. https://doi.org/10.1016/j.aap.2005.12.006.
AMC (Autarquia Municipal de Trânsito e Cidadania). 2013. “Fortaleza em Mapas – Informações Georreferenciadas: Eixos viários.” Accessed March 25, 2014. https://mapas.fortaleza.ce.gov.br.
Amoh-Gyimah, R., M. Saberi, and M. Sarvi. 2017. “The effect of variations in spatial units on unobserved heterogeneity in macroscopic crash models.” Anal. Methods Accid. Res. 13 (Mar): 28–51. https://doi.org/10.1016/j.amar.2016.11.001.
Anselin, L. 1988. Spatial econometrics: Methods and models. Dordrecht, Netherlands: Kluwer.
Anselin, L. 2005. Exploring spatial data with GeoDa: A workbook. Urbana, IL: Univ. of Illinois.
ASTEF (Associaçõo Técnico Científica Engenheiro Paulo de Frontim). 2001. Manual do Usuário do Sistema Computacional do Sistema de Informações de Acidentes de Trânsito—SIAT/FOR. Fortaleza, Brazil: ASTEF.
Assunção, R. M., M. C. Neves, G. Câmara, and C. C. Freitas. 2006. “Efficient regionalisation techniques for socio-economic geographical units using minimum spanning trees.” Int. J. Geog. Inf. Sci. 20 (7): 797–811. https://doi.org/10.1080/13658810600665111.
Briant, A., P. P. Combes, and M. Lafourcade. 2010. “Dots to boxes: Do the size and shape of spatial units jeopardize economic geography estimations?” J. Urban Econ. 67 (3): 287–302. https://doi.org/10.1016/j.jue.2009.09.014.
Cai, Q., M. Abdel-Aty, J. Lee, and N. Eluru. 2017. “Comparative analysis of zonal systems for macro-level crash modeling.” J. Saf. Res. 61 (Jun): 157–166. https://doi.org/10.1016/j.jsr.2017.02.018.
Cai, Q., J. Lee, N. Eluru, and M. Abdel-Aty. 2016. “Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models.” Accid. Anal. Prev. 93 (Aug): 14–22. https://doi.org/10.1016/j.aap.2016.04.018.
Darmofal, D. 2006. Spatial econometrics and political science. Columbia, SC: Univ. of South Carolina.
Fotheringham, A. S., C. Brunsdon, and M. Charlton. 2002. Geographically weighted regression. New York: Wiley.
Huang, H., P. Xu, and M. Abdel-Aty. 2013. “Transportation safety planning: A spatial analysis approach.” In Proc., Transportation Research Board (TRB) 92th Annual Meeting, Washington, DC: Transportation Research Board.
IBGE (Instituto Brasileiro de Geografia e Estatística). 2011. “Base de informações do censo demográfico 2010: Resultados do universo por setor censitário.” Accessed June 15, 2014. ftp://ftp.ibge.gov.br/Censos/Censo_Demografico_2010/Resultados_do_Universo/Agregados_por_Setores_Censitarios/CE_20171016.zip.
Khondakar, B., T. Sayed, and G. Lovegrove. 2010. “Transferability of community-based collision prediction models for use in road safety planning applications.” J. Transp. Eng. 136 (10): 871–880. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000153.
Kim, K., I. M. Brunner, and E. Y. Yamashita. 2006. “Influence of land use, population, employment, and economic activity on accidents.” Transp. Res. Rec. 1953 (1): 56–64. https://doi.org/10.1177/0361198106195300107.
Lee, J., M. Abdel-Aty, and X. Jiang. 2014. “Development of zone system for macro-level traffic safety analysis.” J. Transp. Geogr. 38 (Jun): 13–21. https://doi.org/10.1016/j.jtrangeo.2014.04.018.
Lee, J., M. Abdel-Aty, and X. Jiang. 2015. “Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level.” Accid. Anal. Prev. 78 (May): 146–154. https://doi.org/10.1016/j.aap.2015.03.003.
Li, Z., W. Wang, P. Liu, J. M. Bigham, and D. R. Ragland. 2013. “Using geographically weighted Poisson regression for county-level crash modeling in California.” Saf. Sci. 58 (Oct): 89–97. https://doi.org/10.1016/j.ssci.2013.04.005.
Longley, P. A., M. F. Goodchild, D. J. Maquire, and D. W. Rhind. 2011. Geographic information systems and science. 3rd ed. New York: Wiley.
Lovegrove, G., C. Lim, and T. Sayed. 2010. “Community-based, macrolevel collision prediction model use with a regional transportation plan.” J. Transp. Eng. 136 (2): 120–128. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000081.
Manley, D. 2013. “Scale, aggregation, and the modifiable areal unit problem.” In Handbook of regional science, edited by M. M. Fischer and P. Nijkamp. New York: Springer.
Noland, R. B. 2003. “Traffic fatalities and injuries: The effect of changes in infrastructure and other trends.” Accid. Anal. Prev. 35 (4): 599–611. https://doi.org/10.1016/S0001-4575(02)00040-4.
O’Sullivan, D., and D. J. Unwin. 2010. Geographic information analysis. 2nd ed. New York: Wiley.
Páez, A., and D. M. Scott. 2004. “Spatial statistics for urban analysis: A review of techniques with examples.” GeoJournal 61 (1): 53–67. https://doi.org/10.1007/s10708-005-0877-5.
Pirdavani, A., T. Bellemans, T. Brijs, and G. Wets. 2014. “Application of geographically weighted regression technique in spatial analysis of fatal and injury crashes.” J. Transp. Eng. 140 (8): 04014032. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000680.
SEFIN (Secretaria Municipal de Finanças). 2015. “Fortaleza em Mapas – Informações Georreferenciadas: Área edificada por uso do solo.” Accessed January 10, 2017. https://mapas.fortaleza.ce.gov.br.
Viegas, J. M., L. M. Martinez, and E. A. Silva. 2009. “Effects of the modifiable areal unit problem on the delineation of traffic analysis zones.” Environ. Plann. B: Plann. Des. 36 (4): 625–643. https://doi.org/10.1068/b34033.
Wier, M., J. Weintraub, E. H. Humphreys, E. Seto, and R. Bhatia. 2009. “An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning.” Accid. Anal. Prev. 41 (1): 137–145. https://doi.org/10.1016/j.aap.2008.10.001.
Wong, D. 2009. “The modifiable areal unit problem (MAUP).” In The SAGE handbook of spatial analysis, edited by A. Stewart Fotheringham and P. A. Rogerson. London: Sage.
Xu, P., H. Huang, N. Dong, and M. Abdel-Aty. 2014. “Sensitivity analysis in the context of regional safety modeling: Identifying and assessing the modifiable areal unit problem.” Accid. Anal. Prev. 70 (Sep): 110–120. https://doi.org/10.1016/j.aap.2014.02.012.
Xu, P., H. Huang, N. Dong, and S. C. Wong. 2017. “Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach.” Accid. Anal. Prev. 98 (Jan): 330–337. https://doi.org/10.1016/j.aap.2016.10.015.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 4 • April 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Mar 17, 2019
Accepted: Sep 23, 2019
Published online: Jan 30, 2020
Published in print: Apr 1, 2020
Discussion open until: Jun 30, 2020
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