Azad Abdulhafedh^*

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Department of Civil and Environmental Engineering, University of Missouri, Columbia, MO, USA.
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Abstract: Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index; and Getis-Ord G_i^* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and G_i^* statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.

Keywords: Spatial Autocorrelation, Moran’s Index, Getis-Ord G_i^* Statistic, Vehicle Crashes

Cite this paper: Abdulhafedh, A. (2017) A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord G_i^* Statistic. Open Journal of Civil Engineering, 7, 208-221. doi: 10.4236/ojce.2017.72013.

References

[1]   Black, W. (1992) Network Autocorrelation in Transport Network and Flow Systems. Geographical Analysis, 24, 207-222.
https://doi.org/10.1111/j.1538-4632.1992.tb00262.x

[2]   Bailey, T.C. and Gatrell, A.C. (1995) Interactive Spatial Data Analysis. Addison Wesley Longman Limited, Harlow.

[3]   Lord, D. and Persaud, N. (2000) Accident Prediction Models with and without Trend: Application of the Generalized Estimating Equations Procedure. Transportation Research Record, 1717, 102-108.
https://doi.org/10.3141/1717-13

[4]   Wood, G.R. (2002) Generalized Linear Accident Models and Goodness of Fit Testing. Accident Analysis and Prevention, 34, 417-427.
https://doi.org/10.1016/S0001-4575(01)00037-9

[5]   MacNab, Y.C. (2004) Bayesian Spatial and Ecological Models for Small-Area Accident and Injury Analysis. Accident Analysis and Prevention, 36, 1028-1091.
https://doi.org/10.1016/j.aap.2002.05.001

[6]   El-Basyouny, K. and Sayed, T. (2006) Comparison of Two Negative Binomial Regression Techniques in Developing Accident Prediction Models. Transportation Research Record, 1950, 9-16.
https://doi.org/10.3141/1950-02

[7]   Mitra, S. and Washington, S. (2007) On the Nature of Over-Dispersion in Motor Vehicle Crash Prediction Models. Accident Analysis and Prevention, 39, 459-468.
https://doi.org/10.1016/j.aap.2006.08.002

[8]   Aguero-Valverde, J. and Jovanis, P. (2008) Analysis of Road Crash Frequency with Spatial Models. Transportation Research Record, 2061, 55-63.
https://doi.org/10.3141/2061-07

[9]   Washington, P., Karlaftis, G. and Mannering, F. (2010) Statistical and Econometric Methods for Transportation Data Analysis. 2nd Edition, Chapman Hall, CRC, Boca Raton.

[10]   Lord, D. and Mannering, F. (2010) The Statistical Analysis of Crash Frequency Data: A Review and Assessment of Methodological Alternatives. Accident Analysis and Prevention, 44, 291-305.
https://doi.org/10.1016/j.tra.2010.02.001

[11]   Savolainen, P., Mannering, F., Lord, D. and Quddus, M. (2011) The Statistical Analysis of Highway Crash-Injury Severities: A Review and Assessment of Methodological Alternatives. Accident Analysis and Prevention, 43, 1666-1676.

[12]   Mohammadi, M., Samaranayake, V. and Bham, G. (2014) Crash Frequency Modeling Using Negative Binomial Models: An Application of Generalized Estimating Equation to Longitudinal Data. Accident Analysis and Prevention, 2, 52-69.

[13]   El-Basyouny, K. and Sayed, T. (2009) Collision Prediction Models Using Multivariate Poisson-Lognormal Regression. Accident Analysis and Prevention, 41, 820-828.

[14]   Tobler, W.R. (1970) A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, 234-240.
https://doi.org/10.2307/143141

[15]   Anselin, L. (1988) Spatial Econometrics: Methods and Models. Kluwer Academic, Dordrecht.
https://doi.org/10.1007/978-94-015-7799-1

[16]   LeSage, J.P. and Pace, R.K. (2009) Introduction to Spatial Econometrics. Chapman and Hall, CRC, New York.
https://doi.org/10.1201/9781420064254

[17]   Fotheringham, A.S., Brunsdon, C. and Charlton, M.E. (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationship. Wiley, Chichester.

[18]   Cliff, A.D. and Ord, K. (1981) Spatial Processes: Models and Applications. Pion, London.

[19]   Levine, N., Kim, K.E. and Nitz, L.H. (1995) Spatial Analysis of Honolulu Motor Vehicle Crashes: A Spatial Pattern. Accident Analysis and Prevention, 27, 663-674.

[20]   Black, W.R. and Thomas, I. (1998) Accidents on Belgium’s Motorways: A Network Autocorrelation Analysis. Transport Geography, 6, 23-31.

[21]   Miaou, S.P., Song, J.J. and Mallick, B.K. (2003) Roadway Traffic Crash Mapping: A Space-Time Modeling Approach. Accident Analysis and Prevention, 6, 33-57.

[22]   Wang, X. and Abdel-Aty, M. (2006) Temporal and Spatial Analyses of Rear-End Crashes at Signalized Intersections. Accident Analysis and Prevention, 38, 1137-1150.

[23]   Aguero-Valverde, J. (2013) Multivariate Spatial Models of Excess Crash Frequency at Area Level: Case of Costa Rica. Accident Analysis and Prevention, 59, 365-373.

[24]   Chiou, Y.C., Fu, C. and Chih-Wei, H. (2014) Incorporating Spatial Dependence in Simultaneously Modeling Crash Frequency and Severity. Analytic Methods in Accident Research, 2, 1-11.

[25]   Anselin, L. (1995) Local Indicators of Spatial Association—LISA. Geographic Analysis, 27, 93-115.
https://doi.org/10.1111/j.1538-4632.1995.tb00338.x

[26]   Anselin, L. (1992) Space Stat: A Program for the Statistical Analysis of Spatial Data. National Center for Geographic Information and Analysis, University of California, Santa Barbara.

[27]   Goodchild, M.F. (1987) Spatial Autocorrelation. Concepts and Techniques in Modern Geography, 48, 56-63.

[28]   Griffith, D.A. (1987) Spatial Autocorrelation: A Primer. Resource Publications in Geography. The Association of American Geographers, Washington DC.

[29]   Cliff, A.D. and Ord, K. (1975) The Choice of a Test for Spatial Autocorrelation. Pion, London.

[30]   Getis, A. and Ord, J.K. (1992) The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24, 189-206.
https://doi.org/10.1111/j.1538-4632.1992.tb00261.x

[31]   Getis, A. and Ord, K. (1996) Local Spatial Statistics: An Overview. Geo Information International, Cambridge.

[32]   Fischer, M.M. and Wang, J. (2011) Spatial Data Analysis: Models, Methods, and Techniques. Springer, New York.
https://doi.org/10.1007/978-3-642-21720-3

[33]   Berglund, S. and Karlstrom, A. (1999) Identifying Local Spatial Association in Flow Data Geographical Systems. Transportation Geography, 1, 219-236.

[34]   ESRI (2016) ArcGIS Resources Center.
http://resources.esri.com/help/10.1/ArcGISEngine/java/Gp_ToolRef/Spatial_Statistics_tools/
how_hot_spot_analysis_colon_getis_ord_gi_star_spatial_statistics_works.htm

[35]   ESRI (2016a) ArcGIS Resources Center.
https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-statistics-toolbox/
an-overview-of-the-spatial-statistics-toolbox.htm

[36]   Myers, R., Branas, C., French, C., Nance, L., Kallan, J., Wiebe, J. and Carr, G. (2013) Safety in Numbers: Are Major Cities the Safest Places in the United States? Annals of Emergency Medicine, 62, 408-418.