Science Journal of Public Health

| Peer-Reviewed |

Monitoring Fatal Road Accidents, Using Spatio-Temporal Statistics and GIS Modeling

Received: 26 March 2015    Accepted: 26 March 2015    Published: 11 April 2015
Views:       Downloads:

Share This Article

Abstract

Background: Purpose of this study is to monitor the fatal road accidents (FRA) in the Region of Crete, Greece and capture their dynamics in time and space using the Geographical Information System (GIS) technology. It aims to record the FRA spatially from 2001 to 2012, predict their spatio-temporal variance, estimate the number of FRA that should be expected the next years per region and identify the high risk areas. Methods: It is a spatio-temporal study using data from the National Emergency Center’s database. The SPSS 20 and the Arc map 10 were used for the analysis. Spatio-temporal models were applied; specifically, geographical descriptive, Geary’s C, co-kriging interpolation and the Geographical Weighted regression model. Results: According to the Geary’s C, FRA follow a clustered pattern in Crete, whilst they are not randomly occurred (Geary’s C= 0.42; 95%CI= 0.029-0.873; pvalue<0.001). There was a total of 1,039 FRA cases that presented heterogeneous distribution on the island, gathering within the standard distance and ellipse. Time related factors and age were found to be significant to the risk for FRA (pvalue<0.001), [summer months: ExpB=3.43, 95%CI=1.726-5.027 and the night hours: ExpB=2.43; 1.304-4.487]. High risk areas were identified and the expected number of unrecorded FRA was found to vary from 0.0001 to 5.5 cases per 50km2. Conclusions: The present study inserts, for the first time in the Greek bibliography, a new way of monitoring and capturing the FRA dynamics and highlights the use of the GIS technology and dynamic modeling.

DOI 10.11648/j.sjph.s.2015030301.17
Published in Science Journal of Public Health (Volume 3, Issue 3-1, May 2015)

This article belongs to the Special Issue Spatial Analysis and Mathematics in Health Research, During Times of Global Socio-Economic Instability

Page(s) 35-41
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Fatal Road Accidents, Spatio-Temporal Analysis, High Risk Areas, Interpolation Model, GIS

References
[1] ICMR. Development of a feasibility module for road traffic injuries surveillance. Ind Council Med Res Bul 2009;39(10-12): 42-50.
[2] World Health Organization (WHO). Global Plan for the Decade of Action for Road Safety 2011-2020. Available at: http://www.who.int/roadsafety/decade_of_action/plan/en/index.html (Accessed in 05/02/2013.
[3] Khan, G., Qin, X., Noyce, D.A., 2006, Spatial Analysis Of Weather Crash Patterns In Wisconsin, 85th Annual Meeting of the Transportation Research Board, Washington, USA
[4] Saffet Erdogan, Ibrahim Yilmaz, Tamer Baybura, Mevlut Gullu, Accident Analysis & Prevention. Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar.2008. 40; 174–181
[5] V. Prasannakumar, H. Vijith, R. Charutha, N. Geetha. Spatio-Temporal Clustering of Road Accidents: GIS Based Analysis and Assessment. Procedia - Social and Behavioral Sciences. 2011. 21; 317–325
[6] Spatio-Temporal Clustering of Road Accidents: GIS Based Analysis and Assessment. Procedia - Social and Behavioral Sciences. 2011. 21; 317–325
[7] World Health Organization (WHO). Global Plan for the Decade of Action for Road Safety 2011-2020. Available at: http://www.who.int/roadsafety/decade_of_action/plan/en/index.html (Accessed in 05/02/2013).
[8] E Petridou, H Askitopoulou, D Vourvahakis, Y Skalkidis, D Trichopoulos. Epidemiology of road traffic accidents during pleasure travelling: the evidence from the island of Crete. Accident Analysis & Prevention. 1997. 29; 687–693.
[9] PANACEA,Tountas G., (2007), Portal of asclipios. Available at: http://panacea.med.uoa.gr/topic.aspx?id=807
[10] ICMR. Development of a feasibility module for road traffic injuries surveillance. Ind Council Med Res Bul 2009;39(10-12): 42-50.
[11] Mitchell A. The ESRI Guide to GIS Analysis, Volume 2. ESRI Press. 2005.
[12] Bell SB, Hoskins RE, Pickle LW, Wartenberg D. Current practices in spatial analysis of cancer data: mapping health statistics to inform policymakers and the public. International Journal of Health Geographics. 2006; 5:49.
[13] Geary, R. C. (1954). "The Contiguity Ratio and Statistical Mapping". The Incorporated Statistician (The Incorporated Statistician) 5 (3): 115–145. doi:10.2307/2986645.
[14] A. Stein, F. van der Meer, B. Gorte, Spatial Statistics for remote sensing, Environmental and ecological statistics 83 (2002) 17-33
[15] P.A. Burrough, Principles of Geographical Information Systems for Land Resources Assessment, New York: Oxford University Press. 1986
[16] M.A. Oliver, Kriging: A Method of Interpolation for Geographical Information Systems, International Journal of Geographic Information Systems 4 (1990) 314:332. Basu S, Reinsel GC. Regression models with spatially correlated errors. Journal of the American Statistical Association. 1994; 89: 88-99.
[17] Krieger N. Place, Space, and Health: GIS and Epidemiology. Epidemiology. 2003;12 (4): 384-385.
[18] ChaoWang, Mohammed A. Quddus, Stephen G. Ison. Impact of traffic congestion on road accidents: A spatial analysis of the M25 motorway in England. Accident Analysis and Prevention 41 (2009) 798–808
[19] Ma J, Kockelman, K.M., Damien, P. A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods Accident Analysis and Prevention. 2008; 40(3): 964-975
[20] Levine N, Karle K, Lawrence H N. Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns. Accid. Anal. and Prev., Vol. 27, No. 5, pp. 663-674, 1995.
[21] ChaoWang, Mohammed A. Quddus, Stephen G. Ison. Impact of traffic congestion on road accidents: A spatial analysis of the M25 motorway in England. Accident Analysis and Prevention 41 (2009) 798–808. (Levine N, Karle K, Lawrence
[22] H N. Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns. Accid. Anal. and Prev., Vol. 27, No. 5, pp. 663-674, 1995
[23] World Health Organization (WHO). Global Plan for the Decade of Action for Road Safety 2011-2020. Available at: http://www.who.int/roadsafety/decade_of_action/plan/en/index.html (Accessed in 05/02/2013)
[24] Michael L. Matthews, Andrew R. Moran. Age differences in male drivers' perception of accident risk: The role of perceived driving ability. Accident Analysis & Prevention. 1986; 18 (4): 299–313
[25] Ross Owen Phillips, Fridulv Sagberg. Road accidents caused by sleepy drivers: Update of a Norwegian survey. Accident Analysis & Prevention. 2013; 50:138–146.
Author Information
  • Department of Anaesthesiology, University Hospital of Heraklion, University of Crete, Iraklion, Greece

  • Epidemiology researcher, Faculty of Medicine, University of Crete, Iraklion, Greece

  • Welfare Management, Faculty of Medicine, University of Crete, Iraklion, Greece

  • Associate Professor of Epidemiology, Faculty of Medicine, University of Crete, Iraklion, Greece

Cite This Article
  • APA Style

    Melidoniotis Evangelos, Sifaki-Pistolla Dimitra, Chatzea Vasiliki-Eirini, Tzanakis Nikolaos. (2015). Monitoring Fatal Road Accidents, Using Spatio-Temporal Statistics and GIS Modeling. Science Journal of Public Health, 3(3-1), 35-41. https://doi.org/10.11648/j.sjph.s.2015030301.17

    Copy | Download

    ACS Style

    Melidoniotis Evangelos; Sifaki-Pistolla Dimitra; Chatzea Vasiliki-Eirini; Tzanakis Nikolaos. Monitoring Fatal Road Accidents, Using Spatio-Temporal Statistics and GIS Modeling. Sci. J. Public Health 2015, 3(3-1), 35-41. doi: 10.11648/j.sjph.s.2015030301.17

    Copy | Download

    AMA Style

    Melidoniotis Evangelos, Sifaki-Pistolla Dimitra, Chatzea Vasiliki-Eirini, Tzanakis Nikolaos. Monitoring Fatal Road Accidents, Using Spatio-Temporal Statistics and GIS Modeling. Sci J Public Health. 2015;3(3-1):35-41. doi: 10.11648/j.sjph.s.2015030301.17

    Copy | Download

  • @article{10.11648/j.sjph.s.2015030301.17,
      author = {Melidoniotis Evangelos and Sifaki-Pistolla Dimitra and Chatzea Vasiliki-Eirini and Tzanakis Nikolaos},
      title = {Monitoring Fatal Road Accidents, Using Spatio-Temporal Statistics and GIS Modeling},
      journal = {Science Journal of Public Health},
      volume = {3},
      number = {3-1},
      pages = {35-41},
      doi = {10.11648/j.sjph.s.2015030301.17},
      url = {https://doi.org/10.11648/j.sjph.s.2015030301.17},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.sjph.s.2015030301.17},
      abstract = {Background: Purpose of this study is to monitor the fatal road accidents (FRA) in the Region of Crete, Greece and capture their dynamics in time and space using the Geographical Information System (GIS) technology. It aims to record the FRA spatially from 2001 to 2012, predict their spatio-temporal variance, estimate the number of FRA that should be expected the next years per region and identify the high risk areas. Methods: It is a spatio-temporal study using data from the National Emergency Center’s database. The SPSS 20 and the Arc map 10 were used for the analysis. Spatio-temporal models were applied; specifically, geographical descriptive, Geary’s C, co-kriging interpolation and the Geographical Weighted regression model. Results: According to the Geary’s C, FRA follow a clustered pattern in Crete, whilst they are not randomly occurred (Geary’s C= 0.42; 95%CI= 0.029-0.873; pvalue<0.001). There was a total of 1,039 FRA cases that presented heterogeneous distribution on the island, gathering within the standard distance and ellipse. Time related factors and age were found to be significant to the risk for FRA  (pvalue<0.001), [summer months: ExpB=3.43, 95%CI=1.726-5.027 and the night hours: ExpB=2.43; 1.304-4.487]. High risk areas were identified and the expected number of unrecorded FRA was found to vary from 0.0001 to 5.5 cases per 50km2. Conclusions: The present study inserts, for the first time in the Greek bibliography, a new way of monitoring and capturing the FRA dynamics and highlights the use of the GIS technology and dynamic modeling.},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Monitoring Fatal Road Accidents, Using Spatio-Temporal Statistics and GIS Modeling
    AU  - Melidoniotis Evangelos
    AU  - Sifaki-Pistolla Dimitra
    AU  - Chatzea Vasiliki-Eirini
    AU  - Tzanakis Nikolaos
    Y1  - 2015/04/11
    PY  - 2015
    N1  - https://doi.org/10.11648/j.sjph.s.2015030301.17
    DO  - 10.11648/j.sjph.s.2015030301.17
    T2  - Science Journal of Public Health
    JF  - Science Journal of Public Health
    JO  - Science Journal of Public Health
    SP  - 35
    EP  - 41
    PB  - Science Publishing Group
    SN  - 2328-7950
    UR  - https://doi.org/10.11648/j.sjph.s.2015030301.17
    AB  - Background: Purpose of this study is to monitor the fatal road accidents (FRA) in the Region of Crete, Greece and capture their dynamics in time and space using the Geographical Information System (GIS) technology. It aims to record the FRA spatially from 2001 to 2012, predict their spatio-temporal variance, estimate the number of FRA that should be expected the next years per region and identify the high risk areas. Methods: It is a spatio-temporal study using data from the National Emergency Center’s database. The SPSS 20 and the Arc map 10 were used for the analysis. Spatio-temporal models were applied; specifically, geographical descriptive, Geary’s C, co-kriging interpolation and the Geographical Weighted regression model. Results: According to the Geary’s C, FRA follow a clustered pattern in Crete, whilst they are not randomly occurred (Geary’s C= 0.42; 95%CI= 0.029-0.873; pvalue<0.001). There was a total of 1,039 FRA cases that presented heterogeneous distribution on the island, gathering within the standard distance and ellipse. Time related factors and age were found to be significant to the risk for FRA  (pvalue<0.001), [summer months: ExpB=3.43, 95%CI=1.726-5.027 and the night hours: ExpB=2.43; 1.304-4.487]. High risk areas were identified and the expected number of unrecorded FRA was found to vary from 0.0001 to 5.5 cases per 50km2. Conclusions: The present study inserts, for the first time in the Greek bibliography, a new way of monitoring and capturing the FRA dynamics and highlights the use of the GIS technology and dynamic modeling.
    VL  - 3
    IS  - 3-1
    ER  - 

    Copy | Download

  • Sections