GIS를 이용한 전국 교통사고 공간분석 - 상황적 범죄예방 시각에서 바라본 교통사고 예방방안-

Abstract

Traffic safety is the most important part of building a safe society. Traffic accidents, which pose a risk to traffic safety, can not only be a property damage caused by an accident, but also a secondary and tertiary cost for recovering it, and a great loss in life. The purpose of this study is to present the traffic accident hotspots by analyzing the distribution of traffic accidents on the roads related to these traffic accidents, and to diagnose the macro environment factors influencing the traffic accidents through spatial regression analysis. The dependent variable was the number of traffic accidents and the number of traffic accidents. As a result of the point mapping analysis which maps the point data of the accident place to the map, it was confirmed that there is an area where the traffic accidents occurred in the whole country are occurred. In addition, KDE analysis was conducted to confirm the hotspot area. As a result, hotspots of traffic accidents were distributed around Seoul and Metropolitan City, and hotspot grid clusters with high density were identified in the area. Finally, spatial autocorrelation analysis was performed before spatial regression analysis. As a result of analyzing the global statistic for the number of traffic accidents occurred in the whole country, Moran 's I coefficient was significant and the number of traffic accidents was found to have spatial autocorrelation. As a result of LISA analysis for local statistical analysis, the Gyeonggi province of Seoul was suggested as a region belonging to the HH (high-high) type. As a result of analyzing the spatial autocorrelation based on the number of traffic accidents occurred in the nationwide district, another subordinate variable, HH (high-high) type was also found around Gyeonggi city area near Seoul city. As a result of the spatial regression analysis after confirming the significant spatial autocorrelation of the traffic accidents in the national district, the population density and the number of the transportation enterprises were found to have a statistically significant effect on the number of traffic accidents among the social environmental factors. , And middle school students had significant positive effects. As a result of the spatial regression analysis of the model for the traffic accidents, it was found that the spatial regression model was not significant even though there was spatial dependence on the number of traffic accidents. This study implies that the number of traffic accidents and the number of casualties incurred in 2015 were integrated with the census data based on the official statistics recorded by the police.