GIS-based Spatial and Temporal Analysis of Aging-Involved Accidents: a Case Study of Three Counties in Florida

Abstract

Roadway accidents claim more than 30,000 lives each year in the United States, and they continue adversely affecting people's well-being. This problem becomes even more challenging when aging populations are considered due to their vulnerability to accidents. This is especially a major concern in Florida since the accident risk is increasing proportionally to the population growth of aging Floridians. This study investigates the spatial and temporal patterns of aging people-involved accidents using geographical information systems (GIS)-based methods via a case study of three urban counties in Florida, selected based on their high aging-involved accident rates. A series of spatial analytic methods are utilized to explore accident patterns, including a network distance-based kernel density estimation method, which provides an unbiased distribution of the accidents over the local roadways. An accident density ratio measure is also developed in order to understand how accidents involving aging people occur at different locations than those of the general population. Results indicate that high risk locations for aging-involved accidents show different spatial and temporal patterns than those for other age groups. Investigating these distinct patterns at a high spatio-temporal scale can lead to better aging-focused transportation plans and policies.

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam

“…GIS-based spatial analysis of RTA has been popularly applied to explore hotspots (Anderson 2009;Vemulapalli et al 2017). The hotspots will be showed accurately on the map associated with the attribute information of each accident.…”

“…The KDE method has been commonly applied for identifying high accident risk locations on the road networks (Harirforoush and Bellalite 2016;Vemulapalli et al 2017). In contrast to spatial analysis, temporal analysis of RTA is not seriously considered (Plug, Xia, and Caulfield 2011;Vemulapalli et al 2017). There were several past studies carried out using temporal analysis of traffic accidents.…”

“…It is vital to have a deep understanding of both the temporal and spatial dimensions simultaneously of accidents so that there is a comprehensive view of accident hotspots. Nevertheless, currently, there are very few studies that focus on the spatial-temporal analysis in identifying RTA hotspots (Plug, Xia, and Caulfield 2011;Dai 2012;Vemulapalli et al 2017). The comap method enables us to analyze temporal-spatial integration effectively (Brunsdon, Corcoran, and Higgs 2007).…”

A Selection of High Pedestrian Accident Zones Using Traffic Accident Data and GIS: A Case Study of Seoul

“…이러한 사업을 진행할 때, 어떤 지점에 안전 대책을 우 선적으로 수립하여야 교통안전을 크게 개선할 수 있는지 파 악하는 것은 매우 중요하다 (Khan et al, 2008). 많은 연구들이 교통사고 핫스팟 선정에 KDE를 사용하였 으며 (Anderson, 2009;Flahaut et al, 2003;Plug et al, 2011;Prasannakumar et al, 2011;Truong and Somenahalli, 2011;Vemulapalli, 2015), 일부 연구들은 KDE를 시행하여 보행자 사고 핫스팟을 선정하였다 (Blazquez and Celis, 2013;Jang et al, 2013;Loo et al, 2011;Pulugurtha et al, 2007;Rankavat and Tiwari, 2013 일부 연구들은 Getis-ord Gi*를 시행하여 교통사고 핫스팟 을 선정하였다 (Khan et al, 2008;Kingham et al, 2011;Kuo et al, 2011;Manepalli et al, 2011;Vemulapalli, 2015). 특히, Getis-ord Gi*를 사용한 기존 연구들은 Getis-ord Gi*로 …”

Hotspots Detection From Trajectory Data Based on Spatiotemporal Data Field Clustering

“…There are variety of meanings of hotspots, such as hotspots of crime (Malleson and Andrese., 2015;Newton and Felson 2015;Steil and Parrish., 2009), hotspots of incident (Anderson., 2006;Vemulapalli et al, 2016), hotspots of disease (Wanjala et al 2011;Hu et al, 2013) and hotspots of business (Chen et al, 2016;Turner., 2013). In this paper, the city hotspots refer to the area where city event happen intensively and the function of it would attract more people to travel.…”