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Clustering of Road Traffic Injuries During the 7-Day Songkran Holiday, Thailand: a Spatial Analysis

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Clustering of Road Traffic Injuries During the 7-Day Songkran Holiday, Thailand: a Spatial Analysis Original Article VOL. 17|NO. 3|ISSUE 67|JULY-SEPT. 2019 Clustering of Road Traffic Injuries During the 7-day Songkran Holiday, Thailand: A Spatial Analysis Tubtimhin S,1 Laohasiriwong W,1 Pitaksanurat S,1 Sornlorm K,1 Luenam A2 ABSTRACT Background 1Faculty of Public Health, Road traffic injury (RTI) is a major cause of fatalities around the world and Thailand Khon Kaen University, Khon Kaen, Thailand. is the second leading country. 3Faculty of Public and Environmental Health, Huachiew Chalermprakiet University, Objective Samut Prakan, Thailand. To determine the spatial pattern of road traffic injury during the 7-day Songkran holiday in Thailand. Method Corresponding Author Wongsa Laohasiriwong This study utilized the data obtained from the Information Technology for Emergency Medical System (ITEMS) covering the nationwide road traffic injury during the Faculty of Public Health, Songkran festival, Thai New Year holiday (April 9-15, 2015). The Moran’s I was used Khon Kaen University, to identify global autocorrelation within the country whereas the Local Indicators Khon Kaen, 40002, Thailand. of Spatial Association (LISA) analysis was administered for analyzing the spatial distribution of RTIs and determining the spatial autocorrelation and correlation of E-mail: [email protected] numbers motor vehicles and length of road networks and road traffic injury. Result Citation During Songkran holiday 2015, the univariate Moran’s I of RTIs distribution among Tubtimhin S, Laohasiriwong W, Pitaksanurat S, provinces in Thailand showed a slightly positive spatial autocorrelation, as the Sornlorm K, Luenam A. Clustering of Road Traffic Moran’s I was 0.1701, with statistical significance at 0.05. Local indicators of spatial Injuries During the 7-day Songkran Holiday, Thailand: A Spatial Analysis. Kathmandu Univ Med J. association indicated seven hotspots and five cold spots. In addition, the number of 2019;67(3):184-9. motor vehicles, and length of trunk road (super highway), tertiary roads, secondary roads, and primary roads had positive spatial autocorrelation with road traffic injury, with Moran’s I values of 0.173, 0.117, 0.219, 0.162, and 0.279, respectively. Conclusion This study demonstrates that local indicators of spatial association could detect the spatial pattern of road traffic injury. The number of motor vehicles, length of all roads served as new parameters for determining road traffic injury hotspots. KEY WORDS Road traffic injury, Songkran festival, Spatial analysis Page 184 KATHMANDU UNIVERSITY MEDICAL JOURNAL INTRODUCTION This study was conducted using the road traffic accident data obtained from the Information Technology for Globally road traffic injury (RTI) caused nearly 1.35 million Emergency Medical System (ITEMS), the National Institute casualties and was the 8th leading cause of death.1 In term for Emergency Medical Service, Thailand in 2015. It covered of severity, urban areas were more likely to originate less the road traffic injuries information during the Songkran serious accidents than rural areas and weather conditions festival long holiday or 7-dangerous days of 76 provinces, were associated with increased accident severity.2-4 The except for the Bangkok Metropolitans. The geographical environmental factors could have resulted in the clustering coordinates of administrative areas of Thailand were pattern of RTIs and the clusters indicated the locations retrieved from the DIVA-GIS online (http://www.diva-gis. where accidents occur intensely. However, spatial analysis org/gdata), which is publicly available. The country road of RTI could use different criteria such as type of accident networks were retrieved from DIVA-GIS online.11 The occurrence and number of vehicles. Three methods of data on the number of motor vehicles registered in each spatial accident pattern analysis including kernel density, province was from the Digital Government Development nearest neighbor distance, and the K-function, based on Agency (DGA).12 vehicle involved in the accidents.5 Spatial correlation is important in road segment and intersection level crash The detection of RTI spatial patterns was based on localized models.6 The application of spatial analysis and modeling detection of case frequency spatial patterns. For exploratory of accident statistics and death rates at provincial level spatial data analysis, we used the open GIS software identified provinces with the outstandingly high accident Quantum GIS (QGIS) introduced by Steiniger and Hunter and and death rates.7 GIS is importance as a management GeoDa (https://geodacenter.github.io) to determine spatial system for accident analysis using combination of statistical autocorrelation of variables, and Stata version 10.0 (Stata and spatial methods.8 GIS-based spatial statistics have been Corp, College Station, TX, USA) to calculate the province’s used to identify hot spots and obtain information required distribution of RTI, numbers of motor vehicles and lengths to help decision-makers for taking appropriate measures of roads.13,14 Deciles of RTI distribution among provinces to prevent road accidents.9 Previously, few studies applied was calculated and visualized as the total number of RTIs spatial analysis to determine the influence of environmental occurred in each province during the 7-days Songkran factors on RTIs. Therefore, the purpose of this study was holiday period, 2015. A autocorrelation analysis on the to determine the spatial pattern of RTI during the 7-day spatial distribution of geographic patterns and clusters Songkran holiday in Thailand. The study results could be of RTIs were determined using LISA.15 The province-level used as evidence for relevant policy-making and strategy layers of polygons and points were matched to frequency of development to improve the road safety system. RTIs. The spatial distribution of RTIs was obtained from the province-level polygon map, which included the latitudes and longitudes for each province. The RTIs frequency was METHODS further visualized with province level layers on the polygon map and labelled with the administrative code for each Thailand is located in Southeast Asia with the population of province. Exploratory spatial pattern analysis was also used approximately 68 million. The country is administratively to determine the distribution patterns for the RTI cases divided into 4 main regions: the Central, North, South and using global Moran’s I. The local Moran’s I investigated the the Northeast with 77 provinces, 878 districts, 7,225 sub- local level of spatial autocorrelation of provinces with a high districts, and 74,965 villages. In term of transportation, and low frequency of RTIs. Computation of Local indices there are 4 national highways: Phahon Yothin, Mittraphap, of spatial association (LISA) assessed the local version of Sukhumvit and Phet Kasem and also main road such as Asia Moran’s I for each location to determine the variation in Highway (AH) (fig. 1).10 spatial autocorrelation over the study area. Its significance was evaluated in five categories: high-high (HH), low-low (LL), low-high (LH), high-low (HL), and not significant. A high-high spatial autocorrelation occurred when a high frequency of RTIs correlated with a high frequency in neighboring areas (also known as hotspots), or when a low frequency of RTIs correlated with also a low frequency in neighboring areas (or cold spots). A high-low indicates that high RTI cases are adjacent to low RTI frequency (negative spatial autocorrelation), or when a low frequency of RTIs correlated with also a high frequency in neighboring areas is considered as a low-high cluster, and not significant indicates that there is no spatial autocorrelation. Figure 1. Map of Thailand Page 185 Original Article VOL. 17|NO. 3|ISSUE 67|JULY-SEPT. 2019 This study set the spatial weight matrix of 3k-Nearest neighbors around each province, meaning that the clustering relationship was identified with three neighboring provinces. The statistical significance level was 0.05, and the simulation used 999 permutations to evaluate the sensitivity of the results. The outcome of Moran’s I identifies the intensity of spatial autocorrelation along with the result of statistically significant test, i.e. the p-value. The following mathematical representation exhibits the computation of Moran’s I: Figure 5. Distribution of RTI Figure 6. The Distribution of casualties during the Songkran motor vehicles in Thailand in holidays in Thailand in 2015 2015 Kaen, Udon Thani, Surin, Roi Et and Ubon Ratchathani. The provinces with highest RTIs in other regions were where, Chiang Mai in the North, Chon Buri and Samut Sakhon in Wij is the spatial weight between a RTI cases in provinces i the Central and Nakhon Si Thammarat and Songkhla in the and j;N the total number of RTI cases; S0 the aggregate of South (fig. 2). all spatial weights; and xi, xj the number of RTIs cases in In term of severity the urgent and emergent RTI cases provinces i and j, respectively. were found highest in the same provinces of the total RTI cases, with additional four provinces in the Northeast. Phetchabun and Chiang Rai were additional provinces in RESULTS the North, Suphan Buri and Phetchaburi of the Central and The results showed the distribution pattern of RTIs and Prachuap Khiri Khan and Phangnga in the South. Considering their levels of severity of urgent, emergent and casualty casualty, the provinces with high number of deaths were during the 7-day Songkarn holiday in 2015. The number mostly in the Northeast comprised of Surin, Roi Et, Nakhon of registered motor vehicles as well as the lengths of Ratshasima Provinces, Chiang Mai, Chiang Rai, Phisanulok different types of roads in each province were calculated in the North, Suphan Buri in the Cental and Surat Thani in and visualized. The darker the color indicated the higher the South. (fig. 3,4,5). Among the environmental factors, number of RTIs, numbers of vehicles and lengths of roads the numbers of motor vehicle were different at provincial at provincial level. The deciles distribution of total RTIs level across the countries.
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