Original Article VOL. 17|NO. 3|ISSUE 67|JULY-SEPT. 2019 Clustering of Road Traffic Injuries During the 7-day Songkran Holiday, : 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

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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 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

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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 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. There were highest numbers of cases, urgent and emergent RTI cases; and quartiles of RTIs vehicles in four provinces of the Northeast, three provinces casualties in 76 provinces of Thailand except for Bangkok in the North, five provinces in the Central and three in the Metropolitan during April 9-15, were calculated and South (fig. 6). presented in figure 2,3,4, and 5. The univariate global Moran’s I of RTIs during Songkran Festival holidays in 2015 was 0.117 with the statistically significant level of 0.05. It indicated that there was clustering distribution of RTIs in Thailand. The Local Indicators of Spatial Association (LISA) showed a positive spatial autocorrelation of four cluster types (HH, LL, HL, and LH). Particularly, “HH” represented provinces with the high RTIs surrounded by three neighboring provinces with also had high frequency of RTIs, were founded in Sa Kaeo, Buri Ram,

Surin, Si Sa Ket, Maha Sarakham, Kalasin, and Chaiyaphum

provinces, while “LL” indicated provinces with low numbers Figure 2. Figure 3. Distribution Figure 4. Distribution of RTIs surrounded with three low values provinces were Distribution of RTI of urgent RTI cases of emergent RTI those of Suphan Buri, Nakhon Pathom, Samut Prakan, Ang cases during the during the Songkran cases during the Songkran holidays, holidays, Thailand in Songkran holidays Thong, and Nonthaburi. On the other hand, “LH” identified Thailand in 2015 2015 Thailand in 2015 provinces with low RTIs surrounded by three provines with high number of RTIs were Trat and Amnat Charoen. “HL” Among the 9,415 nationwide RTI cases during the 7-day was a province with high RTIs surrounded by three low Songkran holiday, there were 5,404 urgent RTI, 997 RTIs provinces were found in Pathum Thani and Saraburi emergent (critical) cases with 268 casualties. Provinces (fig. 7). with had the highest numbers of RTI were mostly located in the Northeast region including Nakhon Ratchasima, Khon The Moran’s I indicated clustering patterns of vehicles density with RTIs. The bivariate analysis for the spatial

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Moran’I LISA Cluster Map LISA Significance Map Moran’I LISA Cluster Map LISA Significance Map

Figure 9. Moran’s I scatter plot matrix (Bivariate: length of the Figure 7. Moran’s I scatter plot matrix (univariate) of RTIs during trunk (Super highway) and number of RTIs) during the Songkran the Songkran holidays in Thailand in 2015 holidays, April 2015 autocorrelation between the number of vehicles and RTIs, Moran’I LISA Cluster Map LISA Significance Map indicated seven hotspots in Buri Ram, Surin, Si Sa ket, Maha Sarakham, Roi Et, Yasothon, Kalasin provinces. The hotspot represented provinces with high number of vehicles and had high frequency of RTI surrounded by provinces with the same pattern. The coldspot of spatial autocorrelation between the number of vehicles and RTIs represented provinces that had low number of vehicles and had low frequency of RTI surrounded by provinces with the same Figure 10. Moran’s I scatter plot matrix (Bivariate: length of Tertiary roads and number of RTIs) during the Songkran holidays, pattern, were found in five provinces in the Central and April 2015 Songkhla in the South (fig. 8). Moran’I LISA Cluster Map LISA Significance Map Moran’I LISA Cluster Map LISA Significance Map

Figure 11. Moran’s I scatter plot matrix (Bivariate: Length of Figure 8. Moran’s I scatter plot matrix (Bivariate: number of Secondary roads and number of RTIs) during the Songkran vehicle and number of RTI) of RTAs during the Songkran holidays, holidays, April 2015 April 2015

Moran’I LISA Cluster Map LISA Significance Map Concerning the lengths of roads in provinces, there were spatial autocorrelation between lengths of trunk road (super highway) and RTIs where the hotspots were located in Chiang Rai, Chaiyaphum, Maha Sarakham, and Buri Ram provinces. The coldspots were found in five provinces of the Central and one in the South. Concerning primary road (a national highway that links between provinces), there were two hotspots of spatial autocorrelation between Figure 12. Moran’s I scatter plot matrix (Bivariate: length primary roads and RTIs located in Nakhon Nayok and of Primary roads and number of RTIs) during the Songkran Prachuap Khiri Khan provinces (fig. 9,10,11,12). There holidays, April 2015 were also four hotspots of spatial autocorrelation between million populations were mostly located in the Northeast secondary road (a rural road) and RTIs in the Northeast, region, the biggest region of the country. In addition, these one in the Central and two in the South. However, all six northeastern cities had more than one million vehicles coldspots were located near Bangkok. The four hotspots are located along the Mittraphap highway and AH-12, the spatial autocorrelation between tertiary roads (a local main, straight and long distance roads. In addition, they road) and RTIs were located in Surin, Chaiyaphum, Buri had more than a million population as well as had more Ram, and Si Sa Ket provinces, whereas the coldspots were number of The Central region, has a complex physiography, in six provinces of the Central near Bangkok. including diverging landscape features such as hills, plateaus, high population density and also it has a large number of vehicles, and major road that distribute to other DISCUSSION regions. People are crowded and traveling by many type of About 70% of the total 9,415 RTI cases during the 7 day vehicles, may cause accidents and injuries or deaths, such Songkran holiday were serious cases. The provinces with as in Chon Buri and Pathum Thani. These provinces had high the highest numbers of RTI cases had more than one number of vehicles, of more than 680,000 registered. The

Page 187 Original Article VOL. 17|NO. 3|ISSUE 67|JULY-SEPT. 2019 provincial distribution patterns of RTI cases and distribution as Chiang Mai and Chiang Rai province with high numbers of numbers of vehicles were almost the same. It was also of vehicles as well as having more local roads around the confirmed by the spatial autocorrelation between the city. In addition, Nakhon Sawan and Phitsanulok provinces number of vehicles and RTIs of our study indicated seven are a major route from the Central to the North, causing a hotspots in Buri Ram, Surin, Si Sa Ket, Maha Sarakham, Roi high density of vehicles on the road during 7-day Songkran Et, Yasothon, Kalasin provinces. The hotspot represented holiday of which millions of people use this way to their provinces with high number of vehicles and had high homes. The South is an area of plain, with a mountain range, frequency of RTI surrounded by three provinces with the a peninsula with two sides of the sea, the Gulf of Thailand same pattern. On the other hand, the coldspot of spatial and the Andaman Sea. Phet Kasem road, the longest road autocorrelation between the number of vehicles and RTIs in Thailand, is a major road of the region which the lengths represented provinces that had low number of vehicles and of more than 1,300 kilometers. Surat Thani, Nakhon Si had low frequency of RTI surrounded by three provinces Thammarat and had high numbers of with the same pattern, were found in five provinces RTIs due to the main road (Phet Kasem and AH-2) as well in the Central. A study in Osmaniye, Turkey reported as a large number of vehicles. The level of severity injuries that accidents were clustered rather than occurring by (urgent and emergent) found in the same provinces that chance alone. The clusters indicate the locations where had high numbers of RTIs and number of vehicles such as accidents occur intensely.5 Horizontal curves, junctions, Chiang Mai, Chiang Rai, Nakhon Ratchasima, Surin, Roi Et, road surface conditions were associated with road traffic Ubon Ratchathani, Surat Thani, Nakhon Si Thammarat and accidents.16 Many provinces with higher numbers of RTIs Songkhla. cases had poor road surface due to under maintenances and dangerous curves and junctions especially in the Northeast. Another factors influencing RTIs during CONCLUSION Songkran festival were lengths of roads in provinces The This study shows that there were almost 10,000 RTI cases coldspots of spatial autocorrelation between the lengths of during the 7-day Songkran festival holiday, about 70% were super highway and RTIs were found in five provinces of the severe cases. The provinces with the highest numbers Central and one in the South. These provinces were small of RTI were mainly located in the Northeast. LISA was in term of areas, therefore has shorter lengths of super able to detect the spatial pattern of RTIs. The number of highway. The four coldspots of a primary road or national vehicles, the length of all roads served as new parameters highway that links between provinces and RTI also related for determining RTI hotspots during Songkran holidays with their shorter lengths. In addition, the longer length of in Thailand. The univariate Moran’s I of RTIs distribution rural roads, the higher the RTI cases, with four hotspots of among provinces showed a slightly positive spatial spatial autocorrelation in the Northeast, one in the Central autocorrelation with statistical significance. LISA indicated and two in the South. The shorter lengths of those rural seven hotspots which were mostly in the Northeast and roads reflected its influence with six coldspots in provinces five coldspots, of which almost all were in the Central. The near Bangkok. Similarly, the lengths of local road also had number of the vehicles and road lengths are important impact on RTIs with the hotspots in Surin, Chaiyaphum, factors that contributes to road traffic injury. The hot spot Buri Ram, and Si Sa Ket provinces, whereas the coldspots is indicated to help decision makers for taking appropriate were found in six provinces near Bangkok. The possible measures in order to prevent and reduce road accidents. explanation could be similar to those indicated in the Therefore, policy and strategic of RTAs preventive measures study of Chaparro, Hernández-Vásquez, and Parras related are necessary for reducing the risk of road traffic injury and to the type of road, reporting that 62.8% of places where also need to be properly in the emergency medical service accidents occurred were avenues, with 2.2 times higher for them. frequency than routes. This result was in line with many of studies indicated that road type was related to higher levels of traffic accidents, due to the speeds reached onsuch ACKNOWLEDGEMENT roads that favor accident occurrence. Others factor related to the road were the curves and the direction of the curve The authors are grateful to all contributors to this can condition accident occurrence.17 The landscape of the research, and especially to the Information Technology for North region consists of large and complex mountains. Emergency Medical System (ITEMS), the National Institute There is a national road (Phahol Yothin) and several for Emergency Medical Service (NIEMS) for the data, and secondary roads that are used as the main road between the supports from the Faculty of Public Health, Khon Kaen the provinces. It has a dense population in mega cities such University.

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