Land use data can be used to understand patterns of economic behavior, such as the relationship between land use and property values or the impact of land use on environmental factors like air and water quality. The combination of land use data with other data sources and analysis methods can yield significant insights into economic growth and behavior. In this study, the land use and land cover (LULC) were classified using multi-temporal Sentinel-2 imagery (2019 and 2021) and random forest through the Google Earth Engine platform (GGE) with an overall accuracy of more than 89.79%. According to the results of the change detection analysis, there was a 16.96% increase in miscellaneous surface areas and a 15.50% increase in artificial surface areas. These disclose confirm that the sea salt farm, which are the traditional economic function, are losing 37.40%. Furthermore, the CA-Markov model was utilized to predict alterations in land use patterns in the year 2023 through the extrapolation of existing trends. The predicted LULC map of 2023 publicizes the trend of the sea salt farm decreasing, contrasty the artificial surface areas are increasing. In summary, this research reveals the evidence that LULC is strongly related to traditional living changes, and spatial analysis techniques are reasonable and committing tools for study.
{"title":"Spatial Dynamics Evolution of Land use for the Study of the Local Traditional Living Changes","authors":"","doi":"10.52939/ijg.v19i4.2635","DOIUrl":"https://doi.org/10.52939/ijg.v19i4.2635","url":null,"abstract":"Land use data can be used to understand patterns of economic behavior, such as the relationship between land use and property values or the impact of land use on environmental factors like air and water quality. The combination of land use data with other data sources and analysis methods can yield significant insights into economic growth and behavior. In this study, the land use and land cover (LULC) were classified using multi-temporal Sentinel-2 imagery (2019 and 2021) and random forest through the Google Earth Engine platform (GGE) with an overall accuracy of more than 89.79%. According to the results of the change detection analysis, there was a 16.96% increase in miscellaneous surface areas and a 15.50% increase in artificial surface areas. These disclose confirm that the sea salt farm, which are the traditional economic function, are losing 37.40%. Furthermore, the CA-Markov model was utilized to predict alterations in land use patterns in the year 2023 through the extrapolation of existing trends. The predicted LULC map of 2023 publicizes the trend of the sea salt farm decreasing, contrasty the artificial surface areas are increasing. In summary, this research reveals the evidence that LULC is strongly related to traditional living changes, and spatial analysis techniques are reasonable and committing tools for study.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49576963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of this study was to use spatial statistics and geographic information systems to identify high-risk areas for highway accidents in Nakhon Pathom, Thailand. Secondary data from the Ministry of Transport on the locations of accidents in the road network between 2021-2022 was analyzed using Equivalent Property Damage Only (EPDO), Spatial Autocorrelation, Kernel Density Estimation, and hotspot analysis. The study focused on Nakhon Pathom, a province in Central Thailand, and found that high-risk areas were concentrated along major routes with heavy traffic and high population density, including both urban and community areas. The study also identified specific risk spots, with Kamphaeng Saen District and Highways NO. 321(Kamphaeng Saen-Thung Khok Road), NO. 3231(Den Makham-Bang Len Road), and NO. 3232(Nong Phong Nok - Pai Chedi Road) being particularly affected, as well as Sam Phran District and Highway NO. 375(Ban Bo-Phra Prathon Road). These findings provide important insights into the clustering of accidents and their risk spots, which can be used to improve traffic safety in Nakhon Pathom.
{"title":"Spatial Statistics and Severity of Highway Accidents in Nakhon Pathom, Thailand","authors":"","doi":"10.52939/ijg.v19i4.2629","DOIUrl":"https://doi.org/10.52939/ijg.v19i4.2629","url":null,"abstract":"The aim of this study was to use spatial statistics and geographic information systems to identify high-risk areas for highway accidents in Nakhon Pathom, Thailand. Secondary data from the Ministry of Transport on the locations of accidents in the road network between 2021-2022 was analyzed using Equivalent Property Damage Only (EPDO), Spatial Autocorrelation, Kernel Density Estimation, and hotspot analysis. The study focused on Nakhon Pathom, a province in Central Thailand, and found that high-risk areas were concentrated along major routes with heavy traffic and high population density, including both urban and community areas. The study also identified specific risk spots, with Kamphaeng Saen District and Highways NO. 321(Kamphaeng Saen-Thung Khok Road), NO. 3231(Den Makham-Bang Len Road), and NO. 3232(Nong Phong Nok - Pai Chedi Road) being particularly affected, as well as Sam Phran District and Highway NO. 375(Ban Bo-Phra Prathon Road). These findings provide important insights into the clustering of accidents and their risk spots, which can be used to improve traffic safety in Nakhon Pathom.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48674076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study was a cross-sectional study. The study of spatial association patterns and the influences on the Coronavirus Disease 2019 (COVID-19) epidemic situation in Thailand was performed using secondary data from the COVID-19 interactive dashboard, Department of Disease Control, Ministry of Public Health, between January 1st, 2020, and December 31st, 2021. Moran’s I, Local Indicators of Spatial Association (LISA), and Spatial Regression was applied for statistical analysis. In the epidemic situation of COVID-19, the highest of 11,512.65 per one hundred thousand population, and the spatial association between the nighttime light average, the prevalence of smokers in Thailand, the proportion of population per village health volunteer, and the proportion of population per health care center with the epidemic situation of COVID-19 has Moran’s I = 0.309, 0.396, 0.081 and 0.424, respectively. From the Spatial Lag Model (SLM), a factor that has a spatial association with the epidemic situation of COVID-19 is the nighttime light average, the prevalence of smokers in Thailand, and the proportion of population per healthcare center, which can predict the epidemic situation of COVID-19 by 47.8 percent (R2 =0.478). The growth factor of a large city is an important factor for population density which is a major cause of spread of the coronavirus easily. Moreover, smoking behavior has encouraged the epidemic to spread rapidly. The situation is serious as the number of hospitals is not enough to support the treatment and screening of patients to cover the entire population of Thailand. Therefore, it is urgent that the government plan to mitigate the situation with maximum efficiency by having Covid-19 centers and increase the number of beds and facilities.
{"title":"Spatial Association Patterns with Cultural and Behaviour with the Situations of COVID-19","authors":"","doi":"10.52939/ijg.v19i4.2637","DOIUrl":"https://doi.org/10.52939/ijg.v19i4.2637","url":null,"abstract":"This study was a cross-sectional study. The study of spatial association patterns and the influences on the Coronavirus Disease 2019 (COVID-19) epidemic situation in Thailand was performed using secondary data from the COVID-19 interactive dashboard, Department of Disease Control, Ministry of Public Health, between January 1st, 2020, and December 31st, 2021. Moran’s I, Local Indicators of Spatial Association (LISA), and Spatial Regression was applied for statistical analysis. In the epidemic situation of COVID-19, the highest of 11,512.65 per one hundred thousand population, and the spatial association between the nighttime light average, the prevalence of smokers in Thailand, the proportion of population per village health volunteer, and the proportion of population per health care center with the epidemic situation of COVID-19 has Moran’s I = 0.309, 0.396, 0.081 and 0.424, respectively. From the Spatial Lag Model (SLM), a factor that has a spatial association with the epidemic situation of COVID-19 is the nighttime light average, the prevalence of smokers in Thailand, and the proportion of population per healthcare center, which can predict the epidemic situation of COVID-19 by 47.8 percent (R2 =0.478). The growth factor of a large city is an important factor for population density which is a major cause of spread of the coronavirus easily. Moreover, smoking behavior has encouraged the epidemic to spread rapidly. The situation is serious as the number of hospitals is not enough to support the treatment and screening of patients to cover the entire population of Thailand. Therefore, it is urgent that the government plan to mitigate the situation with maximum efficiency by having Covid-19 centers and increase the number of beds and facilities.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42787753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Making a nautical chart for safe navigation is a bathymetric survey's primary goal. Multifrequency MBES, developed during the last few decades, has dramatically improved the efficiency, accuracy, and spatial resolution of coastal and ocean mapping. The goal of multifrequency MBES is to increase the sub surface’s detection resolution. In order to obtain an accurate picture of the seabed, the user can lessen the impact of this subsidence by running surveys in five different modes at once. With the help of multifrequency MBES, this study will analyze bathymetry in shallow coastal waters. According to this study, each frequency's density equals one-fifth of the raw data. The digital bathymetric model (DBM) has identical frequencies. According to the produced DBM, the study site's depth value ranges from -2.5 m to -23.5 m LWS. Between 200 kHz and other depths, a bathymetric variation of little more than 50 cm. Between 200 kHz and other frequencies to -10 cm, the bathymetry range of 0 cm predominates. Dredging volume inter frequencies falls between 0.042 m3/m2 and 0.068 m3/m2. This amount is negligible compared to the overall dredging volume with a thickness of more than 1 m inside 1 hectare.
{"title":"Dredging Volume Analysis Using Bathymetric Multifrequency","authors":"","doi":"10.52939/ijg.v19i4.2623","DOIUrl":"https://doi.org/10.52939/ijg.v19i4.2623","url":null,"abstract":"Making a nautical chart for safe navigation is a bathymetric survey's primary goal. Multifrequency MBES, developed during the last few decades, has dramatically improved the efficiency, accuracy, and spatial resolution of coastal and ocean mapping. The goal of multifrequency MBES is to increase the sub surface’s detection resolution. In order to obtain an accurate picture of the seabed, the user can lessen the impact of this subsidence by running surveys in five different modes at once. With the help of multifrequency MBES, this study will analyze bathymetry in shallow coastal waters. According to this study, each frequency's density equals one-fifth of the raw data. The digital bathymetric model (DBM) has identical frequencies. According to the produced DBM, the study site's depth value ranges from -2.5 m to -23.5 m LWS. Between 200 kHz and other depths, a bathymetric variation of little more than 50 cm. Between 200 kHz and other frequencies to -10 cm, the bathymetry range of 0 cm predominates. Dredging volume inter frequencies falls between 0.042 m3/m2 and 0.068 m3/m2. This amount is negligible compared to the overall dredging volume with a thickness of more than 1 m inside 1 hectare.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45238759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, there has been a rapid advancement in the use of unmanned aerial vehicles (UAVs) in various aspects of life, especially in the area of automated data collection. These advancements have brought about numerous possibilities. The article describes the use of an unmanned aerial vehicle, specifically the PHANTOM 4 pro, to gather remote sensing data and create digital topographic plans at a scale of 1:2000 in Phu Tho province's Thanh Son district. A method was suggested for improving the current systems for obtaining remote sensing data for cartography using UAVs. The ease of controlling the UAVs and the quality and timeliness of the data they transmit to control points confirm the value of using them to create topographic maps. In addition to UAVs, fieldwork also involves the use of the GNSS brand CHCNAV I50. The high precision GNSS system enables the camera's 3D position to be detected within a few centimeters at the time of each capture. A digital topographic map was compiled of Thanh Son district, covering 165 hectares, and processed using software such as Agisoft and Global Mapper. The digital topographic map that was produced satisfies the documentation requirements of government organizations. The maximum error in height is 4.7 cm, the error of coordinates north and east are 1.8 cm and 1.4 cm respectively. This was achieved by using 590 raster images, which had a resolution of 2.3 cm and a size of 5472x3648 pixels. Based on the findings, the map's accuracy is within an acceptable range of less than 2 cm, which is suitable for a map scale of 1:2000.
{"title":"Creation and Assessment of a Topographic Map from Unmanned Aerial Vehicle Data in the Province Phu Tho, Thanh Son District","authors":"P. T. Thanh, M. A. Elshewy, N. B. Long","doi":"10.52939/ijg.v19i3.2605","DOIUrl":"https://doi.org/10.52939/ijg.v19i3.2605","url":null,"abstract":"In recent years, there has been a rapid advancement in the use of unmanned aerial vehicles (UAVs) in various aspects of life, especially in the area of automated data collection. These advancements have brought about numerous possibilities. The article describes the use of an unmanned aerial vehicle, specifically the PHANTOM 4 pro, to gather remote sensing data and create digital topographic plans at a scale of 1:2000 in Phu Tho province's Thanh Son district. A method was suggested for improving the current systems for obtaining remote sensing data for cartography using UAVs. The ease of controlling the UAVs and the quality and timeliness of the data they transmit to control points confirm the value of using them to create topographic maps. In addition to UAVs, fieldwork also involves the use of the GNSS brand CHCNAV I50. The high precision GNSS system enables the camera's 3D position to be detected within a few centimeters at the time of each capture. A digital topographic map was compiled of Thanh Son district, covering 165 hectares, and processed using software such as Agisoft and Global Mapper. The digital topographic map that was produced satisfies the documentation requirements of government organizations. The maximum error in height is 4.7 cm, the error of coordinates north and east are 1.8 cm and 1.4 cm respectively. This was achieved by using 590 raster images, which had a resolution of 2.3 cm and a size of 5472x3648 pixels. Based on the findings, the map's accuracy is within an acceptable range of less than 2 cm, which is suitable for a map scale of 1:2000.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48497562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Forest fires lead to severe damage to the environment and human health. Monitoring can be applied using remotely sensed data and in combination with Geographical Information Systems (GIS) based spatial analysis. Lately, Iraq subjected to many forest fires. In this study, the aim was to monitor and detect the burned areas in Mosul Park during the latest period which happened in June 2022. The hypothesis of the study was based on using Sentinel-2 images and the Normalized Burn Ratio (NBR) index. Two images have been used to compare burned areas; one during the fire events and another postfire. as well as, Normalized Difference Vegetation Index (NDVI) map has been used to identify the Park's characteristics. Moreover, Pearson's correlation (r) with Air Quality Index (AQI) was determined during the burning period. GIS-based processes resulted in detecting the area of burning where the burned area was 16.76 hectares and lay in the eastern part of the study area. Pearson correlation with AQI has resulted in 0.92, while the collinearity between the burned areas and AQI was 0.84. Accurate and prompt planning for fire-affected regions is essential for supporting fire affect assessment, calculating environmental losses, determining planning strategies, and monitoring vegetation recovery.
{"title":"NBR Index-Based Fire Detection Using Sentinel-2 Images and GIS: A Case Study in Mosul Park, Iraq","authors":"Mahmood","doi":"10.52939/ijg.v19i3.2607","DOIUrl":"https://doi.org/10.52939/ijg.v19i3.2607","url":null,"abstract":"Forest fires lead to severe damage to the environment and human health. Monitoring can be applied using remotely sensed data and in combination with Geographical Information Systems (GIS) based spatial analysis. Lately, Iraq subjected to many forest fires. In this study, the aim was to monitor and detect the burned areas in Mosul Park during the latest period which happened in June 2022. The hypothesis of the study was based on using Sentinel-2 images and the Normalized Burn Ratio (NBR) index. Two images have been used to compare burned areas; one during the fire events and another postfire. as well as, Normalized Difference Vegetation Index (NDVI) map has been used to identify the Park's characteristics. Moreover, Pearson's correlation (r) with Air Quality Index (AQI) was determined during the burning period. GIS-based processes resulted in detecting the area of burning where the burned area was 16.76 hectares and lay in the eastern part of the study area. Pearson correlation with AQI has resulted in 0.92, while the collinearity between the burned areas and AQI was 0.84. Accurate and prompt planning for fire-affected regions is essential for supporting fire affect assessment, calculating environmental losses, determining planning strategies, and monitoring vegetation recovery.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41954634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, three multi-temporal remotely sensed data acquired from Landsat-5 Thematic Mapper (TM) and Landsat -8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) in 1990, 2005, and 2020 were used. The maximum likelihood classifier (MLC) was opted to classify land use and land cover (LULC). Land surface temperature (LST) and LULC spectral indices i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Latent Heat Index (NDLI) and Bare Soil Index (BSI) have been computed and their relationships were examined. The overall accuracy of LULC was more than 93%. The analyses showed a notable transformation in LULC over the study period. For instance, built-up areas increased 103.7% with a rate of 45.5 ha/year and agriculture land increased by 28.9% with a rate of 186.4 ha/year. Whereas, bare soil was sharply decreased by 36.4% at a rate of 227.7ha/year. The minimum and maximum LST values increased by 2.9°C and 4.9°C, respectively, from 1990 to 2020. Furthermore, LST has a negative relationship with NDVI and NDLI (NDVI: 1990: r2 = 0.62; 2005: r2 = 0.62; 2020: r2 = 0.65. NDLI: 1990: r2 = 0.79; 2005: r2 = 0.78; 2020: r2 = 0.61) and a positive relationship with NDBI and BSI (NDBI: 1990: r2 = 0.68; 2005: r2 = 0.73; 2020: r2 = 0.44. BSI: 1990: r2 = 0.77; 2005: r2 = 0.78; 2020: r2 = 0.53). These results provided useful information about LULC changes and its impact on LST, which are necessary for experts and land-use planners to formulate sustainable LST mitigation policies, create an environmental comfort in Nag-Hammadi district, and other geographical locations with similar conditions.
{"title":"Evaluating the Spatiotemporal Dynamics of Land Surface Temperature in Relation to the Land Use/Land Cover changes in Nag-Hammadi District, Egypt, using Remote Sensing and GIS","authors":"","doi":"10.52939/ijg.v19i3.2599","DOIUrl":"https://doi.org/10.52939/ijg.v19i3.2599","url":null,"abstract":"In this study, three multi-temporal remotely sensed data acquired from Landsat-5 Thematic Mapper (TM) and Landsat -8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) in 1990, 2005, and 2020 were used. The maximum likelihood classifier (MLC) was opted to classify land use and land cover (LULC). Land surface temperature (LST) and LULC spectral indices i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Latent Heat Index (NDLI) and Bare Soil Index (BSI) have been computed and their relationships were examined. The overall accuracy of LULC was more than 93%. The analyses showed a notable transformation in LULC over the study period. For instance, built-up areas increased 103.7% with a rate of 45.5 ha/year and agriculture land increased by 28.9% with a rate of 186.4 ha/year. Whereas, bare soil was sharply decreased by 36.4% at a rate of 227.7ha/year. The minimum and maximum LST values increased by 2.9°C and 4.9°C, respectively, from 1990 to 2020. Furthermore, LST has a negative relationship with NDVI and NDLI (NDVI: 1990: r2 = 0.62; 2005: r2 = 0.62; 2020: r2 = 0.65. NDLI: 1990: r2 = 0.79; 2005: r2 = 0.78; 2020: r2 = 0.61) and a positive relationship with NDBI and BSI (NDBI: 1990: r2 = 0.68; 2005: r2 = 0.73; 2020: r2 = 0.44. BSI: 1990: r2 = 0.77; 2005: r2 = 0.78; 2020: r2 = 0.53). These results provided useful information about LULC changes and its impact on LST, which are necessary for experts and land-use planners to formulate sustainable LST mitigation policies, create an environmental comfort in Nag-Hammadi district, and other geographical locations with similar conditions.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48501991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Road accidents are a major global problem, especially accidents from riding a motorcycle, as these affect both human life and property. Therefore, identifying accident sites is important for accident prevention. This study aimed to analyze the density of accident sites involving motorcycles among high school students in 2019 by using Geographic Information System data (GIS) in Sukhothai Province. In the study, in-depth interviews were used with respondents, including high school students who had accidents on motorcycles, and traffic police officers who were responsible for investigating accidents in schools. In addition, reports of accident sites were used to arrange GIS data layers and analyze the density of the accident sites using Kernel Density Estimation (KDE). The study results revealed that accidents occurred at 217 accident sites in the study area. The map of accident sites and density was created by using GIS data. The areas with accidents in heavy traffic were the roads in the three main districts: Mueang, Si Samrong, and Sawankhalok. Regarding the analysis, accidents were caused by fast cut-off riding, narrow road shoulders, and road users’ non-compliance with traffic regulations. The study results were submitted to traffic authorities, schools, departments responsible for rural roads, and local government organizations, and used for planning and developing models to prevent traffic accidents involving motorcycles among high school students through the student council.
道路交通事故是一个重大的全球性问题，特别是骑摩托车的事故，因为这些事故影响到人类的生命和财产。因此，确定事故现场对预防事故非常重要。本研究旨在利用地理信息系统数据(GIS)分析2019年素可泰省高中生摩托车事故现场密度。在研究中，对受访者进行了深度访谈，包括发生过摩托车事故的高中生，以及负责调查学校事故的交通警察。此外，利用事故现场报告对GIS数据层进行排列，并利用核密度估计(Kernel density Estimation, KDE)分析事故现场的密度。研究结果显示，研究区内共发生217起事故。利用GIS数据绘制了事故地点和密度图。交通繁忙时发生事故的地区主要集中在三个主要地区的道路上:Mueang, Si Samrong和Sawankhalok。分析认为，交通事故主要是由于快车道骑行、道路肩窄、道路使用者不遵守交通规则造成的。研究结果被提交给交通当局、学校、农村道路主管部门、地方自治团体，并通过学生会用于规划和开发防止高中生摩托车交通事故的模式。
{"title":"Analysis of Accident Sites from Motorcycles among High School Students Using Geographic Information Systems, Sukhothai Province","authors":"K. Thipthimwong, N. Noosorn","doi":"10.52939/ijg.v19i3.2603","DOIUrl":"https://doi.org/10.52939/ijg.v19i3.2603","url":null,"abstract":"Road accidents are a major global problem, especially accidents from riding a motorcycle, as these affect both human life and property. Therefore, identifying accident sites is important for accident prevention. This study aimed to analyze the density of accident sites involving motorcycles among high school students in 2019 by using Geographic Information System data (GIS) in Sukhothai Province. In the study, in-depth interviews were used with respondents, including high school students who had accidents on motorcycles, and traffic police officers who were responsible for investigating accidents in schools. In addition, reports of accident sites were used to arrange GIS data layers and analyze the density of the accident sites using Kernel Density Estimation (KDE). The study results revealed that accidents occurred at 217 accident sites in the study area. The map of accident sites and density was created by using GIS data. The areas with accidents in heavy traffic were the roads in the three main districts: Mueang, Si Samrong, and Sawankhalok. Regarding the analysis, accidents were caused by fast cut-off riding, narrow road shoulders, and road users’ non-compliance with traffic regulations. The study results were submitted to traffic authorities, schools, departments responsible for rural roads, and local government organizations, and used for planning and developing models to prevent traffic accidents involving motorcycles among high school students through the student council.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42889966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The current study is based on the evaluation of the assessment of rainwater harvesting (RWH) in (semi- arid) regions. Where, this study aimed to assess the implementation of RWH by developing a methodology that can be easily applied to identify rainwater harvesting locations in Hasa Basin in southwest Jordan, through integration between the Multiple Criteria Decision Models (MCDM) using an analytic hierarchy process (AHP), and Geographic Information System (GIS). The main factors considered to achieve the aim of the study were rainfall intensity, runoff, slope, flood susceptibility, soil texture, geology, land use/ cover (LULC), elevation, rivers, faults, settlement centers, roads, wells. These were reclassified and weighted to map the levels of rainwater harvesting in the study area. Rainwater harvesting suitable sites map obtained for the study area showed that areas with high and very high suitability formed, respectively, about 11.14% and 1.17%, while areas with low and very low suitability, in contrast, constituted about 46.09% and 9.68 %, respectively, of the total area of the study area.
{"title":"The Application of the Analytic Hierarchy Process and GIS to Map Suitable Rainwater Harvesting Sites in (Semi-) Arid Regions in Jordan","authors":"","doi":"10.52939/ijg.v19i3.2601","DOIUrl":"https://doi.org/10.52939/ijg.v19i3.2601","url":null,"abstract":"The current study is based on the evaluation of the assessment of rainwater harvesting (RWH) in (semi- arid) regions. Where, this study aimed to assess the implementation of RWH by developing a methodology that can be easily applied to identify rainwater harvesting locations in Hasa Basin in southwest Jordan, through integration between the Multiple Criteria Decision Models (MCDM) using an analytic hierarchy process (AHP), and Geographic Information System (GIS). The main factors considered to achieve the aim of the study were rainfall intensity, runoff, slope, flood susceptibility, soil texture, geology, land use/ cover (LULC), elevation, rivers, faults, settlement centers, roads, wells. These were reclassified and weighted to map the levels of rainwater harvesting in the study area. Rainwater harvesting suitable sites map obtained for the study area showed that areas with high and very high suitability formed, respectively, about 11.14% and 1.17%, while areas with low and very low suitability, in contrast, constituted about 46.09% and 9.68 %, respectively, of the total area of the study area.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48038547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Landslide is the natural problem occur worldwide due to its geological features, climatic characteristics and human activities. With the help of a geographic information system (GIS) and the Analytic Hierarchical Process (AHP) method, this research attempts to develop a map of landslide susceptibility. During the present investigation, a total of ten landslide influencing factors including elevation, slope, curvature, aspect, topographic wetness index (TWI), land cover, lithology, precipitation, distance to the road and drainage, were examined for the present analysis. Using AHP, weights were applied to each factor. The weight over lay approach was used to create the landslide susceptibility map, which was then divided into five classes. According to the research findings of the susceptibility classes, 19.97% of the research 's area was highly susceptible, followed by 61.65% of low susceptible, 17.33% of moderate susceptible, 0.94% of high susceptible, and 0.12% of very high susceptible. The areas with extremely high landslide susceptibility are adjacent to a road system and have a steep slope. The amount of mean annual rainfall is high and lithology belonging to the Jurassic metasediments. The findings for this map showing the research area's vulnerability to landslides in Khao Yai National Park are useful for planners and decision-makers for slope management and future development projects in the area.
{"title":"Landslide Susceptibility Mapping Using LiDAR Data: A Case Study of Khao Yai National Park, Thailand","authors":"","doi":"10.52939/ijg.v19i3.2597","DOIUrl":"https://doi.org/10.52939/ijg.v19i3.2597","url":null,"abstract":"Landslide is the natural problem occur worldwide due to its geological features, climatic characteristics and human activities. With the help of a geographic information system (GIS) and the Analytic Hierarchical Process (AHP) method, this research attempts to develop a map of landslide susceptibility. During the present investigation, a total of ten landslide influencing factors including elevation, slope, curvature, aspect, topographic wetness index (TWI), land cover, lithology, precipitation, distance to the road and drainage, were examined for the present analysis. Using AHP, weights were applied to each factor. The weight over lay approach was used to create the landslide susceptibility map, which was then divided into five classes. According to the research findings of the susceptibility classes, 19.97% of the research 's area was highly susceptible, followed by 61.65% of low susceptible, 17.33% of moderate susceptible, 0.94% of high susceptible, and 0.12% of very high susceptible. The areas with extremely high landslide susceptibility are adjacent to a road system and have a steep slope. The amount of mean annual rainfall is high and lithology belonging to the Jurassic metasediments. The findings for this map showing the research area's vulnerability to landslides in Khao Yai National Park are useful for planners and decision-makers for slope management and future development projects in the area.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43744228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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