Indonesia's geographical position is an advantage compared to other countries, both in terms of geoeconomics, geopolitics and geostrategy. For this reason, it is necessary to develop and use acoustic methods to describe underwater features, carry out underwater communications or to measure oceanographic variables at sea. This research was intended to provide an analytical and visual graphical description with the aim that it can be used for various purposes both in the research, military and other marine fields, as well as to analyze the influence of sediment and different frequencies on acoustic propagation patterns in shallow waters of Kayeli Bay. This research was conducted using CTD data from Kayeli Bay, which is a body of water in Buru Regency, Maluku Province and is located between 3° 15' 55'' – 3° 22' 50" S and 127° 01'35" – 127° 01' 35 "E, using the Monterey-Miami parabolic equation method using 4 types of sediment and 3 different frequencies as model input. From the results of this research it can be concluded that the propagation of sound waves in shallow seas is greatly influenced by the type of sediment and frequenty used. Changes in acoustic impedance at the bottom of the water and within the water column can significantly influence the behavior of acoustic waves in shallow water environments, and accurate acoustic impedance data are critical for effective ray tracing modelling.
{"title":"Underwater Acoustic Propagation using Monterey-Miami Parabolic Equation in Shallow Water Kayeli Bay Buru Distric","authors":"R. Lalita, H. M. Manik, Irsan S Brojonegoro","doi":"10.30871/jagi.v7i2.2802","DOIUrl":"https://doi.org/10.30871/jagi.v7i2.2802","url":null,"abstract":"Indonesia's geographical position is an advantage compared to other countries, both in terms of geoeconomics, geopolitics and geostrategy. For this reason, it is necessary to develop and use acoustic methods to describe underwater features, carry out underwater communications or to measure oceanographic variables at sea. This research was intended to provide an analytical and visual graphical description with the aim that it can be used for various purposes both in the research, military and other marine fields, as well as to analyze the influence of sediment and different frequencies on acoustic propagation patterns in shallow waters of Kayeli Bay. This research was conducted using CTD data from Kayeli Bay, which is a body of water in Buru Regency, Maluku Province and is located between 3° 15' 55'' – 3° 22' 50\" S and 127° 01'35\" – 127° 01' 35 \"E, using the Monterey-Miami parabolic equation method using 4 types of sediment and 3 different frequencies as model input. From the results of this research it can be concluded that the propagation of sound waves in shallow seas is greatly influenced by the type of sediment and frequenty used. Changes in acoustic impedance at the bottom of the water and within the water column can significantly influence the behavior of acoustic waves in shallow water environments, and accurate acoustic impedance data are critical for effective ray tracing modelling. \u0000 ","PeriodicalId":503070,"journal":{"name":"Journal of Applied Geospatial Information","volume":"45 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139390576","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}
Royston Manurung, Tulus Ikhsan Nasution, Syahrul Humaidi, Immanuel Jhonson A. Saragih, Khindi Aufa Hibatullah, M. Situmorang, Yahya Darmawan
Soil temperature is a crucial parameter in monitoring and understanding climate and soil ecosystems. It plays a vital role in various environmental aspects, including agriculture, ecology, and geoscience. Monitoring soil temperature is necessary for planning and managing agriculture and natural resources. Currently, temporal observations of soil temperature by BMKG are limited, conducted only at 07:55, 13:55, and 18:55 local time. This limitation makes it difficult to perform detailed soil temperature analysis. This research was conducted to design a digital soil temperature monitoring device accessible via the internet. Seven DS18B20 sensors were used at depths of 0 cm, 2 cm, 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm, combined with an ESP8266 module using the Arduino system. The implementation of this design resulted in a real-time soil temperature monitoring system with data updates every 10 seconds. The observed data are displayed on a 20x4 LCD and sent to the cloud, making them accessible on the webpage http://monitoringsuhutanah.my.id. Calibration results indicate that the DS18B20 sensors used in this study provide accurate and consistent temperature measurements, with an average correction range of (-0.20) to 0.24, thus suitable for operational use. Field tests show that the digital data are accurate and correspond (linearly correlate) with conventional data. This is based on a correlation value of 0.7, while the RMSE values range from 0.5 to 2.18 and the bias ranges from (-0.69) to 0.08.
{"title":"Design and Development of A Digital Soil Temperature Monitoring System Based on The Internet of Things at North Sumatra Climatological Station","authors":"Royston Manurung, Tulus Ikhsan Nasution, Syahrul Humaidi, Immanuel Jhonson A. Saragih, Khindi Aufa Hibatullah, M. Situmorang, Yahya Darmawan","doi":"10.30871/jagi.v7i2.6545","DOIUrl":"https://doi.org/10.30871/jagi.v7i2.6545","url":null,"abstract":"Soil temperature is a crucial parameter in monitoring and understanding climate and soil ecosystems. It plays a vital role in various environmental aspects, including agriculture, ecology, and geoscience. Monitoring soil temperature is necessary for planning and managing agriculture and natural resources. Currently, temporal observations of soil temperature by BMKG are limited, conducted only at 07:55, 13:55, and 18:55 local time. This limitation makes it difficult to perform detailed soil temperature analysis. This research was conducted to design a digital soil temperature monitoring device accessible via the internet. Seven DS18B20 sensors were used at depths of 0 cm, 2 cm, 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm, combined with an ESP8266 module using the Arduino system. The implementation of this design resulted in a real-time soil temperature monitoring system with data updates every 10 seconds. The observed data are displayed on a 20x4 LCD and sent to the cloud, making them accessible on the webpage http://monitoringsuhutanah.my.id. Calibration results indicate that the DS18B20 sensors used in this study provide accurate and consistent temperature measurements, with an average correction range of (-0.20) to 0.24, thus suitable for operational use. Field tests show that the digital data are accurate and correspond (linearly correlate) with conventional data. This is based on a correlation value of 0.7, while the RMSE values range from 0.5 to 2.18 and the bias ranges from (-0.69) to 0.08.","PeriodicalId":503070,"journal":{"name":"Journal of Applied Geospatial Information","volume":"79 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139153139","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}
Technological developments in this era of globalization are very rapid. This requires humans to enter life together with information and technology. Stunting as a chronic nutritional problem in children, continues to be a global challenge. Geographic Information Systems (GIS) have proven to be effective tools in spatial analysis and distribution mapping stunting. In this context, method Geographically Weighted Regression (GWR) has been used to model the spatial relationship between factors that contribute to stunting. This research will produce a Geographic Information System using the method Geographically Weighted Regression. With this Geographic Information System, it can display location points and affected information stunting. Because of this system, the Padang Lawas Utara District Health Office does not need to store location data stunting in archive form again but digitally. This study underscores the importance of using GIS with the GWR method in mapping patient locations stunting. Through the integration of geographic data and spatial analysis, we can generate a better understanding of the influencing factors stunting at the local level, which in turn can support prevention and response efforts stunting which is more effective.
{"title":"Geographic Information System Mapping Risk Factors Stunting Using Methods Geographically Weighted Regression","authors":"Siska Mayasari Rambe, S. Suendri","doi":"10.30871/jagi.v7i2.6936","DOIUrl":"https://doi.org/10.30871/jagi.v7i2.6936","url":null,"abstract":"Technological developments in this era of globalization are very rapid. This requires humans to enter life together with information and technology. Stunting as a chronic nutritional problem in children, continues to be a global challenge. Geographic Information Systems (GIS) have proven to be effective tools in spatial analysis and distribution mapping stunting. In this context, method Geographically Weighted Regression (GWR) has been used to model the spatial relationship between factors that contribute to stunting. This research will produce a Geographic Information System using the method Geographically Weighted Regression. With this Geographic Information System, it can display location points and affected information stunting. Because of this system, the Padang Lawas Utara District Health Office does not need to store location data stunting in archive form again but digitally. This study underscores the importance of using GIS with the GWR method in mapping patient locations stunting. Through the integration of geographic data and spatial analysis, we can generate a better understanding of the influencing factors stunting at the local level, which in turn can support prevention and response efforts stunting which is more effective.","PeriodicalId":503070,"journal":{"name":"Journal of Applied Geospatial Information","volume":"118 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139154666","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 Human Development Index (HDI) is a statistical measure used to measure and evaluate the progress and quality of human life in a country. For the Government of Indonesia, HDI is important because it is used to create or develop effective policies and programs. In addition, HDI is also used as one of the allocators in determining the General Allocation Fund. The 2022 HDI data released by BPS shows that there has been an increase in the HDI in each district/city over the last 12 years, including in the regions of Eastern Indonesia. High and low HDI values are influenced by several factors, and there are indications that there is spatial diversity where surrounding areas tend to have HDI levels that are not far from the area. The Geographically Weighted Regression method is used in this study because it takes into account spatial aspects. However, the GWR model must be built repeatedly if there is regional expansion. Therefore, a GWR model that applies machine learning methods is needed where the model is built and tested using different datasets, namely training data and test data, so that the model can predict new data better. The results obtained are that the GWR model with test data has a better R-Square value when compared to the GWR model previously trained using training data, which is 0.9946702, based on the linear regression model shows the results that the most influential factor on HDI in Eastern Indonesia is expected years of schooling (X2).
{"title":"Data-Driven Modeling of Human Development Index in Eastern Indonesia's Region Using Gaussian Techniques Empowered by Machine Learning","authors":"Syuhra Putri Ganiswari, Harun Al Azies, Adhitya Nugraha, Ardytha Luthfiarta, Gustian Angga Firmansyah","doi":"10.30871/jagi.v7i2.6757","DOIUrl":"https://doi.org/10.30871/jagi.v7i2.6757","url":null,"abstract":"The Human Development Index (HDI) is a statistical measure used to measure and evaluate the progress and quality of human life in a country. For the Government of Indonesia, HDI is important because it is used to create or develop effective policies and programs. In addition, HDI is also used as one of the allocators in determining the General Allocation Fund. The 2022 HDI data released by BPS shows that there has been an increase in the HDI in each district/city over the last 12 years, including in the regions of Eastern Indonesia. High and low HDI values are influenced by several factors, and there are indications that there is spatial diversity where surrounding areas tend to have HDI levels that are not far from the area. The Geographically Weighted Regression method is used in this study because it takes into account spatial aspects. However, the GWR model must be built repeatedly if there is regional expansion. Therefore, a GWR model that applies machine learning methods is needed where the model is built and tested using different datasets, namely training data and test data, so that the model can predict new data better. The results obtained are that the GWR model with test data has a better R-Square value when compared to the GWR model previously trained using training data, which is 0.9946702, based on the linear regression model shows the results that the most influential factor on HDI in Eastern Indonesia is expected years of schooling (X2).","PeriodicalId":503070,"journal":{"name":"Journal of Applied Geospatial Information","volume":"18 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139238098","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}
Gustian Angga Firmansyah, Junta Zeniarja, Harun Al Azies, Sri Winarno, Syuhra Putri Ganiswari
The HDI (Human Development Index) is one of the important components to measure the level of success in efforts to improve the quality of human life. The human development index is built with three dimensions, namely the longevity and health dimension, the knowledge dimension and the decent standard of living dimension. The longevity and health dimension is measured using Life expectancy at birth. The knowledge dimension is measured using expected years of schooling and average years of schooling. Meanwhile, the decent standard of living dimension is measured using Adjusted per capita expenditure. This study aims to find factors that influence HDI (Human Development Index) in Western Indonesia Region using machine learning models. The results obtained are that HDI is influenced by average years of schooling, expected years of schooling, Life expectancy at birth, and Adjusted per capita expenditure which are sorted from the most significantly influential. The model used in this study is GWR (Geographically Weighted Regression) with evaluation results including, AIC of 215.3162, AICc of 226.5107, and the accuracy level in the form of R-square of 99.38% which means this model is good to use.
{"title":"Machine Learning-Enhanced Geographically Weighted Regression for Spatial Evaluation of Human Development Index across Western Indonesia","authors":"Gustian Angga Firmansyah, Junta Zeniarja, Harun Al Azies, Sri Winarno, Syuhra Putri Ganiswari","doi":"10.30871/jagi.v7i2.6755","DOIUrl":"https://doi.org/10.30871/jagi.v7i2.6755","url":null,"abstract":"The HDI (Human Development Index) is one of the important components to measure the level of success in efforts to improve the quality of human life. The human development index is built with three dimensions, namely the longevity and health dimension, the knowledge dimension and the decent standard of living dimension. The longevity and health dimension is measured using Life expectancy at birth. The knowledge dimension is measured using expected years of schooling and average years of schooling. Meanwhile, the decent standard of living dimension is measured using Adjusted per capita expenditure. This study aims to find factors that influence HDI (Human Development Index) in Western Indonesia Region using machine learning models. The results obtained are that HDI is influenced by average years of schooling, expected years of schooling, Life expectancy at birth, and Adjusted per capita expenditure which are sorted from the most significantly influential. The model used in this study is GWR (Geographically Weighted Regression) with evaluation results including, AIC of 215.3162, AICc of 226.5107, and the accuracy level in the form of R-square of 99.38% which means this model is good to use.","PeriodicalId":503070,"journal":{"name":"Journal of Applied Geospatial Information","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139238470","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}
Nanda Riska Devy, Syamsul Bahri Agus, S. B. Susilo
Rob floods caused by rising sea levels are a natural disaster that can potentially threaten coastal areas, especially in Indonesia. Tidal floods seriously threaten coastal areas, especially East Luwu Regency. Environmental factors and rapid growth on the coast of East Luwu Regency influence the vulnerability and complexity of the environment. This research aims to identify the spatial distribution of tidal flood risk levels and predict tidal flood inundation in 2050 at the highest tide on the coast of Luwu Timur District. This effort is part of a disaster mitigation strategy due to rising sea levels. The modeling approach involves Geographic Information Systems (GIS) overlaying data and integrating DEM, HHWL, and SLR data for 28 years (1992-2020). The research results show that the coastal areas studied have a high risk related to tidal flooding, with locations closest to the coastline being at the highest risk. In contrast, the risk decreases as you move away from the coastline. Apart from that, the modeling results also estimate that in 2050, inundation will reach a height of 1,570 meters. The area affected by tidal flood inundation has increased in each sub-district. The inundation will spread evenly along the coastline and extend inland due to seawater intrusion. Coastal areas dominated by production land, such as ponds and agricultural areas, are predicted to experience the most extensive impact of inundation compared to other land uses. Emphasizes the need for mitigation efforts to minimize the impacts that may be caused by tidal floods in the future.
{"title":"Analysis of Rob Flood Risk on The Coast of East Luwu District Using GIS","authors":"Nanda Riska Devy, Syamsul Bahri Agus, S. B. Susilo","doi":"10.30871/jagi.v7i2.6719","DOIUrl":"https://doi.org/10.30871/jagi.v7i2.6719","url":null,"abstract":"Rob floods caused by rising sea levels are a natural disaster that can potentially threaten coastal areas, especially in Indonesia. Tidal floods seriously threaten coastal areas, especially East Luwu Regency. Environmental factors and rapid growth on the coast of East Luwu Regency influence the vulnerability and complexity of the environment. This research aims to identify the spatial distribution of tidal flood risk levels and predict tidal flood inundation in 2050 at the highest tide on the coast of Luwu Timur District. This effort is part of a disaster mitigation strategy due to rising sea levels. The modeling approach involves Geographic Information Systems (GIS) overlaying data and integrating DEM, HHWL, and SLR data for 28 years (1992-2020). The research results show that the coastal areas studied have a high risk related to tidal flooding, with locations closest to the coastline being at the highest risk. In contrast, the risk decreases as you move away from the coastline. Apart from that, the modeling results also estimate that in 2050, inundation will reach a height of 1,570 meters. The area affected by tidal flood inundation has increased in each sub-district. The inundation will spread evenly along the coastline and extend inland due to seawater intrusion. Coastal areas dominated by production land, such as ponds and agricultural areas, are predicted to experience the most extensive impact of inundation compared to other land uses. Emphasizes the need for mitigation efforts to minimize the impacts that may be caused by tidal floods in the future.","PeriodicalId":503070,"journal":{"name":"Journal of Applied Geospatial Information","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139261521","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}
Dinda Afifah Adinuha, Syamsul Bahri Agus, N. Zamani
The presence of waste in coastal environments can lead to increased coastal damage and burden. Most of the population's activities in Bulukumba Regency are concentrated in coastal areas, thus making this region susceptible to significant pressure from waste pollution. This research aims to determine the level of coastal area sensitivity in Bulukumba towards waste pollution. The study was conducted from October to December 2022. The research location is the coastal area of Bulukumba Regency, which includes seven subdistricts: Gantarang, Ujung Bulu, Ujung Loe, Bonto Bahari, Bontotiro, Herlang, and Kajang. Primary data were obtained through interviews and direct observations at the research locations, while secondary data were collected through literature studies and relevant institutions in Bulukumba. The results of parameter weighting using the expert judgment method indicate that five important parameters are used to assess the sensitivity of the coastal environment to waste pollution. These parameters consist of current velocity (20.27%), distance of the ecosystem from the harbor (18.92%), distance of the ecosystem from settlements (18.92%), distance of the ecosystem from rivers (17.57%), and the presence of waste on the coast (17.57%). The distribution of coastal environmental sensitivity levels to waste pollution shows that the eastern coastal areas are more sensitive to waste pollution than the southern coastal areas. The current velocity is the most significant parameter influencing the coastal environment's sensitivity to waste pollution and holds the highest weight and score across all research areas.
{"title":"The Sensitivity Level of the Coastal Areas in Bulukumba Regency to Waste Pollution","authors":"Dinda Afifah Adinuha, Syamsul Bahri Agus, N. Zamani","doi":"10.30871/jagi.v7i2.6727","DOIUrl":"https://doi.org/10.30871/jagi.v7i2.6727","url":null,"abstract":"The presence of waste in coastal environments can lead to increased coastal damage and burden. Most of the population's activities in Bulukumba Regency are concentrated in coastal areas, thus making this region susceptible to significant pressure from waste pollution. This research aims to determine the level of coastal area sensitivity in Bulukumba towards waste pollution. The study was conducted from October to December 2022. The research location is the coastal area of Bulukumba Regency, which includes seven subdistricts: Gantarang, Ujung Bulu, Ujung Loe, Bonto Bahari, Bontotiro, Herlang, and Kajang. Primary data were obtained through interviews and direct observations at the research locations, while secondary data were collected through literature studies and relevant institutions in Bulukumba. The results of parameter weighting using the expert judgment method indicate that five important parameters are used to assess the sensitivity of the coastal environment to waste pollution. These parameters consist of current velocity (20.27%), distance of the ecosystem from the harbor (18.92%), distance of the ecosystem from settlements (18.92%), distance of the ecosystem from rivers (17.57%), and the presence of waste on the coast (17.57%). The distribution of coastal environmental sensitivity levels to waste pollution shows that the eastern coastal areas are more sensitive to waste pollution than the southern coastal areas. The current velocity is the most significant parameter influencing the coastal environment's sensitivity to waste pollution and holds the highest weight and score across all research areas.","PeriodicalId":503070,"journal":{"name":"Journal of Applied Geospatial Information","volume":"10 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139268583","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}
Angga Dwi Prasetyo, N. Bashit, Muhammad Adnan Yusuf, Farouki Dinda Rassarandi
The Covid-19 pandemic has caused all countries to implement strategies to suppress its spread, one of which is Indonesia, especially DKI Jakarta, which has implemented Large-Scale Social Restrictions (PSBB) since April 10 2020. Apart from being able to suppress the spread of the Covid-19 virus, PSBB is thought to have an impact on the environment, especially air quality in DKI Jakarta. According to research from the BMKG, Jakarta's air quality has improved over the last 5 years with the implementation of the PSBB. Besides analyzing the effect of the PSBB on air quality in DKI Jakarta, this research also aims to help governments in every region of Indonesia that do not have air quality monitoring stations. The method used in this study is to utilize Imagery from Sentinel-5P to measure concentrations of NO2, CO and SO2 gases validated using field data and utilize the NOAA Satellite acquired with Ventusky to analyze the effect of wind on the distribution of air pollution due to the PSBB. The results showed that the ratio of the average concentrations of NO2, CO and SO2 gases in DKI Jakarta decreased respectively to 27.70% ; 10.20% ; 42.06%. This shows an increase in air quality in DKI Jakarta due to the implementation of the PSBB. Comparison of the average concentrations of NO2, CO and SO2 gases in DKI Jakarta during the PSBB and after the PSBB increased slightly respectively to 11.92% ; 1.89% ; 35.84%. This shows that there is a decrease in air quality in DKI Jakarta which was caused after the implementation of the PSBB. Wind also affects the concentration of NO2, CO and SO2 gases. This is evidenced by the results of the correlation where the gas concentration is low when the wind speed is high, and vice versa. It was concluded that during the COVID-19 pandemic the concentrations of NO2, CO and SO2 in DKI Jakarta decreased and slightly increased after the PSBB, and wind could affect the distribution of these gases.
{"title":"Analysis The Effect of Large-Scale Social Restrictions on Air Quality in DKI Jakarta","authors":"Angga Dwi Prasetyo, N. Bashit, Muhammad Adnan Yusuf, Farouki Dinda Rassarandi","doi":"10.30871/jagi.v7i2.5224","DOIUrl":"https://doi.org/10.30871/jagi.v7i2.5224","url":null,"abstract":"The Covid-19 pandemic has caused all countries to implement strategies to suppress its spread, one of which is Indonesia, especially DKI Jakarta, which has implemented Large-Scale Social Restrictions (PSBB) since April 10 2020. Apart from being able to suppress the spread of the Covid-19 virus, PSBB is thought to have an impact on the environment, especially air quality in DKI Jakarta. According to research from the BMKG, Jakarta's air quality has improved over the last 5 years with the implementation of the PSBB. Besides analyzing the effect of the PSBB on air quality in DKI Jakarta, this research also aims to help governments in every region of Indonesia that do not have air quality monitoring stations. The method used in this study is to utilize Imagery from Sentinel-5P to measure concentrations of NO2, CO and SO2 gases validated using field data and utilize the NOAA Satellite acquired with Ventusky to analyze the effect of wind on the distribution of air pollution due to the PSBB. The results showed that the ratio of the average concentrations of NO2, CO and SO2 gases in DKI Jakarta decreased respectively to 27.70% ; 10.20% ; 42.06%. This shows an increase in air quality in DKI Jakarta due to the implementation of the PSBB. Comparison of the average concentrations of NO2, CO and SO2 gases in DKI Jakarta during the PSBB and after the PSBB increased slightly respectively to 11.92% ; 1.89% ; 35.84%. This shows that there is a decrease in air quality in DKI Jakarta which was caused after the implementation of the PSBB. Wind also affects the concentration of NO2, CO and SO2 gases. This is evidenced by the results of the correlation where the gas concentration is low when the wind speed is high, and vice versa. It was concluded that during the COVID-19 pandemic the concentrations of NO2, CO and SO2 in DKI Jakarta decreased and slightly increased after the PSBB, and wind could affect the distribution of these gases.","PeriodicalId":503070,"journal":{"name":"Journal of Applied Geospatial Information","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281183","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}
Parlindungan Harahap, R. Virgana, Targa Sapanji, Ucu Nugraha
Pangandaran Regency has the potential for an earthquake disaster accompanied by a tsunami which occurred on July 17 2006 at 15.19 with a magnitude of 7.7, earthquake data from the USGS shows an earthquake of magnitude 5-9, there are 909 earthquake points between 1918 - 2023, 2 earthquake points above magnitude 7 (7.7 and 7.44), 18 earthquake points above magnitude 6-6.9, below magnitude 6 there were 887 earthquake points, earthquake points in the south of Pangandaran Regency were concentrated between 2 groups of locations. Raster calculation of land surface at 5 meters above sea level and 10 meters above sea level is not recommended as a location to escape for tsunami disaster mitigation, also 20 meters above sea level is not recommended unless there are no other higher areas, 30 meters above sea level is highly recommended with a note if there are higher areas it is better to shift to a higher area, because tsunami waves cannot be predicted when they hit one area, their height can be different when they hit another area, it can be calculated that the potential impact of the tsunami disaster is 90,576 buildings or houses. Several villages could be rescue locations to mitigate potential tsunami disasters in Pangandaran Regency, such as in Cimerak sub-district (Limusgede village and Cimerak village), in Cijulang sub-district (Kertayasa village and Margacinta village), in Parigi sub-district (Parakanmanggu village, Cintakarya village, Selasari village), in Sidamulih subdistrict (Kersaratu village and Kalijati village), in Pangandaran subdistrict (Pagergunung village), in Kalipucang subdistrict (Ciparakan village), in Padaherang subdistrict (Payutran village, Bojongsari village, Karangsari, Kedangwuluh, Pasirgeulis), for Mangunjaya subdistrict all areas in below 30 meters so that mitigation locations must be prepared in several border villages in Ciamis Regency.
{"title":"Geographic Information System for Tsunami Disaster Mitigation Evacuation Routes Moving the Sunda Subduction Megathrust (Case Study: Analysis of Pangandaran Regency)","authors":"Parlindungan Harahap, R. Virgana, Targa Sapanji, Ucu Nugraha","doi":"10.30871/jagi.v7i2.6518","DOIUrl":"https://doi.org/10.30871/jagi.v7i2.6518","url":null,"abstract":"Pangandaran Regency has the potential for an earthquake disaster accompanied by a tsunami which occurred on July 17 2006 at 15.19 with a magnitude of 7.7, earthquake data from the USGS shows an earthquake of magnitude 5-9, there are 909 earthquake points between 1918 - 2023, 2 earthquake points above magnitude 7 (7.7 and 7.44), 18 earthquake points above magnitude 6-6.9, below magnitude 6 there were 887 earthquake points, earthquake points in the south of Pangandaran Regency were concentrated between 2 groups of locations. Raster calculation of land surface at 5 meters above sea level and 10 meters above sea level is not recommended as a location to escape for tsunami disaster mitigation, also 20 meters above sea level is not recommended unless there are no other higher areas, 30 meters above sea level is highly recommended with a note if there are higher areas it is better to shift to a higher area, because tsunami waves cannot be predicted when they hit one area, their height can be different when they hit another area, it can be calculated that the potential impact of the tsunami disaster is 90,576 buildings or houses. Several villages could be rescue locations to mitigate potential tsunami disasters in Pangandaran Regency, such as in Cimerak sub-district (Limusgede village and Cimerak village), in Cijulang sub-district (Kertayasa village and Margacinta village), in Parigi sub-district (Parakanmanggu village, Cintakarya village, Selasari village), in Sidamulih subdistrict (Kersaratu village and Kalijati village), in Pangandaran subdistrict (Pagergunung village), in Kalipucang subdistrict (Ciparakan village), in Padaherang subdistrict (Payutran village, Bojongsari village, Karangsari, Kedangwuluh, Pasirgeulis), for Mangunjaya subdistrict all areas in below 30 meters so that mitigation locations must be prepared in several border villages in Ciamis Regency.","PeriodicalId":503070,"journal":{"name":"Journal of Applied Geospatial Information","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281850","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 investigates land-use patterns and changes in the vicinity of Ilorin Airport in Southwestern Nigeria using spatio-temporal analysis. Geographic information systems (GIS) and remote sensing techniques are employed to analyze land use dynamics from 1972 to 2018, and make a projection to 2078. Satellite images obtained from the United States Geological Survey and primary data collected through GPS serve as the main sources of information for the analysis. The findings reveal significant shifts in land use over the study period. A marked increase in built-up areas indicates urban expansion, while grassland areas experience a corresponding decrease. These changes are attributed to the development and growth of the airport and ongoing urbanization processes in the region. The results provide valuable insights into the impact of airport development and urbanization on land-use patterns in the study area. The study highlights the importance of employing GIS and remote sensing techniques in monitoring and analyzing land-use dynamics, enabling informed decision-making and planning processes. The research contributes to the existing knowledge on land-use changes associated with airport development and urbanization. It provides a foundation for further research in the field of land-use management and spatial planning. The outcomes of this study can inform policy and decision-makers, urban planners, and other stakeholders in developing strategies for sustainable land-use practices and mitigating the potential adverse effects of airport development and urban expansion.
{"title":"Spatio-Temporal Analysis of Ilorin Airport on the Land-Use of Ilorin Metropolis, Southwestern Nigeria","authors":"Nurudeen Onomhoale Ahmed, Oyeniyi Solomon Taiwo","doi":"10.30871/jagi.v7i2.5693","DOIUrl":"https://doi.org/10.30871/jagi.v7i2.5693","url":null,"abstract":"This study investigates land-use patterns and changes in the vicinity of Ilorin Airport in Southwestern Nigeria using spatio-temporal analysis. Geographic information systems (GIS) and remote sensing techniques are employed to analyze land use dynamics from 1972 to 2018, and make a projection to 2078. Satellite images obtained from the United States Geological Survey and primary data collected through GPS serve as the main sources of information for the analysis. The findings reveal significant shifts in land use over the study period. A marked increase in built-up areas indicates urban expansion, while grassland areas experience a corresponding decrease. These changes are attributed to the development and growth of the airport and ongoing urbanization processes in the region. The results provide valuable insights into the impact of airport development and urbanization on land-use patterns in the study area. The study highlights the importance of employing GIS and remote sensing techniques in monitoring and analyzing land-use dynamics, enabling informed decision-making and planning processes. The research contributes to the existing knowledge on land-use changes associated with airport development and urbanization. It provides a foundation for further research in the field of land-use management and spatial planning. The outcomes of this study can inform policy and decision-makers, urban planners, and other stakeholders in developing strategies for sustainable land-use practices and mitigating the potential adverse effects of airport development and urban expansion.","PeriodicalId":503070,"journal":{"name":"Journal of Applied Geospatial Information","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139283934","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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