DOI: 10.3897/jbgs.e155799

Authors: Astisiasari Astisiasari, Wisyanto Wisyanto, Dian Melati, Sukristiyanti Sukristiyanti, Raditya Umbara, Yukni Arifianti, Trinugroho Trinugroho, Lian Andikasari, Taufik Ramdhani

Abstract: Landslide exposure is an adept approach to measuring the consequences of a landslide hazard on elements at risk. Landslides in the Bogor area, Province of West Java, Indonesia, have increased in number and consequences since 2015. The Bogor area also has a fairly large population that may aggravate the impact. Accordingly, this study aims to measure the landslide exposures over two substantial elements (i.e., population and land use). The actual resources for these exposed elements are available from the open-access geodata and the Google Earth Engine (GEE) platform. For the land use classification, this study employs two robust machine-learning (ML) algorithms on a GEE-based Object-based Image Analysis (OBIA), i.e., Random Forest (RF) and Gradient Tree Boosting (GTB). Moreover, the population data were retrieved from WorldPop estimates. Landslide exposures were then analyzed through an overlay between these two elements with a landslide hazard map sourced from the former study. The results show that in 2020, the Bogor area had an exposed population of 885,353 people, with Bogor Selatan District having the highest exposed population (135,475 people). Moreover, in 2021, the Bogor area had a total exposed land use reaching 347.9 km2, with built-up area having the most extensive, reaching 45.9% of the total exposed area. Here, Sukajaya District had the largest exposed land use (39.1 km2). This study is expected to reach multifaceted entities that contribute to strengthening landslide risk reduction. Through this spatial awareness of the highly exposed areas to landslides, mitigation measures can be taken accordingly.

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DOI: 10.3897/jbgs.e89215

Authors: Aleksandra Ravnachka, Velimira Stoyanova

Abstract: Light industry is one of the most important and priority industries in Bulgarian economy. It includes the production of textiles, clothing, and leather. Its development affects the state of the country’s overall economy. Despite the numerous studies that use GIS, in Bulgaria there have been no publications on the application of statistical analysis with the use of ArcGIS software. This study aims to apply Geographic cluster analysis using ArcGIS software to analyze the light industry in Bulgaria as of 2010, 2015, and 2020. The grouping of areas by selected indicators in the present study was performed with the Grouping Analysis tool. NO_SPATIAL_CONSTRAINT was selected for the Spatial Constraints parameter and FIND_SEED_LOCATIONS – for the Initialization Method. In this case, we used the K-Means algorithm to partition features into groups. That algorithm is one of the most popular and widely used clustering algorithms in GIS applications. The areas were grouped into 10 clusters. The selection of indicators on which the clustering procedure was based, is following the generally accepted indicators for assessing the state and importance of the food industry in the structure of the economy. The following indicators were used: output for 2010, 2015, and 2020; number of employees and export earnings as of 2010, 2015, and 2020, for each administrative-territorial unit. The spatial distribution of the population, in combination with the historical and the modern economic development of the settlements, forms the regional differences in the development of the light industry in the country. The cluster analysis of certain indicators for the assessment of the light industry at the NUTS 3 level as of 2010, 2015, and 2020, shows some changes in the spatial development trends of the industry. The cluster analysis shows that there are slight spatial differences in production at the NUTS 3 level, with large consumer centers and markets being the most important.

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