Indonesia was under Dutch colonial authority for several centuries, during which mapping activities were carried out to serve the interests of the ruling government. Indonesia possesses several historical maps with significant potential, although their utilization remains incomplete. This research aims to explore the possible applications of historical maps, particularly in the context of organizing historical coastlines. The historical maps included in this project originate from the years 1896, 1898, 1920, and 1944 and have undergone georeferencing and digitizing processes. The Digital Shoreline Analysis System (DSAS) utilizes the Net Shoreline Movement (NSM) and Linear Regression Rate (LRR) statistical techniques to assess shoreline changes. The outcome of this study is a cartographic representation of the alteration in the coastline of Tangerang Regency, accompanied by a graphical depiction of the shoreline's transformation. The accuracy of the results is compromised due to variations in historical maps originating from different time periods, resulting in differing geographical perspectives across authors. Consequently, the reliability of the accuracy is diminished. Nevertheless, these limits restrict the extent to which geography may be advanced based on each individual map.
The designation of a geographical location serves as a significant historical reference for understanding the socio-cultural and physical characteristics of a certain area. The process of mapping geographical names can offer valuable insights into several aspects, including cultural legacy, linguistic diversity, patterns of migration, and dynamics of metropolitan areas. The disparities observed in geographical nomenclature between ancient maps and contemporary maps might enhance the robustness of historical data by visually illustrating the transformations that have transpired over time. Hence, the objectives of this research endeavor are: The objectives of this study are as follows: 1.) To acquire information on the state of the maps and the completeness of toponymic information in the historical maps that were utilized. 2.) To quantify the number of geographical names that can be derived from these maps. 3.) To compare the number of geographical names extracted from the historical maps with those found in more modern maps. The primary focus of this study included the digitization of geographical names extracted from historical topographic maps. These maps were sourced from the Topografische Dienst van Nederlands Indië, dated 1889, the Army Maps Service, dated 1943, and the Indonesian official topographic maps (Peta Rupabumi Indonesia-RBI), dated 2014. The findings of this research project have yielded a comprehensive geo-database including the names of Nagari in Agam Regency, spanning the years 1889, 1943, and 2014.
Cultural landscapes reflect humanity's creative genius, social evolution, imagination, and spiritual life. The city of Yogyakarta in Indonesia is an ideal example of a cultural landscape reflecting the works of Hindu-Buddhist, Islamic, colonial, reform to contemporary civilization. Reconstructing historical landscapes and regions is critical for preserving historical memory. Geographical names are a possible way to build community identities. Our research aims to trace the multitemporal landscape from historical maps in Yogyakarta. This study conducted a comparative cartographic analysis of several historical maps of Yogyakarta City, focusing on some of the critical changes and phases during the era. We used topographic maps produced by the Topografische Dienst of the Dutch East Indies, the US Army Map Services, and the Indonesian Geospatial Information Agency to trace the historical landscape in Yogyakarta City. We digitized geographical names as they were presented on the historical maps. Indonesia Geographical Features Cataloging was followed to create a geodatabase. The results of this study showed how the dynamics of geographical names change based on historical map tracing. Additionally, there have been changes in the territory boundaries. This dataset of historical geographical names can serve as a database for preserving cultural heritage and as a basis for sustainable development in Yogyakarta City.
Visible Infrared Imaging Radiometer Suite (VIIRS) instruments produce nighttime light (NTL) images showing artificial light emissions, which are closely related to human existence as an indicator of built-up areas, especially settlements. This study was designed to determine the capability of NTL data to estimate population based on its correlation with the intensity of artificial light emission and lit area by conducting multivariate linear regression analysis using Python in Google Colaboratory. The research area consisted of regencies/cities on Java Island, home to the largest population in Indonesia, that had different rates of development. The samples were city/regency population data divided randomly with a 7:3 ratio into training and testing samples. The model was created using a training samples with correlation coefficients of 0.857 for 2015, 0.855 for 2017, and 0.852 for 2019 and then validated by calculating the percent error (% error) between the estimated and actual populations using the testing samples. The results showed an average of 1.44% error, and from this high accuracy indicator, the study concludes that NTL can be used to estimate the population. However, this estimate only serves as an overview because the model was developed based on small-scale cases, resulting in less detailed outcomes.
Batam, one of the largest cities in Kepulauan Riau Province, Indonesia, has been established as a free-trade zone (FTZ). It is undergoing rapid development and is thus characterized by built-up areas expanding every year. This condition is due to the increasing demand for space and leads to conflicts of needs for land use and incompatibility with the allocation plans. The land cover maps of Batam City in 2000 and 2015 were obtained from the Landsat 7 ETM+ and Landsat 8 OLI image interpretation and classification, which used maximum likelihood. These maps were also checked with high-resolution imagery (Google Earth image) to produce a confusion matrix to validate or test their accuracy. Land Change Modeler (LCM) was an instrument used to determine changes in land cover from 2000 to 2015. Based on land cover change from 2000 to 2015, the results showed a total increase of 2,401.65 hectares or 43.48% in the built-up area. From 2000 to 2015, it was persistently expanding toward the city’s outskirts. The confusion matrixes revealed that the land cover maps in 2000 and 2015 had an overall accuracy of 95.5% and 96.3%, respectively.
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