Version 1
: Received: 29 August 2023 / Approved: 29 August 2023 / Online: 30 August 2023 (10:13:56 CEST)
Version 2
: Received: 20 November 2023 / Approved: 22 November 2023 / Online: 23 November 2023 (09:28:13 CET)
How to cite:
Sukmaniar, S.; Listyaningsih, U.; Muhidin, S. Modeling of Spatial Data on Accuracy Values of Slum Area Distribution in Palembang City, Indonesia. Preprints2023, 2023082019. https://doi.org/10.20944/preprints202308.2019.v2
Sukmaniar, S.; Listyaningsih, U.; Muhidin, S. Modeling of Spatial Data on Accuracy Values of Slum Area Distribution in Palembang City, Indonesia. Preprints 2023, 2023082019. https://doi.org/10.20944/preprints202308.2019.v2
Sukmaniar, S.; Listyaningsih, U.; Muhidin, S. Modeling of Spatial Data on Accuracy Values of Slum Area Distribution in Palembang City, Indonesia. Preprints2023, 2023082019. https://doi.org/10.20944/preprints202308.2019.v2
APA Style
Sukmaniar, S., Listyaningsih, U., & Muhidin, S. (2023). Modeling of Spatial Data on Accuracy Values of Slum Area Distribution in Palembang City, Indonesia. Preprints. https://doi.org/10.20944/preprints202308.2019.v2
Chicago/Turabian Style
Sukmaniar, S., Umi Listyaningsih and Salut Muhidin. 2023 "Modeling of Spatial Data on Accuracy Values of Slum Area Distribution in Palembang City, Indonesia" Preprints. https://doi.org/10.20944/preprints202308.2019.v2
Abstract
Urbanization triggers the emergence of slums in urban areas. Palembang is one of the Indonesian cities with slum settlements. This research aimed to analyze the interpolation of slum positions in Palembang and their distribution pattern using kernel density analysis and by modeling spatial data on accuracy values. It employed a quantitative method with a survey approach. Samples were selected using proportional random sampling from families in 64 slum areas across the 13 districts in the city, and their positions were recorded with a GPS device and then processed and mapped in ArcGIS. The data were analyzed with inverse distance weighted, kernel density, and spatial data modeling. Results showed that slum areas near the riverbanks had high population density, while those located further were lower. The values obtained from the kernel density analysis varied from 0 to 58.1123, while the inverse distance weighted showed a value range of 2.26745–380.991. The spatial data modelling analysis demonstrated that the distribution of slum areas was widely spread along the Musi River, with an accuracy value of 96.8%, which falls within the range of >0.9-1.
Keywords
Modeling of Spatial Data; Accuracy Value; Slum Areas Distribution
Subject
Social Sciences, Urban Studies and Planning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
