Identification of Slum Distribution Patterns Based on GWR (Case Study: Panjang District, Bandar Lampung City)
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Abstract
Slums are densely populated settlements that are not habitable, the building density is very high in a very limited area, causing various problems such as being prone to social diseases and diseases. The distribution of slums in an area can be analyzed using geographic information systems as an innovative strategy to visualize geospatial information accurately, allowing for more in-depth and data-based analysis to support decision making. This study uses the Geographically Weighted Regression (GWR) method. Geographically Weighted Regression (GWR) is a statistical analysis that allows researchers to evaluate spatial variations in the relationship between dependent and independent variables, using the Fixed Gaussian kernel function to provide constant weighting across research locations, so that they can understand complex and different spatial relationships in each location. Based on data processing on seven indicators and 16 slum parameters stipulated in PUPR Regulation No. 14 of 2018, Panjang District, Bandar Lampung City, there are 5 sub-districts that are categorized as light slums with slum distribution points spread across various locations without clear concentration. The results of the Geographically Weighted Regression (GWR) model show that the independent variables (X) and dependent variables (Y) reflect a complex relationship with variable X 11 (non-standard wastewater management system) having the highest coefficient of 0.213830. The results of the GWR model in the ANOVA table show that the GWR model does not provide significant improvement.
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References
A. Hariyanto, "Slum Area Handling Strategy as an Effort to Create a Healthy Housing and Settlement Environment (Case Example: Pangkalpinang City)," Jurnal PWK Unisba, pp. 11-37, 2017.
A. Wijaya and F. Ardalia, "Utilization of Communal Space in Urban Slum Areas in Manggarai, South Jakarta," IKRA-ITH TEKNOLOGI, pp. 17-26, 2019.
MR Akbar and N. Novira, "Analysis of Slum Management in Medan Denai District," Jurnal Tunas Geografi, vol. 8, no. 1, pp. 59-70, 2019.
N. Rendhyka, "Identification of Slum Areas in Raja Basa District, Bandar Lampung City," University of Lampung, Bandar Lampung, 2022.
MS Adiputra, E. Rustiadi and AE Pravitasari, "Slum Settlement Distribution Patterns in Tangerang Regency and Spatial Diversity of Influencing Factors," Journal of Regional and Rural Development Planning, vol. 6, no. 2, pp. 146-164, 2021.
R. As'ari and S. Fadjarajani, "Environment-Based Slum Settlement Management," Journal of Geography, vol. 15, no. 1, pp. 56-67, 2018.
N. Fitria and RP Setiawan, "Identification of Characteristics of Slum Settlement Environment in Kapuk Village, West Jakarta," POMITS TECHNICAL JOURNAL, vol. 3, no. 2, pp. 240-244, 2014.
M. Putra, M. Fakhri and K. Furoida, "Information Systems," URECOL Journal, p. 398, 2019.
ARN Diastina, SS Handajani and I. Slamet, "Analysis of Geographically Weighted Regression (GWR) Model in the Case of the Number of Active KB Participants in Central Java Province," PROCEEDINGS OF THE NATIONAL SEMINAR ON GEOTIK, pp. 364-373, 2019.
Irawan, R. Candra and AL Nugraha, "Analysis of Slum Level in Settlements Using Geographically Weighted Regression (GWR) Model (Case Study; Surakarta City, Central Java)," Undip Geodesy Journal, no. 1, 2020.
N. Lutfiani, Sugiman and S. Mariani, "Geographically Weighted Regression (GWR) Modeling With D=Gaussian Kernel Weighting Function and Bi-Square," UNNES Journal of Mathematics , vol. 8, no. 1, pp. 82-91, 2019.
Copyright (c) 2025 Ratna Mustika Sari, Melantika Sihombing, Rizky Ahmad Yudanegara, Meraty Ramadhini
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