Population characteristics and distribution patterns of slum areas in Palembang City: Getis ord gi* analysis

Abstract

The paper aims to describe the population characteristics and the distribution patterns of slums in Palembang City. The research employs a quantitative method with 382 respondents. The data are analyzed using cross-tabulation of IBM SPSS 23 to know the population characteristics. Meanwhile, the distribution patterns of slums are analyzed by observing the sample distribution through the proportional random sampling technique. It is carried out by calculating the number of buildings of each area and noting the coordinates of each sample using GPS essentials application. The data are recorded and inserted into the sample spots on the map, which were then analyzed using the High-Low Clustering Report of Getis Ord General Gi*, to see the distribution pattern, especially the cold spot and hot spot, through ArcMap 103 program. The research found that non-migrant married Moslems dominate the population of Palembang city, with the average occupation is labor or manual worker. The slum distribution forms a low cluster pattern, meaning that it has a low value. The value is due to the government’s effort to manage the city and the development of the market sector, limiting the slum distribution. Getis Ord Gi* analysis revealed that the slum area in the city center and within a dense population is a cold spot (low cluster), while those far from the city center yet are still crowded are hot spots (high cluster).

Emerging Spatio-temporal Hot Spot Analysis of Beijing Subsidence Trend Detection Based on PS-InSAR

“…The average value is filled, and the true meaning of each cube bin (as shown in Figure 2b) is the average deformationin (Prasannakumar et al, 2011) The spatio-temporal hotspot analysis algorithm is shown in Figure 2c. Firstly, the spatio-temporal Getis-Ord Gi * statistics (Sukmaniar et al, 2020) (as Equations 1) are performed on each bin to obtain the z-score value of each bin, that is, the continuous deformation value in the cumulative deformation period is obtained. Then, based on this value, the K nearest neighbor clustering algorithm is used in the space-time domain to obtain the deformation hot and cold spots in the demonstration area.…”

Gis Based Spatial Distribution Map of Petrol Stations Using Geostatistical Analysis in Selangor Malaysia

“…The effect on population density brings the investors to develop petrol stations. The analysis using Getis Ord Gi* reveals that the slum areas in the city centre and the dense population is a cold spot (low cluster), while those that are far from the city centre are hot spots as high cluster (Sukmaniar et al, 2020) . The result obtained from the spatial analysis will determine and identify the strategic location for future planning.…”

Modeling of Spatial Data on Accuracy Values of Slum Area Distribution in Palembang City, Indonesia

“…The riverbanks provide an alternative housing option for residents seeking low rental costs. Like other urban areas, this city always experiences an increase in population yearly (Sukmaniar, Pitoyo et al, 2020). This phenomenon stems from the bustling economic activities in Palembang City service and trade sectors.…”

“…Therefore, establishing strong collaboration becomes essential to maintain orderliness in infrastructural development. Urbanization continues due to the high migration of people to urban areas with increased economic activity (Sukmaniar, Kurniawan et al, 2020). This phenomenon is a result of the strong attraction that urban areas hold for migrants who aspire to enhance their social and economic conditions.…”