Identifying the spatial patterns of housing distribution in Johor Bahru through spatial autocorrelation

• Published in: IOP conference series. Earth and environmental science 2020-07, Vol.540 (1), p.12008
• Main Authors: Mohd Sairi, N A, Burhan, B, Mohd Safian, E E
• Format: Article
• Language: English
• Subjects: Autocorrelation; Clustering; Geographical locations; Geography; Housing; Place preferences; Spatial analysis
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/540/1/012008

Everything that has a location in geographic space will naturally generate a spatial pattern either clustered, dispersed or random. A testable assumption in the concept where geographic location matters is Tobler's First Law of Geography whereas one means for quantifying the law of geography by Tobler is through the measures of spatial autocorrelation. Hence, this study aims to identify the spatial clustering patterns of housing distribution in Johor Bahru by applying spatial autocorrelation methods. Through global spatial autocorrelation analysis, the results depict a high clustering within the housing distribution as the value of Moran's I is 0.993852 which is highly positive and near to 1. Next, the LISA cluster map had successfully identified individual clusters where the housing characteristics and location characteristics in Skudai and Pasir Gudang are similar to its nearby housing units. However, the housing units in Johor Bahru have dissimilar characteristics of housing and locations with its nearest housing units. Based on this analysis, although the housing distribution indicates a clustering pattern overall, the type of clustering is however, locally different. Apart from that, the results also reflects buyer's preferences of housing choices through locational characteristics whilst suggesting for possibility of conducting distance matrix for further analysis.