Enhancing Clustering Technique to Plan Social Infrastructure Services

This article deals with social infrastructure planning problems in urban city. Each facility must serve minimum pre-specified level of demand. The objective is to minimize the distance traveled by users to reach the facilities this means also to maximize the accessibility to facilities. A location model that captures the above features is formulated and different solution methods are tested. Clustering in spatial data mining is to group similar objects based on their connectivity, distance, or their relative density in space. In real word, there exist many physical obstacles such as rivers, lakes, highways and mountains, and their presence may affect the result of clustering significantly. In this paper, we study the problem of clustering in the presence of obstacles to solve location of public service facility problem. In this paper, CSPOD-DBSCAN algorithm (Clustering with short path Obstructed Distance - Density- Based Spatial Clustering of Applications with Noise) is developed in the spirit of DBSCAN clustering algorithms. This algorithm is Density-based clustering algorithm using Dijkstra algorithm to calculate obstructed short path distance. The application of this algorithm is illustrated through a case study involving the location of schools in the districts of Mecca in Saudi Arabia.