Clustering Neighborhoods According To Urban Functions and Development Levels by Different Clustering Algorithms: a Case in Konya

Abstract

Urban functions/activities, which emerged under the influence of the human factor and are in the process of development over time, play a crucial role in the development of neighborhoods. To ensure balanced development status among the neighborhoods, it is necessary to know the development levels of the neighborhoods in advance. This study focuses on the clustering of the 167 central neighborhoods in Konya in terms of urban functions and reveals the similarities or differences in the development status of these neighborhoods. K-means, Hierarchical (agglomerative) and OPTICS clustering analyzes were used to cluster central neighborhoods. 18 features related to urban functions were determined as input parameters in the clustering analyzes. Results showed that cluster analysis can be used in urban studies and determine the development status of cities. It is important to carry out clustering studies to make urban planning by revealing the development differences between the neighborhoods and to provide more appropriate service delivery.