Highlighting concealed communities: detection of urban informal settlements using space syntax method, fuzzy model, and high-resolution imagery

Abstract

The rapid expansion of informal settlements poses a critical challenge to urbanization and underscores the urgent need for innovative and precise methods to map and monitor this global problem. This research leverages a multidimensional framework, incorporating service accessibility, demographic data, and route morphology, and uses the Fuzzy model to map informal settlements in a case study from District 19 of Tehran, Iran, where there is a high limitation of availability and accessibility regarding informal settlement area data. This study also uses high-resolution Google Earth Pro imagery, combined with visual interpretation, to extract the most recent residential blocks of the study area. The results demonstrate robust and transformative performance, with a Kapp value of 0.816 and an overall classification accuracy of 91.8, confirming the method's strong capability to distinguish between formal and informal settlements. This efficient methodology underscores the value of an analytical approach in urban planning, particularly focusing on geospatial data and technology. The informal settlements identified are often blended with adjacent formal ones, complicating their identification. This study's results reveal an efficient and precise technique for detecting informal settlements in urban areas with mixed formal and informal blocks. Consequently, it is demonstrated that the method employed in this study will be a quick, cost effective, and reliable solution in similar areas.