Mapping Informal Settlements Using Machine Learning Techniques, Object-Based Image Analysis and local Knowledge

The existence of informal settlements in Riyadh City, the Kingdom of Saudi Arabia (KSA), has given rise to some urban planning issues. To provide improvements to mapping and planning processes, the current study aims to evaluate and characterize informal settlements within the city using object-based machine learning (ML) techniques (specifically, Random Forest (RF) and Support Vector Machine (SVM)), expert knowledge (EK) and satellite data. An examination of four defined locales has produced a comprehensive, local, informal settlement ontology. Four main categories (shape, geometry, texture, and pattern) were used to build the ontological framework. Expert local knowledge was employed to produce a ruleset to accurately identify and map these areas. Specific indicators identified by the specialists were used in a combined object-based ML and image analysis (OBIA) approach, with high-resolution worldview-3 imagery used as input data. Results demonstrated that combining EK and ML with remotely sensed data can efficiently, effectively and accurately distinguish informal settlement areas. This work has shown that an object-based ML technique (RF), in combination with EK about important local environment indicators, provides a useful method for mapping informal settlements

GIS-based Spatial Accessibility to Islamic Facilities for Muslim Community in the Melbourne Metropolitan Area

The Geographic Information System (GIS) technology has not been widely used in Islamic facilities including mosques, Islamic schools and halal butchers, and is an important issue for the Muslim communities in Melbourne, Australia. This study applied spatial methods to analyze the spatial accessibility to Muslim facilities of the population in Melbourne city. Spatial accessibility is needed because Muslim people believe that they must pray in the mosques, buy halal food and educate their children in Islamic environment. Therefore, this study aimed to (i) identify spatial accessibility by car to the above-mentioned Muslim facilities and (ii) identify disadvantaged Muslim communities and facilities using census data at the fine spatial resolution (i.e. at Mesh Block level). The disadvantaged Muslim commonalities in the Melbourne Metropolitan Area (MMA) were delineated in GIS environment using such techniques as spatial and hot spot analysis, network analysis, mean center and standard distance methods by using ArcGIS 10.3. The spatial accessibility was assessed in terms of travel distance and time, to highlight their differences. This study highlighted the most affected Muslim communities in terms of spatial accessibility, which are located in Hume, Whittlesea, and Melton and Casey suburbs. Furthermore, this study stressed that there is a lack of Mosques and Islamic Schools in the MMA, as approximately 5,000 Muslims do not have proper access to this kind of facilities. The findings of this study could be used by Muslim community when choosing a suburb for their families. Therefore, it is recommended that the urban and regional planners should take the obtained results into consideration to achieve fair and better distribution of Islamic facilities in Melbourne city.Validerad;2020;Nivå 1;2020-05-18 (johcin)</p