Web-based Decision Support System to Evaluate the Living Conditions: A Case Study of the Colombo City

Colombo city hosts almost eight hundred thousand people from various parts of the world and it is one of the fastest growing cities in the Asian continent. The city subjects to heavy migration because of urbanization. Due to this reason, the living conditions also vary from place to place in the city. The immigrants are often concerned about their mobility and accessibility to dierent civic services. Hence, selection of a living area becomes an important factor for an inhabitant physically, mentally and nancially. However, systematic methodology has not been implemented to evaluate these living conditions. This study explicates utilizing of hotspot analysis and Network Analysis extension of ArcGIS to extrapolate crime and the accessibility to six fundamental civic services including education, healthcare, public parks, shopping centres and emergency response (re ghting and ambulance) from dierent neighbourhoods of the city. Weighted overlay approach is utilized to aggregate above the criteria and nd the most inhabitable neighbourhoods in the city. The study indicates the best area as \neighbourhoods with least crime and easiest accessibility to all mentioned fundamental services". Accessibility to each civic service is calculated by the service area and converted to a raster data which further aggregates them into a single raster using the above mentioned weighted overlay approach. After exporting the graphical model as a python script, the system is further developed to handle and return the dynamic in uence rate based on the user inputs and ultimately the user obtains results for the best area. Then, the generated map automatically gets uploaded into the geo-server and the users can view the nal liveability map on a dedicated web platform. Based on the approaches such as network analysis, multi criteria evaluation and decision support system, this study assists in selecting a neighbourhood on the basis of selected criteria by a particular user and also helps urban planners to identify design gaps in urban areas related to each criteria.