Abstract:
Colombo city hosts almost eight hundred thousand people from various parts of the
world and it is one of the fastest growing city in the Asian continent. The city
subjects to heavy migration because of urbanization. Due to this reason, the living
conditions vary from place to place in the city. The immigrants are often concerned
about their mobility and accessibility to different civic services. Hence, selection of
a living area becomes an important factor for an inhabitant physically, mentally and
financially. 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 (firefighting and ambulance) from
different neighbourhoods of the city. Weighted overlay approach is utilized to
aggregate above criteria and to find 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 service area and converted to a raster data which
further aggregates them into a single raster using above mentioned weighted
overlay approach. After exporting the graphical model as a python script, the system
is further developed to handle and return dynamic influence 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 geoserver and the users can view final
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 the selected criteria by a
particular user and also helps urban planners to identify design gaps in urban areas
related to each criterion.