To transform a city into a smart city it is important to focus on civic issues faced by the inhabitants. Civic complaints incorporate problems related to street condition, traffic, noise, water etc. Their analysis can contribute to proactive decisions to be taken by the city authority. Urban Computing is applied in many areas like transportation, environment, and security etc. but there is a need to explore more on urban planning from the perspective to analyze root cause of civic issues and reducing their concentration. In the present work, segregation of different urban areas is done and issues critical in a region are determined. Primarily, two phase clustering has been performed. In first phase, a dynamic grid based clustering is done on the basis of spatial attribute to analyze complaints that may have strong inter-dependency. In second phase, the location based clusters are further clustered based on complaint category which helps in determining regions of city imitating similar complaint behavior. The analysis is done on real world data acquired for two cities New York (USA) and Bangalore (India). Experimental results are visualized to show better interpretation. The results will help in planning strategies to improve inhabitant's satisfaction rate and consequently improving their quality of life.
['Preeti Bansal', 'Durga Toshniwal']
Analyzing civic complaints for proactive maintenance in smart city