Data analytics on city 311 information requests

In the current era of big data, huge volumes of a wide variety of data are generated and collected at a rapid rate. Embedded in these big data is implicit, previously unknown and potentially useful knowledge and information. This calls for data science—which use techniques like data mining, machine learning, etc.—for social good. With popularity of the initiates of open data, more data are made openly accessible to citizens. An example of these open big data is data collected at the 311 contract centre in the City of Winnipeg for the 311 information requests. In this research, we analyze and mine this dataset to find characteristics associated with the callers and the information requests. Knowledge on these characteristics helps users (e.g., decision makers at the City) to get a better understanding of the requests (e.g., why residents request information through 311 instead of other online options). In a longer term, the discovered knowledge and the understanding of the data helps improve the 311 and other online services. Along this direction, this research will add to the growing body of knowledge for all Canadian cities and communities.

Faculty Supervisor:

Carson Leung

Student:

Adam Pazdor

Partner:

City of Winnipeg

Discipline:

Computer science

Sector:

Other services (except public administration)

University:

University of Manitoba