Big Data Analytics Framework for Predictive Analytics using Public Data with Privacy Preserving

There are increasingly leveraging public data with cities increasingly interested in driving both responsiveness to citizen demands and cost savings through data analytics. As public managers seek to augment existing data sources, such as 311 complaints, with existing secondary data, such as US Census products, severe challenges exist. This paper considers the problems inherent in data being collected at divergent geographic levels over different time horizons. An inductive analytical methodology is developed to create units of analysis that are both useful and analytically appropriate for public managers and policy leaders in urban areas. A big data analytics framework for public data, called BDAP, was presented predictive analytics for community need considering data the spatial and temporal location while addressing the data issues such as missing values, privacy-preserving, and predictive modeling. The findings illustrate the power of inductive data curation and privacy-preserving leading to benefits to the big data community. An application for the Open Data Platform was developed using KCMO’s 311 data, crime data and census data.