DATA, DATA EVERYWHERE … HOW CAN CITIES USE IT? DRAWING DESCRIPTIVE AND PREDICTIVE INSIGHTS FROM NEW AND EXISTING BIG DATA

In recent decades, cities have engaged in a more networked governance style of public management which recognizes the value of input from public, private, and individual stakeholders in the pursuit of effective and efficient government. Residents are key players in an inclusive, participatory, and efficient governance process and are uniquely qualified to offer feedback and expertise. However, their input is often underutilized due to public managers’ lack of time or capacity to capture, analyze and interpret it. This can result in the omission of valuable insights from residents in community analysis, policy evaluation, or planning processes. These papers on the use and utility of resident input data represent important early steps in understanding analytics that can enable cities’ use of the big, open-source, and free data available to them. These papers demonstrate how a city can transform resident input into information at scale to inform policy and practice. They model the use of two large sets of resident input data that can facilitate new insights into the current and future states of a modern city. Two chapters explore patterns in resident data generated by use of a city’s 311 service request system and use them to develop a Bayesian Network to predict future service demand. The third chapter evaluates the sentiment of Twitter posts that mention specific neighborhoods to determine their effect on real-word indicators of neighborhood housing change.