Data-driven service planning in the Petabyte Age: the case of Arlington, Texas.

Cities around the world have amassed a variety of data. A main challenge lies in transforming these big data into meaningful knowledge that can inform a city's strategic decisions and enhance urban sustainability. Along with this challenge is the debate about "Wisdom of Crowds" (WOC) in the Petabyte Age. Using the City of Arlington, Texas as a case study, this research explores a hybrid approach for social inquiries with the aid of WOC and spatial learning techniques to leverage data for developing predictive models to support a city's service planning. The results indicate that there exist temporal and spatial patterns of service demands, spatial correlation between demands for code compliance and water services, as well as association with neighborhood characteristics. The findings point to opportunities for further data integration and data mining, organizational collaboration, and resource management to improve the efficiency of service provision in cities. [ABSTRACT FROM AUTHOR]