Predicting demand for 311 non-emergency municipal services: An adaptive space-time kernel approach |
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| Institution: | 1. School of Geographic Sciences, Center of Geographic Information Analysis for Public Security, Guangzhou University, China;2. Department of Geography, University of Cincinnati, Cincinnati, OH 45221-0131, USA;3. Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;4. Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China;1. Institut für Geographie, Universität Augsburg, D-86159 Augsburg, Germany;2. Indo-German Centre of Sustainability, Indian Institute of Technology Madras, Chennai 600 036, India;3. Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, D-24118 Kiel, Germany;4. Environment and Water Resource Engineering Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600 036, India;1. School of Public Policy and Urban Affairs, Northeastern University, Boston, MA 02115, USA;2. Radcliffe Institute for Advanced Study, Harvard University, Cambridge, MA 02138, USA;3. Dept. of Visual and Media Arts, Emerson College, Boston, MA 02116, USA;1. Servicio de Radiodiagnóstico, Hospital Universitario Ramón y Cajal, Madrid, Spain;2. Servicio de Cirugía Torácica, Hospital Universitario Ramón y Cajal, Madrid, Spain |
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| Abstract: | Many cities in the United States and Canada offer a 311 helpline to their residents for submitting requests for non-emergency municipal services. By dialing 311, urban residents can report a range of public issues that require governmental attention, including potholes, graffito, sanitation complaints, and tree debris. The demand for these municipal services fluctuates greatly with time and location, which poses multiple challenges to effective deployment of limited resources. To address these challenges, this study uses a locally adaptive space-time kernel approach to model 311 requests as an inhomogeneous Poisson process and presents an analytical framework to generate predictions of 311 demand in space and time. The predictions can be used to optimally allocate resources and staff, reduce response time, and allow long-term dynamic planning. We use a bivariate spatial kernel to identify the spatial structure and weigh each kernel by corresponding past observations to capture the temporal dynamics. Short-term serial dependency and weekly temporality are modeled through the temporal weights, which are adaptive to local community areas. We also transform the computation-intensive parameter estimation procedure to a low dimensional optimization problem by fitting to the autocorrelation function of historical requests. The presented method is demonstrated and validated with sanitation service requests in Chicago. The results indicate that it performs better than common industry practice and conventional spatial models with a comparable computational cost. |
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| Keywords: | 311 Spatial point process Modeling Space-time kernel estimation |
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