Predicting demand for 311 non-emergency municipal services: An adaptive space-time kernel approach

Xu, Li Xin; Kwan, Mei‐Po; McLafferty, Sara; Wang, Shaowen

doi:10.1016/j.apgeog.2017.10.012

Cited by 30 publications

(19 citation statements)

References 25 publications

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“…Georeferencing for these issues involves automated location referencing in the smartphone app, and users reporting street addresses or closest intersection. For most cities including Columbus, the accurate street address or geographical coordinates of the reported issue are recorded into a database 36,37 . The City of Columbus Department of Neighborhoods maintains a retrospective geocoded dataset of 311 service requests that contains 21 different categories of non-emergency public issues.…”

Section: Methodsmentioning

confidence: 99%

311 service requests as indicators of neighborhood distress and opioid use disorder

Hyder

Southerland

et al. 2020

Sci Rep

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Opioid use disorder and overdose deaths is a public health crisis in the United States, and there is increasing recognition that its etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. We explore the use of municipal service requests (also known as “311” requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008–2017. We find 10 out of 21 types of 311 requests spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively. Since 311 requests are publicly available with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy.

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Section: Methodsmentioning

confidence: 99%

311 service requests as indicators of neighborhood distress and opioid use disorder

Hyder

Southerland

et al. 2020

Sci Rep

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“…Anonymized open data is also available for many US cities such as the City of Chicago, with detailed records of over a decade of crimes, 311 service calls, permits, inspections, traffic flow, and other operational data. Integrating and analyzing these varied data sources will not only enable new questions about, and insights into the interdependencies of urban phenomena, but also new approaches to understanding complex environmental and urban systems (Xu et al 2017). For example, a science question may be posed to explore the relationships between social factors such as crime or school performance and the environmental characteristics of urban neighborhoods (e.g.…”

Section: Urban Sensing Datamentioning

confidence: 99%

Integrating CyberGIS and Urban Sensing for Reproducible Streaming Analytics

et al. 2021

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Increasingly pervasive location-aware sensors interconnected with rapidly advancing wireless network services are motivating the development of near-real-time urban analytics. This development has revealed both tremendous challenges and opportunities for scientific innovation and discovery. However, state-of-the-art urban discovery and innovation are not well equipped to resolve the challenges of such analytics, which in turn limits new research questions from being asked and answered. Specifically, commonly used urban analytics capabilities are typically designed to handle, process, and analyze static datasets that can be treated as map layers and are consequently ill-equipped in (a) resolving the volume and velocity of urban big data; (b) meeting the computing requirements for processing, analyzing, and visualizing these datasets; and (c) providing concurrent online access to such analytics. To tackle these challenges, we have developed a novel cyberGIS framework that includes computationally reproducible approaches to streaming urban analytics. This framework is based on CyberGIS-Jupyter, through integration of cyberGIS and real-time urban sensing, for achieving capabilities that have previously been unavailable toward helping cities solve challenging urban informatics problems.

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“…Xu et al used a locally adaptive space-time kernel approach to model 311 requests. They provided an analytical model to predict 311 demands in space and time [21].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data

et al. 2019

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Citizen Relationship Management (CiRM) is one of the important matters in citizen-centric e-government. In fact, the most important purpose of e-government is to satisfy citizens. The ‘137 system’ is one of the most important ones based on the citizen-centric that is a municipality phone based request/response system. The aim of this research is a data-mining of a ‘137 system’ (citizens’ complaint system) of the first district of Bojnourd municipality in Iran, to prioritize the urban needs and to estimate citizens’ satisfaction. To reach this, the K-means and Bees Algorithms (BA) were used. Each of these two algorithms was executed using two different methods. In the first method, prioritization and estimation of satisfaction were done separately, whereas in the second method, prioritization and estimation of satisfaction were done simultaneously. To compare the clustering results in the two methods, an index was presented quantitatively. The results showed the superiority of the second method. The index of the second method for the first needs in K-means was 0.299 more than the first method and it was the same in two methods in BA. Also, the results of the BA clustering were better at it because of the S (silhouette) and CH (Calinski-Harabasz) indexes. Considering the final prioritization done by the two algorithms in two methods, the primary needs included asphalt, so specific schemes should be considered.

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Predicting demand for 311 non-emergency municipal services: An adaptive space-time kernel approach

Cited by 30 publications

References 25 publications

311 service requests as indicators of neighborhood distress and opioid use disorder

311 service requests as indicators of neighborhood distress and opioid use disorder

Integrating CyberGIS and Urban Sensing for Reproducible Streaming Analytics

Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data

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