Analysis for Smart Urban Management Event Based on SARIMA and Spatiotemporal Clustering
Authors: 
Wanchen Yue, Xutong Liu, Hui SongAuthors Info & Claims
Pages 182 - 186
Abstract
With the development of E-government in China, urban grid management systems produce mass events data every day which are reported by city officers and citizens. Spatiotemporal big data can reveal the major issues in urban operation facing community. To perceive the insights on urban operation data, we build a Season Autoregressive Integrated Moving Average model and improve the spatiotemporal clustering algorithm for discovering the trend of events in time and space. We collaborate with a city management system and work on one year's data. Our spatiotemporal analysis gains high accuracy on events prediction and provides data support for community resource allocation and city governance planning.
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Index Terms
- Analysis for Smart Urban Management Event Based on SARIMA and Spatiotemporal Clustering
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Published In
October 2018
221 pages
ISBN:9781450364768
DOI:10.1145/3291801
Copyright © 2018 ACM.
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In-Cooperation
- Shandong Univ.: Shandong University
- University of Queensland: University of Queensland
- Dalian Maritime University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 October 2018
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- Refereed limited
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ICBDR 2018
ICBDR 2018: 2018 The 2nd International Conference on Big Data Research
October 27 - 29, 2018
Weihai, China
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