A multi-dimensional city data embedding model for improving predictive analytics and urban operations
Industrial Management & Data Systems
ISSN: 0263-5577
Article publication date: 14 June 2022
Issue publication date: 2 November 2022
Abstract
Purpose
A smart city is a potential solution to the problems caused by the unprecedented speed of urbanization. However, the increasing availability of big data is a challenge for transforming a city into a smart one. Conventional statistics and econometric methods may not work well with big data. One promising direction is to leverage advanced machine learning tools in analyzing big data about cities. In this paper, the authors propose a model to learn region embedding. The learned embedding can be used for more accurate prediction by representing discrete variables as continuous vectors that encode the meaning of a region.
Design/methodology/approach
The authors use the random walk and skip-gram methods to learn embedding and update the preliminary embedding generated by graph convolutional network (GCN). The authors apply this model to a real-world dataset from Manhattan, New York, and use the learned embedding for crime event prediction.
Findings
This study’s results show that the proposed model can learn multi-dimensional city data more accurately. Thus, it facilitates cities to transform themselves into smarter ones that are more sustainable and efficient.
Originality/value
The authors propose an embedding model that can learn multi-dimensional city data for improving predictive analytics and urban operations. This model can learn more dimensions of city data, reduce the amount of computation and leverage distributed computing for smart city development and transformation.
Keywords
Acknowledgements
Author contribution: Zhe Jing and Yan Luo contributed equally to this paper.
Funding: The research was funded by the Key Program of NSFC-FRQ Joint Project (no: 72061127002); Hong Kong Research Grants Council “The Diffusion of Online Learning Technology”; (no: 15503719); Natural Science Foundation of Guangdong Province (no: 2019A1515012095) and Shenzhen Humanities & Social Sciences Key Research Bases.
Citation
Jing, Z., Luo, Y., Li, X. and Xu, X. (2022), "A multi-dimensional city data embedding model for improving predictive analytics and urban operations", Industrial Management & Data Systems, Vol. 122 No. 10, pp. 2199-2216. https://doi.org/10.1108/IMDS-01-2022-0020
Publisher
:Emerald Publishing Limited
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