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Open AccessArticle

Site Selection of Digital Signage in Beijing: A Combination of Machine Learning and an Empirical Approach

by Yuxue Wang 1,†, Su Li 1,†, Xun Zhang 1,2,*, Dong Jiang 2, Mengmeng Hao 2,* and Rui Zhou 3
1
Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
2
Key Laboratory of Resources Utilization and Environmental Remediation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2020, 9(4), 217; https://doi.org/10.3390/ijgi9040217
Received: 11 March 2020 / Revised: 29 March 2020 / Accepted: 3 April 2020 / Published: 4 April 2020
(This article belongs to the Special Issue Geovisualization and Social Media)
With the extensive use of digital signage, precise site selection is an urgent issue for digital signage enterprises and management agencies. This research aims to provide an accurate digital signage site-selection model that integrates the spatial characteristics of geographical location and multisource factor data and combines empirical location models with machine learning methods to recommend locations for digital signage. The outdoor commercial digital signage within the Sixth Ring Road area in Beijing was selected as an example and was combined with population census, average house prices, social network check-in data, the centrality of traffic networks, and point of interest (POI) facilities data as research data. The data were divided into 100–1000 m grids for digital signage site-selection modelling. The empirical approach of the improved Huff model was used to calculate the spatial accessibility of digital signage, and machine learning approaches such as back propagation neural network (BP neural networks) were used to calculate the potential location of digital signage. The site of digital signage to be deployed was obtained by overlay analysis. The result shows that the proposed method has a higher true positive rate and a lower false positive rate than the other three site selection models, which indicates that this method has higher accuracy for site selection. The site results show that areas suitable for digital signage are mainly distributed in Sanlitun, Wangfujing, Financial Street, Beijing West Railway Station, and along the main road network within the Sixth Ring Road. The research provides a reference for integrating geographical features and content data into the site-selection algorithm. It can effectively improve the accuracy and scientific nature of digital signage layouts and the efficiency of digital signage to a certain extent. View Full-Text
Keywords: digital signage; site selection; multisource information; multiscale analysis digital signage; site selection; multisource information; multiscale analysis
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Wang, Y.; Li, S.; Zhang, X.; Jiang, D.; Hao, M.; Zhou, R. Site Selection of Digital Signage in Beijing: A Combination of Machine Learning and an Empirical Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 217.

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