Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing
The layout and site selection strategy of commercial facilities are crucial for both enterprise performance and market image, while also significantly impacting the overall planning of urban commercial environments. However, conventional methods of choosing sites sometimes depend on outdated managem...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/13/12/432 |
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| author | Jin Zou Xun Zhang Yangxiao Cong Zhentong Gao Jinlian Shi |
| author_facet | Jin Zou Xun Zhang Yangxiao Cong Zhentong Gao Jinlian Shi |
| author_sort | Jin Zou |
| collection | DOAJ |
| description | The layout and site selection strategy of commercial facilities are crucial for both enterprise performance and market image, while also significantly impacting the overall planning of urban commercial environments. However, conventional methods of choosing sites sometimes depend on outdated management information systems or static statistical models, which may not take into account all relevant factors and have poor data quality. By utilizing geographical big data and geographical artificial intelligence, this study improves the viability of commercial layout and site selection methods. This study utilizes mobile phone signaling data from Beijing combined with point-of-interest (POI) data from within the Sixth Ring Road of Beijing to identify user behaviors using algorithms. Through a combination of BiLSTM-RF and reinforcement learning algorithms, a population location prediction algorithm is constructed to address the issues of inaccurate and outdated population flow data in commercial site selection. The forecast distribution has a high level of accuracy, with a prediction accuracy rate of 73.2%. Additionally, based on geographical big data, the urban landscape is reconstructed to create a 3D model of Beijing. An immersive interactive commercial site selection system is implemented using the Unreal Engine. |
| format | Article |
| id | doaj-art-999efc8a357748cca5c6b362be4782d7 |
| institution | DOAJ |
| issn | 2220-9964 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-999efc8a357748cca5c6b362be4782d72025-08-20T02:53:43ZengMDPI AGISPRS International Journal of Geo-Information2220-99642024-12-01131243210.3390/ijgi13120432Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of BeijingJin Zou0Xun Zhang1Yangxiao Cong2Zhentong Gao3Jinlian Shi4School of Mathematics and Physics, Xinjiang Hetian College, Hetian 848000, ChinaSchool of Mathematics and Physics, Xinjiang Hetian College, Hetian 848000, ChinaRailway Economic and Planning Research Institute, Beijing 100089, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaThe layout and site selection strategy of commercial facilities are crucial for both enterprise performance and market image, while also significantly impacting the overall planning of urban commercial environments. However, conventional methods of choosing sites sometimes depend on outdated management information systems or static statistical models, which may not take into account all relevant factors and have poor data quality. By utilizing geographical big data and geographical artificial intelligence, this study improves the viability of commercial layout and site selection methods. This study utilizes mobile phone signaling data from Beijing combined with point-of-interest (POI) data from within the Sixth Ring Road of Beijing to identify user behaviors using algorithms. Through a combination of BiLSTM-RF and reinforcement learning algorithms, a population location prediction algorithm is constructed to address the issues of inaccurate and outdated population flow data in commercial site selection. The forecast distribution has a high level of accuracy, with a prediction accuracy rate of 73.2%. Additionally, based on geographical big data, the urban landscape is reconstructed to create a 3D model of Beijing. An immersive interactive commercial site selection system is implemented using the Unreal Engine.https://www.mdpi.com/2220-9964/13/12/432geographical big datamobile phone signalingcommercial site selectionalgorithmmodel |
| spellingShingle | Jin Zou Xun Zhang Yangxiao Cong Zhentong Gao Jinlian Shi Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing ISPRS International Journal of Geo-Information geographical big data mobile phone signaling commercial site selection algorithm model |
| title | Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing |
| title_full | Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing |
| title_fullStr | Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing |
| title_full_unstemmed | Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing |
| title_short | Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing |
| title_sort | research and modeling of commercial location selection based on geographic big data and mobile signaling data a case study of the central urban area of beijing |
| topic | geographical big data mobile phone signaling commercial site selection algorithm model |
| url | https://www.mdpi.com/2220-9964/13/12/432 |
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