Site Selection Prediction for Coffee Shops Based on Multi-Source Space Data Using Machine Learning Techniques
Based on a study of the spatial distribution of coffee shops in the main urban area of Beijing, the main influencing factors were selected based on the multi-source space data. Subsequently, three regression models were compared, and the best site selection model was found. A comparison was performe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/12/8/329 |
| _version_ | 1797584467599032320 |
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| author | Jiaqi Zhao Baiyi Zong Ling Wu |
| author_facet | Jiaqi Zhao Baiyi Zong Ling Wu |
| author_sort | Jiaqi Zhao |
| collection | DOAJ |
| description | Based on a study of the spatial distribution of coffee shops in the main urban area of Beijing, the main influencing factors were selected based on the multi-source space data. Subsequently, three regression models were compared, and the best site selection model was found. A comparison was performed between the prediction model functioning with a buffer and without one, and the accuracy of the location model was verified by comparing the actual change trend and the predicted trend in two years. The following conclusions were obtained: (1) coffee shops in the main urban area of Beijing are clustered in an area within 12 km of the main urban center, and also around the core commercial agglomeration area; (2) the random forest (RF) model is the best model in this study, and the accuracy values before and after buffer analysis were 0.915 and 0.929, respectively; and (3) after verifying the accuracy of the model through two years of data, we recommend the establishment of a main road buffer zone for site selection, and the success rate of site selection was found to reach 72.97%. This study provides crucial insight for coffee shop prediction model selection and potential store location selection, which is significant to improving the layout of leisure spaces and promoting economic development. |
| first_indexed | 2024-03-10T23:53:08Z |
| format | Article |
| id | doaj.art-2997d2863a094aecb6c64e783bfc7b46 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T23:53:08Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-2997d2863a094aecb6c64e783bfc7b462023-11-19T01:23:58ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-08-0112832910.3390/ijgi12080329Site Selection Prediction for Coffee Shops Based on Multi-Source Space Data Using Machine Learning TechniquesJiaqi Zhao0Baiyi Zong1Ling Wu2School of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaBased on a study of the spatial distribution of coffee shops in the main urban area of Beijing, the main influencing factors were selected based on the multi-source space data. Subsequently, three regression models were compared, and the best site selection model was found. A comparison was performed between the prediction model functioning with a buffer and without one, and the accuracy of the location model was verified by comparing the actual change trend and the predicted trend in two years. The following conclusions were obtained: (1) coffee shops in the main urban area of Beijing are clustered in an area within 12 km of the main urban center, and also around the core commercial agglomeration area; (2) the random forest (RF) model is the best model in this study, and the accuracy values before and after buffer analysis were 0.915 and 0.929, respectively; and (3) after verifying the accuracy of the model through two years of data, we recommend the establishment of a main road buffer zone for site selection, and the success rate of site selection was found to reach 72.97%. This study provides crucial insight for coffee shop prediction model selection and potential store location selection, which is significant to improving the layout of leisure spaces and promoting economic development.https://www.mdpi.com/2220-9964/12/8/329site selection predictioncoffee shoprandom forestbuffer analysismulti-source space data |
| spellingShingle | Jiaqi Zhao Baiyi Zong Ling Wu Site Selection Prediction for Coffee Shops Based on Multi-Source Space Data Using Machine Learning Techniques ISPRS International Journal of Geo-Information site selection prediction coffee shop random forest buffer analysis multi-source space data |
| title | Site Selection Prediction for Coffee Shops Based on Multi-Source Space Data Using Machine Learning Techniques |
| title_full | Site Selection Prediction for Coffee Shops Based on Multi-Source Space Data Using Machine Learning Techniques |
| title_fullStr | Site Selection Prediction for Coffee Shops Based on Multi-Source Space Data Using Machine Learning Techniques |
| title_full_unstemmed | Site Selection Prediction for Coffee Shops Based on Multi-Source Space Data Using Machine Learning Techniques |
| title_short | Site Selection Prediction for Coffee Shops Based on Multi-Source Space Data Using Machine Learning Techniques |
| title_sort | site selection prediction for coffee shops based on multi source space data using machine learning techniques |
| topic | site selection prediction coffee shop random forest buffer analysis multi-source space data |
| url | https://www.mdpi.com/2220-9964/12/8/329 |
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