IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i12p2113-d1538170.html
   My bibliography  Save this article

A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement

Author

Listed:
  • Bin Zhao

    (Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, China)

  • Hao Zheng

    (Architectural Intelligence Group, Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong SAR, China)

  • Xuesong Cheng

    (Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, China)

Abstract

The selection of construction sites for Cultural and Museum Public Buildings (CMPBs) has a profound impact on their future operations and development. To enhance site selection and planning efficiency, we developed a predictive model integrating Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs). Taking Shanghai as our case study, we utilized over 1.5 million points of interest data from Amap Visiting Vitality Values (VVVs) from Dianping and Shanghai’s administrative area map. We analyzed and compiled data for 344 sites, each containing 39 infrastructure data sets and one visit vitality data set for the ANN model input. The model was then tested with untrained data to predict VVVs based on the 39 input data sets. We conducted a multi-precision analysis to simulate various scenarios, assessing the model’s applicability at different scales. Combining GA with our approach, we predicted vitality improvements. This method and model can significantly contribute to the early planning, design, development, and operational management of CMPBs in the future.

Suggested Citation

  • Bin Zhao & Hao Zheng & Xuesong Cheng, 2024. "A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement," Land, MDPI, vol. 13(12), pages 1-31, December.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2113-:d:1538170
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/12/2113/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/12/2113/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaoxiang Tang & Cheng Zou & Chang Shu & Mengqing Zhang & Huicheng Feng, 2024. "Research on Site Selection Planning of Urban Parks Based on POI and Machine Learning—Taking Guangzhou City as an Example," Land, MDPI, vol. 13(9), pages 1-18, August.
    2. Sakai, Takanori & Beziat, Adrien & Heitz, Adeline, 2020. "Location factors for logistics facilities: Location choice modeling considering activity categories," Journal of Transport Geography, Elsevier, vol. 85(C).
    3. Takanori Sakai & Adrien Beziat & Adeline Heitz, 2020. "Location factors for logistics facilities: Location choice modeling considering activity categories," Post-Print halshs-03196110, HAL.
    4. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    5. Zhenwei Wang & Xiaochun Wang & Zijin Dong & Lisan Li & Wangjun Li & Shicheng Li, 2023. "More Urban Elderly Care Facilities Should Be Placed in Densely Populated Areas for an Aging Wuhan of China," Land, MDPI, vol. 12(1), pages 1-13, January.
    6. Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oliveira, Renata Lúcia Magalhães de & Dablanc, Laetitia & Schorung, Matthieu, 2022. "Changes in warehouse spatial patterns and rental prices: Are they related? Exploring the case of US metropolitan areas," Journal of Transport Geography, Elsevier, vol. 104(C).
    2. Zhouying Song, 2022. "The geography of online shopping in China and its key drivers," Environment and Planning B, , vol. 49(1), pages 259-274, January.
    3. Sakai, Takanori & Kawamura, Kazuya & Hyodo, Tetsuro, 2020. "Logistics facilities for intra and inter-regional shipping: Spatial distributions, location choice factors, and externality," Journal of Transport Geography, Elsevier, vol. 86(C).
    4. Cuccu, Liliana & Pontarollo, Nicola, 2024. "Logistic hubs and support for radical-right populism: Evidence from Italy," European Journal of Political Economy, Elsevier, vol. 82(C).
    5. Yang, Zhiwei & Chen, Xiaohong & Pan, Ruixu & Yuan, Quan, 2022. "Exploring location factors of logistics facilities from a spatiotemporal perspective: A case study from Shanghai," Journal of Transport Geography, Elsevier, vol. 100(C).
    6. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Zhi, Danyue & Song, Dongdong & Chen, Yan & de Bok, Michiel & Tavasszy, Lóránt A. & Gao, Ziyou, 2023. "Uncovering and modeling the hierarchical organization of urban heavy truck flows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    7. David Guerrero & Jean Paul Hubert & Martin Koning & Nicolas Roelandt, 2022. "On the Spatial Scope of Warehouse Activity: An Exploratory Study in France," Post-Print hal-03551270, HAL.
    8. Dalila Ribaudo, 2023. "Tracking the Van: The role of forward linkages in logistics MNEs' location choices across European NUTS 3 regions," Papers in Regional Science, Wiley Blackwell, vol. 102(2), pages 331-362, April.
    9. Wang, Kailai & Chen, Zhenhua & Cheng, Long & Zhu, Pengyu & Shi, Jian & Bian, Zheyong, 2023. "Integrating spatial statistics and machine learning to identify relationships between e-commerce and distribution facilities in Texas, US," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    10. Zhao, Liyuan & Zhou, Cong & Liu, Kaili & Huang, Liyang & Li, Zhi-chun, 2024. "Comparison of the driving mechanism between logistics land use and facilities: A case study from Wuhan metropolitan area," Journal of Transport Geography, Elsevier, vol. 114(C).
    11. Guerrero, D. & Hubert, J.-P. & Koning, M. & Roelandt, N., 2022. "On the spatial scope of warehouse activity: An exploratory study in France," Journal of Transport Geography, Elsevier, vol. 99(C).
    12. Zhang, Xinfang & Liu, Chengliang & Peng, Yan & Lu, Jing, 2023. "Connectivity-based spatial patterns and factors influencing international container multimodal hubs in China under the Belt and Road initiative," Transport Policy, Elsevier, vol. 143(C), pages 10-24.
    13. Samani, Ali Riahi & Mishra, Sabyasachee & Golias, Mihalis & Lee, David J.-H., 2023. "What influences the location choice of establishments? An analysis considering establishment types and activities interactions," Journal of Transport Geography, Elsevier, vol. 111(C).
    14. Chandra, Aitichya & Sharath, M.N. & Pani, Agnivesh & Sahu, Prasanta K., 2021. "A multi-objective genetic algorithm approach to design optimal zoning systems for freight transportation planning," Journal of Transport Geography, Elsevier, vol. 92(C).
    15. Lian-Sheng Tang & Xiang Tan & Peng Guo & Ting-Ting Huang & Tie-Li Liu, 2022. "Comparative Analysis of Locational Factors and Their External Influence on Free-Trade Port Zones in China," Sustainability, MDPI, vol. 14(4), pages 1-19, February.
    16. Wu, Jiabin & Li, Qihang & Bie, Yiming & Zhou, Wei, 2024. "Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm," Energy, Elsevier, vol. 304(C).
    17. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    18. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    19. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    20. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2113-:d:1538170. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.