Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
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.
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.
Urban resilience and urban land use efficiency are inevitable topics in urban planning and development, and the coupling coordination between the two will contribute substantially to urban sustainability. With panel data from 14 cities in Hunan from 2010 to 2021 and by combining the entropy method, the Super-SBM model, and the coupling coordination degree model, this study analyzed the dynamic spatial–temporal evolution pattern of urban resilience and land utilization efficiency and their coupling coordination through a multi-dimensional evaluation index system in 14 cities in Hunan from 2010 to 2021. The main findings were as follows: overall, the urban resilience in Hunan stayed low over the years of the study. Temporally, the mean resilience increased gradually from 0.1962 to 0.3331, and spatially, the urban resilience was higher in the eastern region than in the western area of the province, with Changsha having the highest level of resilience. Second, the urban land use efficiency in Hunan rose with volatility from 0.7162 to 0.9299, and spatially, urban land use efficiency was higher in the northern region than in the southern region, with Zhangjiajie having the highest level of urban land use efficiency. Third, the province had a high coupling degree between urban resilience and urban land use efficiency, and the average coupling value was 0.8531, with higher coupling degrees observed in the southern area and the Chang–Zhu–Tan urban agglomeration in the province. Fourth, the coordination degree between urban resilience and urban land use efficiency stayed moderate across the province, rising from 0.5788 to 0.6453, from marginally coordinated to primarily coordinated, where the northern area had a higher coordination degree. All 14 cities were in a coordinated state by the mean coordination level. Changsha was in a highly coordinated state. The research here is expected to provide some references for urban administrators in Hunan and beyond to release policies that will achieve stronger urban resilience, higher urban land use efficiency, and better coupling coordination.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.