Function Replacement Decision-Making for Parking Space Renewal Based on Association Rules Mining

被引:5
作者
Xia, Bing [1 ,2 ]
Ruan, Yichen [3 ]
机构
[1] Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China
[2] Zhejiang Univ, Ctr Balance Architecture, Hangzhou 310028, Peoples R China
[3] Zhejiang Univ City Coll, Sch Spatial Planning & Design, Hangzhou 310015, Peoples R China
基金
中国国家自然科学基金; 英国科研创新办公室;
关键词
parking space; function replacement; association rule; POIs; urban renewal; URBAN-RENEWAL; SUSTAINABILITY; PATTERNS;
D O I
10.3390/land11020156
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Parking lots are typical urban spaces with a large total area and scattered distribution. With the development of smart cars and shared driving, parking demand is likely to decline. Thus, the reuse of existing parking spaces presents important opportunities and challenges in the process of the digital transformation of future cities. One of the key issues in the sustainable renewal of parking spaces is to make scientific decisions regarding the replacement of functions. Based on relevant data from the urban area of Hangzhou, this study analyzes the spatial co-location relationships between parking spaces and other urban points of interest (POIs). By mining the function association patterns, this research aims to establish a decision-making support model for the function replacement of parking spaces. The following conclusions are drawn: (1) based on charge, size, and affiliation, parking lots can be divided into eight categories; (2) parking lots of different charges, sizes, and affiliations differ in their spatial co-location relationships with POIs; and (3) most parking lots are suitable for catering services, followed by companies and commercial residences. The innovations of this research lie in providing scientific references for the renewal of urban fragmented spaces by mining urban function association rules at the microscale.
引用
收藏
页数:23
相关论文
共 61 条
[1]  
Adamo J., 2012, DATA MING ASS RULES
[2]  
Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170036.170072
[3]  
Asia Development Bank, 2011, PARK POL AS CIT
[4]  
Bai X.Y., 2020, ARCHITECTS J, V67, P8, DOI [10.19819/j.cnki.ISSN0529-1399.202010002, DOI 10.19819/J.CNKI.ISSN0529-1399.202010002]
[5]   Experimenting community impact evaluation (CIE) for assessing urban regeneration programmes: The case study of the area 22@ Barcelona [J].
Bottero, Marta ;
Bragaglia, Francesca ;
Caruso, Nadia ;
Datola, Giulia ;
Dell'Anna, Federico .
CITIES, 2020, 99
[6]  
Breiman L., 2001, Machine Learning, V45, P5
[7]   Adaptive detection of statistically significant regional spatial co-location patterns [J].
Cai, Jiannan ;
Liu, Qiliang ;
Deng, Min ;
Tang, Jianbo ;
He, Zhanjun .
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2018, 68 :53-63
[8]  
Casanova H., 2015, PUBLIC SPACE ACUPUNC
[9]  
Chen Y., 2019, THESIS SOUTHEAST U
[10]   Understanding the spatial organization of urban functions based on co-location patterns mining: A comparative analysis for 25 Chinese cities [J].
Chen, Yimin ;
Chen, Xinyue ;
Liu, Zihui ;
Li, Xia .
CITIES, 2020, 97