The supply of high-quality leisure places is not only an important indicator of livability in cities,but also an important engine for the stable development of urban economy. The article takes 14231 leisure places listed on Dianping APP in Shanghai as research samples,and uses ArcGIS spatial analysis to reveal the clustering level and spatial distribution pattern of high-quality leisure venues in Shanghai. It constructs the convenience evaluation index system to evaluate the convenience of the spatial pattern of leisure places in Shanghai,and explores the formation factors and mechanisms of its formation have also been d. It's found that: 1) High-quality leisure venues in Shanghai generally exhibit a spatial pattern of "multi center agglomeration and axial extension",with high-density agglomeration areas in the center of the main urban areas of Shanghai forming "connected clusters" and "isolated islands",while low-density axial agglomeration areas are formed in other peripheral urban areas. 2) The types of leisure venues in Shanghai are unevenly clustered,with different types of leisure venues clustering in descending order: food and catering type,sports and fitness type, cultural and leisure type,shopping mall type,and characteristic park type. There are significant differences in the clustering patterns,with food and catering type and sports and fitness type forming a "block type" clustering,characteristic park type and shopping mall type forming a "multi center type" clustering,and cultural and leisure type forming a "single center type" clustering. 3) The convenience level of high-quality leisure venues in Shanghai is relatively low,with significant regional differences. High-convenience areas are concentrated in the central area of Shanghai,showing an overall spatial distribution feature of "high in the middle and low in the surrounding area". Finally,it proposes some targeted strategies for optimizing the spatial pattern of high-quality leisure venues.
[1] Liu J,Deng Y,Wang Y,et al.Urban nighttime leisure space mapping with nighttime light images and POI data[J]. Remote Sensing,Multidisciplinary Digital Publishing Institute,2020,12(3):541.
[2] Newman D B,Tay L,Diener E.Leisure and subjective well-being:A model of psychological mechanisms as mediating factors[J]. Journal of Happiness Studies,2014,15(3):555-578.
[3] Lloyd K M,Auld C J.The role of leisure in determining quality of life:Issues of content and measurement[J]. Social Indicators Research,2002,57(1):43-71.
[4] 荆莹. 基于地理大数据的城市休闲空间分布、可达测度和优化配置[D]. 武汉:武汉大学,2019.
[5] Williams A M,Shaw G.Future play:Tourism,recreation and land use[J]. Land Use Policy,2009,26(12):326-335.
[6] 吴必虎,俞曦. 旅游规划原理[M]. 北京:中国旅游出版社,2010.
[7] Yuen F,Johnson A J.Leisure spaces,community,and third places[J]. Leisure Sciences,2017,39(3):295-303.
[8] 王心蕊,孙九霞. 城市居民休闲与主观幸福感研究:以广州市为例[J]. 地理研究,2019,38(7):1566-1580.
[9] 中国旅游研究院. 中国休闲发展年度报告(2022)[M]. 北京:旅游教育出版社,2023.
[10] Pintea M I.Leisure externalities:Implications for growth and welfare[J]. Journal of Macroeconomics,2010,32(4):1025-1040.
[11] Li S,Colson V,Lejeune P,et al.Agent-based modelling of the spatial pattern of leisure visitation in forests:A case study in Wallonia,south Belgium[J]. Environmental Modelling & Software,2015,71:111-125.
[12] Zhao Q,Chen X,Chen G,et al.The socioeconomic contribution of the recreational fishery based on input-output analysis:The case of China[J]. Marine Policy,2022,143:105177.
[13] Ma L,Xiu C.Spatial structure of urban residents’ leisure activities:A case study of shenyang,China[J]. Chinese Geographical Science,2021,31:671-683.
[14] Jing Y,Liu Y,Cai E,et al.Quantifying the spatiality of urban leisure venues in Wuhan,Central China-GIS-based spatial pattern metrics[J]. Sustainable Cities and Society,2018,40:638-647.
[15] Liu Y,Zhang Y,Jin S T,et al.Spatial pattern of leisure activities among residents in Beijing,China:Exploring the impacts of urban environment[J]. Sustainable Cities and Society,2020,52:101806.
[16] Ghasemi K,Behzadfar M,Borhani K.Spatial analysis of leisure land uses in Tehran:Assessing inequity using the MARCOS method within a GIS framework[J]. Heliyon,2023,9(9):e19691.
[17] 陈蔚珊,柳林,梁育填. 基于POI数据的广州零售商业中心热点识别与业态集聚特征分析[J]. 地理研究,2016,35(4):703-716.
[18] 刘逸,陈銮,刘子惠,等. 基于同位模式的休闲产业空间集聚特征研究[J]. 旅游学刊,2022,37(2):94-104.
[19] Leyden K M,Goldberg A,Michelbach P.Understanding the pursuit of happiness in ten major cities[J]. Urban Affairs Review,2011,47(6):861-888.
[20] Zhang A,Li W,Wu J,et al.How can the urban landscape affect urban vitality at the street block level?A case study of 15 metropolises in China[J]. Environment and Planning B:Urban Analytics and City Science,2021,48(5):1245-1262.
[21] 汤放华,朱俊杰. 基于新发展理念的湖南省城镇化质量与水平发展协调度演变研究[J]. 湖南师范大学自然科学学报,2021,44(2):9-16.
[22] 舒伯阳,蒋月华,刘娟. 新时代乡村旅游高质量发展的理论思考及实践路径[J]. 华中师范大学学报:自然科学版,2022,56(1):73-82.
[23] 李剑. 绵竹市乡村旅游高质量发展研究[D]. 绵阳:西南科技大学,2022.
[24] 侯国林,黄震方. 城市商业游憩区旅游开发的原则与产品体系[J]. 城市问题,2001(1):18-20,27.
[25] 王兆峰,史伟杰,苏昌贵. 中国康养旅游地空间分布格局及其影响因素[J]. 经济地理,2020,40(11):196-207.
[26] 潘竟虎,李俊峰. 中国A级旅游景点空间分布特征与可达性[J]. 自然资源学报,2014,29(1):55-66.
[27] 时浩楠,杨雪云. 国家级特色小镇空间分布特征[J]. 干旱区资源与环境,2019,33(3):39-44.
[28] 王珺颖,谢德体,王三,等. 基于POI提取的山地丘陵区乡村旅游空间分布研究——以重庆市农家乐为例[J]. 中国农业资源与区划,2020,41(5):257-267.
[29] 王甫园,王开泳,郑鑫,等. 珠三角城市群生态空间游憩利用扩展格局及影响因素[J]. 生态学报,2021,41(17):7049-7062.
[30] 陈天帅. 基于POI数据的郑州市吧式休闲场所空间分布及驱动力研究[D]. 福州:福建师范大学,2019.
[31] 黎雅悦,戈大专,牛博,等. 广州市休闲旅游资源空间分布及其可达性特征[J]. 热带地理,2022,42(10):1701-1712.
[32] 李华. 上海城市生态游憩空间格局及其优化研究[J]. 经济地理,2014,34(1):174-180.
[33] 孙枫,章锦河,王昶,等. 城市商业型健身休闲场所空间格局及影响机理——以上海市为例[J]. 地理科学,2021,41(2):198-206.
[34] 李华. 城市生态游憩空间服务功能评价与优化对策[J]. 城市规划,2015,39(8):63-69.
[35] 倪嘉琦. 轨道交通短期客流预测分析系统的设计与实现[D]. 上海:上海交通大学,2016.
[36] 李花,张志斌,王伟军. 兰州市大中型超市的空间分布格局及其影响因素[J]. 经济地理,2016,36(9):85-93.
[37] 湛东升,张文忠,张娟锋,等. 北京市公共服务设施集聚中心识别分析[J]. 地理研究,2020,39(3):554-569.
[38] 康思奇. 合肥市主城区公园绿地可达性及服务设施特征分析[D]. 合肥:安徽农业大学,2022.
[39] Xue K,Yu K,Zhang H,et al.Research on health promotion strategies of public recreation space in the coastal area of Qingdao City Center,China[J]. Sustainable Energy Technologies and Assessments,2022,52:102144.
[40] 汤惠君. 广州城市规划的气候条件分析[J]. 经济地理,2004,24(4):490-493,576.
