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干旱区地理 >

2023 , Vol. 46 >Issue 10: 1744 - 1756

DOI: https://doi.org/10.12118/j.issn.1000-6060.2022.672

区域发展

基于手机定位数据的西宁市老年人公园绿地可达性预测

  • 赵志远 ,
  • 丁逸尘 ,
  • 杨喜平 ,
  • 吴升
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  • 1.福州大学数字中国研究院（福建），福建 福州 350003
    2.空间数据挖掘与信息共享教育部重点实验室，福建 福州 350003
    3.海西政务大数据应用协同创新中心，福建 福州 350002
    4.自然资源部城市国土资源监测与仿真重点实验室，广东 深圳 518000
    5.陕西师范大学地理科学与旅游学院，陕西 西安 710119
赵志远（1989-），男，博士，副研究员，主要从事人群动态观测与应用建模等方面的研究. E-mail: zyzhao@fzu.edu.cn

收稿日期: 2022-12-22

  修回日期: 2023-02-22

  网络出版日期: 2023-11-10

基金资助

国家自然科学基金项目(42201500);国家自然科学基金项目(42271468);福建省中央引导地方科技发展专项(2020L3005);空间数据挖掘与信息共享教育部重点实验室开放基金(2022LSDMIS03);自然资源部城市国土资源监测与仿真重点实验室开放基金资助课题(KF-2022-07-005)

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Prediction of the accessibility of parks and green spaces for the elderly in Xining City based on mobile phone location data

  • Zhiyuan ZHAO ,
  • Yichen DING ,
  • Xiping YANG ,
  • Sheng WU
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  • 1. Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350003, Fujian, China
    2. Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou 350003, Fujian, China
    3. Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350002, Fujian, China
    4. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, Guangdong, China
    5. School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, Shaanxi, China

Received date: 2022-12-22

  Revised date: 2023-02-22

  Online published: 2023-11-10

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摘要

预测并分析老年人公园绿地资源可获取性对提高老年人生活品质、有效应对我国老龄化社会带来的有关挑战具有重要意义。为使城市公园绿地的分布更具“适老性”，基于西宁市带有年龄标识信息的手机位置数据，识别并预测老年人口的空间分布，运用高斯两步移动搜索法对老年人公园绿地可达性进行研究及预测。结果表明：（1） 2018—2028年老年人数显著增加，且老年人数变化量呈现中心城区及远郊区较低、近郊区高的环状分布特征。（2） 10 a间，老年人公园绿地可达性的空间分布总体格局未发生显著变化，但可达性水平总体下降。（3） 2018—2028年老年人公园绿地可达性相对变化量较大，步行和公交30 min条件下，约87%的空间单元的公园绿地可达性降低超过70%。研究结果补充了高空间精度下城市老年人公园绿地可达性研究的不足，提供了面向老龄化发展需求的城市未来公园绿地规划建议。

关键词： 老年人; 公园绿地; 手机数据; 可达性; 高斯两步移动搜索法; 西宁市

本文引用格式

赵志远 , 丁逸尘 , 杨喜平 , 吴升 . 基于手机定位数据的西宁市老年人公园绿地可达性预测[J]. 干旱区地理, 2023 , 46(10) : 1744 -1756 . DOI: 10.12118/j.issn.1000-6060.2022.672

Abstract

Predicting and analyzing the availability of green space resources for the elderly are crucial for improving their quality of life and addressing the challenges of an aging society. Taking Xining City, the area with the largest elderly population in Qinghai Province, as the study area, this study identifies and predicts the spatial distribution of older people based on mobile phone location data with age identification information. The Gaussian-based two-step floating catchment area (G2SFCA) method was then employed to study and predict the accessibility of parks and green spaces for the elderly. The following results were observed: (1) The elderly population increasing rate exhibited a circular distribution, displaying a low rate in the central city and outer suburbs and a high rate in the inner suburbs. (2) The overall spatial distribution pattern of accessibility of parks and green spaces for the elderly did not change significantly over the 10-year forecast period, but the general accessibility level declined. Under the condition of walking for 15 min, the population covered by relatively high and high grades of accessibility decreased from 17.58% to 6.70%. Moreover, under the condition of public transportation for 30 min, the population covered by relatively high and high grades of accessibility decreased from 26.41% to 9.28%. (3) It was found that the relative variability of accessibility of parks and green spaces for the elderly is significant from 2018 to 2028, with approximately 87% of the parks and green spaces experiencing a reduction of >70% in accessibility under both walking and public transportation conditions for 30 min. This study provides valuable insights for future urban park and green space planning, particularly in response to the needs of an aging population.

Key words： the elderly people; park and green space; mobile phone data; accessibility; Gaussian-based two-step floating catchment area method; Xining City

参考文献

[1] 刘厚莲. 世界和中国人口老龄化发展态势[J]. 老龄科学研究, 2021, 9(12): 1-16.
[1] [Liu Houlian. Trends of population aging in China and the world as a whole[J]. Scientific Research on Aging, 2021, 9(12): 1-16. ]
[2] Hansen W G. How accessibility shapes land-use[J]. Journal of the American Institute of Planners, 1959, 25(2): 73-76.
[3] Hillsdon M, Panter J, Foster C, et al. The relationship between access and quality of urban green space with population physical activity[J]. Public Health, 2006, 120(12): 1127-1132.
[4] Moyano A, Martínez H, Coronado J M. From network to services: A comparative accessibility analysis of the Spanish high-speed rail system[J]. Transport Policy, 2018, 63: 51-60.
[5] Hu L, He S, Han Z, et al. Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies[J]. Land Use Policy, 2019, 82: 657-673.
[6] 李鑫, 马晓冬, 薛小同, 等. 城市绿地空间供需评价与布局优化——以徐州中心城区为例[J]. 地理科学, 2019, 39(11): 1771-1779.
[6] [Li Xin, Ma Xiaodong, Xue Xiaotong, et al. Spatial supply-demand evaluation and layout optimization for urban green space: A case study of Xuzhou central district[J]. Scientia Geographica Sinica, 2019, 39(11): 1771-1779. ]
[7] Chen X, Jia P. A comparative analysis of accessibility measures by the two-step floating catchment area (2SFCA) method[J]. International Journal of Geographical Information Science, 2019, 33(9): 1739-1758.
[8] Radke J, Mu L. Spatial decompositions, modeling and mapping service regions to predict access to social programs[J]. Geographic Information Sciences, 2000, 6(2): 105-112.
[9] Wang F. Measurement, optimization, and impact of health care accessibility: A methodological review[J]. Annals of the Association of American Geographers, 2012, 102(5): 1104-1112.
[10] Li M, Wang F, Kwan M, et al. Equalizing the spatial accessibility of emergency medical services in Shanghai: A trade-off perspective[J]. Computers, Environment and Urban Systems, 2022, 92(2): 101745, doi: 10.1016/j.compenvurbsys.2021.101745.
[11] 周海涛, 马钰松, 樊亚宇, 等. 内蒙古红色旅游资源空间分布及可达性分析[J]. 干旱区地理, 2023, 46(5): 814-822.
[11] [Zhou Haitao, Ma Yusong, Fan Yayu, et al. Spatial distribution and accessibility analysis of red tourism resources in Inner Mongolia[J]. Arid Land Geography, 2023, 46(5): 814-822. ]
[12] Dai D. Racial/ethnic and socioeconomic disparities in urban green space accessibility: Where to intervene?[J]. Landscape and Urban Planning, 2011, 102(4): 234-244.
[13] Zhang J, Cheng Y, Wei W, et al. Evaluating spatial disparity of access to public parks in gated and open communities with an improved G2SFCA model[J]. Sustainability, 2019, 11(21): 5910, doi: 10.3390/su11215910.
[14] Shaw S, Sahoo H. Accessibility to primary health centre in a Tribal District of Gujarat, India: Application of two step floating catchment area model[J]. GeoJournal, 2020, 85(2): 505-514.
[15] 韩非, 罗仁朝. 基于可达性测度的城市社区居家养老服务供需匹配研究——以南京为例[J]. 经济地理, 2020, 40(9): 91-101.
[15] [Han Fei, Luo Renchao. Matching of supply and demand for community service-oriented home care facilities based on accessibility measurement: A case study of Nanjing[J]. Economic Geography, 2020, 40(9): 91-101. ]
[16] Kabisch N, Haase D. Green justice or just green? Provision of urban green spaces in Berlin, Germany[J]. Landscape and Urban Planning, 2014, 122: 129-139.
[17] Rigolon, Alessandro. Parks and young people: An environmental justice study of park proximity, acreage, and quality in Denver, Colorado[J]. Landscape and Urban Planning, 2017, 165: 73-83.
[18] Lee A, Maheswaran R. The health benefits of urban green spaces: A review of the evidence[J]. Journal of Public Health Oxford University Press, 2011, 33(2): 212, doi: 10.1093/pubmed/fdq068.
[19] Payne L, Mowen A, Orsega-Smith E. An examination of park preferences and behaviors among urban residents: The role of residential location, race, and age[J]. Leisure Sciences, 2002, 24(2): 181-198.
[20] Guo S, Song C, Pei T, et al. Accessibility to urban parks for elderly residents: Perspectives from mobile phone data[J]. Landscape and Urban Planning, 2019, 191: 103642, doi: 10.1016/j.landurbplan. 2019.103642.
[21] Wen C, Albert C, Haaren C V. Equality in access to urban green spaces: A case study in Hannover, Germany, with a focus on the elderly population[J]. Urban Forestry & Urban Greening, 2020, 55: 126820, doi: 10.1016/j.ufug.2020.126820.
[22] 韦易伶. 成都市老旧居住区绿地系统适老化改造研究[D]. 成都: 西南交通大学, 2019.
[22] [Wei Yilin. Study on suitable aging transformation of old residential greenbelt system in Chengdu[D]. Chengdu: Southwest Jiaotong University, 2019. ]
[23] 程功. 山地城市老年群体公园绿地可达性研究[D]. 重庆: 重庆大学, 2020.
[23] [Cheng Gong. Research on green space accessibility of park for elderly people in mountain city[D]. Chongqing: Chongqing University, 2020. ]
[24] 高雪梅, 杨续超, 陈柏儒, 等. 基于随机森林模型的环渤海地区人口空间化模拟[J]. 地球信息科学学报, 2022, 24(6): 1150-1162.
[24] [Gao Xuemei, Yang Xuchao, Chen Bairu, et al. Spatialization of population in the Bohai Rim region using random forest model[J]. Journal of Geo-information Science, 2022, 24(6): 1150-1162. ]
[25] 侯亚丽, 匡文慧, 窦银银. 全球超大城市空间扩张及分形特征研究[J]. 地理学报, 2022, 77(11): 2687-2702.
[25] [Hou Yali, Kuang Wenhui, Dou Yinyin. Analysis of urban expansion and fractal features in global 33 megacities from 2000—2020[J]. Journal of Geographical Sciences, 2022, 77(11): 2687-2702. ]
[26] 牛强, 盛富斌, 刘晓阳, 等. 基于手机信令数据的城内迁居活跃度识别方法研究——以武汉市为例[J]. 地理研究, 2022, 41(8): 2142-2154.
[26] [Niu Qiang, Sheng Fubin, Liu Xiaoyang, et al. Research on the identification method of relocation activity degree in inner city based on mobile phone signaling data: A case study of Wuhan[J]. Geographical Research, 2022, 41(8): 2142-2154. ]
[27] 张雪霞, 吴升, 赵志远, 等. 基于手机信令数据的城市小活动空间人群空间分布特征[J]. 地球信息科学学报, 2021, 23(8): 1433-1445.
[27] [Zhang Xuexia, Wu Sheng, Zhao Zhiyuan, et al. Spatial distribution characteristics of people with small activity space in urban based on mobile phone signaling data[J]. Journal of Geo-information Science, 2021, 23(8): 1433-1445. ]
[28] 朱菁, 张怡文, 樊帆, 等. 基于智能手机数据的城市建成环境对居民通勤方式选择的影响——以西安市为例[J]. 陕西师范大学学报(自然科学版), 2021, 49(2): 55-66.
[28] [Zhu Jing, Zhang Yiwen, Fan Fan, et al. Impact of urban built environment on commute mode choice by smart phone app data: A case study of Xi’an City[J]. Journal of Shaanxi Normal University (Natural Science Edition), 2021, 49(2): 55-66. ]
[29] 杨喜平, 杨鸿海, 李彬, 等. 基于手机数据的西部城市居民出行特征研究——以西宁市为例[J]. 人文地理, 2021, 36(1): 115-124.
[29] [Yang Xiping, Yang Honghai, Li Bin, et al. Characteristics of urban human mobility of western China based on mobile phone data: A case study of Xining[J]. Human Geography, 2021, 36(1): 115-124. ]
[30] Wesolowski A, Stresman G, Eagle N, et al. Quantifying travel behavior for infectious disease research: A comparison of data from surveys and mobile phones[J]. Scientific Reports, 2014, 4(1): 5678, doi: 10.1038/srep05678.
[31] 仝德, 孙裔煜, 谢苗苗. 基于改进高斯两步移动搜索法的深圳市公园绿地可达性评价[J]. 地理科学进展, 2021, 40(7): 1113-1126.
[31] [Tong De, Sun Yiyu, Xie Miaomiao. Evaluation of green space accessibility based on improved Gaussian two-step floating catchment area method: A case study of Shenzhen City, China[J]. Progress in Geography, 2021, 40(7): 1113-1126. ]
[32] Lin Y, Zhou Y, Lin M, et al. Exploring the disparities in park accessibility through mobile phone data: Evidence from Fuzhou of China[J]. Journal of Environmental Management, 2021, 281: 111849, doi: 10.1016/j.jenvman.2020.111849.
[33] 黄玖菊, 林伊婷, 陶卓霖, 等. 社会公平视角下深圳公园绿地可达性研究[J]. 地理科学, 2022, 42(5): 896-906.
[33] [Huang Jiuju, Lin Yiting, Tao Zhuolin, et al. Evaluation of park green space accessibility in Shenzhen from the perspective of social equity[J]. Scientia Geographica Sinica, 2022, 42(5): 896-906. ]
[34] Spaccapietra S, Parent C, Damiani M L, et al. A conceptual view on trajectories[J]. Data & Knowledge Engineering, 2008, 65(1): 126-146.
[35] Cao J, Li Q, Tu W, et al. Characterizing preferred motif choices and distance impacts[J]. PLoS ONE, 2019, 14(4): 1-17.
[36] 赵颖祺, 陈玉虎, 张晰, 等. 一种基于箱线图的SAR图像舰船检测算法研究[J]. 中国海洋大学学报(自然科学版), 2021, 51(10): 130-140.
[36] [Zhao Yingqi, Chen Yuhu, Zhang Xi, et al. A ship detection algorithm for SAR image based on box-plot[J]. Periodical of Ocean University of China (Natural Science Edition), 2021, 51(10): 130-140. ]
[37] 上海交通大学媒体与传播学院. 2021老年人手机使用报告[R]. 上海: 上海交通大学, 2021.
[37] [School of Mmedia & Communication, Shanghai Jiao Tong University. Report on mobile phone use among the elderly in 2021[R]. Shanghai: Shanghai Jiao Tong University, 2021. ]
[38] 中关村在线. 我国仍有1.4亿老年人使用功能机[EB/OL]. [2021-12-15]. https://mobile.zol.com.cn/783/7831904.html.
[38] [ZOL. There are still 140 million elderly people in China using functional phones[EB/OL]. [2021-12-15]. https://mobile.zol.com.cn/783/7831904.html. ]
[39] 陶卓霖, 程杨. 基于人口自然增长的北京市老年人口空间分布预测研究[J]. 人口与发展, 2016, 22(2): 91-97.
[39] [Tao Zhuolin, Cheng Yang. Projection of elderly population distribution in Beijing based on natural population growth[J]. Population and Development, 2016, 22(2): 91-97. ]
[40] 翟石艳, 何新新, 孔云峰, 等. 基于最优供需分配的公共设施空间可达性分析[J]. 地理学报, 2022, 77(4): 1028-1038.
[40] [Zhai Shiyan, He Xinxin, Kong Yunfeng, et al. Measuring the spatial accessibility of public services by optimal supply-demand allocation[J]. Journal of Geographical Sciences, 2022, 77(4): 1028-1038. ]
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