中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Green space exposure inequality between diverse age and gender groups in rapid urbanization area: Evidence from mobile signaling data

文献类型:期刊论文

作者Yao, Xia2,3,4,5; Zhang, Junmao2,3,4; Pei, Tao1,3; Zhou, Hao6; Lin, Tao2,3,4,8; Sun, Caige7; Chen, Yuan2,4; Li, Fangfang2,4; Zhang, Guoqin2,3,4
刊名CITIES
出版日期2026-02-01
卷号169页码:106545
关键词Dynamic green space exposure Age and gender group difference Spatial and temporal pattern Green space inequality Mobile signaling data
ISSN号0264-2751
DOI10.1016/j.cities.2025.106545
产权排序5
文献子类Article
英文摘要Understanding green space exposure (GSE) inequality is critical for advancing green justice. Previous studies relied on static exposure assessments, ignoring human mobility, while dynamic GSE studies lacked fine-grained analysis. This study integrated high-resolution population mobility through hourly mobile signaling data to examine spatiotemporal GSE patterns and inequality across age and gender groups on Xiamen Island, China. Temporal GSE patterns varied by age. Young adulthood (16-24), middle adulthood (25-44), and older adulthood (45-64) exhibited similar patterns with peak exposure during 16:00-18:00. Conversely, retirement (>= 65) showed highest GSE during 09:00-10:00, while youth (7-15) were most active during 00:00-01:00 and 13:00-14:00. Spatially, GSE hotspots concentrated in the old town and new district, particularly within highvitality blocks. GSE inequality differed across multiple dimensions. Young adulthood experienced the greatest inequality (Gini = 0.7637), followed by middle adulthood (Gini = 0.7324), older adulthood (Gini = 0.7230), retirement (Gini = 0.6985), and youth (Gini = 0.5755). Gender analysis revealed females (Gini = 0.7707) generally experienced greater GSE than males (Gini = 0.7337). This female-dominant inequality pattern persisted across young (Gini_females = 0.7759, Gini_males = 0.7368), middle (Gini_females = 0.7486, Gini_males = 0.7074), and older adulthood (Gini_females = 0.7411, Gini_males = 0.7038). However, this pattern was inverted among youth (Gini_females = 0.5613, Gini_males = 0.5745) and retirees (Gini_females = 0.6639, Gini_males = 0.7064), where males exhibited greater GSE inequality than females. Our results provide novel insights into dynamic GSE, contributing valuable knowledge for environmental justice and sustainable urban development.
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WOS关键词POPULATION-DISTRIBUTION ; URBAN GREENSPACE ; TRAVEL MODE ; HEALTH ; TIME ; INTERPOLATION ; PATTERNS ; ASSESSMENTS ; LANDSCAN ; STRESS
WOS研究方向Urban Studies
语种英语
WOS记录号WOS:001598060400001
出版者ELSEVIER SCI LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/217426]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Lin, Tao
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;
2.Chinese Acad Sci, Inst Urban Environm, State Key Lab Reg & Urban Ecol, Xiamen 361021, Peoples R China;
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
4.Xiamen Key Lab Smart Management Urban Environm, Xiamen 361021, Peoples R China;
5.Tech Univ Munich, Sch Life Sci, Dept Life Sci Syst, Terr Ecol Res Grp, D-85354 Freising Weihenstephan, Germany;
6.Minist Ecol & Environm Peoples Republ China, Nanjing Inst Environm Sci, Nanjing 210042, Peoples R China;
7.South China Normal Univ, Sch Geog, Guangzhou 510631, Peoples R China;
8.Fujian Key Lab Digital Technol Terr Space Anal & S, Fuzhou 350108, Peoples R China
推荐引用方式
GB/T 7714
Yao, Xia,Zhang, Junmao,Pei, Tao,et al. Green space exposure inequality between diverse age and gender groups in rapid urbanization area: Evidence from mobile signaling data[J]. CITIES,2026,169:106545.
APA Yao, Xia.,Zhang, Junmao.,Pei, Tao.,Zhou, Hao.,Lin, Tao.,...&Zhang, Guoqin.(2026).Green space exposure inequality between diverse age and gender groups in rapid urbanization area: Evidence from mobile signaling data.CITIES,169,106545.
MLA Yao, Xia,et al."Green space exposure inequality between diverse age and gender groups in rapid urbanization area: Evidence from mobile signaling data".CITIES 169(2026):106545.

入库方式: OAI收割

来源:地理科学与资源研究所

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