Measuring community resilience inequality to inland flooding using location aware big data
文献类型：期刊论文
| 作者 | Qian, Jiale2,3; Du, Yunyan2,3; Liang, Fuyuan3; Yi, Jiawei2,3; Zhang, Xueqin2,3; Jiang, Jianxiang1; Wang, Nan2,3; Tu, Wenna2,3; Huang, Sheng2,3; Pei, Tao2,3 |
| 刊名 | CITIES
 |
| 出版日期 | 2024-06-01 |
| 卷号 | 149页码:104915 |
| 关键词 | Community resilience Inequality Priority Inland flooding Location aware big data |
| DOI | 10.1016/j.cities.2024.104915 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Understanding community resilience is critical for effective resource allocation and planning. However, classical resilience measures often fall short in capturing the diverse characteristics of each stage: preparation, resistance, recovery, and adaptability. We propose a multi-stage resilience assessment framework integrating morphological indicators to measure community resilience. Applying this framework to evaluate disparities in community resilience after the inland flooding in Changsha, we find that morphological indicators provide a more accurate description of resilience priority and ability compared to speed indicators. The analysis reveals stronger complementarity and lower redundancy among resilience indicators. Moreover, we establish a strong connection between resilience, driving factors, and government actions. Neighborhoods with high flood risk and socioeconomic status encounter the severest impact and highest recovery priority, leading to substantial government engineering measures. Conversely, vulnerable communities facing high flood risk and low socioeconomic status display the lowest resistance and recovery ability, emphasizing the necessity for tailored initiatives like increasing green infrastructure and constructing flood buffer zones. Additionally, low river flood risk communities with high socioeconomic status experience minimal flood impact, underscoring the significance of prioritizing low-lying regions susceptible to surface water flooding. The study informs government decision-making, emphasizing differentiated strategies for enhancing community resilience across regions and stages. |
| WOS关键词 | RISK ; CITIES ; MODEL |
| WOS研究方向 | Urban Studies |
| WOS记录号 | WOS:001218923600001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205209]  |
| 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 | Du, Yunyan |
| 作者单位 | 1.Changsha Planning Survey Design & Res Inst, Changsha 410007, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qian, Jiale,Du, Yunyan,Liang, Fuyuan,et al. Measuring community resilience inequality to inland flooding using location aware big data[J]. CITIES,2024,149:104915. |
| APA | Qian, Jiale.,Du, Yunyan.,Liang, Fuyuan.,Yi, Jiawei.,Zhang, Xueqin.,...&Ma, Ting.(2024).Measuring community resilience inequality to inland flooding using location aware big data.CITIES,149,104915. |
| MLA | Qian, Jiale,et al."Measuring community resilience inequality to inland flooding using location aware big data".CITIES 149(2024):104915. |
入库方式： OAI收割
来源：地理科学与资源研究所
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