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| 基于社交媒体签到数据的城市居民暴雨洪涝响应时空分析 | |
| 引用本文： | 王波,甄峰,孙鸿鹄.基于社交媒体签到数据的城市居民暴雨洪涝响应时空分析[J].地理科学,2020,40(9):1543-1552. |
| 作者姓名： | 王波 甄峰 孙鸿鹄 |
| 作者单位： | 1. 中山大学地理科学与规划学院，广东 广州 510275 2. 南京大学建筑与城市规划学院，江苏 南京 210093 3. 江苏省智慧城市设计仿真与可视化技术工程实验室，江苏 南京 210093 |
| 基金项目： | 国家自然科学基金项目（41901191）、国家社会科学重点基金（20AZD040）、中央高校基本科研业务费项目（19lgpy42）资助 |
| 摘 要： | 暴雨洪涝等小型地域性气候灾害给城市韧性带来挑战。以南京暴雨洪涝为例，通过挖掘新浪微博签到数据，构建公众感知指数和公众情绪指数，分析居民对暴雨洪涝响应的时空格局。在时间维度上，居民对暴雨洪涝的响应主要集中在暴雨洪涝期，并随灾害的严重程度而变化；在暴雨洪涝期内，居民在社交媒体上对暴雨洪涝的响应集中在早、晚高峰。在空间维度上，居民对暴雨洪涝的响应集中在主城区和3个新市区；重要交通基础枢纽地区和低海拔、经历快速城市化的新市区的居民对暴雨洪涝担忧程度更高。时空分析表明，暴雨洪涝对居民的交通出行影响最明显。基于时空间分析，最后从硬件和软件设施上为提升暴雨洪涝的城市韧性提供相关政策建议。 |
| 关 键 词： | 城市韧性 社交媒体 时空间分析 暴雨洪涝 大数据 |
| 收稿时间： | 2019-11-23 |
The Spatio-temporal Patterns of Public Responses Towards Rainstorms and Associated Floods Based on Social Media Check-in Data |
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| Wang Bo,Zhen Feng,Sun Honghu.The Spatio-temporal Patterns of Public Responses Towards Rainstorms and Associated Floods Based on Social Media Check-in Data[J].Scientia Geographica Sinica,2020,40(9):1543-1552. | |
| Authors: | Wang Bo Zhen Feng Sun Honghu |
| Institution: | 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, Guangdong, China 2. School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, Jiangsu, China 3. Jiangsu Provincial Engineering Laboratory of Smart City Design Simulation & Visualization, Nanjing University, Nanjing 210093, Jiangsu, China |
| Abstract: | Recently, user-generated content with spatial inference such as the check-in data in social media has been used in disaster research and planning. The volunteered geographical information has been used in representing the spatio-temporal patterns of people’s response towards large-scale hazards. This study focus on a small-scale but repeatedly occurred hazard – rainstorms and associated floods (RF). RF has posed a tremendous challenge to urban resilience, especially for cities in developing countries experiencing rapid land use changes. Using the RF in Nanjing City as a case, Sina Weibo check-in data during July 1-21, 2016 was collected and their content were analyzed. Specifically, the RF-related keywords were compiled and used to identify RF-related tweets from background check-in records. Then sentiment analysis was applied to show attitude expressed in each FR-related keywords. Then, the public awareness index and public sentiment index were used for revealing the spatio-temporal patterns of pubic responses towards RF. Our findings show that: 1) Temporally, the majority of RF-related tweets were posted during the rainstorm period; within a day, the RF concern was most discernable during the morning and evening peak hours; 2) Spatially, high public concern towards RF was found in areas within the main city and three new districts; and the major transport hubs and new districts with low altitude and rapid urban construction in recent years showed a higher level of concern on RF; 3) Both the temporal and spatial analysis suggest that RF adversely influence people’s travel-related behavior the most. Based on the knowledge gained from this analysis, policy implications were proposed to increase the urban resilience towards RF covering both hardware and software parts. It is suggested that these critical facilities including drainage facilities and other underground infrastructure, transport systems, and communications systems require advanced planning and long-term maintenance, especially for new city districts and key regional and urban transport hubs. Furthermore, it is of significance that information management and easily accessible communication function well during the RF, making the government, industry and the general public well-informed about the ongoing situation. Methodologically, this study further confirms that social media data can provide valuable real-time spatial information on understanding people’s responses towards RF, which could be used by local governments in emergency management. To enhance urban resilience, a quick decision making based on collection and analysis of social media data could be a good example of the big data use in smart city development. |
| Keywords: | urban resilience social media spatio-temporal analysis rainstorm and associated floods big data |
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