Next Article in Journal
Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the “8.8” Jiuzhaigou Earthquake
Next Article in Special Issue
Clustering Complex Trajectories Based on Topologic Similarity and Spatial Proximity: A Case Study of the Mesoscale Ocean Eddies in the South China Sea
Previous Article in Journal
Planning Sustainable Economic Development in the Russian Arctic
Previous Article in Special Issue
Weighted Dynamic Time Warping for Grid-Based Travel-Demand-Pattern Clustering: Case Study of Beijing Bicycle-Sharing System
Article

Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data

by 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4 and 1,2,3,4,*
1
Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China
2
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
3
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
4
Center for Geodata and Analysis, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(8), 358; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8080358
Received: 28 June 2019 / Revised: 7 August 2019 / Accepted: 11 August 2019 / Published: 13 August 2019
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
Complex natural disasters often cause people to suffer hardships, and they can cause a large number of casualties. A population that has been affected by a natural disaster is at high risk and desperately in need of help. Even with the timely assessment and knowledge of the degree that natural disasters affect populations, challenges arise during emergency response in the aftermath of a natural disaster. This paper proposes an approach to assessing the near-real-time intensity of the affected population using social media data. Because of its fatal impact on the Philippines, Typhoon Haiyan was selected as a case study. The results show that the normalized affected population index (NAPI) has a significant ability to indicate the affected population intensity. With the geographic information of disasters, more accurate and relevant disaster relief information can be extracted from social media data. The method proposed in this paper will benefit disaster relief operations and decision-making, which can be executed in a timely manner. View Full-Text
Keywords: social media; natural disasters; emergency response; affected people intensity social media; natural disasters; emergency response; affected people intensity
Show Figures

Graphical abstract

MDPI and ACS Style

Cheng, C.; Zhang, T.; Su, K.; Gao, P.; Shen, S. Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data. ISPRS Int. J. Geo-Inf. 2019, 8, 358. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8080358

AMA Style

Cheng C, Zhang T, Su K, Gao P, Shen S. Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data. ISPRS International Journal of Geo-Information. 2019; 8(8):358. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8080358

Chicago/Turabian Style

Cheng, Changxiu, Ting Zhang, Kai Su, Peichao Gao, and Shi Shen. 2019. "Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data" ISPRS International Journal of Geo-Information 8, no. 8: 358. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8080358

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop