Relief Supply-Demand Estimation Based on Social Media in Typhoon Disasters Using Deep Learning and a Spatial Information Diffusion Model
Bibliographic Data
| ID | 22033747 |
|---|---|
| Authors | Shaopan Li (0000-0001-7554-4289, Tsinghua University), Yiping Lin (Tsinghua University), Hong Huang (0000-0001-6428-1364, Tsinghua University, corresponding author) |
| Year | 2024 |
| Volume | 13 |
| Issue | 1 |
| Pages | 29 |
| Publication date | 2024-01-16 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi13010029 |
| OpenAlex | W4390906420 |
| Language | EN |
| Citations received | 3 |
| References cited | 25 |
Estimating disaster relief supplies is crucial for governments coordinating and executing disaster relief operations. Rapid and accurate estimation of disaster relief supplies can assist the government to optimize the allocation of resources and better organize relief efforts. Traditional approaches for estimating disaster supplies are based on census data and regional risk assessments. However, these methods are often static and lack timely updates, which can result in significant disparities between the availability and demand of relief supplies. Social media, network maps, and other sources of big data contain a large amount of real-time disaster-related information that can promptly reflect the occurrence of a disaster and the relief requirements of the affected residents in a given region. Based on this information, this study presents a model to estimate the demand for disaster relief supplies using social media data. This study employs a deep learning approach to extract real-time disaster information from social media big data and integrates it with a spatial information diffusion model to estimate the population in need of relief in the affected regions. Additionally, this study estimates the demand for emergency materials based on the population in need of relief. These findings indicate that social media data can capture information on the demand for relief materials in disaster-affected regions. Moreover, integrating social media big data with traditional static data can effectively improve the accuracy and timeliness of estimating the demand for disaster relief supplies
Big data · Business · Data mining · Disaster area · Economics · Emergency management · Estimation · Geography · Meteorology · Population · Relief Work · Social media · Typhoon · World Wide Web · Computer Science · Disaster Management and Resilience · Engineering · Evacuation and Crowd Dynamics · Human Mobility and Location-Based Analysis
| Unique citing works | 3 |
|---|---|
| Citations per year | 1,5 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |