Implementing a routine and standard approach for the automatic collection of socio-economic impact observations for impact-based forecasting and warning
Bibliographic Data
| ID | 22026075 |
|---|---|
| Authors | Faye Wyatt (0000-0001-7562-3411, Met Office, corresponding author), Joanne Robbins (0000-0003-2225-0548, Met Office), Simon Eaton (0000-0003-0892-9204), S V Eaton (0009-0008-3765-5664, Met Office) |
| Year | 2024 |
| Volume | 110 |
| Pages | 104608 |
| Publication date | 2024-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Disaster Risk Reduction (JOURNAL) |
| Journal identifiers | ISSN: 2212-4209 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijdrr.2024.104608 |
| OpenAlex | W4399601976 |
| Language | EN |
| Citations received | 2 |
| References cited | 31 |
Globally, there are different scientific methods being developed to support the provision of Impact-based Forecasts and Warnings. Impact data is critically valuable to many of these methods enabling the development of new hazard impact models and forecasting applications, and the evaluation of these different products. However, routine and consistent approaches to collect socio-economic impact information remain challenging, with traditional approaches being labour intensive, error prone and unsustainable. This paper describes the design of an automated workflow that can collect, standardise, and harmonise impact observations from a range of sources, routinely. The workflow is a 6-step process including web scraping, Natural Language Processing (NLP) and geoparsing. The workflow enables essential criteria (i.e., date, location, types and details of the societal impacts and the hazard(s) which led to them) to be captured faster than previous manual approaches. It also allows a wider range of hazard and impact types to be collected than was previously practical to do. Testing of the workflow indicates that it could benefit from further refinement of the NLP and geoparse modules to allow for the detection of finer scale impact locations. This in turn would allow for different scales of event detection to be developed and tested. Additionally, the applicability of the workflow for different types of impact data sources such as news and social media remains to be tested. Despite the need for further refinement, this workflow represents a step change in the speed and efficiency of impact data collection within the Met Office
Business · Data collection · Data mining · Data science · Database · Hazard · Hazard analysis · Reliability engineering · Warning system · Workflow · Computer Science · Data-Driven Disease Surveillance · Disaster Management and Resilience · Engineering · Risk Perception and Management
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| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |