Charactering Human-Caused Fires Using GIS-Based Dimensionality Reduction Techniques in Keelung City, Taiwan
Source abstract
Abstract
Fires resulting from human activities, encompassing arson, electrical problems, smoking, cooking mishaps, and industrial accidents, necessitate understanding to facilitate effective prevention. This study investigates human-caused fires in Keelung City, Taiwan, employing geographic information system (GIS)-based dimensionality reduction techniques. By analyzing eleven diverse factors, including fire incident density, population-related, building-related and economic-related features, valuable insights are gained for enhancing fire prevention. Utilizing principal component analysis (PCA), factor analysis (FA), and out-of-bag (OOB) predictor importance, our algorithm identifies key factors explaining dataset variance. Results from three approaches reveal a significant link between fire incidents and the elderly population, buildings over 40 years old, and the tertiary sector in the economy, contributing to developing effective measures for mitigating and managing fire occurrences.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fire effects on ecosystems · Underground infrastructure and sustainability · Applied Advanced Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung 202301, Taiwan
Department of Civil Engineering, National Central University, Taoyuan 320317, Taiwan
References (62)
Show source-provided references
1. An easy, accurate and efficient procedure to create Forest Fire Risk Maps using Modeler (SEXTANTE plugin)10.1007/s11676-016-0267-5 ↗
2. Exploring spatial–temporal dynamics of fire regime features in mainland Spain10.5194/nhess-17-1697-2017 ↗
3. Comparative analysis of post-earthquake fires in Japan from 1995 to 201710.1007/s10694-018-00813-5 ↗
4. Evaluating the severity of building fires with the analytical hierarchy process, big data analysis, and remote sensing10.1007/s11069-020-04057-x ↗
5. Spatial planning of fire-agency stations as a function of wildfire likelihood in Thasos, Greece10.1016/j.scitotenv.2020.139004 ↗
6. Current wildland fire patterns and challenges in Europe: A synthesis of national perspectives10.1177/11786221211028185 ↗
7. Past fires and post-fire impacts reconstructed from a southwest Australian stalagmite10.1016/j.gca.2022.03.020 ↗
8. Stack effects in tall building fires: A case study of Taiwan old apartment fire10.1038/s41598-022-13118-z ↗
9. Challenges to the Fire Service–Centric Emergency Management System10.1061/(ASCE)NH.1527-6996.0000523 ↗
10. Understanding Brazil’s catastrophic fires: Causes, consequences and policy needed to prevent future tragedies
11. The older adult: Associated fire risks and current challenges for the development of future fire safety intervention strategies10.1002/fam.2823 ↗
12. Elderly about home fire safety: A qualitative study into home fire safety knowledge and behaviour10.1016/j.firesaf.2021.103391 ↗
13. Fire statistics and risk analysis in wooden building structures in Serbia10.1080/20426445.2017.1309808 ↗
14. Federal Emergency Management Agency (1997). Socioeconomic Factors and the Incidence of Fire, Federal Emergency Management Agency.
15. National Fire Agency, Ministry of the Interior, Taiwan (2021). Analysis of National Fire Statistics for the Year, Ministry of the Interior.
16. Keelung City Fire Bureau (2021). Overview of Fire Statistical Analysis in Keelung City for the Year, Keelung City Fire Bureau.
17. (2023, November 01). National Fire Agency, Ministry of the Interior, (In Chinese).
18. (2023, November 01). Keelung City Fire Bureau, (In Chinese).
19. Joint principal component and discriminant analysis for dimensionality reduction10.1109/TNNLS.2019.2904701 ↗
20. Tsutsumida, N., Murakami, D., Yoshida, T., Nakaya, T., Lu, B., and Harris, P. (2019). Geographically Weighted Non-negative Principal Components Analysis for Exploring Spatial Variation in Multidimensional Composite Index. GeoComputation, 2019.
21. Acal, C., Aguilera, A.M., and Escabias, M. (2020). New modeling approaches based on varimax rotation of functional principal components. Mathematics, 8.10.3390/math8112085 ↗
22. A review of principal component analysis algorithm for dimensionality reduction
23. Understanding wildfires in mainland Spain. A comprehensive analysis of fire regime features in a climate-human context10.1016/j.apgeog.2017.10.007 ↗
24. What controls fire spatial patterns? Predictability of fire characteristics in the Canadian boreal plains ecozone10.1002/ecs2.2985 ↗
25. Factors affecting the behavior of large forest fires in Turkey10.1007/s00267-020-01389-z ↗
26. Spatiotemporal patterns of changes in fire regime and climate: Defining the pyroclimates of south-eastern France (Mediterranean Basin)10.1007/s10584-015-1332-3 ↗
27. Optimized hot spot and directional distribution analyses characterize the spatiotemporal variation of large wildfires in Washington, USA, 1970–202010.1007/s13753-022-00396-4 ↗
28. GIS method for characterizing fire flow capacity10.1016/j.firesaf.2015.02.001 ↗
29. GIS-based fuzzy multi-criteria approach for optimal site selection of fire stations in Istanbul, Turkey10.1016/j.seps.2020.100860 ↗
30. Truong, T.X., Nhu, V.H., Phuong DT, N., Nghi, L.T., Hung, N.N., Hoa, P.V., and Bui, D.T. (2023). A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas. Remote Sens., 15.10.3390/rs15143458 ↗
31. Evaluation of optical remote sensing data in burned areas mapping of Thasos Island, Greece10.1007/s41748-020-00195-1 ↗
32. Principal component analysis and self-organizing map for visualizing and classifying fire risks in forest regions
33. Temporal record and spatial distribution of fire foci in State of Minas Gerais, Brazil10.1016/j.jenvman.2020.111707 ↗
34. Fire regime dynamics in mainland Spain. Part, 1: Drivers of change10.1016/j.scitotenv.2019.135841 ↗
35. Assessing the impact of ecological security and forest fire susceptibility on carbon stocks in Bo Trach district, Quang Binh province, Vietnam10.1016/j.ecoinf.2022.101962 ↗
36. Developing and validating a scale for anxiety over land and forest fire10.1016/j.ijdrr.2023.103850 ↗
37. Optimal trees selection for classification via out-of-bag assessment and sub-bagging10.1109/ACCESS.2021.3055992 ↗
38. Goretzko, D., and Ruscio, J. (2023). The comparison data forest: A new comparison data approach to determine the number of factors in exploratory factor analysis. Behav. Res. Methods, 1–14.10.3758/s13428-023-02122-4 ↗
39. Exploring population responses to environmental change when there is never enough data: A factor analytic approach10.1111/2041-210X.13085 ↗
40. Random forests for big data10.1016/j.bdr.2017.07.003 ↗
41. Untangling fuel, weather and management effects on fire severity: Insights from large-sample LiDAR remote sensing analysis of conditions preceding the 2019–2020 Australian wildfires10.1016/j.jenvman.2023.119474 ↗
42. Characterizing past fire occurrence in longleaf pine ecosystems with the Mid-Infrared Burn Index and a Random Forest classifier10.1016/j.foreco.2021.119635 ↗
43. (2023, November 01). Department of Civil Affairs, Keelung City Government, (In Chinese).
44. Mobile GIS applications for environmental field surveys: A state of the art
45. Assessing small hydropower sites in Nigeria for sustainable development using ArcGIS10.1016/j.egyr.2023.09.102 ↗
46. (2023, November 01). Department of Social Affairs, Keelung City Government, (In Chinese).
47. (2023, November 01). Department of Urban Development, Keelung City Government, (In Chinese).
48. (2023, November 01). Department of Economic Affairs, Keelung City Government, (In Chinese).
49. World Health Organization (2015). World Report on Ageing and Health, World Health Organization.
50. Causative factor analysis of passenger ship accident (Fire/Explosion) in Indonesia
51. Moler, C., and Little, J. (2020). Proceedings of the ACM on Programming Languages, 4(HOPL), Association for Computing Machinery.10.1145/3386331 ↗
52. Collapse of the 16-story Plasco building in Tehran due to fire10.1007/s10694-019-00903-y ↗
53. Farinha, J., Cunha, L., and Dimuccio, L.A. (2022). Exploratory Spatial Analysis of Social Vulnerability and Forest Fire Risk in the Pinhal Interior Sul (Central Portugal). Sustainability, 14.10.3390/su14053010 ↗
54. Fire in heritage and historic buildings, a major challenge for the 21st century10.1016/j.dibe.2022.100102 ↗
55. A review and analysis of the thermal exposure in large compartment fire experiments
56. A thorough classification and discussion of approaches for modeling and managing domino effects in the process industries10.1016/j.ssci.2020.104618 ↗
57. Fire risk reduction on the margins of an urbanizing world10.1108/DPM-06-2020-0191 ↗
58. Ren, X., Li, C., Ma, X., Chen, F., Wang, H., Sharma, A., and Masud, M. (2021). Design of multi-information fusion based intelligent electrical fire detection system for green buildings. Sustainability, 13.10.3390/su13063405 ↗
59. Determining factors affecting perceived effectiveness among filipinos for fire prevention preparedness in the national capital region, Philippines: Integrating protection motivation theory and extended theory of planned behavior10.1016/j.ijdrr.2022.103497 ↗
60. Risk-based safety measure allocation to prevent and mitigate storage fire hazards10.1016/j.psep.2020.01.008 ↗
61. Resilient landscapes to prevent catastrophic forest fires: Socioeconomic insights towards a new paradigm10.1016/j.forpol.2021.102458 ↗
62. Learning from fire investigations and research–A Norwegian perspective on moving from a reactive to a proactive fire safety management10.1016/j.firesaf.2020.103047 ↗