Detecting changes in spatial characteristics of Colorado human-caused wildfires using APRIORI-based frequent itemset mining. (April 2023)

• Record Type: Journal Article
• Authors: Cleland, Zachary W.; Dao, Khac An; Dao, Thi Hong Diep
• Is Part Of: Computers, environment and urban systems. Volume 101(2023)
• Publication Date: 2023-04
• Subjects: Human-caused wildfires -- Characteristics -- Changes -- Spatial data mining -- APRIORI-based -- Spatial-temporal association mining -- Visual analytics; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2023.101941
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: The phenomenon of human-caused wildfire presents significant dilemmas for states like Colorado, given a growing population and large portions of forested mountain ecoregions. This study seeks to understand the changes in characteristics of Western Colorado human-caused wildfire (HCF) occurrences from 1992 to 2015 while considering their locations and spatial context. Fires are grouped into three seven-year study periods, 1992–1999, 2000–2007, and 2008–2015. First, we constructed an extensive spatial database documenting the spatial characteristics of these fires. The spatial characteristics represent the spatial relationships and interactivity of fire occurrence locations with various environmental and anthropogenic entities. Second, an unsupervised data mining approach (APRIORI-based itemset mining) is used to extract the most frequent characteristics of the fire occurrences during these periods. Finally, we present a comprehensive visual graph-based evaluation approach to evaluate the mined results effectively. Our findings indicate explicit and fresh insights into changes in human-caused wildfire characteristics for the study area. The method presented and enhanced here is proven robust and very effective at extracting an extensive and comprehensive set of characteristics of HCFs. The operative spatial predication schemas and the result evaluation approach based on visual analytics are this study's successful and novel aspects. Highlights: Robust APRIORI-based spatial data mining approach to extract characteristics of human-caused wildfire (HCF) Extracted HCF characteristic patterns and their changes over time, considering their locations and spatial context Novel aspects include the operative spatial predication schemas and the result evaluation approach based on visual analytics. This study serves as an interpretable spatial-temporal data mining framework for change detection.