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The Wikipedia database is a data source of immense richness and variety. Included in this database are thousands of geotaggedarticles, including, for example, almost real-time updates on current and historic natural hazards. This includes usercontributedinformation about the location of natural hazards, the extent of the disasters, and many details relating toresponse, impact, and recovery. In this research, a computational framework is proposed to detect spatial patterns of naturalhazards from the Wikipedia database by combining topic modeling methods with spatial analysis techniques. Thecomputation is performed on the Neon Cluster, a high performance-computing cluster at the University of Iowa. This workuses wildfires as the exemplar hazard, but this framework is easily generalizable to other types of hazards, such ashurricanes or flooding. Latent Dirichlet Allocation (LDA) modeling is first employed to train the entire English Wikipediadump, transforming the database dump into a 500-dimension topic model. Over 230,000 geo-tagged articles are thenextracted from the Wikipedia database, spatially covering the contiguous United States. The geo-tagged articles areconverted into an LDA topic space based on the topic model, with each article being represented as a weighted multidimensiontopic vector. By treating each article’s topic vector as an observed point in geographic space, a probabilitysurface is calculated for each of the topics. In this work, Wikipedia articles about wildfires are extracted from the Wikipediadatabase, forming a wildfire corpus and creating a basis for the topic vector analysis. The spatial distribution of wildfireoutbreaks in the US is estimated by calculating the weighted sum of the topic probability surfaces using a map algebraapproach, and mapped using GIS. To provide an evaluation of the approach, the estimation is compared to wildfire hazardpotential maps created by the USDA Forest service

DETECTING SPATIAL PATTERNS OF NATURAL HAZARDS FROM THE WIKIPEDIA KNOWLEDGE BASE