## 1 Introduction
This integration establishes a robust framework for characterizing fires [32; 33], integrating spatial analysis, dimensionality reduction, pattern recognition, risk factor identification, and effective visualization and communication of results [29; 30; 31]. In addition to employing PCA to investigate spatiotemporal trends in fires, previous studies utilize various other dimensionality reduction methods like factor analysis (FA) and linear discriminant analysis to investigate the factors contributing to fires [19; 22; 32]. Dimensionality reduction aims to decrease the number of features in a dataset while retaining as much information as possible. PCA and FA are both techniques used for dimensionality reduction, but they adopt different methods to project the data onto a lower-dimensional space while preserving important information [19; 20]. PCA has been employed to analyze fire incidents and regional attributes, identifying various factors including human activities, industrial behaviors, building types, and other pertinent variables for evaluating fire-related characteristics. The use of PCA has been effective in summarizing temporal behavior and exploring potential connections in the evolution of fire features [19]. Furthermore, PCA is beneficial for characterizing fires by simplifying intricate datasets, identifying key factors, uncovering dominant trends, assisting in visualization, and establishing a foundation for more efficient and accurate analyses [20; 21]. In recent years, climate change has frequently increased the susceptibility of certain areas to wildfires, simultaneously escalating the severity of fire incidents [23; 25; 34; 35]. In light of the influence of global warming and extreme climate changes, PCA has found widespread application in the analysis of fire characteristics and the examination of spatiotemporal trends in fires [19; 20; 35; 36]. Within the realm of machine learning methods such as PCA and FA, random forest (RF) stands out as a robust algorithm known for its strong performance with default settings [7; 37; 38]. RFs offer the benefit of estimating predictor importance through the permutation of out-of-bag (OOB) predictor observations across the ensemble of trees [39; 40]. The OOB predictor refers to a method used for estimating the prediction error of a model trained with bootstrap aggregation (bagging) [41]. This involves computing the increase in OOB errors resulting from randomly permuting the observations of predictor variables in the OOB sample for each tree. The averaged value over all trees indicates the importance of each variable within the RF. The higher the importance measure, the greater the influence of the variable in predicting the response [42]. In this study, we analyze the contributing factors of fire incidents and combine the spatial distribution of fires with factors such as elderly population, population density, and building age to identify potential areas for strengthening preventive measures. By exploring the various factors associated with fires, we aim to provide insights for enhancing fire prevention strategies in the future. Human-caused fires, stemming from activities such as negligence, arson, electrical malfunctions, or industrial accidents, are a significant concern requiring in-depth analysis for effective prevention and risk assessment. This article presents an innovative approach to analyzing human-caused fires in Keelung city, Taiwan, utilizing GIS-based dimensionality reduction techniques. Given the substantial risks associated with these fires, comprehending their characteristics and influencing factors is paramount. The proposed method involves an examination of multiple factors, including fire incident density, population density, the proportion of elderly individuals, the proportion of old buildings, the proportion of old non-reinforced concrete (RC) buildings, and the economic contributions of the secondary and tertiary sectors.