火災是都市地區常見的災害之一。過往研究多聚焦於直轄市的公共或重大傷亡建築，並專注於結構與使用類型的模擬分析。然而，對於建築物外部環境的研究，常限於描述性統計與傳統線性迴歸，缺乏對火災的空間異質性和空間相依性的深入探討，限制了建築物火災理論的解釋力。本研究選取彰化縣作為案例，利用Ripley's K函數檢定建築火災為群聚分布的空間型態。透過核密度估計（KDE）及Local G-statistic（Gi*）來識別建築物火災發生潛勢區與熱區，發現主要群聚在都市計畫區。本研究進一步探討了建築物火災發生潛勢因子的關聯性，並選取人口結構特徵、社會經濟層面及建物土地使用分區等變項。利用最小統計區為樣本，透過傳統線性迴歸（OLS）、空間落遲模式（SLM）、空間誤差模式（SEM）及地理加權迴歸（GWR）進行分析。GWR的解釋力最為顯著，證實了建築物火災發生與區域空間有顯著的空間變異性。GWR分析亦顯示，除了人口密度、老年人口數、中低收入戶數、商業區面積比及混合使用住宅區面積比，與以往常見影響因子相符外。兒童人口數的影響與預期相反，可能由於家長選擇專業托育及消防局落實防災教育，減少了火災發生的機率。基於實證結果，本研究建議短中長期的策略。短期策略包括為弱勢群體提供住宅用火災警報器和滅火器的補助。中期策略建議對新建的商業區和混合使用住宅區新建築採用防火構造，並鼓勵老舊複合式住宅進行修繕加固。長期策略則將火災發生潛勢區和熱區，以及GWR分析高影響顯著區域納入地區災害防救計畫，提升都市計劃與地區災害防救計畫的整合。此外，從防火宣導開始，逐步建立一個公助、自助、互助的防災體系社區，增強社區的自主防火能力。本研究的方法與結果增強了分析的客觀性與可靠性，亦為現行的災害防救法及都市計畫法提供了具體的火災潛勢分析與實證基礎。

This study investigates the spatial distribution and factors influencing building fires in Changhua County, Taiwan, focusing on spatial heterogeneity and dependency. Prior research often emphasized public buildings in metropolitan areas, but this study broadens the scope to analyze the urban environment. Spatial data analysis methods, including Ripley’s K-function, Kernel Density Estimation (KDE), Moran’s I, and the Local G-statistic (Gi*), are employed to identify clustering patterns, as well as high-fire potential areas and fire hotspot areas concentrated within urban planning districts. The research analyzes relationships between building fire incidence and variables, such as demographic characteristics, socioeconomic conditions, and building land-use zoning. Using statistical areas as samples, regression models—Ordinary Least Squares (OLS), Spatial Lag Model (SLM), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR)—reveal significant spatial variability in fire incidence. GWR results indicate that factors such as population density, elderly population, lower-middle income households, commercial districts, and mixed-use residential districts strongly impact fire potential. Notably, the effect of the child population deviates from expectations. This deviation may result from parents opting for professional childcare and the fire bureau implementing disaster education, reducing fire incidence. The study concludes with disaster prevention recommendations for government and public stakeholders, proposing short-term, medium-term, and long-term strategies. These include fire prevention subsidies, adopting fire-resistant structures, and integrating high-fire potential areas into disaster prevention plans.

1. 內政部消防署(2024a)。101至112年全國火災統計分析。
2. 內政部消防署(2024b)。106至112年消防統計年報。
3. 內政部國土測繪中心(2023)。土地利用分級分類系統表。
4. 內政部國土測繪中心(2024年1月20日)。國土利用現況調查成果資訊專區。https://www.nlsc.gov.tw/cl.aspx?n=13705
5. 內政部統計處(2016)。統計區分類系統資料標準。
6. 火災原因調查鑑定書及火災原因紀錄製作規定 (2024年3月11日修正)。
7. 火災案件搶救出勤紀錄表填寫作業原則(2016年3月17日修正)。
8. 王怡驊(2019)。地理資訊系統應用於縱火犯罪案件分析之研究-以臺中市為例〔碩士論文〕。國立中興大學。
9. 王建元(2020)。利用地理加權迴歸分析住宅火災之環境因素〔碩士論文〕。國立清華大學。
10. 王素芬、陳毅青、宋承恩(2020)。整合多尺度自然與行政單元評估南投縣崩塌災害風險。地理學報，(96)，27-54。https://doi.org/10.6161/jgs.202008_(96).0002
11. 王翎雅(2015)。桃園縣建築物火警空間特性分析〔碩士論文〕。國立中央大學。
12. 朱慶大(2007)。土地混合使用後火災風險改變分析方法之研究—系統動力學觀點〔碩士論文〕。國立臺灣大學。
13. 余宗庭(2020)。基隆市火災特性之研究〔碩士論文〕。國立臺灣海洋大學。
14. 吳杰穎、何天河、蔡綽芳、黃繼霆(2017) 。現行各縣市區域計畫中防災規劃之課題。建築與規劃學報，18(2)，93-112。
15. 吳榮平(2007)。都市火災空間分布及發生因素之研究〔博士論文〕。國立臺北大學。
16. 吳榮平、熊光華(2009)。都市火災風險約空間解析。中華建築學刊，3(3)，1-12。https://doi.org/10.6371/CHJA.200903.0001
17. 李孟晉(2020)。有形文化資產火災延燒風險分析及消防安全因應對策 -以北港朝天宮為例〔碩士論文〕。中央警察大學。
18. 李宗儒、陳昭榮、李妙純(2021) 。台灣大腸癌死亡率之空間分析。台灣衛誌， 40(2)，225-240。https://doi.org/10.6288/TJPH.202104_40(2).109105
19. 李珮均(2022)。以人因工程角度分析火災原因─以高雄市為例〔碩士論文〕。中原大學。
20. 李清安(2018)。火災風險評估之研究-以新北市泰山區為例〔博士論文〕。國立臺北科技大學。
21. 沈成隆(2019)。火災資料之統計分析研究-以臺中市為例〔碩士論文〕。亞洲大學。
22. 災害防救法(2022年6月15日修正)。
23. 易逸波、張嘉成、鄭達鴻(2019)。FDS 軟體對於透天厝火災模擬之差異性分析。勞動及職業安全衛生研究季刊，27(1)，77-85。
24. 林君亭(2012)。城市火災安全評估之研究〔碩士論文〕。國立臺北科技大學。
25. 林季芸(2016) 。空間的價值向度：實質環境形塑。華藝數位股份有限公司。
26. 林政宏(2012) 。金門火災空間特性分析〔碩士論文〕。國立金門大學。
27. 林晏辰(2016) 。工廠火災相關因素探討-以新北市為例〔碩士論文〕。國立政治大學。
28. 林淑芬(2010) 。建築物火災潛勢模型之建立-以台南縣新市鄉、新化鎮為例〔碩士論文〕。國立成功大學。
29. 林智偉(2017) 。以迴歸分析探討六都執行預防火災類業務之成效〔碩士論文〕。亞洲大學。
30. 姜驎容(2021) 。地理空間資訊分析在消防火警及緊急 救護資源分派之初探—以桃園市為例〔碩士論文〕。中央警察大學。
31. 建築法(2022年5月11日修正)。
32. 洪玉如(2010)。建築物火災潛勢模型之建構-以台南市為例〔碩士論文〕。國立成功大學。
33. 科學園區私有廠房與有關建築物輔導使用及強制拍賣辦法(2019年2月26日)。
34. 風災震災火災爆炸火山災害潛勢資料公開辦法 (2018年12月5日修正)。
35. 孫郁喬(2020)。相關係數的相似性檢定〔碩士論文〕。國立交通大學。
36. 馬敏(2020)。火災安全的研究〔博士論文〕。元智大學。
37. 高資婷(2015) 。臺灣高空屋率現象探討-空間分析方法的應用〔碩士論文〕。國立臺灣大學。
38. 許佳莉(2020)。地理資訊系統應用於火災熱區空間分析之研究—以臺中市為例〔碩士論文〕。國立中興大學。
39. 郭文田、邱榮振(2008)。高雄市火災發生潛勢分析。建築學報，(62)，47-72。https://doi.org/10.6377/JA.200803.0047
40. 郭修安、宋立垚(2019)。應用 GIS 分析火災風險評價及消防佈局-以臺北士林區為例。地理資訊系統季刊，13(2)，8-16。https://doi.org/10.6628/GIS.201904_13(2).0002
41. 郭銘諭(2021)。住宅用火災警報器與火災危害影響之研究–以新竹縣為例〔碩士論文〕。中央警察大學。
42. 都市計畫定期通盤檢討實施辦法(2017年4月18日修正)。
43. 都市計畫法 (2021年5月26日修正)。
44. 陳弘毅、紀人豪(2016)。火災學。鼎茂圖書出版股份有限公司。
45. 陳威哲、陳建元(2018，11月9日)。嘉義縣火災易致災區域分析[會議場次]。2018臺灣災害管理研討會，新北市。https://doi.org/10.6857/CDMT.201811.0059
46. 陳彥仲、吳舒凱、吳榮平(2023)。中型城市建築物火災空間資料分析－以彰化市為例。規劃學報，41(2)，35-59。
47. 陳彥仲、吳舒凱、施順仁(2024)。文化古城鎮建築物火災潛勢空間辨認-以鹿港鎮為例。建築學報，(127)，45-63。https://doi.org/10.53106/101632122024030127003
48. 陳彥仲、吳舒凱、盧鏡臣(2023)。臺灣中型城市建築火災空間分析－以員林市為例。規劃學報，41(1)，35-59。
49. 陳德育(2019)。火災因子分析─以臺中市為例〔碩士論文〕。國立中興大學。
50. 陳錦嫣、黃國展(2013)。地理資訊系統入門與應用。新文京開發出版股份有限公司。
51. 黃信超、陳建元 (2016，11月4日)。臺南市2011至2015年災害熱點分析[會議場次]。2016 臺灣災害管理學會十週年年會，新北市。https://doi.org/10.6149/JDM.2016.SI.b07
52. 黃國慶、詹士樑、蘇冠臻、王姿懿(2021)。臺灣都市階層體系變動趨勢之探討。都市與計劃，48(1)，1-26。https://doi.org/10.6128/CP.202103_48(1).0001
53. 黃煒(2019)。應用事件樹分析方法探討火災風險評估之研究—以臺北市為例〔碩士論文〕。國立臺北科技大學。
54. 溫在弘(2015)。空間分析：方法與應用。雙葉書廊有限公司。
55. 經濟部產業園區管理局自行興建建築物租售辦法(2023年11月6日修正)。
56. 葉碧翠(2016)。少年及成年施用第三、四級毒品犯罪空間初探-以臺北市為例。中央警察大學警學叢刊，47(3)，57-58。
57. 鄒克萬、張曜麟(2004)。都市土地使用變遷空間動態模型之研究。地理學報，(35)，35-51。
58. 趙祐詩(2012)。城郊處火災分佈特性之研究-以臺中市后里區為例〔碩士論文〕。中央警察大學。
59. 劉擇昌(2011)。住宅竊盜犯罪熱區空間分析與環境特性之研究--以台北市大安區為例〔博士論文〕。中央警察大學。
60. 劉麗雯(2009)。地理資訊系統做爲社區服務方案規劃與執行的輔助工具。社會政策與社會工作學刊，13(1)，53-92。https://doi.org/10.6785/SPSW.200906.0053
61. 蔡宛容(2013)。運用地理加權迴歸方法探討都市土地混合使用空間分佈之影響因素〔碩士論文〕。國立成功大學。
62. 蔣得心、林峰田(2008)。都市地區火災風險分區劃設方法之研究。危機管理學刊，5(2)。https://doi.org/10.6459/JCM.200809_5(2).0008
63. 謝京辰、廖興中、楊銘欽、董鈺琪(2019)。全民健康保險醫療資源潛在空間可近性分析-以台灣北部四縣市為例。台灣衛誌，38(3)，316-327。https://doi.org/10.6288/TJPH.201906_38(3).108016
64. 謝濠光(2013)。建築物火災風險因子建模之研究〔碩士論文〕。中央警察大學。
65. 譚至皙(2002)。台灣中部地區國小教師對自然災害的防備態度及因應行為之研究〔碩士論文〕。臺中師範學院。
66. 觀光地區及風景特定區建築物及廣告物攤位設置規劃限制辦法(2004年4月7日修正)。
67. Akaike, H. (1973). Information Theory and an Extension of the Maximum Likelihood Principle. In B. N. Petrov, & F. Csaki (Eds.), Proceedings of the 2nd International Symposium on Information Theory. (pp.199-213). https://doi.org/10.1007/978-1-4612-1694-0_15
68. Andresen, M. A., & Malleson, N. (2013). Spatial heterogeneity in crime analysis. Crime modeling and mapping using geospatial technologies, 8, 3-23. https://doi.org/10.1007/978-94-007-4997-9_1
69. Anselin, L. (1988a). Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity. Geographical analysis, 20(1), 1-17. https://doi.org/10.1111/j.1538-4632.1988.tb00159.x
70. Anselin, L. (1988b). Spatial econometrics: methods and models(Vol. 4). Springer Science & Business Media.
71. Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical analysis, 27(2), 93-115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
72. Anselin, L. (2005). Exploring spatial data with GeoDaTM: a workbook. University of Illinois, center for spatially integrated social science. https://www.geos.ed.ac.uk/~gisteac/fspat/geodaworkbook.pdf
73. Anselin, L., Bera, A. K., Florax, R., & Yoon, M. J. (1996). Simple diagnostic tests for spatial dependence. Regional science and urban economics, 26(1), 77-104. https://doi.org/10.1016/0166-0462(95)02111-6
74. Anselin, L., & Griffith, D. A. (1988). Do spatial effects really matter in regression analysis? Papers of the Regional Science Association, 65(1), 11-34. https://doi.org/10.1111/j.1435-5597.1988.tb01155.x
75. Anselin, L., & Rey, S. (1991). Properties of tests for spatial dependence in linear regression models. Geographical analysis, 23(2), 112-131. https://doi.org/10.1111/j.1538-4632.1991.tb00228.x
76. Ardianto, R. (2018). Modelling spatial temporal patterns and drivers of urban residential fire risk [Doctoral dissertation]. RMIT University.
77. Ardianto, R., & Chhetri, P. (2019). Modeling spatial–temporal dynamics of urban residential fire risk using a Markov chain technique. International Journal of Disaster Risk Science, 10, 57-73. https://doi.org/10.1007/s13753-018-0209-2
78. Bae, G., Jung, Y., & Yoo, H. (2015). An analysis on the characteristics of spatial clustering distribution in the urban fire of Gyeongsangnam-do, Korea [Paper presentation]. The ACRS 2015-36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, 2015. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=236be5af0d15b6cac091bfb815928ebd40c8a8d7
79. Besag, J., & Diggle, P. J. (1977). Simple Monte Carlo tests for spatial pattern. Journal of the Royal Statistical Society Series C: Applied Statistics, 26(3), 327-333. https://doi.org/10.2307/2346974
80. Besag, J. E. (1977). Contribution to the discussion on Dr. Ripley's paper. Journals of the Royal Statistical Society, 39, 193-195.
81. Brisson, D., & Usher, C. L. (2007). The effects of informal neighborhood bonding social capital and neighborhood context on homeownership for families living in poverty. Journal of Urban Affairs, 29(1), 65-75. https://doi.org/10.1111/j.1467-9906.2007.00323.x
82. Brunsdon, C., Fotheringham, S., & Charlton, M. (1998). Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician), 47(3), 431-443. https://doi.org/10.1111/1467-9884.00145
83. Buffington, T., Scott, J. G., & Ezekoye, O. A. (2021). Combining spatial and sociodemographic regression techniques to predict residential fire counts at the census tract level. Computers, Environment and Urban Systems, 88, 101633. https://doi.org/10.1016/j.compenvurbsys.2021.101633
84. Butsic, V., Kelly, M., & Moritz, M. A. (2015). Land use and wildfire: A review of local interactions and teleconnections. Land, 4(1), 140-156. https://doi.org/10.3390/land4010140
85. Carmona, M. (2021). Public places urban spaces: The dimensions of urban design. Routledge. https://doi.org/10.4324/9781315158457
86. Casetti, E. (1972). Generating models by the expansion method: applications to geographical research. Geographical analysis, 4(1), 81-91. https://doi.org/10.1111/j.1538-4632.1972.tb00458.x
87. Casetti, E. (1997). The expansion method, mathematical modeling, and spatial econometrics. International regional science review, 20(1-2), 9-33. https://doi.org/10.1177/016001769702000102
88. Ceyhan, E., Ertuğay, K., & Düzgün, Ş. (2013). Exploratory and inferential methods for spatio-temporal analysis of residential fire clustering in urban areas. Fire Safety Journal, 58, 226-239. https://doi.org/10.1016/j.firesaf.2013.01.024
89. Chhetri, P., Corcoran, J., Stimson, R. J., & Inbakaran, R. (2010). Modelling potential Socio‐economic determinants of building fires in south east Queensland. Geographical Research, 48(1), 75-85. https://doi.org/10.1111/j.1745-5871.2009.00587.x
90. Chiang, T.-H., & Lin, F.-T. (2007). A Delineation of Fire Risk Zones in Urban Area [Paper presentation]. The 2nd International Conference on Urban Disaster Reduction. https://www.researchgate.net/profile/Feng-Tyan-Lin/publication/266526361_A_Delineation_of_Fire_Risk_Zones_in_Urban_Area/links/552856460cf2779ab78dffbe/A-Delineation-of-Fire-Risk-Zones-in-Urban-Area.pdf
91. Chisty, M. A., & Rahman, M. M. (2020). Coping capacity assessment of urban fire disaster: An exploratory study on ward no: 30 of Old Dhaka area. International Journal of Disaster Risk Reduction, 51, 101878. https://doi.org/10.1016/j.ijdrr.2020.101878
92. Clark, A., Smith, J., & Conroy, C. (2015). Domestic fire risk: a narrative review of social science literature and implications for further research. Journal of Risk Research, 18(9), 1113-1129. https://doi.org/10.1080/13669877.2014.913660
93. Cliff, A., & Ord, K. (1972). Testing for spatial autocorrelation among regression residuals. Geographical analysis, 4(3), 267-284. https://doi.org/10.1111/j.1538-4632.1972.tb00475.x
94. Conzen, M. P. (2018). Core Concepts in Town-Plan Analysis. In V. Oliveira (Ed.), Teaching Urban Morphology (pp. 123-143). Springer International Publishing.
95. Conzen, M. R. G. (1960). Alnwick, Northumberland: a study in town-plan analysis. Transactions and Papers [Institute of British Geographers](27), iii-122. https://www.jstor.org/stable/621094
96. Corcoran, J., Higgs, G., Brunsdon, C., Ware, A., & Norman, P. (2007). The use of spatial analytical techniques to explore patterns of fire incidence: A South Wales case study. Computers, Environment and Urban Systems, 31(6), 623-647. https://doi.org/10.1016/j.compenvurbsys.2007.01.002
97. Corcoran, J., Higgs, G., & Higginson, A. (2011). Fire incidence in metropolitan areas: A comparative study of Brisbane (Australia) and Cardiff (United Kingdom). Applied Geography, 31(1), 65-75. https://doi.org/10.1016/j.apgeog.2010.02.003
98. Corcoran, J., Higgs, G., Rohde, D., & Chhetri, P. (2011). Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an Australian case study. Journal of Geographical Systems, 13(2), 193-226. https://doi.org/10.1007/s10109-009-0102-z
99. Cordes, J., & Castro, M. C. (2020). Spatial analysis of COVID-19 clusters and contextual factors in New York City. Spatial and spatio-temporal epidemiology, 34, 100355. https://doi.org/10.1016/j.sste.2020.100355
100. Coty, M.-B., McCammon, C., Lehna, C., Twyman, S., & Fahey, E. (2015). Home fire safety beliefs and practices in homes of urban older adults. Geriatric nursing, 36(3), 177-181. https://doi.org/10.1016/j.gerinurse.2014.12.013
101. Crawford, B. A. (2005). Reducing fire risks for the poor. Fire engineering Magazine, 158(1), 83-88. https://www.fireengineering.com/leadership/reducing-fire-risks-for-the-poor/
102. de Oliveira Santos, C. J., de Sá Alencar, V., & Vasconcelos, B. (2022). Fluid Dynamics Analysis and Fire Evacuation through Computer Simulation: A Case Study. American Academic Scientific Research Journal for Engineering, Technology, and Sciences, 88(1), 26-41. https://core.ac.uk/download/pdf/524625554.pdf
103. Dekker, K., & Bolt, G. (2005). Social cohesion in post-war estates in the Netherlands: Differences between socioeconomic and ethnic groups. Urban studies, 42(13), 2447-2470. https://doi.org/10.1080/00420980500380360
104. Dixon, P. M. (2002). Ripley’s K function. In A. H. E.-S. a. W. W. Piegorsch (Ed.), Encyclopedia of Environmetrics (Vol. 3, pp. 1796–1803). John Wiley & Sons, Ltd. https://www3.nd.edu/~mhaenggi/ee87021/Dixon-K-Function.pdf
105. Drysdale, D. (2011). An introduction to fire dynamics. John wiley & sons. https://doi.org/10.1002/9781119975465
106. Duncanson, M., Woodward, A., & Reid, P. (2002). Socioeconomic deprivation and fatal unintentional domestic fire incidents in New Zealand 1993–1998. Fire Safety Journal, 37(2), 165-179. https://doi.org/10.1016/S0379-7112(01)00033-9
107. Fahy, R., & Maheshwari, R. (2021). Poverty and the Risk of Fire. National Fire Protection Association. https://www.nfpa.org/education-and-research/research/nfpa-research/fire-statistical-reports/poverty-and-the-risk-of-fire
108. Fischer, C. S. (1982). To dwell among friends: Personal networks in town and city. University of chicago Press.
109. Florax, R. J. G. M., & Nijkamp, P. (2005). Misspecification in Linear Spatial Regression Models. In K. Kempf-Leonard (Ed.), Encyclopedia of Social Measurement (pp. 695-707). Elsevier.
110. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons.
111. Fotheringham, A. S., Charlton, M. E., & Brunsdon, C. (1998). Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A, 30(11), 1905-1927. https://doi.org/10.1068/a301905
112. Gerell, M., Hallin, P.-O., Nilvall, K., & Westerdahl, S. (2020). Att vända utvecklingen: från utsatta områden till trygghet och delaktighet. Malmö universitet.
113. Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical analysis, 24(3), 189-206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
114. Guldåker, N., & Hallin, P.-O. (2013). Stadens bränder. D. 1, Anlagda bränder och Malmös sociala geografi. Malmö högskola, Institutionen för urbana studier. https://www.diva-portal.org/smash/get/diva2:1404906/FULLTEXT01.pdf
115. Guldåker, N., & Hallin, P.-O. (2014). Spatio-temporal patterns of intentional fires, social stress and socio-economic determinants: A case study of Malmö, Sweden. Fire Safety Journal, 70, 71-80. https://doi.org/10.1016/j.firesaf.2014.08.015
116. Guldåker, N., Hallin, P.-O., Klubien, M. T., & Nilsson, J. (2022). Residential Fires in Metropolitan Areas: Living Conditions and Fire Prevention. Residential Fire Safety (pp. 307-326). The Society of Fire Protection Engineers Series. Springer, Cham. https://doi.org/10.1007/978-3-031-06325-1_18
117. Guldåker, N., Per-Olof, H., Nilsson, J., & Tykesson, M. (2018). Bostadsbränder i storstadsområden. Myndigheten för samhällsskydd och beredskap. https://lucris.lub.lu.se/ws/portalfiles/portal/39907995/Bostadsbra_nder_i_storstadsomra_den.pdf
118. Hantson, S., Lasslop, G., Kloster, S., & Chuvieco, E. (2015). Anthropogenic effects on global mean fire size. International Journal of Wildland Fire, 24(5), 589-596. https://doi.org/10.1071/WF14208
119. Hantson, S., Pueyo, S., & Chuvieco, E. (2015). Global fire size distribution is driven by human impact and climate. Global Ecology and Biogeography, 24(1), 77-86. https://doi.org/10.1111/geb.12246
120. Harinam, V., Bavcevic, Z., & Ariel, B. (2022). Spatial distribution and developmental trajectories of crime versus crime severity: do not abandon the count-based model just yet. Crime Science, 11(1), 1-15. https://doi.org/10.1186/s40163-022-00176-x
121. Hasan, M. R., Kauser, M. R. H., & Ibrahim, J. (2021). Fire Hazard in Chattogram City Corporation Area: A Critical Analysis of Its Causes and Mitigation Measures. Frontiers in Sustainable Cities, 3, 683468. https://doi.org/10.3389/frsc.2021.683468
122. Hastie, C., & Searle, R. (2016). Socio-economic and demographic predictors of accidental dwelling fire rates. Fire Safety Journal, 84, 50-56. https://doi.org/10.1016/j.firesaf.2016.07.002
123. Hu, J., Shu, X., Xie, S., Tang, S., Wu, J., & Deng, B. (2019). Socioeconomic determinants of urban fire risk: a city-wide analysis of 283 Chinese cities from 2013 to 2016. Fire Safety Journal, 110, e102890-e102890. https://doi.org/10.1016/j.firesaf.2019.102890
124. Huang, K. (2009). Population and building factors that impact residential fire rates in large U.S. cities [Master's thesis]. Texas State University. https://hdl.handle.net/10877/3592
125. Jain, P., Coogan, S. C. P., Subramanian, S. G., Crowley, M., Taylor, S., & Flannigan, M. D. (2020). A review of machine learning applications in wildfire science and management. Environmental Reviews, 28(4), 478-505. https://doi.org/10.1139/er-2020-0019
126. Jennings, C. R. (1999). Socioeconomic characteristics and their relationship to fire incidence: a review of the literature. Fire technology, 35(1), 7-34. https://doi.org/10.1023/A:1015330931387
127. Jennings, C. R. (2013). Social and economic characteristics as determinants of residential fire risk in urban neighborhoods: A review of the literature. Fire Safety Journal, 62, 13-19. https://doi.org/10.1016/j.firesaf.2013.07.002
128. Kalinic, M., & Krisp, J. M. (2018). Kernel density estimation (KDE) vs. hot-spot analysis–detecting criminal hot spots in the City of San Francisco [Paper presentation]. Proceeding of the 21st Conference on Geo-Information Science. https://www.researchgate.net/profile/Maja-Kalinic-2/publication/325825793_Kernel_Density_Estimation_KDE_vs_Hot-Spot_Analysis_-_Detecting_Criminal_Hot_Spots_in_the_City_of_San_Francisco/links/5b27de230f7e9b332a31af55/Kernel-Density-Estimation-KDE-vs-Hot-Spot-Analysis-Detecting-Criminal-Hot-Spots-in-the-City-of-San-Francisco.pdf
129. Kocher, S. D., & Butsic, V. (2017). Governance of land use planning to reduce fire risk to homes Mediterranean France and California. Land, 6(2), 24. https://doi.org/10.3390/land6020024
130. Kounadi, O., Ristea, A., Araujo, A., & Leitner, M. (2020). A systematic review on spatial crime forecasting. Crime Science, 9, 1-22. https://doi.org/10.1186/s40163-020-00116-7
131. Laris, P. (2013). Integrating land change science and savanna fire models in West Africa. Land, 2(4), 609-636. https://doi.org/10.3390/land2040609
132. Li, S., & Banerjee, T. (2021). Spatial and temporal patterns of wildfires in California from 2000 to 2019. Authorea Preprints. https://doi.org/10.1002/essoar.10504419.2
133. Li, S., Zhao, Z., Miaomiao, X., & Wang, Y. (2010). Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression. Environmental Modelling & Software, 25(12), 1789-1800. https://doi.org/10.1016/j.envsoft.2010.06.011
134. Lim, D.-H., Na, W.-J., Hong, W.-H., & Bae, Y.-H. (2023). Development of a fire prediction model at the urban planning stage: Ordinary least squares regression analysis of the area of urban land use and fire damage data in South Korea. Fire Safety Journal, 136, 103761. https://doi.org/10.1016/j.firesaf.2023.103761
135. Lindgren, S.-Å., Björk, M., Ekbrand, H., Persson, S., & Uhnoo, S. (2013). Barn/ungdomar som anlägger brand–orsaker och motåtgärder. In: Slutrapport, Göteborg: Göteborgs universitet/Brandforsk. https://www.brandskyddsforeningen.se/globalassets/brandforsk/anlagd-brand/rapporter/rapport_908_091.pdf
136. Liu, K., Wang, J., & Tang, P. (2015). Sprawling Urban Form and Expanding Living Space: A Study on the Relationship of Residential Space Development and Urban Built-up Area Expansion in Nanjing, China. Journal of Asian Architecture and Building Engineering, 14(2), 387-394. https://doi.org/10.3130/jaabe.14.387
137. Liu, Y., Wang, K., Xing, X., Guo, H., Zhang, W., Luo, Q., Gao, S., Huang, Z., Li, H., Li, X., Wang, J., Wang, J., Zhu, D. (2023). On spatial effects in geographical analysis. Acta Geographica Sinica, 78(3), 517-531. https://doi.org/10.11821/dlxb202303001
138. Marcon, E., & Puech, F. (2003). Evaluating the geographic concentration of industries using distance-based methods. Journal of economic geography, 3(4), 409-428. https://doi.org/10.1093/jeg/lbg016
139. Matthews, S., & Yang, T.-C. (2012). Mapping the results of local statistics: Using geographically weighted regression. Demographic Research, 26(6), 151-166. https://doi.org/10.4054/DemRes.2012.26.6
140. Moise, I. K., & Piquero, A. R. (2021). Geographic disparities in violent crime during the COVID-19 lockdown in Miami-Dade County, Florida, 2018–2020. Journal of experimental criminology, 1-10. https://doi.org/10.1007/s11292-021-09474-x
141. Monjarás-Vega, N. A., Briones-Herrera, C. I., Vega-Nieva, D. J., Calleros-Flores, E., Corral-Rivas, J. J., López-Serrano, P. M., Pompa-García, Marín, Rodríguez-Trejo, D. A., Carrillo-Parra, A., González-Cabán, A. (2020). Predicting forest fire kernel density at multiple scales with geographically weighted regression in Mexico. Science of the Total Environment, 718, 137313. https://doi.org/10.1016/j.scitotenv.2020.137313
142. Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17-23. https://doi.org/10.2307/2332142
143. Nakaya, T., Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2005). Geographically weighted Poisson regression for disease association mapping. Statistics in medicine, 24(17), 2695-2717. https://doi.org/10.1002/sim.2129
144. Noori, S., Mohammadi, A., Miguel Ferreira, T., Ghaffari Gilandeh, A., & Mirahmadzadeh Ardabili, S. J. (2023). Modelling and mapping urban vulnerability index against potential structural fire-related risks: an integrated GIS-MCDM approach. Fire, 6(3), 107. https://doi.org/10.3390/fire6030107
145. Openshaw, S. (1984). The modifiable areal unit problem. Concepts and techniques in modern geography. Geo Books. https://www.uio.no/studier/emner/sv/iss/SGO9010/openshaw1983.pdf
146. Ord, J., & Cliff, A. (1973). Spatial autocorrelation. Pion. https://doi.org/10.1177/030913259501900205
147. Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical analysis, 27(4), 286-306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
148. World Health Organization (2018). WHO housing and health guidelines. https://iris.who.int/bitstream/handle/10665/276001/9789241550376-eng.pdf?utm_source=miragenews&utm_medium=miragenews&utm_campaign=news
149. Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6), 269. https://doi.org/10.3390/ijgi8060269
150. Parzen, E. (1962). On estimation of a probability density function and mode. The annals of mathematical statistics, 33(3), 1065-1076. https://doi.org/10.1214/aoms/1177704472
151. Pearson, E., Windsor, T., Crisp, D., Butterworth, P., & Anstey, K. (2012). Neighbourhood characteristics: Shaping the wellbeing of older Australians. NSPAC Research Monograph 2, National Seniors Productive Ageing Centre: Canberra. https://cepar.edu.au/sites/default/files/Neighbourhood_Characteristics_and_Ageing_Well.pdf
152. Ramachandran, G., & Charters, D. (2011). Quantitative risk assessment in fire safety: Routledge. https://doi.org/10.4324/9780203937693
153. Rencher, A. C., & Christensen, W. F. (2012). Multivariate regression. Methods of multivariate analysis (3rd ed.) (pp. 339-383). John Wiley & Sons. https://doi.org/10.1002/9781118391686.ch10
154. Ripley, B. D. (1976). The second-order analysis of stationary point processes. Journal of applied probability, 13(2), 255-266. https://doi.org/10.2307/3212829
155. Ripley, B. D. (1981). Spatial Statistics. John Wiley & Sons. https://doi.org/10.1002/0471725218
156. Rohde, D., Corcoran, J., & Chhetri, P. (2010). Spatial forecasting of residential urban fires: A Bayesian approach. Computers, Environment and Urban Systems, 34(1), 58-69. https://doi.org/10.1016/j.compenvurbsys.2009.09.001
157. Rosenblatt, M. (1956). Remarks on some nonparametric estimates of a density function. The annals of mathematical statistics, 27, 832-837. http://dx.doi.org/10.1214/aoms/1177728190
158. Sabel, C. (1998). Peaks and Troughs: Space-Time Cluster Detection in Rare Diseases [Paper presentation]. The ESRI User's Conference, San Diego, Calif. https://proceedings.esri.com/library/userconf/proc98/PROCEED/TO250/PAP246/P246.HTM
159. Saffary, T., Adegboye, O. A., Gayawan, E., Elfaki, F., Kuddus, M. A., & Saffary, R. (2020). Analysis of COVID-19 cases' spatial dependence in US counties reveals health inequalities. Frontiers in public health, 8, 579190. https://doi.org/10.3389/fpubh.2020.579190
160. Sandhu, H., Singh, G., Sisodia, M., & Chauhan, R. (2016). Identification of black spots on highway with kernel density estimation method. Journal of the Indian Society of Remote Sensing, 44(3), 457-464. https://doi.org/10.1007/s12524-015-0500-2
161. Sannigrahi, S., Pilla, F., Basu, B., Basu, A. S., & Molter, A. (2020). Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustainable cities and society, 62, 102418. https://doi.org/10.1016/j.scs.2020.102418
162. Scherer, C. W., & Cho, H. (2003). A social network contagion theory of risk perception. Risk Analysis: An International Journal, 23(2), 261-267. https://doi.org/10.1111/1539-6924.00306
163. Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461-464.
164. Silverman, B. W. (1986). Density estimation for statistics and data analysis (Vol. 26). Chapman and Hall. https://ned.ipac.caltech.edu/level5/March02/Silverman/paper.pdf
165. Silverman, B. W. (2018). Density estimation for statistics and data analysis. Routledge. https://doi.org/10.1201/9781315140919
166. Singh, P. P., Sabnani, C. S., & Kapse, V. S. (2021). Hotspot analysis of structure fires in urban agglomeration: A case of Nagpur City, India. Fire, 4(3), 38. https://doi.org/10.3390/fire4030038
167. Song, C., Kwan, M.-P., & Zhu, J. (2017). Modeling fire occurrence at the city scale: A comparison between geographically weighted regression and global linear regression. Int J Environ Res Public Health, 14(4), 396. https://doi.org/10.3390/ijerph14040396
168. Song, Y., Niu, L., & Li, Y. (2019). Combinatorial spatial data model for building fire simulation and analysis. ISPRS International Journal of Geo-Information, 8(9), 408. https://doi.org/10.3390/ijgi8090408
169. Špatenková, O., & Virrantaus, K. (2013). Discovering spatio-temporal relationships in the distribution of building fires. Fire Safety Journal, 62, 49-63. https://doi.org/10.1016/j.firesaf.2013.07.001
170. Stewart Fotheringham, A., Charlton, M., & Brunsdon, C. (1996). The geography of parameter space: an investigation of spatial non-stationarity. International Journal of Geographical Information Systems, 10(5), 605-627. https://doi.org/10.1080/02693799608902100
171. Swift, A., Liu, L., & Uber, J. (2008). Reducing MAUP bias of correlation statistics between water quality and GI illness. Computers, Environment and Urban Systems, 32(2), 134-148. https://doi.org/10.1016/j.compenvurbsys.2008.01.002
172. Syphard, A. D., Bar Massada, A., Butsic, V., & Keeley, J. E. (2013). Land use planning and wildfire: development policies influence future probability of housing loss. PloS one, 8(8), e71708. https://doi.org/10.1371/journal.pone.0071708
173. Syphard, A. D., Rustigian-Romsos, H., Mann, M., Conlisk, E., Moritz, M. A., & Ackerly, D. (2019). The relative influence of climate and housing development on current and projected future fire patterns and structure loss across three California landscapes. Global Environmental Change, 56, 41-55. https://doi.org/10.1016/j.gloenvcha.2019.03.007
174. Taridalaa, S., Yudono, A., Ramlia, M. I., & Akil, A. (2016). A GIS-based Model for Urban Fire Risk and Fire Station Allocation Assessment. The Case of Central Business District (CBD), Kendari City,Indonesia. The 1st Geoplanning International Conferences, Bali. https://pakdosen.unhas.ac.id/storage/dokumen/artikel-1618936844-33%20A%20GIS-based.pdf
175. Thompson, O. F., Galea, E. R., & Hulse, L. M. (2018). A review of the literature on human behaviour in dwelling fires. Safety science, 109, 303-312. https://doi.org/10.1016/j.ssci.2018.06.016
176. Tishi, T. R. (2015). A Study on frequency of fire incidents and fire fighting capacity in different land use categories of Dhaka metropolitan area. [Master's thesis]. Bangladesh University of Engineering and Technology. http://lib.buet.ac.bd:8080/xmlui/handle/123456789/4518
177. Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic geography, 46(sup1), 234-240. https://doi.org/10.2307/143141
178. Todorovic, S. (2020). Modelling risk factors in urban residential fires in Helsinki. [Master's thesis]. University of Helsinki. http://urn.fi/URN:NBN:fi:hulib-202006112709
179. Todorovic, S., Rekola, H., Muukkonen, P., & Bernelius, V. (2020). Asuinrakennuspalon riskin ja siihen vaikuttavien tekijöiden maantieteellinen mallinnus Helsingin pelastustoimen alueella. Helsingin kaupungin pelastuslaitos. http://hdl.handle.net/10138/326501
180. Turner, S. L., Johnson, R. D., Weightman, A. L., Rodgers, S. E., Arthur, G., Bailey, R., & Lyons, R. A. (2017). Risk factors associated with unintentional house fire incidents, injuries and deaths in high-income countries: a systematic review. Injury Prevention, 23(2), 131-137. https://doi.org/10.1136/injuryprev-2016-042174
181. U.S.F.A. (1997). Socioeconomic factors and the incidence of fire. Federal Emergency Management Agency,United States Fire Administration,National Fire Data Center. https://www.usfa.fema.gov/downloads/pdf/statistics/socio.pdf
182. UN. (2018). 68% of the world population projected to live in urban areas by 2050, says UN. United Nation Department of Economic and social affairs. https://www.un.org/development/desa/en/news/population/2018-revision-ofworld-urbanization-prospects.html
183. Weisburd, D., Wilson, D. B., Wooditch, A., & Britt, C. L. (2022). Advanced statistics in criminology and criminal justice. Springer. https://link.springer.com/content/pdf/10.1007/978-3-030-67738-1.pdf
184. Wong, L. T., & Lau, S. (2007). A fire safety evaluation system for prioritizing fire improvements in old high-rise buildings in Hong Kong. Fire technology, 43, 233-249. https://doi.org/10.1007/s10694-007-0014-8
185. Wuschke, K., Clare, J., & Garis, L. (2013). Temporal and geographic clustering of residential structure fires: A theoretical platform for targeted fire prevention. Fire Safety Journal, 62, 3-12. https://doi.org/10.1016/j.firesaf.2013.07.003
186. Xia, Z., Li, H., Chen, Y., & Yu, W. (2019). Detecting urban fire high-risk regions using colocation pattern measures. Sustainable cities and society, 49, 101607. https://doi.org/10.1016/j.scs.2019.101607
187. ZahranEl-Said, M., Jiann, T. S., Mohamad’Asri, N. A. A. B., Tan, E. H. A., Yap, Y. H., & Rahman, E. K. A. (2019). Evaluation of various GIS-based methods for the analysis of road traffic accident hotspot. MATEC web of conferences. https://doi.org/10.1051/matecconf/201925803008
188. Zhang, H., Qi, P., & Guo, G. (2014). Improvement of fire danger modelling with geographically weighted logistic model. International Journal of Wildland Fire, 23(8), 1130-1146. https://doi.org/10.1071/WF13195
189. Zhang, X., Yao, J., & Sila-Nowicka, K. (2018). Exploring spatiotemporal dynamics of urban fires: A case of Nanjing, China. ISPRS International Journal of Geo-Information, 7(1), 7. https://doi.org/10.3390/ijgi7010007
190. Zhang, X., Yao, J., Sila-Nowicka, K., & Jin, Y. (2020). Urban fire dynamics and its association with urban growth: Evidence from Nanjing, China. ISPRS International Journal of Geo-Information, 9(4), 218. https://doi.org/10.3390/ijgi9040218