127 citations as recorded by crossref.

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30. Predicting forest fire kernel density at multiple scales with geographically weighted regression in Mexico N. Monjarás-Vega et al. https://doi.org/10.1016/j.scitotenv.2020.137313
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41. A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence C. Song et al. https://doi.org/10.3390/su9050819
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45. The Influence of Socioeconomic Factors on Human Wildfire Ignitions in the Pacific Northwest, USA C. Reilley et al. https://doi.org/10.3390/fire6080300
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49. Drivers of forest fire occurrence in the cultural landscape of Central Europe M. Adámek et al. https://doi.org/10.1007/s10980-018-0712-2
50. GIS-Based Local Spatial Statistical Model of Cholera Occurrence: Using Geographically Weighted Regression F. Nkeki & A. Osirike https://doi.org/10.4236/jgis.2013.56050
51. Wildfires as a major challenge for natural regeneration in Atlantic Forest J. dos Santos et al. https://doi.org/10.1016/j.scitotenv.2018.09.016
52. Fire danger assessment in Iran based on geospatial information S. Eskandari & E. Chuvieco https://doi.org/10.1016/j.jag.2015.05.006
53. Human-caused fire occurrence modelling in perspective: a review S. Costafreda-Aumedes et al. https://doi.org/10.1071/WF17026
54. Modeling Fire Occurrence at the City Scale: A Comparison between Geographically Weighted Regression and Global Linear Regression C. Song et al. https://doi.org/10.3390/ijerph14040396
55. Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression L. Ríos-Pena et al. https://doi.org/10.1016/j.envsoft.2018.03.008
56. Spatial-temporal analysis of pedestrian injury severity with geographically and temporally weighted regression model in Hong Kong X. Xu et al. https://doi.org/10.1016/j.trf.2020.02.003
57. Developing Lebanon's fire danger forecast G. Mitri et al. https://doi.org/10.1016/j.ijdrr.2017.06.028
58. Probabilistic modelling of wildfire occurrence based on logistic regression, Niassa Reserve, Mozambique E. Nhongo et al. https://doi.org/10.1080/19475705.2019.1615559
59. Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model D. Xiao et al. https://doi.org/10.1155/2019/8521649
60. Cross-regional modelling of fire occurrence in the Alps and the Mediterranean Basin İ. Bekar et al. https://doi.org/10.1071/WF19158
61. Regional livestock grazing, human demography and fire incidence in the Portuguese landscape F. Torres-Manso et al. https://doi.org/10.5424/fs/2014231-02758
62. Early prediction of forest fire events integrating multi-sensor datasets and machine learning techniques D. Bera et al. https://doi.org/10.1007/s00477-025-03070-5
63. Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain Y. Martín et al. https://doi.org/10.1080/19475705.2018.1526219
64. Exploring Multi-Driver Influences on Indonesia's Biomass Fire Patterns from 2002 to 2019 through Geographically Weighted Regression S. Wee et al. https://doi.org/10.1007/s41651-023-00166-w
65. Comparative analysis of CORINE and climate change initiative land cover maps in Europe: Implications for wildfire occurrence estimation at regional and local scales L. Vilar et al. https://doi.org/10.1016/j.jag.2019.01.019
66. Geographically weighted logistic regression approach to explore the spatial variability in travel behaviour and built environment interactions: Accounting simultaneously for demographic and socioeconomic characteristics F. Nkeki & M. Asikhia https://doi.org/10.1016/j.apgeog.2019.05.008
67. Characteristics of human-induced forest fires in China by integrating information from court sentencing records and multiple geospatial data K. Li et al. https://doi.org/10.1080/19475705.2026.2634214
68. Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey D. GÜLÇİN & B. DENİZ https://doi.org/10.18182/tjf.649747
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72. Mapping Spatial Drivers of Predicted Active Fires Kernel Density with Geographically Weighted Regression in Mexico N. Monjarás-Vega et al. https://doi.org/10.3390/f17020264
73. A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China) H. Hong et al. https://doi.org/10.1007/s12665-017-6981-2
74. Different Factors for Different Causes: Analysis of the Spatial Aggregations of Fire Ignitions in Catalonia (Spain) J. González‐Olabarria et al. https://doi.org/10.1111/risa.12339
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79. Spatial heterogeneity of county-level grain protein content in winter wheat in the Huang-Huai-Hai region of China Y. Zhao et al. https://doi.org/10.1016/j.eja.2022.126466
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