128 citations as recorded by crossref.

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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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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
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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
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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
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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
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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
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