Cite
Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling.
MLA
Georganos, Stefanos, et al. “Geographical Random Forests: A Spatial Extension of the Random Forest Algorithm to Address Spatial Heterogeneity in Remote Sensing and Population Modelling.” Geocarto International, vol. 36, no. 2, Feb. 2021, pp. 121–36. EBSCOhost, https://doi.org/10.1080/10106049.2019.1595177.
APA
Georganos, S., Grippa, T., Niang Gadiaga, A., Linard, C., Lennert, M., Vanhuysse, S., Mboga, N., Wolff, E., & Kalogirou, S. (2021). Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto International, 36(2), 121–136. https://doi.org/10.1080/10106049.2019.1595177
Chicago
Georganos, Stefanos, Tais Grippa, Assane Niang Gadiaga, Catherine Linard, Moritz Lennert, Sabine Vanhuysse, Nicholus Mboga, Eléonore Wolff, and Stamatis Kalogirou. 2021. “Geographical Random Forests: A Spatial Extension of the Random Forest Algorithm to Address Spatial Heterogeneity in Remote Sensing and Population Modelling.” Geocarto International 36 (2): 121–36. doi:10.1080/10106049.2019.1595177.