Referencias

Anselin, L., Li, X., and Koschinsky, J. (2021). GeoDa, from the desktop to an ecosystem for exploring spatial data. Geographical Analysis, 54(3), 439–466. https://doi.org/10.1111/gean.12311

Anselin, L., Syabri, I., and Kho, Y. (2005). GeoDa: An introduction to spatial data analysis. Geographical Analysis, 38(1), 5–22. https://doi.org/10.1111/j.0016-7363.2005.00671.x

Berhane, T. M., Lane, C. R., Wu, Q., Autrey, B. C., Anenkhonov, O. A., Chepinoga, V. V., and Liu, H. (2018). Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory. Remote Sensing, 10(4). https://doi.org/10.3390/rs10040580

Bivand, R., Keitt, T., and Rowlingson, B. (2017). Rgdal: Bindings for the ’geospatial’ data abstraction library. https://CRAN.R-project.org/package=rgdal

Bivand, R., Pebesma, E., and Gómez-Rubio, V. (2013). Applied spatial data analysis with R, second edition. Springer, NY. http://www.asdar-book.org/

Bivand, R., and Yu, D. (2024). Spgwr: Geographically weighted regression. https://CRAN.R-project.org/package=spgwr

Cao, J.-H., Xie, C., Zhou, Y., Wang, G.-J., and Zhu, Y. (2025). Forecasting carbon price: A novel multi-factor spatial-temporal GNN framework integrating graph WaveNet and self-attention mechanism. Energy Economics, 144, 108318. https://doi.org/10.1016/j.eneco.2025.108318

Choi, Y. (2023). GeoAI: Integration of artificial intelligence, machine learning, and deep learning with GIS. Applied Sciences, 13(6). https://doi.org/10.3390/app13063895

Cressie, N. (1993). Statistics for spatial data. John Wiley & Sons, Inc.

Diggle, P. (2003). Spatial analysis of spatial point patterns. CRC Press.

Dorling, D. (2011). Area cartograms: Their use and creation. In The map reader (pp. 252–260). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470979587.ch33

Dueker, K. J., and Kjerne, D. (1989). Multipurpose cadastre: Terms and definitions. American Soc. for Photogrammetry; Remote Sensing.

Gelfand, A. E., Schmidt, A. M., Banerjee, S., and Sirmans, C. F. (2004). Nonstationary multivariate process modeling through spatially varying coregionalization. Test, 13, 263–312. https://doi.org/10.1007/BF02595775

Georganos, S., Grippa, T., Gadiaga, A. N., Linard, C., Lennert, M., Vanhuysse, S., Mboga, N., Wolff, E., and and, S. K. (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

Hernangómez, D. (2025). mapSpain: Administrative boundaries of spain (Version 0.10.0). https://doi.org/10.5281/zenodo.5366622

Hijmans, R. J. (2023). Raster: Geographic data analysis and modeling. https://rspatial.org/raster

Hijmans, R. J. (2025). Terra: Spatial data analysis. https://CRAN.R-project.org/package=terra

Koh, J. (2023). Gradient boosting with extreme-value theory for wildfire prediction. Extremes, 26(2), 273–299. https://doi.org/10.1007/s10687-022-00454-6

Kopczewska, K. (2022). Spatial machine learning: New opportunities for regional science. The Annals of Regional Science, 68(3), 713–755. https://doi.org/10.1007/s00168-021-01101-x

Koutsos, T. M., Menexes, G. C., and Mamolos, A. P. (2021). The use of crop yield autocorrelation data as a sustainable approach to adjust agronomic inputs. Sustainability, 13(4). https://doi.org/10.3390/su13042362

Li, S., Jiang, Y., Ke, S., Nie, K., and Wu, C. (2021). Understanding the effects of influential factors on housing prices by combining extreme gradient boosting and a hedonic price model (XGBoost-HPM). Land, 10(5). https://doi.org/10.3390/land10050533

Pebesma, E. (2018). Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal, 10(1), 439–446. https://doi.org/10.32614/RJ-2018-009

Pebesma, E. (2022a). Sf: Simple features for r. https://CRAN.R-project.org/package=sf

Pebesma, E. (2022b). Spacetime: Classes and methods for spatio-temporal data. https://github.com/edzer/spacetime

Pebesma, E. (2022c). Stars: Spatiotemporal arrays, raster and vector data cubes. https://CRAN.R-project.org/package=stars

Pebesma, E., and Bivand, R. (2023). Sp: Classes and methods for spatial data. https://CRAN.R-project.org/package=sp

Perpiñán, O., and Hijmans, R. (2023). rasterVis. https://oscarperpinan.github.io/rastervis/

Shabanpour, N., Razavi-Termeh, S. V., Sadeghi-Niaraki, A., Choi, S.-M., and Abuhmed, T. (2022). Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping. International Journal of Applied Earth Observation and Geoinformation, 112, 102854. https://doi.org/10.1016/j.jag.2022.102854

Song, A., Liu, Y., Feng, T., Li, H., Zhang, Y., Wang, X., Liu, L., Zhang, B.-P., and Li, J.-F. (2022). Simultaneous enhancement of piezoelectricity and temperature stability in KNN-based lead-free ceramics via layered distribution of dopants. Advanced Functional Materials, 32(34), 2204385. https://doi.org/10.1002/adfm.202204385

Taskin Kavzoglu, and Alihan Teke. (2022). Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost). Bulletin of Engineering Geology and the Environment, 81(5). https://doi.org/10.1007/s10064-022-02708-w

Tiefelsdorf, G., M., and Boots, B. (1999). Environment and Planning A: Economy and Space, 31(1), 165–180. https://doi.org/10.1068/a310165

Tobler, W. (2004). Thirty five years of computer cartograms. Annals of the Association of American Geographers, 94(1), 58–73. https://doi.org/10.1111/j.1467-8306.2004.09401004.x

Wang, F., Wang, Y., Zhang, K., Hu, M., Weng, Q., and Zhang, H. (2021). Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation. Environmental Research, 202, 111660. https://doi.org/10.1016/j.envres.2021.111660

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer.

Wickham, H. (2023). Stringr: Simple, consistent wrappers for common string operations. https://CRAN.R-project.org/package=stringr

Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., Woo, K., Yutani, H., and Dunnington, D. (2022). ggplot2: Create elegant data visualisations using the grammar of graphics. https://CRAN.R-project.org/package=ggplot2

Wickham, H., François, R., Henry, L., Müller, K., and Vaughan, D. (2023). Dplyr: A grammar of data manipulation. https://CRAN.R-project.org/package=dplyr

Wickham, H., Vaughan, D., and Girlich, M. (2023). Tidyr: Tidy messy data. https://CRAN.R-project.org/package=tidyr

Yan, S., Jing, L., and Wang, H. (2021). A new individual tree species recognition method based on a convolutional neural network and high-spatial resolution remote sensing imagery. Remote Sensing, 13(3). https://doi.org/10.3390/rs13030479

Zhou, Y., Zhang, Y., Pang, R., and Xu, B. (2021). Seismic fragility analysis of high concrete faced rockfill dams based on plastic failure with support vector machine. Soil Dynamics and Earthquake Engineering, 144, 106587. https://doi.org/10.1016/j.soildyn.2021.106587