To do list:
(1) Zhu, D., Liu, Y., Yao, X. and Fischer, M.M., 2022. Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. GeoInformatica, 26(4), pp.645-676.
Compare this with OLS, Spatial Lag, and Spatial Error models.
(2) Zhu, D., Cheng, X., Zhang, F., Yao, X., Gao, Y. and Liu, Y., 2020. Spatial interpolation using conditional generative adversarial neural networks. International Journal of Geographical Information Science, 34(4), pp.735-758. Compare this with Kridging model and other spatial interpolation models.
(3) Mai, G., Janowicz, K., Yan, B., Zhu, R., Cai, L. and Lao, N., 2020. Multi-scale representation learning for spatial feature distributions using grid cells. arXiv preprint arXiv:2003.00824. Encoding locations.
(4) Du, Z., Wang, Z., Wu, S., Zhang, F. and Liu, R., 2020. Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. International Journal of Geographical Information Science, 34(7), pp.1353-1377. Compare this with GWR.
(5) Wu, S., Wang, Z., Du, Z., Huang, B., Zhang, F. and Liu, R., 2021. Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. International Journal of Geographical Information Science, 35(3), pp.582-608. Compare this with GTWR.