Spatial Empirical Dynamic Modeling
spEDM is an R package for spatial causal discovery. It extends Empirical Dynamic Modeling (EDM) from time series to spatial cross-sectional data, provides seamless support for vector and raster spatial data via tight integration with the sf and terra packages, and enables data-driven causal inference from spatial snapshots.
Refer to the package documentation https://stscl.github.io/spEDM/ for more detailed information.
- Install from CRAN with:
install.packages("spEDM", dependencies = TRUE)- Install binary version from R-universe with:
install.packages("spEDM",
repos = c("https://stscl.r-universe.dev",
"https://cloud.r-project.org"),
dependencies = TRUE)- Install from source code on GitHub with:
if (!requireNamespace("pak", quietly = TRUE)) {
install.packages("pak")
}
pak::pak("stscl/spEDM", dependencies = TRUE)Please cite spEDM as:
Lyu, W., Dai, S., Song, Y., Zhao, W., Yi, W., Xiao, Y., Jia, N., 2026. Measuring causal strengths from spatial cross-sectional data with geographical cross mapping cardinality. International Journal of Geographical Information Science 1–23. https://doi.org/10.1080/13658816.2026.2687121
A BibTeX entry for LaTeX users is:
@article{lyu2026gcmc,
title = {Measuring causal strengths from spatial cross-sectional data with geographical cross mapping cardinality},
ISSN = {1362-3087},
DOI = {10.1080/13658816.2026.2687121},
journal = {International Journal of Geographical Information Science},
publisher = {Informa UK Limited},
author = {Lyu, Wenbo and Dai, Shaoqing and Song, Yongze and Zhao, Wufan and Yi, Wen and Xiao, Yumiao and Jia, Nan},
year = {2026},
month = {June},
pages = {1–23}
}Lyu, W., Dai, S., Song, Y., Zhao, W., Yi, W., Xiao, Y., Jia, N., 2026. Measuring causal strengths from spatial cross-sectional data with geographical cross mapping cardinality. International Journal of Geographical Information Science 1–23. https://doi.org/10.1080/13658816.2026.2687121.
Lyu, W., Lei, Y., Yi, W., Song, Y., Li, X., Dai, S., Qin, Y., Zhao, W., 2026. Causal discovery in urban data with temporal empirical dynamic modeling: The R package tEDM. Computers, Environment and Urban Systems 127, 102435. https://doi.org/10.1016/j.compenvurbsys.2026.102435.
Gao, B., Yang, J., Chen, Z., Sugihara, G., Li, M., Stein, A., Kwan, M.-P., Wang, J., 2023. Causal inference from cross-sectional earth system data with geographical convergent cross mapping. Nature Communications 14. https://doi.org/10.1038/s41467-023-41619-6.
Herrera, M., Mur, J., Ruiz, M., 2016. Detecting causal relationships between spatial processes. Papers in Regional Science 95, 577–595. https://doi.org/10.1111/pirs.12144.
Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, M., Munch, S., 2012. Detecting Causality in Complex Ecosystems. Science 338, 496–500. https://doi.org/10.1126/science.1227079.