How Many Significant LISA?: A multiple testing correction
Abstract
In spatial data analysis, global measures of spatial correlation provide useful insights into the general spatial patterns, but fail to capture local variations. Local Indicators of Spatial Association (LISA) address this limitation by identifying local clusters and spatial outliers, offering valuable applications in many fields of research such as public health, urban planning and environmental monitoring, to name only a few. However, the application of LISA tools in hypothesis testing is undermined by the presence of the multiple testing problem which dramatically increases the risk of committing Type I errors especially when dealing with very large datasets. Standard corrections procedure (like, e. g., Bonferroni and Benjamini-Hochberg procedures) fail to consider the spatial dependence among the various LISA tests and, furthermore, they are overly conservative. This paper proposes an adjustment that, by incorporating the notion of the "effective sample size", improves statistical inference balancing false discovery rate control and statistical power.
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BibTeX
@misc{arbia2026many,
title = {{How Many Significant LISA?: A multiple testing correction}},
author = {Giuseppe Arbia and Francesca Cardile and Vincenzo Nardelli and Niccolò Salvini},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.13472},
eprint = {2610.13472},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.13472},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - How Many Significant LISA?: A multiple testing correction
AU - Giuseppe Arbia
AU - Francesca Cardile
AU - Vincenzo Nardelli
AU - Niccolò Salvini
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.13472
UR - https://arxiv.org/abs/2610.13472
AB - In spatial data analysis, global measures of spatial correlation provide useful insights into the general spatial patterns, but fail to capture local variations. Local Indicators of Spatial Association (LISA) address this limitation by identifying local clusters and spatial outliers, offering valuable applications in many fields of research such as public health, urban planning and environmental monitoring, to name only a few. However, the application of LISA tools in hypothesis testing is undermined by the presence of the multiple testing problem which dramatically increases the risk of committing Type I errors especially when dealing with very large datasets. Standard corrections procedure (like, e. g., Bonferroni and Benjamini-Hochberg procedures) fail to consider the spatial dependence among the various LISA tests and, furthermore, they are overly conservative. This paper proposes an adjustment that, by incorporating the notion of the "effective sample size", improves statistical inference balancing false discovery rate control and statistical power.
ER -