The Quantile Snapshot Scan: Comparing Quantiles of Spatial Data from Two Snapshots in Time

Travis Moore, Wong Weng-Keen
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2677-2686, 2020.

Abstract

We introduce the Quantile Snapshot Scan (Qsnap), a spatial scan algorithm which identifies spatial regions that differ the most between two snapshots in time. Qsnap is designed for spatial data with a numeric response and a vector of associated covariates for each spatial data point. Qsnap focuses on differences involving a specific quantile of the data distribution. A naive implementation of Qsnap is too computationally expensive for large datasets but our novel incremental update provides an order of magnitude speedup. We demonstrate Qsnap’s effectiveness over an extensive set of experiments on simulated data. In addition, we apply Qsnap to two real-world problems: discovering bird migration paths and identifying regions with dramatic changes in drought conditions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v108-moore20a, title = {The Quantile Snapshot Scan: Comparing Quantiles of Spatial Data from Two Snapshots in Time}, author = {Moore, Travis and Weng-Keen, Wong}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {2677--2686}, year = {2020}, editor = {Chiappa, Silvia and Calandra, Roberto}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/moore20a/moore20a.pdf}, url = {https://proceedings.mlr.press/v108/moore20a.html}, abstract = {We introduce the Quantile Snapshot Scan (Qsnap), a spatial scan algorithm which identifies spatial regions that differ the most between two snapshots in time. Qsnap is designed for spatial data with a numeric response and a vector of associated covariates for each spatial data point. Qsnap focuses on differences involving a specific quantile of the data distribution. A naive implementation of Qsnap is too computationally expensive for large datasets but our novel incremental update provides an order of magnitude speedup. We demonstrate Qsnap’s effectiveness over an extensive set of experiments on simulated data. In addition, we apply Qsnap to two real-world problems: discovering bird migration paths and identifying regions with dramatic changes in drought conditions.} }
Endnote
%0 Conference Paper %T The Quantile Snapshot Scan: Comparing Quantiles of Spatial Data from Two Snapshots in Time %A Travis Moore %A Wong Weng-Keen %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-moore20a %I PMLR %P 2677--2686 %U https://proceedings.mlr.press/v108/moore20a.html %V 108 %X We introduce the Quantile Snapshot Scan (Qsnap), a spatial scan algorithm which identifies spatial regions that differ the most between two snapshots in time. Qsnap is designed for spatial data with a numeric response and a vector of associated covariates for each spatial data point. Qsnap focuses on differences involving a specific quantile of the data distribution. A naive implementation of Qsnap is too computationally expensive for large datasets but our novel incremental update provides an order of magnitude speedup. We demonstrate Qsnap’s effectiveness over an extensive set of experiments on simulated data. In addition, we apply Qsnap to two real-world problems: discovering bird migration paths and identifying regions with dramatic changes in drought conditions.
APA
Moore, T. & Weng-Keen, W.. (2020). The Quantile Snapshot Scan: Comparing Quantiles of Spatial Data from Two Snapshots in Time. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:2677-2686 Available from https://proceedings.mlr.press/v108/moore20a.html.

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