Contrasting spatial co-location pattern discovery aims to find subsets of spatial features whose prevalences are substantially different in two spatial domains. This problem is important for generating hypotheses in many spatial applications, including oncology, regional economics, ecology, and epidemiology. In oncology, for example, this problem is important in developing immune-checkpoint inhibitor therapy for cancer treatment. This problem is challenging due to the large number of potential patterns that are exponentially related to the number of input spatial features. Traditional methods of co-location pattern detection require multiple runs, making computationally expensive and do not scale to large datasets. To address these limitations, we propose a Contrasting Spatial Co-location Discovery (CSCD) framework and contribute two filter-refine algorithms that exploit a novel interest measure; the participation index distribution difference (PIDD). Experiments on multiple cancer datasets (e.g., MxIF) show that the proposed algorithm yields substantial computational time savings compared with a baseline algorithm. A real-world case study demonstrates that the proposed work discovers patterns that are missed by the related work and have the potential to inspire new scientific discovery.
|Original language||English (US)|
|Title of host publication||Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2022|
|Editors||Ashwin Shashidharan, Krishna Karthik Gadiraju, Varun Chandola, Ranga Raju Vatsavai|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||11|
|State||Published - Nov 1 2022|
|Event||10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2022 - Seattle, United States|
Duration: Nov 1 2022 → …
|Name||Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2022|
|Conference||10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2022|
|Period||11/1/22 → …|
Bibliographical noteFunding Information:
This material is based upon work supported by the NSF under Grants No. 2040459, 1737633, 1901099, and 1916518. We also thank Kim Koolt and the Spatial Computing Research Group for valuable comments and renements.
© 2022 ACM.
- contrasting spatial co-location
- participation index distribution difference (PIDD)