Conference proceeding
A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity
2021 IEEE International Conference on Data Mining (ICDM), Vol.2021-, pp.767-776
12/2021
DOI: 10.1109/ICDM51629.2021.00088
Abstract
Spatial data are ubiquitous, massively collected, and widely used to support critical decision-making in many societal domains, including public health (e.g., COVID-19 pandemic control), agricultural crop monitoring, transportation, etc. While recent advances in machine learning and deep learning offer new promising ways to mine such rich datasets (e.g., satellite imagery, COVID statistics), spatial heterogeneity - an intrinsic characteristic embedded in spatial data - poses a major challenge as data distributions or generative processes often vary across space at different scales, with their spatial extents unknown. Recent studies (e.g., SVANN, spatial ensemble) targeting this difficult problem either require a known space-partitioning as the input, or can only support very limited number of partitions or classes (e.g., two) due to the decrease in training data size and the complexity of analysis. To address these limitations, we propose a model-agnostic framework to automatically transform a deep learning model into a spatial-heterogeneity-aware architecture, where the learning of arbitrary space partitionings is guided by a learning-engaged generalization of multivariate scan statistic and parameters are shared based on spatial relationships. We also propose a spatial moderator to generalize learned space partitionings to new test regions. Experiment results on real-world datasets show that the spatial transformation and moderation framework can effectively capture flexibly-shaped heterogeneous footprints and substantially improve prediction performances.
Details
- Title: Subtitle
- A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity
- Creators
- Yiqun Xie - University of MarylandErhu He - University of PittsburghXiaowei Jia - University of PittsburghHan Bao - University of IowaXun Zhou - University of IowaRahul Ghosh - University of Minnesota SystemPraveen Ravirathinam - University of Minnesota System
- Resource Type
- Conference proceeding
- Publication Details
- 2021 IEEE International Conference on Data Mining (ICDM), Vol.2021-, pp.767-776
- Publisher
- IEEE
- DOI
- 10.1109/ICDM51629.2021.00088
- ISSN
- 1550-4786
- eISSN
- 2374-8486
- Language
- English
- Date published
- 12/2021
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380498802771
Metrics
3 Record Views