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Geographic datasets are usually accompanied by spatial non-stationarity – a phenomenon that the relationship between features varies across space. Naturally, nonstationarity can be interpreted as the underlying rule that decides how data are generated and alters over space. Therefore, traditional machine learning algorithms are not suitable for handling non-stationary geographic datasets, as they only render a single global model. To solve this problem, researchers often adopt the multiple-local-model approach, which uses different models to account for different sub-regions of space. This approach has been proven efficient but not optimal, as it is inherently difficult to decide the size of subregions. Additionally, the fact that local models are only trained on a subset of data also limits their potential. This paper proposes an entirely different strategy that interprets nonstationarity as a lack of data and addresses it by introducing latent variables to the original dataset. Backpropagation is then used to find the best values for these latent variables. Experiments show that this method is at least as efficient as multiple-local-model-based approaches and has even greater potential.
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Geographic Boosting Tree: Modeling Non-Stationary Spatial Data
Non-stationarity is often observed in Geographic datasets. One way to explain non-stationarity is to think of it as a hidden "local knowledge" that varies across space. It is inherently difficult to model such data as models built for one region do not necessarily fit another area as the local knowledge could be different. A solution for this problem is to construct multiple local models at various locations, with each local model accounting for a sub-region within which the data remains relatively stationary. However, this approach is sensitive to the size of data, as the local models are only trained from a subset of observations from a particular region. In this paper, we present a novel approach that addresses this problem by aggregating spatially similar sub-regions into relatively large partitions. Our insight is that although local knowledge shifts over space, it is possible for multiple regions to share the same local knowledge. Data from these regions can be aggregated to train a more accurate model. Experiments show that this method can handle non-stationary and outperforms when the dataset is relatively small.
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- PAR ID:
- 10275854
- Date Published:
- Journal Name:
- 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
- Page Range / eLocation ID:
- 1205 to 1210
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICMLA51294.2020.00190
