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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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Inverse Distance Weighted Random Forests: Modeling Unevenly Distributed Non-Stationary Geographic Data
Recent years saw explosive growth of Human Geography Data, in which spatial non-stationarity is often observed, i.e., relationships between features depend on the location. For these datasets, a single global model cannot accurately describe the relationships among features that vary across space. To address this problem, a viable solution- that has been adopted by many studies-is to create multiple local models instead of a global one, with each local model representing a subregion of the space. However, the challenge with this approach is that the local models are only fitted to nearby observations. For sparsely sampled regions, the data could be too few to generate any high-quality model. This is especially true for Human Geography datasets, as human activities tend to cluster at a few locations. In this paper, we present a modeling method that addresses this problem by letting local models operate within relatively large subregions, where overlapping is allowed. Results from all local models are then fused using an inverse distance weighted approach, to minimize the impact brought by overlapping. Experiments showed that this method handles non-stationary geographic data very Well, even When they are unevenly distributed.
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- PAR ID:
- 10275853
- Date Published:
- Journal Name:
- 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS)
- Page Range / eLocation ID:
- 41 to 46
- 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/ICACSIS51025.2020.9263208
