Machine-learning-enabled spatial pattern mining: evaluating the impact of imperfect inputs

doi:10.1080/13658816.2025.2490687

Machine-learning-enabled spatial pattern mining: evaluating the impact of imperfect inputs

2025-04-21

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DOI：10.1080/13658816.2025.2490687

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Spatial pattern mining (SPM) aims to detect geographic locations or areas that present interesting, nontrivial, and potentially useful patterns. Traditional formulations of point-based SPM tasks are mainly based on true observations, which tend to have limited spatial coverage, availability, and timeliness. While machine learning (ML) has the potential to extend the range of usable data, the uncertainty of model-predicted labels presents new challenges for their usability in the SPM context. This paper formulates the task of ML-enabled SPM using predicted labels by ML models. Given the ever-expanding family of spatial patterns, we consider four widely-adopted patterns - hotspots, co-locations, mixture patterns, and spatial outliers - to scope our study to make the discussion concrete. We develop soft-label versions of SPM algorithms that can directly execute on uncertain predictions generated by ML models. Additionally, we evaluate the ML-enabled SPM results for both categorical and real-valued datasets across a spectrum of prediction quality. The results show that certain spatial patterns such as multinomial scan statistic-based mixture patterns and normal-model-based hotspots can more robustly maintain the detection quality at different error levels, while others such as spatial outliers are more sensitive to incorrect predictions. This provides helpful guidance on using learning-based predictions for SPM.

Keywords:

Machine learning

pattern mining

imperfect labels

evaluation

Machine-learning-enabled spatial pattern mining: evaluating the impact of imperfect inputs

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