# Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime prediction

arXiv: https://arxiv.org/abs/1902.03237 · HTML: https://mlsanity.com/paper/1902.03237/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 6 of 6)

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- [A Survey on Societal Event Forecasting with Deep Learning](https://mlsanity.com/paper/2112.06345/) (2021)
- [Web Mining to Inform Locations of Charging Stations for Electric Vehicles](https://mlsanity.com/paper/2203.07081/) (2022)
- [Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Survey](https://mlsanity.com/paper/2403.00780/) (2024)
- [Geo-Semantic-Parsing: AI-powered geoparsing by traversing semantic knowledge graphs](https://mlsanity.com/paper/2503.01386/) (2025)

## Abstract (arXiv)

Crime events are known to reveal spatio-temporal patterns, which can be used for predictive modeling and subsequent decision support. While the focus has hitherto been placed on areas with high population density, we address the challenging undertaking of predicting crime hotspots in regions with low population densities and highly unequally-distributed crime.This results in a severe sparsity (i.e., class imbalance) of the outcome variable, which impedes predictive modeling. To alleviate this, we develop machine learning models for spatio-temporal prediction that are specifically adjusted for an imbalanced distribution of the class labels and test them in an actual setting with state-of-the-art predictors (i.e., socio-economic, geographical, temporal, meteorological, and crime variables in fine resolution). The proposed imbalance-aware hyper-ensemble increases the hit ratio considerably from 18.1% to 24.6% when aiming for the top 5% of hotspots, and from 53.1% to 60.4% when aiming for the top 20% of hotspots.