# Active Geospatial Search for Efficient Tenant Eviction Outreach

arXiv: https://arxiv.org/abs/2412.17854 · HTML: https://mlsanity.com/paper/2412.17854/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Dynamic Traffic Modeling From Overhead Imagery](https://mlsanity.com/paper/2012.10530/) (2020)
- [A Visual Active Search Framework for Geospatial Exploration](https://mlsanity.com/paper/2211.15788/) (2022)
- [A Partially Supervised Reinforcement Learning Framework for Visual Active Search](https://mlsanity.com/paper/2310.09689/) (2023)
- [Fine-Grained Property Value Assessment using Probabilistic Disaggregation](https://mlsanity.com/paper/2306.00246/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Scalable Approach to Solving Simulation-Based Network Security Games](https://mlsanity.com/paper/2602.16564/) (2026)
- [Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery](https://mlsanity.com/paper/2602.17605/) (2026)

## Abstract (arXiv)

Tenant evictions threaten housing stability and are a major concern for many cities. An open question concerns whether data-driven methods enhance outreach programs that target at-risk tenants to mitigate their risk of eviction. We propose a novel active geospatial search (AGS) modeling framework for this problem. AGS integrates property-level information in a search policy that identifies a sequence of rental units to canvas to both determine their eviction risk and provide support if needed. We propose a hierarchical reinforcement learning approach to learn a search policy for AGS that scales to large urban areas containing thousands of parcels, balancing exploration and exploitation and accounting for travel costs and a budget constraint. Crucially, the search policy adapts online to newly discovered information about evictions. Evaluation using eviction data for a large urban area demonstrates that the proposed framework and algorithmic approach are considerably more effective at sequentially identifying eviction cases than baseline methods.