Summary of Active Geospatial Search For Efficient Tenant Eviction Outreach, by Anindya Sarkar et al.
Active Geospatial Search for Efficient Tenant Eviction Outreach
by Anindya Sarkar, Alex DiChristofano, Sanmay Das, Patrick J. Fowler, Nathan Jacobs, Yevgeniy Vorobeychik
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel active geospatial search (AGS) modeling framework to mitigate the risk of tenant eviction by targeting at-risk tenants with data-driven outreach programs. The AGS framework integrates property-level information to identify a sequence of rental units to canvas, determining their eviction risk and providing support if needed. A hierarchical reinforcement learning approach is used to learn a search policy that balances exploration and exploitation, accounting for travel costs and budget constraints. The algorithm adapts online to newly discovered information about evictions. Evaluation on a large urban area demonstrates the effectiveness of the proposed framework in identifying eviction cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to help cities prevent tenants from being kicked out of their homes by using special computer programs that find at-risk people. These programs look at property details and figure out which places to visit to help those people stay safe. The researchers developed a new way to train these programs, so they can learn as they go along and get better at finding the right people to help. This approach works really well in big cities with lots of homes. |
Keywords
» Artificial intelligence » Reinforcement learning
