Geospatial Active Search for Preventing Evictions

Anindya Sarkar, Alex DiChristofano, Sanmay Das, Patrick J. Fowler, Nathan Jacobs, Yevgeniy Vorobeychik

Research output: Contribution to journalConference articlepeer-review

Abstract

Evictions are a threat to housing stability and a major concern for many cities. An open question is whether data-driven methods can enhance door-to-door outreach programs to target at-risk tenants. We model this problem using a new framework we term geospatial active search. Geospatial Active Search integrates visual information such as satellite imagery along with tabular data such as property and neighborhood-level information to create an online exploration plan. We develop an approach for the implementation of Geospatial Active Search in St. Louis to find properties containing tenants who will have an eviction filed against them.

Original languageEnglish
Pages (from-to)2456-2458
Number of pages3
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2024-May
StatePublished - 2024
Event23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024 - Auckland, New Zealand
Duration: May 6 2024May 10 2024

Keywords

  • active search
  • eviction
  • geospatial
  • housing
  • reinforcement learning

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