# LotRec: A Recommender for Urban Vacant Lot Conversion

arXiv: https://arxiv.org/abs/2202.02481 · HTML: https://mlsanity.com/paper/2202.02481/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 5 Feb 2022.

## Builds on (most cited first, 1 of 1)

- [Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation](https://mlsanity.com/paper/1607.04373/) (2016)

## Built on by (most cited first, 1 of 1)

- [Community Learning: Understanding A Community Through NLP for Positive Impact](https://mlsanity.com/paper/2210.00590/) (2022)

## Abstract (arXiv)

Vacant lots are neglected properties in a city that lead to environmental hazards and poor standard of living for the community. Thus, reclaiming vacant lots and putting them to productive use is an important consideration for many cities. Given a large number of vacant lots and resource constraints for conversion, two key questions for a city are (1) whether to convert a vacant lot or not; and (2) what to convert a vacant lot as. We seek to provide computational support to answer these questions. To this end, we identify the determinants of a vacant lot conversion and build a recommender based on those determinants. We evaluate our models on real-world vacant lot datasets from the US cities of Philadelphia,PA and Baltimore, MD. Our results indicate that our recommender yields mean F-measures of (1) 90% in predicting whether a vacant lot should be converted or not within a single city, (2) 91% in predicting what a vacant lot should be converted to, within a single city and, (3) 85% in predicting whether a vacant lot should be converted or not across two cities.