| Autor: |
Liang, Xiaofan, Brainerd, Brian, Hicks, Tara, Andris, Clio |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal of Planning Education and Research (2024) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1177/0739456X241273945 |
| Popis: |
Addressing strategies for managing vacant, abandoned, and deteriorated (VAD) properties is important for maintaining healthy communities. Yet, the process of identifying these properties can be difficult. Here, we create a human-in-the-loop machine learning (HITLML) model called VADecide and apply it to a parcel-level case study in Savannah, Georgia. The results show a higher prediction accuracy than was achieved when using a machine learning model without human input in the training. The HITLML approach also reveals differences between machine vs. human-generated results. Our findings contribute to knowledge about the advantages and challenges of HITLML in urban planning. [Accepted for Publication at a Peer Review Journal] |
| Databáze: |
arXiv |
| Externí odkaz: |
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