An evaluation framework for predictive models of neighbourhood change with applications to predicting residential sales in Buffalo, NY (Record no. 133549)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01842nas a2200253Ia 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240802c99999999xx |||||||||||| ||und|| |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER | |
| International Standard Serial Number | 0042-0980 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Vergara, Jan Voltaire |
| 9 (RLIN) | 119786 |
| 245 #3 - TITLE STATEMENT | |
| Title | An evaluation framework for predictive models of neighbourhood change with applications to predicting residential sales in Buffalo, NY |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Name of publisher, distributor, etc. | Urban Studies |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 838-858 |
| 520 ## - SUMMARY, ETC. | |
| Abstract | New data and technologies, in particular machine learning, may make it possible to forecast neighbourhood change. Doing so may help, for example, to prevent the negative impacts of gentrification on marginalised communities. However, predictive models of neighbourhood change face four challenges: accuracy (are they right?), granularity (are they right at spatial or temporal scales that actually matter for a policy response?), bias (are they equitable?) and expert validity (do models and their predictions make sense to domain experts?). The present work provides a framework to evaluate the performance of predictive models of neighbourhood change along these four dimensions. We illustrate the application of our evaluation framework via a case study of Buffalo, NY, where we consider the following prediction task: given historical data, can we predict the percentage of residential buildings that will be sold or foreclosed on in a given area over a fixed amount of time into the future? |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Displacement/Gentrification |
| 9 (RLIN) | 119787 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine Learning |
| 9 (RLIN) | 71338 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Method |
| 9 (RLIN) | 51936 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Neighbourhood |
| 9 (RLIN) | 16535 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Housing |
| 9 (RLIN) | 284 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Dohler, Ehren |
| 9 (RLIN) | 119788 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Phillips, Jonathan |
| 9 (RLIN) | 119789 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Rodriguez, Maria Y |
| 9 (RLIN) | 119790 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Villodas, Melissa L |
| 9 (RLIN) | 119791 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1177/00420980231189403">https://doi.org/10.1177/00420980231189403</a> |
| 999 ## - SYSTEM CONTROL NUMBERS (KOHA) | |
| Koha biblionumber | 133549 |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Date acquired | Serial Enumeration / chronology | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dr VKRV Rao Library | Dr VKRV Rao Library | 02/08/2024 | Vol. 61, No. 5 | AI202 | 02/08/2024 | 02/08/2024 | Article Index |