Kema, Steven (2020) Geography and AI: a happy marriage? Exploring the potential of Machine Learning as a new method in geographic research. Master thesis.
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Abstract
Big data analytics can offer Geography new ways of understanding complex socio-spatial processes, especially with the increasingly available amount of data that is produced by society. This thesis explores the potential of machine learning in Geography via a case study of neighbourhood level gentrification prediction. Several machine learning algorithms are compared; XGBoost, CatBoost, and Random Forest regression outperform standard quantitative methods. The implementation of SHapley Additive exPlanations (SHAP) as a way of interpreting machine learning models is explored, and suggests that SHAP is a promising solution to the need for explainable machine learning models. Future gentrification prediction reveals that model specification has substantial impact on result interpretation and practical applicability, and suggests that theoretical foundation remains a key factor in future development of the research field. Machine learning provides a lot of new opportunities for geography but it is also important to be critical of its promises.
| Item Type: | Thesis (Master) |
|---|---|
| Degree programme: | Economic Geography |
| Supervisor: | Koster, S. and Ballas, D. |
| Date Deposited: | 17 Sep 2020 10:08 |
| Last Modified: | 17 Sep 2020 10:08 |
| URI: | https://frw.studenttheses.ub.rug.nl/id/eprint/3365 |
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