Identifying Localized Amenities for Gentrification Using a Machine Learning-based Framework


The process of gentrification changes the composition and character of urban neighbourhoods in cities worldwide. Amenities such as art galleries, designer boutiques interact with most gentrification processes and could act as indicators for measuring gentrification. Previous literature has explored the role of amenities in gentrification, and some have found distinctive amenity landscapes in different spatial contexts. However, there is a lack of a more generalized approach for identifying gentrification-related amenities across different regions. This study proposed a machine learning-based framework to identify localized gentrification amenities. Specifically, amenities were represented by Points of Interest (POIs) and matched to the North American Industry Classification System (NAICS). Given typical gentrification neighborhoods in an area, featured amenities can be identified via a supervised gradient boosting method. The framework was applied to Shenzhen, a major Chinese city. Results showed that Shenzhen has a distinct amenity landscape in its gentrified neighborhoods; for example, bubble tea beverage shops were recognized as a dominant amenity, as well as financial institutions, digital electronics, and car-related amenities. The proposed machine learning-based framework provides a generalized approach to identifying gentrification-related amenities in different regions, and enables dynamic and fine-grained tracking of gentrification on the basis of big data.

In Applied Geography
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Create your slides in Markdown - click the Slides button to check out the example.

Supplementary notes can be added here, including code, math, and images.

Qili Gao (高琦丽)
Qili Gao (高琦丽)
Assistant Professor

My research interests include human dynamics and urban informatics.