Assessing Spatial Equity and Potential Green Gentrification in Urban Farming Using GIS-based Machine Learning: A Case Study of Taipei’s Garden City Program

Urban Farm DB summary

This study examines the spatial equity and socio-spatial implications of urban agriculture by analysing the distribution of community gardens under Taipei's Garden City Program (TGCP) in relation to neighborhood-level income growth, using unsupervised machine learning (DBSCAN) combined with spatial statistics (Moran's I and LISA). It finds that garden service density is highly concentrated in central districts with significant service gaps in peripheral areas, while at the citywide scale no significant correlation is found between garden density and income growth -- consistent with the "just green enough" view that low-cost, small-scale, decentralized community gardens are less likely to trigger conventional green gentrification -- though several local areas do show potential spatial coupling between garden presence and income change, pointing to a need for policy attention to distributional equity.