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Developing a machine learning model to map new-build gentrification: A mixed-methods approach.

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Summary
This summary is machine-generated.

This study uses Artificial Intelligence (AI) and community input to identify new-build gentrification in Philadelphia. Machine learning models accurately detect development reflecting local gentrification cues, improving urban trend mapping.

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Area of Science:

  • Urban Studies
  • Geographic Information Systems (GIS)
  • Artificial Intelligence (AI)

Background:

  • New-build gentrification significantly alters urban landscapes in the US.
  • Existing literature often overlooks the built environment's role, leading to imprecise gentrification mapping.
  • Machine learning (ML), particularly computer vision, offers advanced capabilities for analyzing urban streetscape changes.

Purpose of the Study:

  • To develop and validate an ML model for identifying new-build gentrification based on community-defined architectural cues.
  • To integrate local resident insights with AI for a nuanced understanding of gentrification.
  • To improve the accuracy of gentrification trend mapping and projections.

Main Methods:

  • Community-based focus groups in Philadelphia identified local visual indicators of new-build gentrification.
  • A ResNet-50 deep learning model was trained to recognize these identified architectural traits.
  • Model performance was evaluated using test accuracy and Area Under the Curve (AUC) scores.
  • Results were compared with municipal permit data using Kernel Density Estimate (KDE) maps.

Main Results:

  • The fine-tuned ResNet-50 model achieved 84.0% test accuracy and an 84.0% AUC score.
  • The study successfully integrated community-derived data with AI for localized gentrification identification.
  • Visualizations using KDE maps highlighted spatial trends of new-build development.

Conclusions:

  • A novel mixed-methods approach combining AI and community input can accurately identify locally specific new-build gentrification.
  • This methodology enhances the precision of urban development and gentrification analysis.
  • The findings contribute to more accurate urban planning and policy-making regarding neighborhood change.