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Summary of Network-based Transfer Learning Helps Improve Short-term Crime Prediction Accuracy, by Jiahui Wu and Vanessa Frias-martinez


Network-Based Transfer Learning Helps Improve Short-Term Crime Prediction Accuracy

by Jiahui Wu, Vanessa Frias-Martinez

First submitted to arxiv on: 10 Jun 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Computers and Society (cs.CY)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This novel transfer learning framework enhances short-term crime prediction models by leveraging human mobility data from source regions with abundant mobility information to fine-tune local crime prediction models in target regions. By transferring weights from deep learning architectures trained on historical crime data, this approach improves accuracy in regions where mobility data is scarce. The results demonstrate F1 score improvements for target cities with limited mobility data, across various types of crimes and US cities. This framework’s ability to adapt to diverse cities and crime types underscores its potential for real-world applications.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper helps predict where crimes will happen soon. They used special computer models that learn from past crimes, but these models need data about how people move around a city. In some places, this data is hard to get, which makes the models less accurate. To solve this problem, they came up with a new way to use the model from one place and adapt it for another. This helps make predictions better in cities where we don’t have much mobility data.

Keywords

» Artificial intelligence  » Deep learning  » F1 score  » Transfer learning