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GeezerVison is showing github.com โ€” mikeqfu - Overview

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Qian Fu mikeqfu

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Research Fellow
35 followers ยท 69 following
  • University of Birmingham
  • 12:36 (UTC)
  • https://orcid.org/0000-0002-6502-9934
  • in/qianfu
  • @mikeqfu
  • @mikeqfu.bsky.social

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mikeqfu/README.md

๐Ÿ‘‹ Hi, I'm Qian!

๐ŸŽ“ Postdoctoral researcher specialising in data integration, engineering and analytics.
๐Ÿ”ฌ Designing and building data-driven solutions to solve practical transport and energy challenges.
๐Ÿ› ๏ธ Focusing on bridging the gap between analytical methodologies and real-world applications via robust data pipelines.
๐ŸŒฑ Developing and maintaining open-source Python packages designed to streamline research workflows.

Transport Energy Data Geospatial Analytics Open Source

๐Ÿ“ฆ Python Packages

Package Description
PyHelpers An open-source toolkit for facilitating Python users' data manipulation tasks.
PyPI version Conda-Forge version License DOI Read The Docs Open Issues
PyRCS An open-source tool for collecting railway codes used in different UK rail industry systems.
PyPI version Conda-Forge version License DOI Read The Docs Open Issues
PyDriosm An open-source tool for downloading, reading and PostgreSQL-based I/O of OpenStreetMap data.
PyPI version License DOI Read The Docs Open Issues

๐Ÿ“Š GitHub Stats (Public Repos)

GitHub Stats Most Used Languages

๐Ÿ“š Selected Publications

  • Huang, Y., Jaroszweski, D., Fu, Q., & Chapman, L. (2027). Disproportionate weather sensitivity in bus travel: Evidence from smart card data in the West Midlands, UK. Journal of Transport & Health, 52, 102461. doi:10.1016/j.jth.2026.102461 | ๐Ÿ“„
  • Jiang, Y., Fu, Q., Thomopoulos, N., & Chen, J. L. (2025). Understanding the influence of past driving experience on electric vehicle purchase intention in China. Transport Policy, 270-282. doi:10.1016/j.tranpol.2024.11.025 | ๐Ÿ“„
  • Fu, Q., Nicholson, G. L., & Easton, J. M. (2024). Understanding data quality in a data-driven industry context: Insights from the fundamentals. Journal of Industrial Information Integration, 42, 100729. doi:10.1016/j.jii.2024.100729 | ๐Ÿ“„
  • Yu, Y., Fu, Q., Zhang, D., & Gu, Q. (2024). Understanding User Experience with Smart Home Products. Journal of Computer Information Systems, 1โ€“23. doi:10.1080/08874417.2024.2408006 | ๐Ÿ“„ | ๐Ÿ’ป
  • Fu, Q., Easton, J. M., & Burrow, M. P. N. (2024). Development of an Integrated Computing Platform for Measuring, Predicting, and Analyzing the Profile-Specific Fixity of Railway Tracks. Transportation Research Record, 2678(6), 1-13. doi:10.1177/03611981231191521 | ๐Ÿ“„ | ๐Ÿ’ป
  • Fu, Q., & Easton, J. M. (2018). Prediction of weather-related incidents on the rail network: prototype data model for wind-related delays in Great Britain. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 4(3), 04018027. doi:10.1061/AJRUA6.0000975 | ๐Ÿ“„ | ๐Ÿ’ป

Pinned Loading

  1. network-rail-weather-rail-incident-prediction network-rail-weather-rail-incident-prediction Public

    A (prototype) model for prediction of weather-related rail incidents in Great Britain.

    Python 1 1

  2. network-rail-track-fixity-layer network-rail-track-fixity-layer Public

    This project develops a data mining tool for analysing and predicting track movements using asset data, environmental factors and track design knowledge to model key parameters and generate fixity โ€ฆ

    Python

  3. smart-home-product-reviews-analysis smart-home-product-reviews-analysis Public

    Understanding user experience using text mining & analysis: A case study of Amazon.com reviews for smart-home products.

    Python

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