Dani Arribas-bel
Co Director at The Alan Turing Institute
Greater Liverpool Area United Kingdom
Summary
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Rockstar🎓
Top SchoolDani Arribas-bel is an urban economist and spatial data scientist with 14 years of experience bridging academic research and open-source geocomputation. As Professor and Co-Director based in the Greater Liverpool Area, he leads urban analytics programmes at The Alan Turing Institute and an imagery data service while maintaining an active research and teaching portfolio. He is a seasoned Python developer and contributor to major geospatial projects such as PySAL and GeoPandas, where his work on plotting, classification schemes and bug fixes has improved reproducible spatial visualization and analysis. His background in spatial econometrics and long involvement with GeoDa and European research centres give him a rare combination of rigorous econometric training and practical tooling expertise. Colleagues value him for turning complex spatial methods into usable, well-tested code that supports both academic insight and applied decision-making.
14 years of coding experience
3 years of employment as a software developer
Master, Economics, Master, Economics at Universidad de ZaragozaSummer School, Summer School at University of SeoulSpatial Econometrics Advanced Institute, Spatial Econometrics, Spatial Econometrics Advanced Institute, Spatial Econometrics at Sapienza Università di RomaErasmus, Economics, Erasmus, Economics at Stockholm University
Github Skills (13)
data-visualizations10
pandas10
data-visualization10
data-visualisation10
spatial-data-analysis10
geospatial10
python10
spatial-analysis10
geospatial-analysis10
matplotlib10
pytest9
testing9
bokeh8
Programming languages (18)
Github contributions (5)
pysal/pysal
Mar 2013 - Jul 2019
PySAL: Python Spatial Analysis Library Meta-Package
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 review, 141 commits, 29 PRs in 6 years 5 months
Contributions summary:Dani primarily contributes to the PySAL project by implementing and refining spatial analysis tools. Their work includes adding methods to read and plot shapefiles (lines and points), improving existing mapping functionalities, and fixing bugs to ensure that functions within the project work in a consistent way. The user has demonstrated experience with different classification schemes, as well as visualization libraries like matplotlib and bokeh, for choropleth and LISA cluster maps. The commits also highlight their contributions to bug fixing and adding features to support PySAL's mission.
pysalpythonspatial-analysis
geopandas/geopandas
Oct 2015 - Oct 2017
Python tools for geographic data
Role in this project:Data Scientist / Software Engineer
Data Scientist / Software EngineerContributions:1 review, 6 commits, 3 PRs in 2 years
Contributions summary:Dani primarily contributed to the `geopandas` library by addressing reported issues and improving code consistency. Their commits involved refactoring plotting functionalities, specifically modifying the use of `axes` and `colormap/cmap` parameters, as well as fixing bugs related to the plotting of empty GeoDataFrames. Additionally, the user adapted the testing suite, indicating a focus on maintaining code quality and ensuring the library functions as intended. These changes focused on the plotting module, which is crucial for visualizing geographic data.
pythongeoparquetgeospatialpandasspatial
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