Taylor Oshan
Associate Professor
Washington, District of Columbia, United States
Summary
🤩
Rockstar🎓
Top SchoolTaylor Oshan is an Associate Professor and quantitative geographer specializing in computational social science and geographic information science, with 11 years of experience translating spatial data into actionable insights. He develops and applies spatial interaction models to understand urban movement and individual decision-making in dense environments, and contributes core calibration and diagnostic functionality to the widely used PySAL spatial analysis library. His work spans GIS, spatial statistics, remote sensing, web mapping, and urban informatics, and he builds open-source tools to make complex movement modeling accessible. Taylor has a strong track record of producing end-to-end systems—from field GPS collection and geodatabases to web applications and analytic pipelines—and teaching GIS in applied contexts. Based in Washington, DC, he combines rigorous PhD-level research with pragmatic software development and cross-disciplinary communication. A less obvious strength is his experience bridging archaeological field GIS and modern urban analytics, demonstrating versatility in both historical and contemporary spatial problems.
11 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (PhD), Geography, Doctor of Philosophy (PhD), Geography at Arizona State UniversityUniversity of DelawareMaster's degree, Geography/G.I.Sci., A, Master's degree, Geography/G.I.Sci., A at Hunter College
Github Skills (4)
lm10
glm10
generalized-linear-models10
python10
Programming languages (5)
Github contributions (5)
pysal/pysal
Mar 2015 - Nov 2017
PySAL: Python Spatial Analysis Library Meta-Package
Role in this project:
Back-end Developer
Back-end DeveloperContributions:266 commits, 19 PRs, 1 push in 2 years 8 months
Contributions summary:Taylor contributed to the development of core spatial interaction modeling functionalities for the PySAL library, specifically focusing on the maximum likelihood estimation of gravity models. This involved implementing the core logic for unconstrained, production-constrained, attraction-constrained, and doubly-constrained models, with support for exponential and power function distance-decay. The code changes reflect the creation of classes and methods for model calibration using iteratively re-weighted least squares and inclusion of diagnostic measures, with the goal of expanding the SpInt module capabilities within PySAL.
meta-packagepythonspatial-analysispysalspatial
TaylorOshan/spint
Jan 2017 - Jan 2021
Contributions:62 pushes, 4 branches in 4 years
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