Syllabus (Semester 1, 2026)

GISCI 343: Geo-Spatial Data Science

University of Auckland, 2026
Instructor: Dr Hyesop Shin
Semester 1: 12 weeks (2 March - 2 June 2026)
Book: https://dataandcrowd.github.io/python-gis/

Course overview

This course provides comprehensive training in Python for urban analytics, progressing from foundational geospatial programming to production-ready research software. Students will learn to analyse urban data, create interactive web dashboards using Shiny for Python, and publish Python packages to PyPI. The course emphasises reproducible research, open science practices, and software development skills essential for modern geospatial research and practice.

Level: Intermediate to Advanced
Contact hours:

Lecture: 2 hours per week (Monday 13:00-15:00)
Lab: 2 hours per week (Tuesday 13:00-15:00)

Prerequisites: - Nothing in principle (but previous exposure helps) - Recommended: basic programming experience (any language) - Familiarity with GIS concepts - Statistical thinking

Learning objectives

By the end of this course, students will be able to:

Manipulate and analyse geospatial data using Python (GeoPandas, OSMnx, momepy)
Apply spatial analysis techniques to urban analytics problems
Design and build interactive web dashboards using Shiny for Python
Structure, document, and test Python packages following best practices
Publish Python packages to PyPI using modern tooling (uv)
Deploy web applications to production environments
Apply reproducible research workflows and version control
Communicate spatial analysis results through visualisations, dashboards, and blog posts

Software requirements

Essential

Python 3.10 or higher
Positron IDE (or VS Code)
Quarto for literate programming
uv for package management
Git and GitHub account
TestPyPI and PyPI accounts (free registration)

Python libraries (managed via uv)

Core geospatial: geopandas, shapely, pyproj, rasterio, contextily
Network analysis: osmnx, networkx, momepy
Dashboard: shiny, shinylive, ipyleaflet, plotly, shinyswatch

Platforms

GitHub for version control (essential across assignments)
Substack account for blogging (Assignment 1)
shinylive account for dashboard hosting (Assignment 2)

Assessment structure

Assessment	Weight	Due date
Weekly quizzes	15%	Weeks 2-10 (not wk8)
Assignment 1: Data Blog Post	15%	5 April (Week 5)
Canvas Mid-sem Test	15%	27-29 March (Week 4)
Assignment 2: Shiny Dashboard	15%	10 May (Week 8)
Poster Showcase	10%	26th May (Week 11, B301 Ground floor)
Assignment 3: PyPI Package	30%	7 June (Week 12)

Total: 100%

Note on Assignment 2: Due date adjusted to 10 May to account for ANZAC Day week having no lecture. Students receive Week 7 (ANZAC week) as dedicated lab time for dashboard development.

Note on Poster Showcase: Students will present an A1 poster describing their packages at a designated showcase event (B301-G10).

Course schedule

Part 1: Introduction to geospatial analysis in Python

Week 1: Course introduction and Python setup (2-3rd March)

Lecture topics

Course overview and expectations
Python for geospatial analysis: why and how?
Python fundamentals review: modules, packages, environments
Introduction to Quarto for literate programming
Modern package management with uv (in lab session)
Basic Python that you need to know

Lab activities

Install Python, Positron, and Quarto
Set up development environment with uv
Install essential geospatial packages
Create your first Quarto document
Test installations with simple examples

Learning resources - uv documentation: https://docs.astral.sh/uv/ - Quarto documentation: https://quarto.org/docs/get-started/ - Python refresher materials (provided)

Key skills: Environment setup, package management, Quarto basics

Week 2: Fundamentals of Python (9 March)

Lecture topics

Python skills

Module imports: import, from...import, import...as
Data structures: long vs wide formatting
Data manipulation fundamentals: filtering, selecting, sorting
Pandas operations: groupby, merge, join
Data visualisation
Dealing with possible errors
Importing geospatial datasets (GeoJSON, Shapefiles, GeoPackage)
Vector and raster data visualisation with GeoPandas and matplotlib
Calculating basic distance measures (Euclidean, haversine)

Urban analytics

Data quality issues in real-world urban data
Identifying outliers and missing data in urban datasets
Statistical imputation methods
Working with census geographies (SA2 level in NZ)

Lab activities

Disclosing Assignment 1
Install and configure Git for version control
Long/wide formatting with pedestrian footfall datasets (Heart of City)
Data manipulation exercises: filtering, groupby, merge operations
Work with messy pedestrian count data (intro to Assignment 1 dataset)
Imputation practice: mean/median/forward fill
Descriptive statistics and exploratory visualisations
Intro to folium for interactive web maps
Write your first Python module

Key skills: Data wrangling, distance calculations, spatial aggregation, imputation, exploratory analysis

Week 3: Advanced geospatial operations (16 March)

Lecture topics

Python skills

Vector and raster data visualisation with GeoPandas and matplotlib
Choropleths and classification (Jenks, quantiles, equal interval)
Interactive web mapping with folium
Spatial aggregation: counting points within polygons (SA2)
Spatial joins across geographic levels
Distance-based analysis: buffers, proximity calculations
CRS and transformations
Spatial indexing and performance

Urban analytics context

Introduction to pedestrian mobility
Introduction to urban morphology with momepy
Walkability indices and street connectivity
Census geography in NZ (SA1, SA2, SA3) and aggregation choices

Lab activities

Download SA2 boundaries for Auckland (Stats NZ)
Count sensors by SA2 and compute average footfall per SA2
Distance matrix between sensors and key destinations
Intro to momepy: basic morphometrics
Street connectivity metrics and relate to footfall
Choropleths with Jenks
Interactive folium maps with popups/tooltips

Key skills: Spatial joins, buffers, network analysis, momepy, choropleths

Week 4: Urban morphology and mid-term assessment (23 March)

Guest lecture: Shriv (Monday 23 March, 13:00-14:00) - Urban morphology using momepy: concepts and applications - Case studies of morphology and walkability analysis - Connecting morphology to pedestrian behaviour - Walkability frameworks with OSMnx

Test guide

Test preparation guidance

Lab - Assignment 1 work session and test preparation

Canvas test (27-29 March) - Practical Python coding assessment covering Weeks 1-4

Key skills: Morphology concepts, revision, coding under assessment conditions

Week 5: Functions and workflows (30 March)

Lecture topics

Writing effective functions and code organisation - important for Assignment 1 and 3
Functions for extremes (max/min) and grouped summaries
GitHub workflows and reproducibility

Lab activities

Reusable functions for blog analysis
Comparing years (pre/post COVID)
Refactoring and code review
Quarto structure and formatting
Final Assignment 1 work session

Assignment 1 due: 5 April Sunday 23:59

Easter and mid-semester break (3 April - 17 April 2026)

No classes. Classes resume Monday 20 April (Week 6).

Part 2: Deploying geospatial work online

Week 6: Introduction to WebGIS and Shiny (20 April)

Lecture topics

WebGIS fundamentals, Wasm overview
Shiny for Python
Interactive maps with ipyleaflet
Building your architecture: Ideation is key!

Guest lecture: Alex Raichev (14:15-15:00)

Lab activities

Announcement of Assignment 2
Build a first Shiny app locally
Reactive outputs (maps, plots, tables)
Controls (sliders, dropdowns)

Key skills: Reactive programming, Shiny fundamentals

Week 7: Dashboard work session (ANZAC week) (27 April)

ANZAC Day: Monday 27 April 2026

Watch Video of urban mobility OD flows.

No lecture. Labs run as normal.

Lab activities

Intensive dashboard development
One-on-one consultations, peer code review
OSM data workshop and network analysis support
Deployment preparation for shinylive

Week 8: Advanced dashboards, networks, and temporal analysis (4 May)

Lecture topics

Multi-page apps (ui.nav_panel())
shinylive deployment workflow
Temporal controls and animation patterns
Dashboard UX principles and performance

Lab activities

Final refinement, performance testing
Deployment troubleshooting
Submission preparation

Assignment 2 due: 10th May Sunday 23:59

Part 3: Publishing your first Python package

Week 9: Built environment and pedestrian policy (11 May)

Lecture topics

The built environment and walking: land-use mix, density, street design, and pedestrian infrastructure
Policy levers for pedestrian mobility: low-traffic neighbourhoods, shared spaces, speed management, pedestrianisation
International case studies: Superblocks (Barcelona), School Streets, 15-minute city (Paris)
Package structure and pyproject.toml, modular design, src layout

Lab activities

Scoping your package: what policy-relevant pedestrian analysis could your tool support?
uv init, package scaffolding, module organisation
Pair work on design architecture

Week 10: Pedestrian equity, safety, and testing geospatial code (18 May)

Lecture topics

Equity in pedestrian infrastructure provision: who benefits, who is underserved?
Pedestrian safety: collisions, exposure to air/noise pollution
Accessibility for diverse populations: older adults, children, disabled walkers
Writing and structuring tests for spatial functions

Lab activities

Write tests for geofencing, distances, OD aggregation
Build docs and tutorials
Final docs, demo notebook, poster finalisation
Poster finalisation

Week 11: CI/CD and pre-publication (25 May)

Lecture topics

Walkability policy and practice
GitHub Actions, pre-commit, semantic versioning
Build and publish to TestPyPI with uv
Licensing and release workflow
Poster design basics

Poster showcase: 26th May (B301-G10)

Week 12: Final publication and poster showcase prep (1 June)

King’s Birthday: Monday 1 June 2026

No lecture.

Lab activities.

Q&A
CI setup, uv build, TestPyPI publish and install testing
Publish to PyPI (if not already)

Assignment 3 due: 7 June Sunday 23:59

Course Policies

Open Science and Reproducibility

All code must be version controlled using Git from Week 1. Students are required to make their packages open source under an appropriate licence (MIT recommended). Dashboards and blog posts should be publicly accessible ~~unless they contain sensitive data~~.

Code Quality Standards

Code submitted for Assignments 2 and 3 must: - Pass automated linting (ruff/black) - Include type hints for public functions (Assignment 3) - Have comprehensive docstrings - Follow PEP 8 style guidelines (https://peps.python.org/pep-0008/) - Be properly documented

Academic Integrity

Standard university academic integrity policies apply. All work must be your own. You may:

Discuss concepts and approaches with peers
Use AI assistants (GitHub Copilot, ChatGPT, Claude) for debugging and learning
Reference external code with proper attribution

You may not:

Copy code from peers or external sources without attribution
Submit work that you don’t understand
Share solution code before deadlines

Data Ethics

When working with urban data:

Respect individual privacy (avoid identifying individuals)
Consider impacts on communities represented in data
Acknowledge data sources and limitations
Be transparent about assumptions and uncertainties
Include ethical reflection in project documentation

Accessibility

Dashboards must consider accessibility:

Colourblind-safe palettes
Keyboard navigation
Clear labels and instructions
Responsive design

Late Submissions

Late submissions incur 10% penalty per day (including weekends)
Extensions available for documented circumstances (apply in advance)
Technical issues: contact instructor immediately, not after deadline

Use of AI Tools

AI tools are permitted for:

Debugging and error resolution
Learning new concepts
Documentation writing
Code suggestions and completion

However:

You must understand all code you submit
Your writing must be your own (if you have used AI in your work, do mention that)
Creative design and analytical approach must be your own
Over-reliance on AI without understanding is apparent and penalised

Weekly Time Expectations

Students should expect to spend approximately 10-12 hours per week:

Lecture: 2 hours
Lab: 2 hours
Independent work: 6-8 hours (readings, assignments, practise)

Peak workload weeks:

Week 5: Assignment 1 completion (12-15 hours)
Week 8: Assignment 2 completion (15-20 hours)
Week 11-12: Assignment 3 completion (20+ hours)

Learning Resources

Essential Documentation

GeoPandas: https://geopandas.org/
Shiny for Python: https://shiny.posit.co/py/
uv Documentation: https://docs.astral.sh/uv/
OSMnx: https://osmnx.readthedocs.io/
Quarto: https://quarto.org/
Python Packaging Guide: https://packaging.python.org/
pytest: https://docs.pytest.org/
MkDocs: https://www.mkdocs.org/

Recommended Books

Rey, S.J., Arribas-Bel, D., & Wolf, L.J. (2020). Geographic Data Science with Python. Available online.
Percival, H. (2017). Test-Driven Development with Python. O’Reilly.
The Turing Way: https://the-turing-way.netlify.app/

Data Sources

OpenStreetMap (via OSMnx or Overpass API)
Stats NZ (New Zealand census and geographic data)
Auckland Council open data portal
Urban Observatory platforms
Natural Earth (global datasets)

Example Projects

Awesome Geospatial: https://github.com/sacridini/Awesome-Geospatial
Python for Geographic Data Science course materials
PyPI urban analytics packages: momepy, urbanaccess, pandana

Technical Support and Troubleshooting

Getting Help

Before Asking:

Check official documentation
Search Stack Overflow and GitHub issues
Review lecture materials and labs
Try debugging with print statements or debugger

When Asking:

Canvas Discussion Forum: General questions, peer support (response: 24-48 hours)
Lab Sessions: Hands-on help from tutors and instructor
Office Hours for Hyesop: Mondays 3-4pm (by appointment)
Email: hyesop.shin@auckland.ac.nz (allow 48 hours for response)

For Technical Issues:

ITS Service Desk: Software installation problems
Course tutors: Python/GIS questions, project opinions
Instructor: Project-specific guidance, conceptual questions

Common Issues

uv Problems:

Update: uv self update
Clear cache: uv cache clean
Check Python version compatibility

Shiny Deployment:

Test locally first
Check file sizes (shinylive has limits)
Use relative paths, not absolute
Verify all dependencies

Testing Spatial Data:

Use appropriate geometric tolerances
Mock external data sources
Create small test fixtures
Test edge cases

PyPI Publication:

Test on TestPyPI first
Ensure unique package name
Complete all metadata in pyproject.toml
Verify README renders correctly

Technical Prerequisites Check

Before Week 1, ensure you can:

Access a computer with Python 3.10+
Install software (admin rights may be needed)
Create GitHub account
Call Powershell (Windows) or Terminal (MacOSX/Linux)
Understand basic Git concepts (or willing to learn)

Contact Information

Instructor: Dr Hyesop Shin
Email: hyesop.shin@auckland.ac.nz
Office: Room 301-529B, Building 301
Office Hours: Mondays 3-4pm (book via email)

Course Materials: - Canvas: Main course hub for announcements, assignments, and resources - Webbook: https://dataandcrowd.github.io/python-gis/ - Discussion Forum: Ed Disucssion board

Getting Help: - During Lab Sessions: Ask tutors and instructor for immediate help - Canvas Discussion Forum: Post questions for peer and instructor support (24-48 hour response) - Office Hours: Mondays 3-5pm or email to arrange another time - Email: For personal matters or urgent issues (allow 48 hours for response)

Course Schedule Summary

Important Dates: - Easter/Mid-Semester Break: 3 April - 17 April 2026 (two weeks, no classes) - ANZAC Day: Monday 27 April 2026 (no lecture) - King’s Birthday: Monday 1 June 2026

Week	Dates	Topic	Assessment
1	2 Mar	Course intro, Python setup	-
2	9 Mar	Geospatial Python fundamentals	-
3	16 Mar	Advanced geospatial operations	-
4	23 Mar	Guest (1-2pm) + Walkability	Canvas Test (27-28 Mar)
5	30 Mar	Functions and workflows	A1 Due (3 Apr)
-	3-17 Apr	EASTER/MID-SEMESTER BREAK	-
6	20 Apr	WebGIS and Shiny intro (+ Guest)	-
7	27 Apr	NO LECTURE (ANZAC Day) - Labs only	-
8	4 May	Advanced dashboards and networks	A2 Due (10 May)
9	11 May	Package structure built environment	Pair work on design architecture
10	18 May	Adding functions, data, and documentation Pedestrian safety
11	25 May	Walkability and Policy Poster showcase	-
12	1 June	NO LECTURE (King’s Birthday) - Labs only	Package submission due

Note on Assignment 2: Due date moved to 8 May (Thursday of Week 8) to account for ANZAC Day week having no lecture. Students get Week 7 labs to work on dashboards.

Note on Assignment 3 & Poster: Both due 7 June. Poster showcase event at separate venue (details to be announced).

Statement on Course Evolution

This syllabus reflects the current course plan. Minor adjustments may be made based on class progress, student needs, or external factors. Significant changes will be communicated via Canvas with adequate notice. Students will always be notified of any modifications to assessment requirements or due dates.

The integration of modern tools (uv, Positron, shinylive) represents the evolving landscape of geospatial Python. We embrace these changes to ensure students learn current best practices.

Last Updated: March 2026
Version: 3.0

This course emphasises practical skills for urban analytics research, planning, and policy support. The combination of spatial analysis, interactive visualisation, and software development prepares students for careers in academia, government, consulting, and technology sectors.