- Introduction
- Full Syllabus
- Short Course / Practical Labs
- Other Bits...
- Online Course / Tool Providers
In this repo we have aimed to provide links to useful teaching resources for teaching Geographic / Spatial Data Science, GIS and Statistics.
With the spread of COVID-19 and the ongoing international response efforts, it is looking like a lot of the academic community will be delivering teaching remotely and online. This might be a problem for some. As such, we have decided to create a living document containing useful teaching resources. Some of these are from the lab, and others are from people who we have reached out to while compiling this.
We do not claim that any of the resources are fully fledged online courses, or that these are a comprehensive list of what is out there, but we think that these might be helpful to the community to list and share.
If you have anything that you think is useful then send us a tweet @geodatascience or issue a PR to this repository!
This provides 15 practicals in R with Slides - associated with (but independent from the book) https://github.com/alexsingleton/urban_analytics ; topics include R, SQL, Descriptive Statistics, Charts and Graphs, Mapping Areas, Mapping Points, Web Mapping, Mapping Flows, Geodemographics, Indices, Spatial Relationships, Regression, Agent Based Models and Network Analysis.
Material and website for the course Geographic Data Science'19, taught at the University of Liverpool. The course was open to last year undergraduate and master students and uses Python.
This repository contains reproducible materials to teach geographic information and data science in R.
OPEN.ED@PSU offers high-quality learning materials written by Penn State faculty. These materials are freely available for you to use, reuse, revise, remix, and redistribute under a creative commons license.
All the practical instructions and data for the CASA, UCL module - Geographic Information Systems and Science.
This is a nice module written by David O' Sullivan with the objectives of 1) Articulate the theoretical and practical considerations in the application of spatial analysis methods and spatial modelling; 2) Prepare, manipulate, analyse, and display spatial data; 3) Apply existing tools to derive meaningful spatial models; 4) Identify and perform appropriate spatial analysis
This is an upcoming book being developed in the open to cover an introduction to Geographic Data Science using PySAL and the rest of the Python stack for data science.
An introduction to programming in Python for geography and geology BSc/MSc students from the University of Helsinki. Introduced basic Python programming concepts (variables, data structures, loops, conditional statements, data analysis with Pandas, basic data visualization) assuming no prior coding experience. Also introduces use of git and GitHub.com, and has lecture videos available. Part of the forthcoming book Introduction to Python for Geographic Data Analysis.
Teaches you how to do different GIS-related tasks in the Python programming language. Each lesson is a tutorial with specific topic(s) where the aim is to learn how to solve common GIS-related problems and tasks using Python tools. We are using only publicly available data which can be used and downloaded by anyone anywhere. We also provide a computing environment which allows you to instantly start programming and trying out the materials yourself, directly in your browser (no installations needed). Also part of the forthcoming book Introduction to Python for Geographic Data Analysis.
A set of video lectures from a course on Geographical Analysis delivered by Dr. Steven Farber at the University of Utah
This book is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization and geospatial capabilities. The book is open source and has been used as the basis of many courses.
- Geocomputation with R book website
- Binder link to get started with RStudio Server
- Source code
- Guest book
This is a book in progress used for the MSc-level course Spatial Analysis taught at the University of Liverpool, part of the MSc in Geographic Data Science programme.
This course provides you with a toolkit for telling stories with urban data. It will introduce basic code, stats, and reasoning with evidence. The course takes a computational social science approach to working with data. It uses Python and Jupyter notebooks to introduce coding and statistical methods that students can reproduce and experiment with in real-time in the classroom. We start the semester with the basics of coding, then move on to data loading and analysis, then on to basic statistics, then hypotheses and the scientific method, and finally a critical assessment of smart cities and urban informatics.
Recent years have seen the (re)emergence of programmatic approaches to geographical information science and the de-emphasis of established desktop 'GIS' packages, both in research settings and in the commercial world. This class introduces the Python programming language and the Python geospatial ecosystem to prepare students for conducting research in this new context.
This tutorial series is designed to provide an accessible introduction to techniques for handling, analysing and visualising spatial data in R. 12 Practicals including basic descriptive statistics, making maps (or various sorts), Geographically Weighted Regression, point pattern analysis and some help with loops.
A set of online lectures alongside tutorials on Geoda and Spatial R from the Centre for Spatial Data Science at the University of Chicago
A nice intro course in Python....
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Book code and data online at https://asdar-book.org
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December 2019 ECS530 https://www.nhh.no/en/courses/analysing-spatial-data/ (Roger Bivand), course materials: https://github.com/rsbivand/ECS530_h19, slides: https://rsbivand.github.io/ECS530_h19/, videos: https://www.youtube.com/playlist?list=PLXUoTpMa_9s10NVk4dBQljNOaOXAOhcE0
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September 2019 https://github.com/rsbivand/ectqg19-workshop R&Py Spatial Analysis Workshop: European Colloquium on Theoretical and Quantitative Geography (Dani Arribas and Roger Bivand)
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August 2019 Workshop at GEOMED 2019, Glasgow (Roger Bivand): https://github.com/rsbivand/geomed19-workshop, slides: https://rsbivand.github.io/geomed19-workshop/
- https://www.youtube.com/playlist?list=PLXUoTpMa_9s1npXD6S9M0_2pUgnTd6cqV; https://opengeohub.org/summer_school_2019
Course materials, Jupyter notebooks, tutorials, guides, and demos for a Python-based urban data science course.
This tutorial provides you with the basic skills to build your own geodemographic classification using R.
- URL: https://data.cdrc.ac.uk/dataset/creating-geodemographic-classification-using-k-means-clustering-r
Lots of content is available here - https://github.com/kingsgeocomp ; but some highlights include:
- https://github.com/kingsgeocomp/code-camp (Introduction to Programming)
- Some general help to install Anaconda and/or Docker: https://github.com/kingsgeocomp/gsa_env
- https://kingsgeocomputation.org/teaching/code-camp/code-camp-python/ (Introduction to Python - with Binder)
- https://github.com/kingsgeocomp/geocomputation (Foundational Module: Pandas, Seaborn and basic data science...)
- https://github.com/kingsgeocomp/spatial-analysis (Building on the above - some specific spatial content)
- https://github.com/kingsgeocomp/applied_gsa (Some advanced content including clustering; not a full course, but useful content)
A nice set of R materials for looking at satellite data...
These are some really nice resources from Monica Stephens for teaching very elementary GIS to students (first-year undergrads with little math/computer skills); including concepts without computers.
An introduction to QGIS and spatial data, no previous knowledge required
An introduction to using R as a GIS, with no previous experience of coding or programming required.
Some practicals form the Friendly Cities Lab at Georgia Tech
This is a short course being taught over the next 2 weeks on advanced spatial modelling. The course is open for remote attendance by the links detailed in the website below:
Here's a near full, and a bit unstructured, set of materials used to teach spatial data with R at UCL both to geographers and those enrolled on the Q-Step programme. https://jamescheshire.github.io/learningR/intro-to-r.html. Materials are taken from the CDRC tutorials too and the links to data may be broken due to a recent upgrade. Full data available here: https://dataviz.spatial.ly/worksheets/
This resource has over 200 lessons and paths (groups of lessons) on lotsof topics including some R / Data Science. In the gallery you can filter by product - e.g. Arcgis Pro, topic, etc.
This repository contains the materials and instructions for the PySAL workshop that was delivered at SciPy 2020.
Two past Scientific Python Conferences have been recorded and put on Youtube, alongside instructional materials for the course. These are approximately 3-hour long tutorials in how to do spatial data analysis in Python.
- SciPy 2019: https://www.youtube.com/watch?v=fc1f4MNLzdQ
- SciPy 2018: https://www.youtube.com/watch?v=kJXUUO5M4ok
Given the current challenge of moving your GIS courses to a fully online environment, Joseph Kerski has put together these great materials: https://github.com/GDSL-UL/Teaching_Links/blob/master/ESRI_Links.md
This comes from the Leeds Data Society (based in University of Leeds), plus lots of other useful resources
A variety of materials have been put online from the University of Leeds including
Programming for Geographical Information Analysis
A slightly older course on Programming in Java for Social Scientists
GIS, Geocomputation and Geoplanning - practicals in NetLogo (ABM)
Lots of additional resources free online can be found under each chapter for the ABM and GIS book by Crooks et al. - https://www.abmgis.org
A 6 week free online course in fundamental cartography. If you take the course 'live' you get access to exercises, data, videos, forums, software etc. Alternatively, you can download just the data and exercises, and see links to the videos from the download link. Resources cna be used in your own work and courses.
Sign up to take the next course offering:
Go to the basic resource download:
- Agent Based Models in Urban Systems: https://www.youtube.com/watch?v=DQihVrHiulY
This Body of Knowledge documents the domain of geographic information science and its associated technologies (GIS&T). By providing this content in a new digital format, UCGIS aims to continue supporting the GIS&T higher education community and its connections with the practitioners.
The University of Manchester has a spatial skills portal that features screencast tutorials on some of the common features of ArcMap and IDRISI and the new Urban Design Toolkit.
Coursera are providing free access to impacted universities: https://blog.coursera.org/helping-universities-and-colleges-go-fully-online-in-response-to-the-coronavirus/ - There is a lot of GIS content.
Some useful guidance here about how to use Teams for remote learning: https://docs.microsoft.com/en-us/MicrosoftTeams/remote-learning-edu
Carto do a lot of great things with their platform - students and educators can free accounts here: https://carto.com/help/getting-started/student-accounts/ - also don't forget their new Kepler.gl link.
Alasdair Rae's Tweet is excellent - https://twitter.com/undertheraedar/status/1238735124524675072?s=20
Open tutorials and course materials covering topics including data integration, GIS and data intensive science. Uses Python and/or R.
Thanks to Jon Reades, Alex Singleton, Stephano De Sabbata, James Cheshire, Mike Gould, Steve Farber, Joseph Kerski, Robin Lovelace, Francisco Rowe, Dani Arribas-Bel, Adam Dennett, Anthony Robinson, Richard Kingston, Andrew Maclachlan, Alasdair Rae, Monica Stephens, Sergio Rey, Ed Manley, Alison Heppenstall, Ken Steif, Andrew Crooks, Luc Anselin, Marynia Kolak, Clio Andris, Angela Li, Kevin Credit, Roger Bivand, David O'Sulivan, Eli Knaap, Henrikki Tenkanen, David Whipp, Kyle Walker, Kenneth Field, Geoff Boeing, Levi Wolf, Nick Bearman who have all supplied content for this list.... plus anyone we have forgotten!