- 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.
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.
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
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)
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.
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.
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
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.
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
Thanks to Jon Reades, Alex Singleton, Stephano De Sabbata, James Cheshire, Mike Gould, Steve Farber, Joseph Kerski, Robin Lovelace, Francisco Rowe, Dani Arribas-Bel, Adnam Dennett, Anthony Robinson, Andrew Maclachla, Alasdair Rae, Monica Stephens, Sergio Rey, Ed Manley, Alison Heppenstall, Andrew Crooks, Luc Anselin, Marynia Kolak, Angela Li, Kevin Credit, David O'Sulivan, Eli Knaap who have all supplied content for this list.... plus anyone we have forgotten!