Geoscripting

Solve geographical and any spatial applied problems through scripting.
Professionals in private companies and research institutes are increasingly using scripting languages for applied spatial analysis. Many students still have a "scripting" barrier to overcome. In Geoscripting you will be introduced to programming in a gentle and applied fashion aimed to make scripting more accessible.
Geoscripting will help advanced programmers to delve into geospatial problems, but the focus is on scripting, so the course is also suitable for early stage programmers as well. During Geoscripting you will learn how to use free and open-source software to deal with spatial vector and raster data, such as field survey point data and satellite imagery used in remote sensing studies. You will replace the traditional memorising cookbook approach to scripting with an explorative approach. You will learn how to search for solutions both online, and among your peers, and you are encouraged to have a try-out mindset during the project weeks. Good scripting habits are paramount to great collaborative programming projects, so you will learn about version control and other key tools for a successful project.

• Demonstrate scripting skills by writing clear, documented and reproducible scripts to read, write and visualise spatial data

• Apply version control supporting effective collaboration in a team

• Use documentation, search tools and contribute to discussion platforms with reproducible examples as an aid to solve scripting challenges effectively

• Use and create open-source software with an appropriate software license

• Combine functions from novel libraries to efficiently process spatial data

• Create an integrated software project that solves a spatial challenge

• Assignment other (20%) Per pair: Handed-in practical scripting assignments and their peer review
• Assignment oral presentation (50%) In teams of four, creating a reproducible script to solve a spatial challenge and its oral defence.
• Written test with open and closed questions (30%) Open book, on-campus, Bring Your Own Device, digital exam using the CodeGrade system (not Ans). Use of chat programs and chat-based AI systems is not allowed.

Students should have basic programming knowledge (equivalent to INF22306 Programming in Python or MAT27803 R for Statistics), ideally in both R and Python, and also have a good understanding of spatial data (vector, raster) via GRS10306 (Introduction Geo-Information Science) or equivalent.

• * Applied spatial data analysis with R (2nd ed.). This book can be downloaded via the WUR library as a pdf. Via Springer books link (WUR Library); * Spatial Data Science: With Applications in R (https://r-spatial.org/book/).