Programming for Geospatial Data Science

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Develop workflows for spatial analyses using Python via open source and commercial software.
• Demonstrate an understanding of the basic principles of writing useful code for spatial analyses.
• Demonstrate a basic understanding of the principles of data science and geospatial cloud computing and how to implement these using Python.
• Demonstrate an understanding of how to programme in the open source language Python.

This module is heavily practical-based to maximize the opportunities for students to learn and apply programming skills for geospatial analysis with real datasets. It starts with several practicals introducing the basics of programming in Python, before then moving to geospatial data science applications using Python. This will include working with Arcpy, the Python library for the industry leading software ArcGIS Pro. A major emphasis is on general good practice designing workflows and organizing code for spatial analyses, which will be useful when working on spatial analyses in other packages (e.g. QGIS; which is also based on Python code, as well as R). Introductory lecture This one hour lecture will introduce the students to the main reasons why programming is so useful for spatial analysis, using real examples from the published literature. Topics covered will include reproducibility of code and the ability to batch process analyses. Practical classes – 3 hours each Part 1: Basics of Python (3 weeks) Practical 1: Introduction into the basics of Python Practical 2: Introduction into Python II. Introduction into control statements and dictionaries. Practical 3: Introduction into Python III. More on control statements, and an introduction to writing functions. Part 2: Geospatial analyses using Python – part 1 (3 weeks) Practical 4: Working with spatial files and directories. Introduction to command line [dos prompt and examples of Linux/cookbook). Get them used to ‘what’s under the hood’ with computers, and how to access this. -Introduction to Arcpy geopandas and directories and path structure. Move files around using both Arcpy and geopandas. Practical 5 – Introduction to geospatial analyses GIS based on Week3 in GGES6013. [define projection; reproject; buffer; clean up files.]. Will be taught to do this both via Arcpy and Geopandas Practical 6 – Functions and loops for geospatial analyses. Builds on the material in Practical 5. Again with Arcpy and geopandas Part 3: Intro to Data science using Python (3 weeks) Practical 7: -- Introduction to data science, [building on material from GGES6103 Practical 4], Jupyter Notebook, IDE, NumPy, Matplotlib & Seaborn, Sklearn, e.g., simple random forest regression Practical 8: Introduction to advanced data science deep learning (Keras, Tensorflow, Pytorch) e.g., land cover classifications. Practical 9: Geospatial cloud computing platforms (e.g., GEE, alternatively Planetary Computer) – filtering data, basic GIS processes, and data retrieval. Part 4: Geospatial analyses using Python – part 2 (2 weeks) Practical 10 An ecological workflow. Based on Arcpy and Geopandas. Practical 11 – Functions and loops based on the material in Practical 10

The module will consist of : a)An introductory lecture that outlines why programming is such a valuable skill for spatial analyses b)Practical classes in the computing laboratories. Each of these will consist of a short lecture briefly outlining any necessary background information the datasets being used as well as the key concepts that the practical will cover. Dissemination of course information via Blackboard that will include lecture and practical handouts, the relevant datasets and coursework information.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Class practicals

• Assessment Type: Formative
• Feedback: Students will receive regular formative feedback during practicals by being able to compare their work to worked examples and being able to discuss discrepancies between their work and the worked code.
• Final Assessment: No
• Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.