Python Training

Intermediate Geospatial Analysis in Python

This is a course for GIS analysts, scientists, engineers, surveyors, and other data analysts with prior experience working with spatial data in Python.

Prerequisites

Completion of the Python Charmers Python for Geospatial Analysis course and six months Python programming experience.

Expected Outcomes

This course will let you take your spatial analysis further using powerful methods to discover new information using location.

At the end of the course you will understand scientifically and statistically grounded methods of geospatial analysis that you can use to aid in your interpretation of real-world data and to solve real-world problems.

You will learn fundamentals of network analysis through automation of common geospatial tasks, the basics of dealing with network data, as well as more advanced spatial statistics such as measures of spatial autocorrelation and multi-dimensional interpolation and regression. You will also learn techniques for dealing with very large datasets through parallel processing and visualization.

Course Syllabus

Day 1: Automating geospatial processes

Day 1 of the course will revise core concepts, introduce network analysis, and look at common geospatial analysis tasks to automate your workflow and analysis:

  • Revision of key concepts and tools for Geospatial Analysis in Python with geopandas and xarray
  • Mapping the locations of addresses with geocoding with geopy
  • Rasterization, vectorization, and skeletonization: converting old map images to vector data with rasterio and scikit-image
  • Finding the most cost effective path with cost-path analysis.
  • Measuring the flow and capacity of a network: an introduction to network analysis with NetworkX and osmnx
    • How many people can reach the city on trains in rush-hour?
    • How many houses will be without water if a water-main is broken?
  • Automating an analysis process in Python. Updating from remote data sources, analysis, automatically produce a map of your results.

Day 2: Extended analysis of spatial data

Day 2 looks at extending this analysis. You will learn how to perform spatial autoregression tests for spatial dependence, work with point pattern datasets for optimisation and to interpolate surfaces, before finally techniques for managing and visualising large spatial datasets:

  • Measuring spatial dependence with pysal:
    • How much do property prices depend on the location of different schools?
    • Can you detect spatial dependence in illegal graffiti locations?
    • Mesuring air pollution hotspots
  • Creating better boundaries from points as concave hulls. Delaunay triangulation, Voronoi tesselation, and point pattern analysis with scipy, shapely and pysal
  • How many pedestrians are in walking through the city of Melbourne right now? Spatial data interpolation and regression with scipy and scikit-learn
  • Large-scale raster analysis: memory-efficient and parallel techniques with dask, rasterio, xarray. Example: remote-sensing indexing
  • Visualization of large-scale datasets with datashader

Personal help

We are happy to offer on-the-spot problem-solving after each day of the training for you to ask one-on-one questions — whether about the course content and exercises or about specific problems you face in your work and how to solve them. If you would like us to prepare for this in advance, you are welcome to send us background info before the course.

Other information

Format:

Live, online, interactive instructor-led training with video streaming (via Zoom) and a cloud server for coding during the course.

Some sessions are jointly offered both online and face-to-face; see locations and dates below.

Materials:

You will have access to all the course materials via the cloud server: PDF of the course notes, Jupyter notebooks, sample datasets.

We will also send you a bound copy of the course notes, cheat sheets, and a USB stick containing kitchen-sink Python installers for multiple platforms, solutions to the programming exercises, and reference documentation on Python and the third-party packages covered in the course.

Computer (online participants):

Hardware: we recommend ≥ 8 GB of RAM, a headset mic, webcam. Preferably also a quiet room and multiple screens.

Software: a modern browser: Chrome, Firefox, or Safari (not IE or Edge) and Zoom. You do not need to install Python on your own computer.

Coding: we have a cloud-based coding server that supports running code and sharing code with the trainer(s).

Timing:

The course will run from 9:00 to roughly 17:00 each day, with breaks of 50 minutes for lunch and 20 minutes each for morning and afternoon tea.

Certificate of completion:

We will provide you a certificate if you complete the course and successfully answer the majority of the exercise questions.

For face-to-face participants (where applicable):

Venue: modern computer-based training facilities (CBD venues)

Computer: An internet-connected computer will be provided for you.

Food and drink: We will provide lunch, morning and afternoon tea, and drinks.

Testimonials

“The course was delivered by trainers who were extremely knowledgeable in their field. It was really good to learn from the best!”

- Marius Roman

“Great course. Enjoyed it and will follow up with some practical implementation of some of the work.”

- Adam Grace

“One of the best training courses I've been on.”

- David Scurrah

“I loved it. Ed was inspiring.”

- Onoriode Coast

“Really impressed by Python's capability and excited to use as alternative to MatLab, as is free and better supported.”

- Carsten Hofmann

“Very comprehensive intro to every aspect of python. Highly qualified trainer. Beyond my expectation on every aspect.”

- Baichuan Sun

“The VM setup and USB is great. Ed is an excellent instructor - he presents well and welcomes any questions. He is clearly a super smart guy who has a great grasp on what he is teaching - able to just prototype on the fly and the course overall really opened my eyes to python.”

- Jack Hendy

“Course content was well presented and easily digested. Practical exercises were an essential part of the course – a good ratio of lecture/play was achieved. Well done Ed and Henry!”

- Steve Zegelin

“Simply awesome!!”

- James Park

“Both Ed and Henry presented well…. The course structure was adjusted to suit the participants quickly and easily.”

- Jenet Austin

“Excellent training course, excellently presented. Perhaps the best that I have had in the area of IT / programming.”

- George Grozev

“One of the best programming courses I have attended - thanks!”

- Giant Billen

“This course has shown me how I could have done the work I was doing just last week 10x more efficiently in Python.”

- Maruf Rahman

“It was a pleasure ... Shared feedback from all involved was that it’s been one of the most beneficial and well delivered training courses we’ve been a part of.”

- Dylan Matthews

“Was the most fulfilling and rewarding class I have taken since "general relativity" at uni. Was extremely well run. Excellent all round!”

- Dr Millicent Maier

“Very impressed with the course, delivery. And depth of knowledge of Ed and Henry. Far exceeded my expectations and has greatly improved my core skills as well as inspired so many new ideas for my current work / projects. Thank you!”

- Kelsey Druken