I am very pleased to announce a Spatial Data Analysis tutorial for current graduate students. Spatial Data Analysis comprises a body of quantitative and statistical approaches for the analysis of geographically referenced datasets found across the geographical and spatial sciences including: census data, transportation networks, parcel and land-use records, remotely sensed earth imagery, survey microdata, economic and environmental indicators, etc. These are the methods that are used in quantitative inquiry across all domains of geography, urban planning, and increasingly in other social sciences, and expertise in their application is becoming a key determinant of research success and attraction in the academic and professional job markets for grads.

In recent years, PySAL has emerged as the leading open source library for conducting spatial data analysis using the Python programming language, garnering more than 70,000 downloads by 2015. The hands-on tutorial will be comprised of 3 main phases. First, participants will be introduced to Python and the related tools for manipulating and analyzing spatial data using PySAL. Second, participants will be instructed on how to carry out advanced exploratory spatial data analysis using the tools within PySAL. Third, graduate students will develop and workshop their preliminary analysis plans for their own research projects, and receive expert guidance and feedback from the workshop instructor as well as a panel of faculty advisors drawn from the Department of Geography and Planning (including Drs. Steven Farber and Michael Widener).

## Agenda

### Day 1 - Tuesday RW117

- **9:00/10:30**: the Scientific Python stack: Jupyter and the Notebook

- **10:30/12:00**: interacting with data in Python

[LUNCH]

- **13:00/14:00**: Data visualisation in Python

- **14:00/16:00**: manipulating spatial data in Python

### Day 2 - Wednesday RW117

- **9:00/10:30**: choropleth mapping

- **10:30/12:00**: spatial weights matrices

[LUNCH]

- **13:00/14:30**: exploratory data analysis - Global

- **14:30/16:00**: exploratory data analysis - Local

### Day 3 - Thursday RW117

- **9:00/10:30**: points

- **10:30/12:00**: clustering

[LUNCH]

- **13:00/14:00**: Spatial clustering

- **14:00/16:30**: Data studio

Dr. Dani Arribas-Bel is Lecturer in Geographic Data Science and member of the Geographic Data Science Lab at the University of Liverpool (UK). Dani is interested in computers, cities and data. In particular, his work focuses on the spatial dimension of cities, from their physical structure to how socio-economic phenomena are spatially distributed. Methodologically, Dani is interested in incorporating new forms of data becoming available into the study of cities, as well as in computational methods such as spatial statistics and machine learning. Dani regularly teaches Geographic Data Science and Python courses, and is member of the development team of PySAL, the Python library for spatial analysis.

FAQs

What should I bring to the tutorial?

You must bring a wifi enabled laptop. Bring a power chord.

How can I contact the organizer with any questions?

You can contact via email at steven.farber@utoronto.ca

What if I can't make it to all dates and times?

Please email Steven Farber to discuss, as we would like to prioritize seats for those intending to participate all three days.

What background knowledge and skills do I require?

No prior knowledge of Python or spatial analysis is required. Recommended prerequisite is a grad-level stats course. But it's not a formal requirement.

How many spots are there, and who is this open to?

Participation is limited to current graduate students at University of Toronto (including postdocs). The workshop is capped at 30 participants, with an anticipated mix of 20 Geography and Planning students, and 10 from elsewhere in the University. Please add yourself to the waitlist and we will hopefully be able to serve you.