🧐 Lectures#

Table of Contents! 👇🏽

  1. Lecture 1: Spatial Data Science

  2. Lecture 2: Data Grammar for Spatial & Urban Data

  3. Lecture 3: Data Engineering

  4. Lecture 4: Exploratory Spatial Data Analysis

  5. Lecture 5: Networks

Tip

A GUIDE TO FOLLOW THIS PAGE

  • The slides will be updated latest a night before the lecture in pdf format.

  • Lectures will not be recorded or delivered online.

  • The section To do before class provides content that is useful for following the lectures. We expect you to follow it before every lecture. It will take about 1 hour of prep at home.

  • Section Extra Material is exactly extra. It is not required for this course but can prove really helpful for gaining extra knowledge either during or after this course. Sometimes I use it to build the contents of the lecture and at others I find them helpful in my research related to the weekly topics but I will never question your knowledge on that.

Let’s begin#

Before starting the course, watch this video by Khalid Kadir about a reflection on poverty (an example of a social problem), expertise and equity. This representation is an example of how experts create boxes around their craft. As a data scientist or a future expert (consultant, data analyst, policymaker, etc.), it is our responsibility to step out of those boxes and engage with communities to strive for just outcomes. If you have seen this video, send me a meme about poverty, inequality and data. The best submission will win a prize at the end of the course.

Lecture 1 - Spatial Data Science#

To do before class [Takes about 1 hour of prep at home]#

As a way to whet your appetite about the content of the first class, I recommend you:

Extra Material [Always to learn more but never needed for the course]#

Lecture 2 - Data Grammar for Spatial & Urban Data#

Slides#

To do before class [Takes about 1 hour of prep at home]#

Extra Material [Always to learn more but never needed for the course]#

Lecture 3 - Data Engineering#

Slides#

To do before class [Takes about 1 hour of prep at home]#

Extra Material [Always to learn more but never needed for the course]#

The contents of this lecture are loosely based on, and explored into further detail, in the following two references :

Lecture 4 - Exploratory Spatial Data Analysis#

Slides#

To do before class [Takes about 1 hour of prep at home]#

  • Read Eli Knaap’s blog on Measuring Urban Segregation with Spatial Computation

  • Watch this lecture on “Spatial Weights” by Luc Anselin (link to 34min video). Keep in mind the motivation, in this case, is focused on spatial regression.

  • Lecture on “Spatial lag” by Luc Anselin (link to video, you can ignore the last five minutes as they are a bit more advanced).

  • Watch this lecture on “Spatial Autocorrelation (Background)” by Luc Anselin. [Part I][Part II]

    Extra Material [Always to learn more but never needed for the course]#

    Lecture 5 - Networks#

    Slides#

    To do before class [Takes about 1 hour of prep at home]#

    Extra Material [Always to learn more but never needed for the course]#