Concepts#
This blocks explore spatial data, old and new. We start with an overview of traditional datasets, discussing their benefits and challenges for social scientists; then we move on to new forms of data, and how they pose different challenges, but also exciting opportunities. These two areas are covered with clips and slides that can be complemented with readings. Once conceptual areas are covered, we jump into working with spatial data in Python, which will prepare you for your own adventure in exploring spatial data.
“Good old” (geo) data#
To understand what is new in new forms of data, it is useful to begin by considering traditional data. In this section we look at the main characteristics of traditional data available to Social Scientists. Warm up before the main part coming up next!
Before you jump on the clip, please watch the following video by the US Census Burearu, which will be discussed:
Then go on to the following clip, which will help you put the Census Bureau’s view in perspective:
New forms of (geo) data#
This section discusses two references in particular:
“Data Ex-Machina”, by Lazer & Radford lazer2017data
And the accidental data paper by Dani Arribas-Bel ArribasBel201445
Although both papers are discussed in the clip, if you are interested in the ideas mentioned, do go to the original sources as they provide much more detail and nuance.