Introduced the (geo-)data revolution
The need of (geo-)data science to make sense of it all
[source]
Traditionally, datasets used in the (social) sciences are:
But also:
Tied into the (geo-)data revolution, new sources are appearing that are:
Different ways to categorise them...
Three levels, based on how they originate:
Government institutions release (part of) their internal data in open format. Motivations (Shadbolt, 2010):
In pairs, 2 minutes to discuss the origin of the following sources of (geo-)data:
From Lazer & Radford (2017):
Usually, a different set of skills is required to tap into their power
The nature of these data is not exactly the same as that of more traditional datasets. For example:
Some of this does not "play well" with techniques employed traditionally to analyze data in Geography.
To be able to extract as much insight as possible from these new sources of data --> borrow techniques from other disciplines, or even create new ones
Examples:
But also others like bayesian inference, network science...
Traditional data:
Accidental data:
--> 1 + 1 > 2
Geographic Data Science'15 - Lecture 1 by Dani Arribas-Bel is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.