“Day 1”

• Introduced the (geo-)data revolution

  • What is it?
  • Why now?

• The need of (geo-)data science to make sense of it all

Today

• Types of (geo-)data: refresher
• Traditional and new sources of spatial data
• New ways for traditional approaches

Representing the World Digitally

GIS Data Models

Traditionally, geographic information is represented as:

• Vector finite set of entities (shapes/geometries)
• Raster images encoding surfaces (values, colours, etc.)

Vector

Raster

Good old spatial data

Good old spatial data

[source]

Good old spatial data (+)

Traditionally, datasets used in the (social) sciences are:

• Collected for the purpose –> carefully designed
• Detailed in information (“…rich profiles and portraits of the country…”)
• High quality

Good old spatial data (-)

But also:

• Massive enterprises ("…every single person…) –> costly
• But coarse in resolution (to preserve pricacy they need to be aggregated)
• Slow: the more detailed, the less frequent they are available

Examples

• Decenial census (and census geographies)
• Longitudinal surveys
• Customly collected surveys, interviews, etc.
• Economic indicators
• …

New sources of (spatial) data

New sources of (spatial) data

Tied into the (geo-)data revolution, new sources are appearing that are:

• Accidental –> created for different purposes but available for analysis as a side effect
• Very diverse in nature, resolution, and quality but, potentially, much more detailed in both space and time

Different ways to categorise them…

Lazer & Radford (2017)

• Digital life: digital actions (Twitter, Facebook, WikiPedia…)
• Digital traces: record of digital actions (CDRs, metadata…)
• Digitalised life: nonintrinsically digital life in digital form (Government records, web…)

Arribas-Bel (2014)

Three levels, based on how they originate:

• Bottom up: “Citizens as sensors”
• Intermediate: Digital businesses/businesses going digital
• Top down: Open Government Data

Opportunities (Lazer & Radford, 2017)

• Massive, passive
• Nowcasting
• Data on social systems
• Natural and field experiments (“always-on” observatory of human behaviour)
• Making big data small

Challenges (Arribas-Bel, 2014)

• Bias
• Technical barriers
• Methodological “mismatch”

Old/New, raster/vector…

Old/New, raster/vector…

Traditional approaches to represent the world in a computer are blending thanks to new forms of data

Keep an open mind to tools, approaches, and methods