“Yesterday”

• Introduced the (geo-)data revolution

  • What is it?
  • Why now?

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

Today

• Traditional data: refresher
• New sources of spatial data
• Opportunities & Challenges

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 detail but, potentially, much more detailed in both space and time
• Quality also varies greatly

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

Citizens as sensors

• Technology has allowed widespread adoption of sensors (bands, smartphones, tablets…)
• (Almost) every aspect of human life is subject to leave a digital trace that can be collected, stored and analyzed
• Individuals become content/data creators (sensors, Goodchild, 2007)
• Why relevant for geographers? –> Most of it (80%?) has some form of spatial dimension

Example: Livehoods

Businesses moving online

• Many of the elements and parts of bussiness activities have been computerized in the last decades
• This implies, without any change in the final product or activity per se, a lot more digital data is “available” about their operations
• In addition, enirely new business activities have been created based on the new technologies (“internet natives”)
• Much of these data can help researchers better understand how cities work

Example: Walkscore

Open data for open governments

Government institutions release (part of) their internal data in open format. Motivations (Shadbolt, 2010):

• Transparency and accountability
• Economic and social value
• Public service improvement
• Creation of new industries and jobs

Example: BikeShare Map

Source

Class Quiz

Class Quiz

In pairs, 2 minutes to discuss the origin of the following sources of (geo-)data:

• Geo-referenced tweets –> Bottom-up
• Land-registry house transaction values –> Open Government
• Google maps restaurant listing –> Digital businesses
• ONS Deprivation Indices –> Traditional (not accidental!)
• Liverpool bikeshare service station status –> Open Government Data

Opportunities & Challenges

Opportunities

From 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

• Bias
• Technical barriers to access
• The need of new methods

Bias

• Traditional data meet some quality standards (representativity, accuracy…)
• Because they’re accidental, new data sources might not
• Researchers need to have extra care and put more thought into what conclusions they can reach from analyses with new sources of data
• In some cases, bias can run in favour of researchers, but this should never be taken for granted

Technical barriers to access

• Much of these data are available
• However, their accidental nature makes them not be directly available

• Usually, a different set of skills is required to tap into their power

  • Basic programming
  • Computing literacy (understanding of the internet, APIs, databases…)
  • Software savvy-ness (a.k.a. “go beyond Word and Excel”)

(New) Methods

The nature of these data is not exactly the same as that of more traditional datasets. For example:

• Spatial aggregation: Polygons Vs. Points
• Temporal aggregation(frequency): Decadal Vs. Real-time

Some of this does not “play well” with techniques employed traditionally to analyze data in Geography –> borrow techniques from other disciplines, or even create new ones

(New) Methods

New + Old

Traditional data:

• High quality, detailed, and reliable
• Costly, coarse, and slow

Accidental data:

• Cheap, fine-grained, and fast
• Less reliable, harder to access, and potentially uninteresting

–> 1 + 1 > 2