"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
• Challenges
• (Cool) examples

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

New sources of (spatial) data

We can split them at 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

Global Open Data Index'14

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

Challenges

Challenges

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

Bias

• Traditionally, data used by urban researchers meets some quality standards (representativity, accuracy...)
• The accidental nature means new data sources will not always meet such standards
• This implies 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 even 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.

(New) Methods

(New) Methods

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:

• Visualization
• Machine learning

But also others like bayesian inference, network science...

Methods - Visualization

• Display of graphical summaries
• Arguably, not new to Geography, but more emphasis should be put on it
• Powerful to both obtain (explore the data) and communicate findings (tell stories with data)

Example: Public Transit in Boston

Methods - Machine learning

• Originated in computer science, blended with statistics
• Focus on prediction and pattern recognition

• Two main types of learning:

  • Supervised: present the computer some true relationships to "learn" a model, then use the model to infer others where no prediction is available (e.g. Google flu trends)
  • Unsupervised: "let the data speak"... and the machine pick up the structure (e.g. Livehoods)

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

Avoid the streetlight effect