• Recap on progress with the coursework
• A quick look at Big Data for SSPA
• Practical working on your projects
• (And an opportunity to ask questions)

• Many examples of indicators presented (none right or wrong)
• Why bother creating indicators?
• What different types of indicators are there?
• How can geographic data analysis help?

Key resources:

• Slides by Ian on indicators
• Slides from me - recap on R

Source: (Boisjoly and El-Geneidy 2017)

• Most indicators emphasise time saving and economic growth, leading to the misled emphasis on motorised modes and speed (Banister 2008)

• Different indicators can lead to different policies.

• The Propensity to Cycle Tool (PCT) is changing how millions of £ is being invested in cycling, making it more evidence-based (Lovelace et al. 2017)

See www.pct.bike

• How good is this indicator? (source: Mattingly and Morrissey 2014)

• Who plans to use QGIS?
• Who plans to use R?
• Who plans to use a bit of both?
• Study area decisions
• Indicator aims decided
• Data access
• Data analysis started

• Gantt charts are useful!

Source: Dean Attali and Robin Lovelace

• But first 5 commandments of Big Data:

1. thou shalt remember the purpose of thine research regardless of the size of thine dataset
2. thou shalt not spend excessive amounts of time making visualising big data for the sake of it (or social media clickbait)
3. thou shalt not do big data until thou has done 'small data' first
4. thou shalt not hide thine ideas behind complex terminology associated with the terms 'big data' or 'data science', the meaning of which has not been clearly identified.
5. if thou wants to be a data scientist thou must program … "for documentation, sharing and scientific repeatability" (mount 2016).

• Another commandment: do not get distracted by Big Data
• High priority: crack-on with the coursework
• check-out the big data and big data archives slides
• CRACK-ON with the coursework!
• I'll present on Big Data in due course…