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Data and localization

Slides of the course on big data by Clement Levallois from EMLYON Business School. For business students. Check the online video connected with these slides. -> How data opens vast perspectives on localization

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MK99 – Big Data 1 
Big data & cross-platform analytics 
MOOC lectures Pr. Clement Levallois
MK99 – Big Data 2 
Data & Localization
MK99 – Big Data 3 
Data & Localization 
1. 
Localization -> new dimensions! 
2. 
Maps, maps, maps 
3. 
Geospatial data and the need for new data structures 
4. 
Two companions: personalization and real time 
5. 
Territories: data is local 
6. 
Distributed systems: beyond local? 
Looking at localization in different ways
MK99 – Big Data 4 
1. New dimensions 
• 
Localization connects activities to physical space 
• 
This adds at least 4 interesting dimensions to data 
Place: Where is this activity happening? 
Distance: Are these two agents neighbors? 
Movement: Is this agent travelling? 
(together with speed and acceleration) 
Structure: How are these agents and activities configured in space?
MK99 – Big Data 5 
Example 1 
• 
Facebook new ad feature: 
– 
“Helping Local Businesses Reach More Customers” 
– 
Target ads to people living in a radius around your store. 
– 
Can also target people who have been recently in this radius. 
– 
https://www.facebook.com/business/news/facebook-local-awareness
MK99 – Big Data 6 
Example 2 
• 
Lyon Smart Data 
– 
An initiative by the city of Lyon 
– 
Making data open to foster innovation for citizens and businesses 
– 
Includes many datasets with geographical relevance 
– 
Similar initiatives in large cities: 
• 
Beijing City Lab

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Data and localization

  • 1. MK99 – Big Data 1 Big data & cross-platform analytics MOOC lectures Pr. Clement Levallois
  • 2. MK99 – Big Data 2 Data & Localization
  • 3. MK99 – Big Data 3 Data & Localization 1. Localization -> new dimensions! 2. Maps, maps, maps 3. Geospatial data and the need for new data structures 4. Two companions: personalization and real time 5. Territories: data is local 6. Distributed systems: beyond local? Looking at localization in different ways
  • 4. MK99 – Big Data 4 1. New dimensions • Localization connects activities to physical space • This adds at least 4 interesting dimensions to data Place: Where is this activity happening? Distance: Are these two agents neighbors? Movement: Is this agent travelling? (together with speed and acceleration) Structure: How are these agents and activities configured in space?
  • 5. MK99 – Big Data 5 Example 1 • Facebook new ad feature: – “Helping Local Businesses Reach More Customers” – Target ads to people living in a radius around your store. – Can also target people who have been recently in this radius. – https://www.facebook.com/business/news/facebook-local-awareness
  • 6. MK99 – Big Data 6 Example 2 • Lyon Smart Data – An initiative by the city of Lyon – Making data open to foster innovation for citizens and businesses – Includes many datasets with geographical relevance – Similar initiatives in large cities: • Beijing City Lab
  • 7. MK99 – Big Data 7 2. Maps, maps, maps • Maps speed up understanding – Maps make data understandable by a wide audience – All visible at once, while zoom allows for details as well – Multiple information layers (colors, symbols, …) • Keep in mind: maps are always political – Watch this extract from the TV series "The West Wing“, Season 2, Episode 16: https://www.youtube.com/watch?v=vVX-PrBRtTY
  • 8. MK99 – Big Data 8 Example • Every single building of the Netherlands on a map • Colored by year of construction • With function (retail or housing?) and surface highlighted • Zoomable and draggable. The city center of Leiden: http://code.waag.org/buildings/
  • 9. MK99 – Big Data 9 Key resources in map-making • Stamen – Agency based in San Francisco – Hire them or check their work • Mapbox.com – SaaS to create interactive maps in web pages and mobile apps. • OpenStreetMap – A crowd sourced open source map of the world. Available through API.
  • 10. MK99 – Big Data 10 3. How to represent “space” in data format? • Traditionally stored in tables in relational databases, queried with SQL. • Query on the table, then exported to a Geographical Information System (GIS) for representation and analysis – Leaders: ArcGIS an QGIS • Problem at the query stage. How to ask to extract these data from the table? – « Return all customers living between point A and B » – « List all customers who live at less than one mile from each other » -> Traditional relational databases, which are made of tables like the one above, cannot process this kind of query efficiently. Customer Address Customer 1 67 Pelikaanstraar, Leiden 2314 CR Customer 2 12 Breestraat, Rotterdam 3046 DM
  • 11. MK99 – Big Data 11 Emerging solutions to work with space 1. SQL solutions – Microsoft SQL server since 2008 • Possible to store and query “geometric” and “geographic” objects • Possible to use complex queries on these objects 2. NoSQL solutions – CartoDB: specializing in geospatial data + mapping. – Neo4J Spatial enables to mix the logics of networks with places in the data, so that you can make such queries on your data: • "Select all streets in the Municipality of NYC where at least 2 of my friends are walking right now." 3. Javascript leading the way! – GeoJSon and TopoJSon: 2 data formats to represent geometric and geographic data developed for Javascript applications – and beyond.
  • 12. MK99 – Big Data 12 4. Two friends for localization: personalization and real-time • Knowing the person, its location, at a precise time unlocks meaningful push notifications • Push notifications are these alerts sent by an app on your mobile, visible as transient icons. • Gets “push marketing” back on solid foundations: – Push marketing actions only to the right person, at the right place, at the right time (and at the right frequency!) You’ve got mail!
  • 13. MK99 – Big Data 13 Now for different (opposite?) approaches to localization • Territories – Not just people are localized. Data is local, too. • Distributed systems – Some projects attempt to build completely decentralized systems of transactions, functioning freely and immune from local regulations. ≠
  • 14. MK99 – Big Data 14 5. Localization is about people and territories Data is a fungible and universal material (just 0s and 1s) and yet … The logic of territories is shaping data: there is a geography of data. Cultural, social, political, linguistic, economic dimensions to data. Frederic Martel Published in French in 2014, Available in English in 2015.
  • 15. MK99 – Big Data 15 Examples • Data protection: not all countries are equal – http://www.darkreading.com/cloud/privacy-security-and- the-geography-of-data-protection-/a/d-id/1315480 • Data handling devices – India and Africa have ++ share of mobile devices • Data production – The uneven geography of Mechanical Turk
  • 16. MK99 – Big Data 16 6. Distributed systems – the end of territories? • Libertarian dream of the cypher-punks: – Individuals transact without consideration of their nationality, currency, legal system, political regime. • Bitcoin – the currency for these transactions? • Torrents – The exchange platform for numeric goods? • Ethereum – the platform where these transactions are created and exchanged? • In practice – Organizations, banking, voting systems, … any aggregated human activity could emerge without reference to local territories or institutions. Just groups of individuals transacting voluntarily and securely.
  • 17. MK99 – Big Data 17 This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com) Contact Clement Levallois (levallois [at] em-lyon.com) for more information.