Clément Levallois <email@example.com> 2017-31-07
last modified: 2018-07-02
1. Localization brings interesting new dimensions
Localization relates activities to physical space, in at least 4 different ways:
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?
a. Example - Facebook Local Awareness 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.
b. Example - Placemeter
“Using computer vision to analyze real life activity”:
Cameras placed in public places (possibly at the windows of private households)
Video is treated on the device attached to the camera, not saved.
measures pedestrian traffic in front of stores to provide "main street analytics"
2. The visual power of maps
a. Map: useful metaphors with a political dimension
The visual metaphor of the map is widely understood
Makes exploration easy: all visible at once, while zoom allows for details as well
Multiple information cues (colors, symbols, shapes, layers, etc.)
Keep in mind: maps are always political
Watch this extract from the TV series "The West Wing“, Season 2, Episode 16:
b. Example: how to explore the real estate market in the Netherlands
Every single building of the Netherlands on a map
Colored by year of construction
With role (retail or housing?) and surface highlighted
Zoomable and draggable
c. Key resources to work with maps
Agency based in San Francisco
Famous for cutting research in map design
SaaS to create interactive maps in web pages and mobile apps.
A crowd sourced open source map of the world. Available through API.
3. How to represent “space” in data format?
a. The specifity of geospatial data
Data is traditionally stored in tables in relational databases, taking this form:
A table can have millions of rows. How to retrieve information such as "get all customers living in Rotterdam"?
"SQL" (Structured Query Language) is a system to express these kinds of queries.
In the table shown above, a query written in SQL look in the "Address" column and inspect all the text to find if "Rotterdam" is present or not.
This is highly inefficient (slow), and more complex queries would not work.
For example, the table above could not be queried for "get all customers living in a 10 miles radius around Rotterdam".
So how to store geospatial data in a way that makes it easy to retrieve?
b. Solutions to store and retrieve geospatial data
Even if SQL does not perform well on geospatial data "out of the box", extra modules have been developed to deal with it.
Microsoft SQL server since 2008:
Possible to store and query “geometric” and “geographic” objects
Possible to use complex queries on these objects
Since ~ 2005, new types of databases have been developed, which don’t follow a table structure in order to facilitate the query of special kinds of data, like geospatial data or network data.
These new databases are called "NoSQL databases"
Carto (ex 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."
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!)
5. Ending with a provocation: Challenging the usefulness of location
a. Localization is about people and territories
Data is a fungible and universal material (just 0s and 1s)
Geographical coordinates are perfectly universal (just need a longitude and latitude)
and yet …
The logic of territories is shaping data: there is a geography of data.
Cultural, social, political, linguistic, economic dimensions to data.
→ representations with a supposedly universal and transparent coordinate system blinds us to this fact.
This argument is made by Frederic Martel in his book "Smart": Internet does not flatten everything into one big model. There are several Internets with their geography, politics and sociology.
Data protection: not all countries are equal
Data handling devices
India and Africa have ++ share of mobile devices
Amazon Mechanical Turk is a service of data production through the hiring of a distributed crowd of workers. Tends to "erase distance".
Yet, the geographical distribution of workers on Amazon Mechanical Turk is far from even. The following figure is taken from this study:
b. Distributed systems – the end of territories?
The libertarian dream of the cypher-punks: individuals transact without consideration for their nationality, currency, legal system, political regime.
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, without a notion of place or distance.
Bitcoin: the currency for these transactions?
Torrent: The exchange platform for numeric goods?
Ethereum: the platform where contracts are made and executed?