The document discusses the evolution of qualitative and quantitative GIS, highlighting its origins, technological advances, and the shift in data producers and users from government entities to private companies and developers. It notes the increasing complexity and capability of GIS tools, with mentions of specific satellite imagery and data management technologies. The author emphasizes the change in analytical approaches brought about by data-driven methodologies and machine learning, along with current projects that leverage these advancements.

1. Quanti-litative Revolution
in GIS

2. @Noootsab
Pre-NextLab
Licence in Maths, ULg
Licence in Computer Sciences, ULg
Geospatial specialization, ULg
Many years in GIS (Ionic Software and its mutations)
Always in data management and semantic
New technologies enthusiast
NextLab age
Project in for the SPW
Project in for Virdata
Wrote a about Play! 2
evangelist (OKFN, ...), speaker
Distributed + Distributed expert
co-founder | organizer
And the list goes on... and on...

3. @NextLab_be

4. Qualitative GIS
Coined in 1963

5. Raster
vulgus: Big Fat
MultiBand Images
SPOT (see later)
IKONOS (greek: image)
LANDSAT
ALOS
QuickBird: resolution 64cm
→ 2m44cm
Vector
vulgus... nah, vector
Feature extraction
Landmeters (rare)
Field surveying
~GPS (GNS)

6. hence, Producers were...
Govs!

7. thus, Users were
Govs!
Oh yeah, researchers as well... huh!? wait!
Okay, very feeeew privates

8. in consequence, Analyses were
Performed by experimented folks...
... or nerds

9. finally, Tools were...
You don't even know them!
ESRI, Oracle Spatial,
OGC, RedSpider (yeah), ...

10. Qualitative GIS
"Started" around 2000
Actually, not really before 2010

11. GIS Revolution
Induce theories from data
The river has flooded on 3km
The river will flood if it rains more than...

12. New producers
Google, 4², OSM, GPS
old: Mappy, Michelin, ...

13. New Users
Devs, Lambda
Billioooooons

14. Analyses
Routing, Accurate GWR¹, ...
Disaster prediction, flood prevention, crash probability, ...
¹Geospatial Weight Regression

15. Tools?
User friendly and/or Open Source
OSM
GeoServer
OpenLayers
Google Maps

16. Quantilitative GIS
Coined by @Noootsab^^

17. WTF?!

18. Rasters today (f.i. Spot7)
Red Green Blue + Near Infra Red Bands
One shot: 60x60km (3.600 sq km)
Takes 3.600.000 sq km of geodata per DAY
Resolution 2 satellites at and 2 others at
Revolution: 110 minutes
26 days to complete the geoid (all pieces of crap covered)
1 single f*****g file for a 60x60km tile is worth up to

19. Vector (mostly position)
everywhere and everytime
Twitter, Facebook, ...
Foursquare, Instagram
Waze
Google (in its whole)
Connected Devices

20. Presidente
Model-Driven
Deductive
Top-Down
Quantitative
Lagged Time
GIS Putsch
Commandante
Data-Driven
Inductive
Bottom-Up
Qualitative
Real Time
Marcelino
Data Lake
Machine Learning
Variety
Value
Velocity
VOLUME? It was there for ages!

21. Who, What, How
You thought I was joking, huh!

22. Socrata
Evan Chan using Spark
Customers have point data (many millions of
rows)
PostGIS: point-in-polygon and other does
Partitioning point data in (tiling, Z-curve)
Partitions into for quick analysis
Adding to Spark for speeding up

23. Azavea
Rob Emanuele on Geotrellis
Run of climate data against daily
temperature and precipitation data out to 2009
Create suitability maps over high-res raster layers
spanning the for Urban Forestry
Modeling.
GeoTrellis is providing with geospatial capabilities.
Ingest, mosaic and pyramid raster data into
for fast (sub-500ms) tile fetching

24. Snips
Rand Hindi in his Labs
Tranquilien: seating availability in public
transport
RiskContext: Determining the risk of bicycle and car
Using technologies that can scale linearly
(Akka, Scala)
Thanks to the bottom up of the architecture, the
clusters keeps crunching data in a resilient manner

25. Done!
Thx & cu on Twitter
@noootsab
@NextLab_be