This document discusses developments in big geodata and its role in GIS. It encourages students to pay closer attention to data aspects of GIS and gain experience with large geodata sets. The presentation explains how data collection is changing from expert-driven and closed to crowd-sourced and open. Examples of big geodata sources include sensor data, social media, and the Internet of Things. Tips are provided for students to explore big geodata through MOOCs and recommended readings.

1. GET
GEO DATA (now) ! #gimaday

2. Get BIG geo data!
The data explosion has enabled GIS on the Web to grow into
new application areas. The Internet of Things, the Sensorweb
and open data repositories are leading us into an era of big
geodata. Apart from the amount of data, the role of data in
GIS and our concept of data are also changing.
This presentation explains a few of these developments. It
calls for students to pay closer attention to the data aspect of
GIS and to gain experience with big geodata. The
presentation ends with a few tips for (eternal) students.
GIMA day, Wageningen U, April 6th 2016

3. Ουτοπία
Not-a-place
A-good-place

4. Port of Rotterdam
(2001)

5. M A P S
Pictures of the way
things are?
“moments in the process
of decision making”*
Maps are changing..

7. GET (1)
Understand

8. 3D
Visualization
Analytics
Apps
Collaborative
Applications
Open
Configurable
Agile Easier
Ready to Use
Implementation
GPS
UAVs
Real-Time
Crowdsourcing
Sensors Scientific Data
Remote Sensing
Field Survey
Social Media
Content
Big Data
Cloud
Virtualization
IoT
Consumerization
Smart Devices
Technology
Integrating Many Innovations
A Whole New Pattern Is Emerging
GIS Is Evolving

9. Data is/was…
collected by experts
consciously
closed
static
structurecreated by software
subset of reality
send as input to an analytical process
on your computer
Ignoringthequantitativeview

10. Data is/becoming…
collected by a crowd
unconsciously
open
real-time
no-structurecreated by data
the analytical process is send to the data
closer to realitythink clusters and cloud

11. #open

14. #3D

15. #crowd

16. BIG#BIG

17. #realtime

20. 160 000 datapoints per minute
Duur 10.000
features/minuut
10.000
features/minuut data
opslag
160 000 features 160 000
features/minuut
dataopslag
1 minuut 10 000 0.0007 GB 160 000 0.01 GB
1 uur 600 000 0.04 GB 9.6 mljn 0.64 GB
1 dag 14,4 miljoen 1 GB 230 mljn 16 GB
1 week 100 miljoen 7 GB 1,6 mljd 112 GB
4 weken 400 miljoen 28 GB 6,8 mljd 448 GB
1 jaar 5,2 mljd 364 GB 83 mljd 5,8 TB
10 52 mljd 3,4 TB 830 mljd 54.4 TB
Credit: Boudewijn Boogaard
Average speed
In one minute
Motorized vehicles
Length over 12,80 m
In lane 3

23. #on-the-fly aggregation

24. BIG DATA WHAT IT IS ABOUT
Guide us to different insights about our world
Why? Lower costs. Improved decision making.
Innovation of products and services.
Real-time data. Stored. Streams. Unstructured (..)
Fairly big
Big data at work, T. Davenport

25. GET (2)
Collect

26. Sensing the City
The main dataset used in this research is the sensed data retrieved by van der Hoeven and
Wandl (2015) for the ‘Hotterdam’ project. In the summer of 2014, they measured the
temperature in Rotterdam by using sensors. About 1300 sensors were distributed.
Be like Iris Theunisse! (check out her thesis).

28. 28

29. M A P S
Becoming better models
of reality!
But still limited by that
model ... and the maker
of the model

30. A FEW TIPS
Mind yr T-shaped profile
Follow a MOOC, read a book
Take a deep dive into spatial temporal land

31. Recommended reading
Books
Big data at Work, Thomas Davenport, Harverd Busines Review Press
Big Data, Viktor Mayer-Schönberger, Kenneth Cukier, HMH
Social Physics, Alex Pentland, Penguin Press
MOOCs
Unlock Value in Massive Datasets, UC San Diego, Coursera
Knowledge Management and Big Data in Business, HK Poly U, Edx
Papers
Do pm me if you are considering to include Big data / GeoAnalytics in your thesis!
Esri : Discover great value from your big data (here)

33. Get BIG Geo Data!
GIMA Day 2016 @ Wageningen U
(GIS on the Web)
Jan Willem van Eck