This document summarizes Erik van der Zee's upcoming presentation on spatial big data. The presentation will cover what constitutes big data, examples of spatial vector and raster big data, techniques for analyzing these different types of spatial big data, visualizing spatial big data, and tools for working with spatial big data. It will also provide examples of analyzing real-time sensor and social media data, as well as visualizing big data using new interfaces like D3.js.

1. Presentatie 2 juni 2014
IM Themadag te GeoFort (Herwijnen)
Erik van der Zee (Geodan)

2. Drs. Erik van der Zee
◦Physical Geographer and Business Economist
◦Senior Consultant at Geodan (www.geodan.nl)
◦PhD Candidate “Added value of location in the Internet of Things and Smart Cities”
◦Subject matter expert in the Geonovum pilot Making Sense for Society (#labms4s)
E-mail Erik.van.der.Zee@geodan.nl
Twitter @erikvanderzee

3. What is Big Data?
Spatial Big Data
◦Big Vector Data (examples)
◦Big Raster Data (examples)
Big Data Analysis
Spatial Analysis of Big Data
◦Big Vector Data Analysis (real-time spatial analytics)
◦Big Raster Data Analysis
Spatial Big Data Visualization (3DJS)
Spatial Big Data Tools and Technologies

4. Wat is Big Data?

5. Haseeb Budhani, 2008 “a blanket term for any collection of data sets so large and complex that it becomes difficult to process using on- hand database management tools or traditional data processing applications”
Gartner “high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making”

8. Big Vector Data

9. 2D3D4D datasets (XYZ+Time)
“Grote” Geodatasets (BGT/BAG)
Lidar Pointclouds
◦AHN2 (aerial)
◦Laserscanners op autos (360°)
Real-time Event Streams
◦Sensors measurements
◦Actuator Control information
◦Social Media APIs (twitter, etc.)
◦“Pasjes en poortjes” (scanners bvb OV checkins)
…

11. Miljarden laserpunten (terrestrial/aerial)
Rotterdam Demo

12. “Internet of Things”
Real-time sensor and control data (XYZT)

13. Sensor-Actuator Networks (Nationaal Datawarehouse Wegverkeergegevens)
Real-time data van tienduizenden sensoren elke seconde

14. Content can be Tekst (Twitter/Whatsapp), Photos (Instagram/Flickr), videos (YouTube), sounds (Soundcloud), …etcetera

15. Georeferenced Social Media Content

16. Big Raster Data

17. Steeds hogere resoluties
Steeds hogere update frequenties (Dynamic HR satellite photos and video)
Steeds meer “banden” (hyperspectral)
Overal land / op water/onder water / in de lucht
Voorbeelden
◦Luchtfoto’s (5cm resolutie…)
◦Gigapans
◦Videobeelden (PTZcams, drones, cars, bodycams)
◦360° Panoramas
◦Multispectral raster data (o.a. satellite imagery)

18. Real-time Raster data
T=16:00
T=17:00
T=18:00
T=19:00
T=20:00
T=21:00
T=22:00
T=23:00

19. Hyperspectral data cubes (High Resolution Multispectral Rasters ) = Big Data (petabytes of data)
Hyperspectral data cubes created yearmonthweekday…

21. Zeer hoge resolutie images die grote gebieden afdekken
http://www.gigapan.com/galleries/11203/gigapans/152220 (demo)

22. E.g. Google Streetview
Cycomedia Cyclorama/aquarama

23. DARPA’s big eye: ARGUS-IS 1.8-gigapixel camera for air surveillance
◦clear images of objects as small as 15 centimeters from an altitude of six kilometers
◦One gigapixel is equal to 1,000 megapixels. For comparison: Modern professional digital cameras have a resolution of about 20 megapixels
Also City Wide Video Surveillance… http://youtu.be/6VkKeM-OK6g?t=8m6s

25. Past – Present - Future

26. Types of analysis (Past – Present – Future…)
Naald in enorme (BIG) hooiberg vinden (bvb 1 specifiek nummerbord)
Soms Trends en patronen destilleren uit berg informatie

27. Big Vector Data Analysis

28. (Real-time) analyse van vector data (Event Steam Processing)
Sensing
Analysis
Act(uat)ing
raw events
meaningful
events
Data creation
Controlling / alerting / notification / routing of objects and people
Data usage

29. Big Raster Data Analysis

30. Analyse van rasterdata m.b.v. beeldherkenningsalgoritmen
Meer en meer informatie kan real-time worden afgeleid uit images and videos (creates new derived data that can be analysed in event processing engine)
◦Face recognition (ov Rotterdam)
◦Number plates (ANPR)
◦Traffic signs (Google)
◦...

31. Gezichtsherkenning

32. Project Ground Truth Google  Feature extractie van informatie uit panorama images t.b.v. map Generation and validation
http://youtu.be/FsbLEtS0uls?t=3m52s
◦Verkeersborden
◦Rijrichtingen
◦Bedrijfslogo’s
◦Straatnamen
◦Huisnummers
◦…

33. Automated Number Plate Recognition (ANPR)

34. Spatial Big Data Visualization

35. Trend van visualiseren van BI (big) Data in kaarten (bvb o.b.v. postcode / adres / woonplaats / wijk) naar Ruimtelijke Analyse (spatial analysis) van gegeorefereerde BI Data

36. Big Data needs New User Interfaces

37. Nieuwe visualisatie mogelijkheden
Voorbeelden http://d3js.org/

38. Spatial Big Data Tooling

39. Spatial Extensions voor NoSQL Databases
◦Neo4J Spatial Extensions (graph database)
◦MonetDB Spatial Extensions (column store)
◦Spatial Hadoop http://spatialhadoop.cs.umn.edu/
Spatial Event Stream Processing engines
◦Esri GeoEvent Processor
◦Oracle Spatial CEP Engine
◦Microsoft Stream Insight…

40. Vragen?