Big Data for Local Context

®
Big Data for Local Context
George Percivall
OGC Chief Engineer, CTO
Location and Context 2015
3 November 2015
Copyright © 2015 Open Geospatial Consortium

OGC
®
Power of Location
•  “Location targeting is holy grail for marketers”
– Sir Martin Sorrell, CEO WPP at Mobile World Congress
•  By measuring entropy of individual’s trajectory, we
find 93% potential predictability in user mobility
– Limits of Predictability in Human Mobility, Science 2010
•  1st law of geography:
"Everything is related to everything else, but near
things are more related than distant things.”
–  Waldo Tobler
© 2013 Open Geospatial Consortium 2

OGC
®
Commercial
39%
Government
27%
NGO
8%
Research
6%
University
20%
The Open Geospatial Consortium
Not-for-profit, international voluntary consensus standards
organization; leading development of geospatial standards
•  Founded in 1994.
•  515+ members and growing
•  48 standards
•  Thousands of implementations
•  Broad user community
implementation worldwide
•  Alliances and collaborative activities
with ISO and many other SDO’s
Africa
4
Asia
Pacific
86
Europe
209
Middle
East
34
North
America
182
South
America
3

Contexts &
Possibilities
PRESENT
Behaviors &
Actuals
PAST
Predictions &
Potentials
FUTURE
Source: Jon Spinney, Location Intelligence, Pitney Bowes

5
Marketing
Automaton

6
By Analyzing Large Volumes of
Historical Mobile Location Data
Married with Place & Demographic
Data, We Can Create Audience
Segments and New Enriched
Lifestyle Profiles for Targeting.

7
Risk
Analysis

8
By Analyzing Large Volumes of
Historical Travel Data coupled with
Historical Accident Observations, and
Present Observations, We Can
Assign Estimated Risk & Associated
Insurance Premiums to Auto Plans.

OGC
®
Geospatial Context via “Space Time Boxes”
Jeff Jonas, IBM Fellow. Chief Scientist, Context Computing Strata + Hadoop World 2015

OGC
®
Spatial Bins used for Local Context
Canberra region with hexagon grid.

OGC
®
Discrete Global Grid Systems
Source: Matt Purss, Geoscience Australia
National
Nested
Grid
SCENZ-Grid
Earth System Spatial Grid
Snyder
Grid

OGC
®
OGC Discrete Global Grid Systems (DGGS)
• Common criteria and terms for spatial grids
• Facilitate interoperability and data fusion
• Support marketplaces of spatial data
• Public comment period to begin next week
http://www.opengeospatial.org/projects/groups/dggsswg

OGC
®
Big Data Spatial Analytics - GeoWave
Geographic objects and operators in Apache Accumulo
Advanced support for OGC spatial types: 3D and temporal
GeoServer plugin for OGC Web Services
http://ngageoint.github.io/geowave/

OGC
®
Detecting proximity and colocation in time
time
Spatial

plane
1
prism
=
1
leaf
+
1
sweep
(&attribute)
End
leaf
of
tracks
id=1
Id=2
11:11:20.835 11:11:26.215 11:11:28.021 11:11:30.127
(C)
(B)
(D)
(A)
OGC Moving Features Standard implements ISO 19141

OGC
®
OGC Moving Features Encoding Standard
•  "Moving features" data describes such things as vehicles,
pedestrians, airplanes and ships.
–  This is Big Data – high volume, high velocity.
•  CSV and XML encodings of ISO 19141

OGC
®
Transportation Survey
•  GPS tracks encoded in OGC Moving Features
•  Sharing data by local governments, transit companies, etc.
•  Reduce traffic congestion using transportation surveys.
Transportation
survey
1234
3456
2345
4567
1234
3456
12hr
total
24hr
total
123
234
3456
4567
3456
7890
Traffic
DemandsTraffic
Congestions

Smart
phones
People
in
the
city

Tracks
measured
by
GPS
(encoded
by
Moving
Features)

OGC
®
Crime Pattern
• Predicting regions
more vulnerable to
crime based on
Routine Activity
Theory

OGC
®
Layout design
Pedestrian-tracking to understand movements in facilities,
e.g., shopping malls and transit terminals

OGC
®
Indoor Geo-Portal
Indoor mCommerce
Emergency
Control
Services for
handicapped persons
Cruise Ship
Hospital
Indoor LBS
Indoor Security
Indoor Robot
Indoor Location Application
© 2015 Open Geospatial Consortium

OGC
®
Retail Location Analytics
Source: HP Viewpoint paper - Retail location analytics
As an example, Figure 1 shows a department store that has been set up in a grid. Each square in
the grid can be labeled with attributes that are significant to the retailer.
By tracking the frequency of visits and time spent in a particular department, the interests of
nalytics
Figure 1. Location grid

OGC
®
National Retail Federation
In-Store Use Cases
•  Marketing
–  drive traffic to store
–  In-store targeting
•  Customer Service
–  Locate a product in store
–  Shopping List - multi-channel
–  Request store associate
•  Operations
–  Store setup
–  Stocking shelves
Copyright 2013 NRF. All rights reserved.Page 11
Verbatim reproduction and distribution of this document is permitted in any medium,
provided this notice is preserved.
Interest. This gives the flexibility to report the information in the most relevant format.
2.2Recommended Coordinate System
These different systems are in contention, so the recommendation presented in this best
practices paper defines one common coordinate system, and one consistent origin system.
Given that the ARTS Location Standard’s objective is to locate objects within a store as a
whole, the recommended coordinate system has to apply across all ARTS standards. The
floor-plan coordinate system is the recommendation as modified herein.
The origin is located at the bottom back left position of the object. This is the same
origin used within planograms and the ARTS Video Analytics Standard.
This presents an interesting solution to a sloping shelf. The position of the origin of the
shelf doesn’t change, since it is anchored at the back. If the bottom front left position
was used, then when a shelf is sloped it would change the location of the origin, which
would complicate the transformation algorithms.
The following table highlights how existing system would be affected by adopting this
standard:
Floor Plan System Planogram System
Origin Bottom Back Left of Fixture Bottom Back Left of Fixture
Coordinate
System
Left-Hand Coordinate System Left-Hand Coordinate System
X axis Left to Right Left to Right
Y axis Front to Back Front to Back
Z axis Bottom to Top Bottom to Top
Copyright 2013 NRF. All rights reserved.Page 11
Verbatim reproduction and distribution of this document is permitted in any medium,
provided this notice is preserved.
The origin is located at the bottom back left position of the object. This is the same
origin used within planograms and the ARTS Video Analytics Standard.
This presents an interesting solution to a sloping shelf. The position of the origin of the
shelf doesn’t change, since it is anchored at the back. If the bottom front left position
was used, then when a shelf is sloped it would change the location of the origin, which
would complicate the transformation algorithms.
The following table highlights how existing system would be affected by adopting this
standard:
Floor Plan System Planogram System
Origin Bottom Back Left of Fixture Bottom Back Left of Fixture
Coordinate
System
Left-Hand Coordinate System Left-Hand Coordinate System
X axis Left to Right Left to Right
Y axis Front to Back Front to Back
Z axis Bottom to Top Bottom to Top
Graphics source: ARTS Coordinate System & Insertion Points Best Practices Version 1.0
ARTS Coordinate S
Points Best Practices
Version 1.0
November 29, 2013 – Last Call Working Draft
Chairman:
Dennis Blankenship V
Phuc Do T
Members:
Graeme Shaw (Team Captain) O
Curtis Philbrook J
Amit Chetal C
Amnon Ribak I
Andy Mattice A
Arun Hampapur I
Bart McGlothin C
Marc MacDonald E
Doug Wick D
In-store coordinate
Reference Systems

OGC
®
CityGML and IndoorGML
1st layer: Topographic space model
–  building structure
–  geometric-topological model
–  network for route planning
2nd layer: Sensor space model
–  Radio/Beacon footprints
–  coverage of sensor areas
–  transition between sensor areas
•  Builds on existing International standards CityGML and IFC
© 2015 Open Geospatial Consortium 22

}  The ILA promotes
the deployment of
indoor location
based services in
mobile environment
}  ILA and OGC
working together on
standards based
architecture

OGC
®
Context Discovery across IoT Data Flow
IEEE ACCESS
Data Archive
User
Smart
Things
Sensors
Smart
Wearable
Visualisation / Presentation /
Recommendation
Cloud
Intermediary
Communication
and Processing
Devices
=Context Discovery
Real Time
Processing
Fig. 6. Data Flow in IoT Solutions in High-level. Context can be discovers in different stages / phases in the data ﬂow. A typical IoT solution may
some part of the data ﬂow architecture depending on the their intended functionalities.
First we introduce the name of the IoT solution in the
column (1) in Table II. We also provide the web page link
of the each product / solution. It is important to note that,
these products does not have any related academic publication.
Therefore, we believe that web page links are the most reliable
collects and processes context information such as locatio
temperature, light, relative humidity and biometric pressu
in order to enhance the visibility and transparency of t
supply chain. SenseAware uses both hardware and softwa
components in their sensor-based logistic solution. such d
Perera, C.; Liu, C.H.; Jayawardena, S.; Min Chen,
"A Survey on Internet of Things From Industrial Market Perspective,”
Access, IEEE , vol.2, no., pp.1660-1679, 2014
doi: 10.1109/ACCESS.2015.2389854

OGC
®
OGC SensorThings API for IoT
•  Builds on OGC Sensor Web Enablement (SWE) standards
that are operational around the world
•  Builds on Web protocols; easy-to-use RESTful style
•  OGC candidate standard for open access to IoT devices
Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm!
Emergence of the Internet of Things
Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm!Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm! Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm!
Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm!Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm!
Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm!
Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm!
Graphic from Dr. Steve Liang
Associate Professor / AITF-Microsoft Industry Chair"
OGC SensorThings SWG Chair
Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm!
Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm!
Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm!
Temperature: 29 C !
Humidity: 29%!
Windspeed: 11 km/h!
CO: 0.23 ppm!
NO: 0.22 ppm!
(Graphic source: Steve Liang, University of Calgary)

OGC
®
Region-Centric
Geospatial
Information
Feature-Centric
Geospatial
Information
Human-Centric
Geospatial
Information
Device-Centric
Geospatial
Information
1980s
1990s
2000s
2010s
Source
of
slide:

Steve
Liang,
Univ.
Calgary
and
chairman
of
OGC
Sensor
Web
4
IoT

Progression of geospatial information
© 2013 Open Geospatial Consortium, Inc. 26
èTowards Micro-geography

OGC
®
Scale of 1 to 1 by Lewis Carroll, 1893
“And then came the grandest idea of all!
We actually made a map of the country,
on the scale of a mile to the mile!”
“Have you used it much?” I enquired.
“It has never been spread out, yet. The farmers object:
they said it would cover the whole country,
and shut out the sunlight!
So we now use the country itself, as its own map,
and I assure you it does nearly as well.”

OGC
®
Join the OGC
Open Geospatial Consortium
www.opengeospatial.org
OGC Standards - freely available
www.opengeospatial.org/standards
OGC on YouTube
http://www.youtube.com/user/ogcvideo
George Percivall
gpercivall@opengeospatial.org

Big Data for Local Context

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