This document discusses how geospatial information and location analytics can transform asset management. It provides three case studies showing how GI has helped with: 1) Capturing highway asset data to create an asset register, 2) Surveying rail track condition and correlating data, 3) Creating an asset ownership framework. The future holds opportunities to leverage new data sources like remote sensing, crowdsourcing, and real-time asset monitoring to improve asset management decisions.

1. G E O S P A T I A L
Old Tricks in a New World
Julian Watts VGCPAM, BApplSc
Principal Consultant, Atkins Management Consultants
Committee Member AGI AM SIG, Council Member IAM, Fellow RGS
6th September 2013
AGI Asset Management Special Interest Group
The Future of Geospatial and Location Analytics for Asset Management

2. Presentation Objective: Transformation
The Future
• Utilise new international standards like ISO55000 and
PAS1192-3 to drive corporate support
• Use alignment to maximise value from geographic information
to deliver asset management and organisational objectives
• Exploit access to Big Data
The Past
• GI was an add-on to projects with no ring-fenced budget
• Not able to sell the benefits beyond a visual front end to a
database
• Leads to proof of concepts that aren’t sustainable
• Usually preconceived ideas about a solution

3. Why is quality GI needed?
•It is an essential enabler for
effective asset management...
•Better information, better
decisions, better performance...
GFMAM Asset Management
Landscape
Geographic Information

4. IAM Asset Management Competency
Framework
Who needs to be trained in GI?
Geographic Information

5. Where GI fits in the
Management System
vv
Source: ISO Guide 83 Management Systems
Source: IAM Conference 2013 Proceedings - View from FDIS for ISO55001
Geographic Information

6. Where does AM fit in the Information
Delivery Cycle?
Source: Bsi PAS 1192-2:2013
Asset Management Information

7. G E O S P A T I A L
Case Study 1
Islington Asset Capture for the HAMP
Initial Demand Drivers
The need for transparent ‘Whole of
Government Accounts’
Create an asset register for 217 kilometres
of highway for:
– Signs, posts, road markings, bus
cages, carriageway and footway
– Condition, dimensions, diagram
number, material, colour

8. How Geographic Information
Helped
• Digitised visible information from aerial photography using stereo
photogrammetric techniques
• Data capture and editing tools were created to work within
ArcGIS that facilitated digitisation of assets and some attributes
• Surveyors worked with tablet PCs loaded with ArcView software
• New assets could be added from a pre-defined list with workflow
to force front-end attribution
Digitised Assets Field Application User Interface

9. Results
• The HAMP has been developed from a user’s perspective
with a user hierarchy for investment
• Behavioural, social, economic and activity data can be overlaid
enabling rich information
• Data maintenance requirements were identified to cover 10% per
year plus new developments
How did it score?Almost there!!

10. What the Future Holds
• Image based mobile 3D mapping
• High-resolution panoramic images
• 3D data for every pixel
• Utilise social media imagery for historic condition
• Utilise access to increasing remotely sensed data

11. G E O S P A T I A L
Case Study 2
DLR Rail Condition Survey
Initial Demand Drivers
The need to manage budget and
possessions
The need to understand track degradation
Visualise the track measurements for:
– Head Wear
– Inner Side Wear
– Outer Side Wear

12. How Geographic Information
Helped
• Visually showed measurements on a map – snapped to the track
• Additional functionality using a smartphone accelerometer and
GPS to measure speed profiles, longitudinal and lateral
acceleration and gradient
Head Wear Side Wear Altitude Speed lateral Accel

13. • Able to correlate high wear with speed, gradient and rolling stock
hunting
• Provided a baseline for subsequent inspections
Track Head and Side Wear Speed lateral Accel
Results

14. What the Future Holds
• Link the value of information with economic gains
• Model degradation vs. cost of replacement and journey time
• Agreed data capture specifications and timeframes
• A ‘network of things’ where assets return real time monitoring info
• Intelligent infrastructure & real time remote condition monitoring
• Maximise use of crowd sourcing from smartphones and social media
How did it score?It could do better!!

15. G E O S P A T I A L
Case Study 3
Local Authority Asset Ownership Framework
Initial Demand Drivers
The need for accountable cost centres
The need for contract packaging
Needed to understand optimum teams for:
– Ownership
– Operations
– Contractor

16. How Geographic Information
Helped
• Enabled visual confirmation and verification of asset boundaries
• Created a platform to set database level rules for asset
stewardship

17. Results
• Clear framework for understanding most appropriate party to
manage each asset
• Identified a platform for how to identify the steward when new
assets enter the EAMS and how to maintain data relationships
Stewardship Register
Process for Identification

18. What the Future Holds
• A wider team involved with more cross organisation
collaboration
• Clear line of sight of benefits for understanding stewardship
• Dynamic analysis of the best party to undertake maintenance
vs. renewals
How did it score?It could do better!!

19. How I see the Future of Geospatial &
Location Analytics for Asset Management
• The Board room will recognise the value that GI brings
to meeting organisational objectives leading to:
»Better
»Faster
»Supported
»Structured
»More