As presented by Roland Martin

1. GIS Data Management for
Large Infrastructure Projects
Roland Martin
Senior GIS Consultant, Arup
19 April 2012

2. Introduction
1. Who am I?
2. What is a large engineering project?
3. How does GIS help?
4. Managing GIS data
5. GIS analysis

3. We are a global, integrated, multidisciplinary firm of
professionals working together to tackle complex planning,
infrastructure and building design challenges
10,000 engineers, designers, planners, management
consultants and economists
90 networked offices in 37 countries
Full service from concept through completion
Dedicated to delivering value through expertise, global
resources and local delivery

4. Large infrastructure projects
 Forth Road Crossing, Scotland
 Antwerp Scheldt Crossing, Belgium
 California High Speed Train Project
 Transbay Terminal, San Francisco

5. Typical GIS tasks
Gather Manage
information information
GIS
Analyze Share
information information

6. Data gathering
Civils
Geotechnics
GIS
Utilities
Base Data
General
Information

7. Project data flows
On an example Primary data flows
rail project... Secondary data flows
GIS
Air
Noise
Quality
Utility
Environmental Engineering
teams
Ecology Rail
Engineering
Highway
Geotechnical Engineering
Engineering Bridge
Engineering

8. Project data flows
Rail Aerial Land Existing
Alignment Imagery Parcels Infrastructure
ALG TIFF MDB Mixed
Source Modelled using InRoads Purchased files Personal Geodatabase SHP, MDB and DWG
DGN
DGN
Labelled with AIN, owner name,
Generated in InRoads DGN
etc
Assessed for accuracy
fGDB
Required Classified by construction
JPEG fGDB
output method
With files as small as possible Classified by land use
fGDB
MDB Various
KMZ
Database of ownership
For quick visualisation
information, right of way

9. Processing alignment into GIS

10. Land use analysis
 Urban areas can be
extremely complex
 Lots of constraints
 Parks
 Churches
 Prison
 Healthcare
facilities

11. Route analysis
 Terrain data
 Faults
 Land use data
 Engineering
constraints:
 Minimum curve
radii
 Maximum gradient
 Keep costs down!

12. Property counts
Output from acoustic model
 Noise impact (Ln) from road
Level Properties People Affected
50-55 dB 420 917
55-60 dB 265 606
60-65 dB 317 715
65-70 dB 232 569
>= 70 dB 114 222
Output from air quality model
• Areas with NO2 levels above threshold
Level Properties People Affected
< 40 PPM 823 1782
>= 40 PPM 525 1247

13. Environmental modeling
 Noise mapping
 Air quality modeling
 Ecology assessments
 Odormodeling

14. Environmental modeling with FME
Extracting data from Oracle
Noise modeling Air quality modeling

15. Thank You!
 Questions?
 For more information:
 roland.martin@arup.com
 www.arup.com