Geographic
Information Science
and the Built
Environment
Presented at the
2014 ASHRAE Winter Conference
Dr. Budhendra Bhaduri
Corporate Research Fellow
January 19, 2014
New York, NY
Acknowledgement
• People who do the real work
–
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Eddie Bright
Raju Vatsavai
Anil Cheriyadat
Amy Rose
Marie Urban
Steve Fernandez
Mark Tuttle
Devin White
… and many others
• People who make it possible
– Department of Energy
– Department of Defense
– … any many more
Managed by UT-Battelle
for the Department of Energy
Overview of presentation
• Background
• Geospatial data driven computing
– Modeling population distribution and dynamics
• Energy and transportation
– Bioenergy assurance
• Crop monitoring from streaming data
– Climate change science
• Geovisualization
• Challenges and Future directions
Managed by UT-Battelle
for the Department of Energy
Geographic data has driven innovation
1790
First US Census. 3.9 million people counted.
1810
1850
Census collects data on manufacturing, quantity, and value of products.
Census collects data on taxation, churches, pauperism, and crime.
1890
1896
Punched-card tabulating machines are used to count 63 million people.
Tabulating Machine Company (TMC) formed.
1911
1924
TMC becomes C-T-R (Computing-Tabulating-Recording) Company.
C-T-R becomes International Business Machines (IBM) Corporation.
1963
1969
First use of the phrase “geographic information system”.
First commercial GIS software companies formed.
1972
1985
NASA launches first earth observation satellite (Landsat 1).
First GPS satellite launched.
2001
2004
Keyhole Corp. creates dynamic 3D mapping of geographic information.
Google Earth initiates the “Web Wide World” and visual discovery.
20??
Intelligent Locational Awareness: Real time access to multidimensional
information of locations, states, and environments of entities.
Managed by UT-Battelle
for the Department of Energy
Cross-disciplinary Approach will foster
Innovation
Philosophy
Cognitive
Science
Mathematics
Statistics
Landscape
Architecture
Geography
Climate
Geospatial Information Science & Technology
Geographic
Information
Science
Applications of
GI Science &
Technology
Geospatial
Technology
Managed by UT-Battelle
for the Department of Energy
National &
Homeland
Security
Ecology
Homeland
Transportation
Homeland
Security
Security
Computer
Science
Physics
Energy
Information
Science &
Technology
(Modified from GIS&T Body of Knowledge by AAG and UCGIS)
Engineering
Our energy challenges and solutions
are often local to regional
Energy savings potential is a
macro-level (regional to
national) phenomenon driven
by individual socioeconomic
behavior at the micro-level
(local)
Homes
Work
Useful insights will come
from characterizing
interactions among
human, energy, and
transportation networks
Gas stations
(regional)
Shop
Banks
(state)
Gas stations
(regional)
Managed by UT-Battelle
for the Department of Energy
Cleaners
Schools
Homes
Success of future strategies
depends on understanding
complexity and consequences
of proposed systems in which
energy, environment and
mobility interests are
simultaneously optimized
Knowledge discovery through high
resolution data and modeling
Deforestation
in Brazilian Amazonia *
• Mesoscale circulation leading
to more clouds and rain over
cleared patches was validated
using satellite data
Managed by UT-Battelle
for the Department of Energy
Wind turbines
and energy policy **
• Turbine motor turbulence
increases surface temperature
and decreases humidity
• Impact mitigated by low
turbulence rotors that are
economically more efficient
* Baidya Roy and Avissar, 2002
** Baidya Roy et al. 2004
Vehicular congestion and
evacuation planning
strategy
• High resolution population data
coupled with dynamic traffic
assignment model improved
congestion and evacuation time
estimations
A number of ongoing activities
Population Assessment
• Web service for LandScan population data
• Population Density Tables portal
Settlement and Damage
Assessment
• Dynamic computation for settlement mapping and
damage assessment
Collaborative
Knowledge Discovery
• Bioenergy KDF: Cyber platform for sharing data
and resources
• Sensorpedia: Wikipedia for sensors
Transportation Analysis
Energy Security
Visualization (iGlobe)
Managed by UT-Battelle
for the Department of Energy
• Routing and contingency analysis
• Evacuation modeling
• EARSS: Real time monitoring for the electric grid
• Large scale biomass monitoring
• Climate data analytics and visualization
• Spatio-temporal data mining as a service
ASHRAE TC 4.2 – Weather data
Weather station cells based on
closest geographical distance to ISH weather stations
Average individual’s distance
to nearest ISH weather station
Location
KML publicly available - http://go.usa.gov/ZV7G
Station points, cell populations, Thiessen polygons via Platte Carree projection
By: Eddie Bright, Yu (Joe) Huang, Jibo Sanyal, Joshua New
Managed by UT-Battelle
for the Department of Energy
D (km)
D (miles)
World
34.5
21.4
S. Korea
12.2
7.6
Japan
17.4
10.8
USA
18.1
11.2
Europe
21.8
13.5
Canada
31.0
19.3
Iran
31.9
19.8
Mexico
35.2
21.9
Venezuela
39.5
24.5
China
40.2
25.0
Saudi Arabia
42.3
26.3
India
62.8
39.0
Brazil
62.8
39.0
LandScan Population Distribution
and Dynamics Model and Database
LandScan Global
Census
Gridded
Night
Day
LandScan USA
As the finest population distribution data ever produced for
the world and the US, LandScan Global and LandScan USA
Managed by UT-Battelle
are
the community
standard for estimating population at risk
for the Department
of Energy
Disparate data integration improving knowledge
of population distribution and dynamics
Population
Road
Railroads
• Census
Polygons
• VMAP
• 1:100K
national
railway
network
• TeleAtlas
Multinet
• Tract-totract worker • TIGER;
flow
• NTAD
• BLS
quarterly
updates
Land cover/
land use
Slope
• Geocover
• DTED
Academic
institutions
Prisons
Hospitals
Business
employment
• Department • National Jail • American
• InfoUSA
of Education Census
Hospital
• MODIS
• LiDAR
• Pitney
Association
Bowes
•
HSIP
•
HSIP
• National
• National
(AHA)
Schools
Prisons
Land Cover Elevation
• Dunn and
Bradstreet
Data
Data (NED) • ESRI
(NLCD)
• GDT
• State GIS
Night
Day
LandScan Global
• Spatial resolution of 30 arc seconds (~1km)
• Ambient population (average of 24 hours)
• Remote sensing based global data modeling and mapping
LandScan USA
Managed by UT-Battelle
for the Department of Energy
• Spatial resolution of 3 arc seconds (~90m)
• Nighttime and daytime population
• Integration of infrastructure and activity databases
Imagery
• EarthViewer
• Terra Server
• Google
Managed by UT-Battelle
for the Department of Energy
night
day
Managed by UT-Battelle
for the Department of Energy
Spatial refinement of LandScan Global
Managed by UT-Battelle
for the Department of Energy
HPC Based Imagery Analysis
Moving from Modeling to Mapping
Urban
Urban
Managed by UT-Battelle
for the Department of Energy
“Developed Land Cover” Examples
Managed by UT-Battelle
for the Department of Energy
Addis Ababa, Ethiopia

2 Xeon Quad core 2.4GHz
CPUs + 4 Tesla GPUs +
48GB

Image analyzed (0.3m)
 40,000x40,000 pixels
(800 sq. km)
 RGB bands

Overall accuracy 93%
 Settlement class 89%
 Non-settlement class
94%

Total processing time
Managed by UT-Battelle
for the Department of Energy
 27 seconds
Settlements are economic indicators
Managed by UT-Battelle
for the Department of Energy
Patterns in overhead imagery
Higher Income
Middle Income
Managed by UT-Battelle
for the Department of Energy
Lower Income
Neighborhood mapping: From local
interactions to global realizations
•Unstructured
Settlements
•Lowest to lower
middle income
•Rural migrants
•Very loosely structured
•Historical ethnic
quarters/neighborhoods
•Poor residents currently
being displaced in some
areas with urban
development/tourism
Damascus, Syria
Managed by UT-Battelle
for the Department of Energy
•Formal Urban Planning
•Typical Urban Services
•Middle to Upper Income
Edge Orientation Distribution
Unplanned Settlement
0.025
Probability
0.02
0.015
0.01
0.005
0
-100
-80
-60
-40
-20
0
theta
20
40
60
80
100
Edge Orientations
Planned Settlement
0.045
0.04
Peakness in the distribution around edge orientations
separated by 90 degrees is a good indicator for planned
settlements.
Managed by UT-Battelle
for the Department of Energy
Probability
0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
-100
-80
-60
-40
-20
0
theta
20
40
Edge Orientations
60
80
100
Line Length Distribution Parameters
Image tiles representing different neighborhoods projected on the
line length distribution parameter space.
Low income neighborhood
Middle income neighborhood
Lognormal distribution parameter (variance)
High income neighborhood
Managed by UT-Battelle
for the Department of Energy
Lognormal distribution parameter (mean)
Length distribution
parameters based on
lines with local
perpendicular support
are promising features
for separating regions
with different socioeconomic attributes.
Rapid
Scene
Analysis
Play uavrun1output.avi
Managed by UT-Battelle
for the Department of Energy
Assessing Population Dynamics
• Dynamic tracking of people and
vehicle fleet movement from
multisensor data
– Video, cell phones, social media
• Travel behavior modeling
– Congestion and safety
– Energy demand and supply
• Feedback among climate, land
use, and population distribution
– Where will people go after
displacement (hurricane
evacuation to sea level rise)
Managed by UT-Battelle
for the Department of Energy
Population Dynamics Video Slide
Managed by UT-Battelle
for the Department of Energy
Critical infrastructure data development
We develop and maintain spatially enabled, foundation level data for a number
of critical infrastructures for research and operational communities.
Hospitals
Managed by UT-Battelle
for the Department of Energy
Prisons
Day-care
Centers
Rail lines/Rail
points
Solid Waste
Landfills
Mobile Home
Parks
Energy Data
Layers
U.S. mobile home parks database
High within-class variation
Need for scalable solutions
Managed by UT-Battelle
for the Department of Energy
Total Area: 9.827 million sq. km
Covered by ≈ 9.8 Trillion pixels of resolution 1m x 1m
Automated and scalable detection
• Data: NAIP 1m imagery
• 8423 samples of 300m x 300m
patches
• State of TX
– Order of magnitude improvement
compared to open source compilation
Database point
Detected point
Managed by UT-Battelle
for the Department of Energy
• ~1500 points by manual
compilation (9 manweeks)
• ~15,000 points by
automated detection (1
week)
Online Detection of Anomaly, Change and
Change Point from Space-Time Data
Potere, D., Feierabend, N., Bright, E., Strahler, A. “Walmart from Space: A New Source for
Land Cover Change Validation” Photogrametric Engineering and Remote Sensing. Vol 74. July
2008.
Managed by UT-Battelle
for the Department of Energy
Household Generation
Managed by UT-Battelle
for the Department of Energy
Worker Commute and Shoppers Flows
•Worker commute modeled with LandScan USA
•Shoppers commute using a double constraint
interactive model
Managed by UT-Battelle
for the Department of Energy
Each Way to Work
Managed by UT-Battelle
for the Department of Energy
Scenario Based Insights
On any working day:
•Total work commute trips : 178,923
•Average travel time : 19.02 minutes
(Census 2005-2007 travel time 21.1)
•Total travel time : 3,403,545 minutes
•Average travel distance : 11.43 miles
•Total travel distance : 1,859,173 miles
Under a scenario of 10% workers distributed
over the entire county switching to more
fuel efficient vehicles
– Impacted efficiency is on 186,000 miles
per trip per day
• However, allocating that 10% to workers
from the higher income groups impacts
significantly more miles
Family income level
Difference with county average
$50,000–$60,000
12%
$60,000–$80,000
15%
$80,000–$90,000
24%
$90,000–$100,000
29%
>$100,000
29%
Managed by UT-Battelle
for the Department of Energy
Managed by UT-Battelle
for the Department of Energy
Energy Assurance Slide with Video
Managed by UT-Battelle
for the Department of Energy
CoNNECT Video Slide
Managed by UT-Battelle
for the Department of Energy
CoNNECT: focus on future homeowners
 What is the trend of my home energy
use?
 What can I do to increase EE and
reduce my energy usage?
 Are my utilities higher than my
neighbors?
 How does my home compare with
similar ones in the area?
 Am I getting the same results as
others?
 Who can provide EERE services in my
area?
Uses Monthly
and Smart
Meter Data
New User
Registration
Managed by UT-Battelle
for the Department of Energy
Visual-SOLAR
Managed by UT-Battelle
for the Department of Energy
Hurricane Preparedness and Response
Total
population
Significant damage (< 5 ft surge)
1,007,676
Catastrophic damage (5–20 ft surge)
1,513,871
DAMAGE
Individual &
Public Assistance
Public
Assistance
Alabama
575,133
56,801
Mississippi
707,506
1,391,233
Louisiana
3,153,293
1,362,477
Managed by UT-Battelle
for the Department of Energy
FEMA Impacted Areas
Source: FEMA Impacted Areas, August 31, 2005
TOTAL
POPULATION
SENIOR
100%
791,361
90,773
75%
1,594,806
187,677
50%
1,505,196
184,372
25%
1,701,593
224,279
Total
5,592,956
687,101
EARSS: Energy Awareness and
Resiliency Standardized Services
• Open energy (electric grid) status data
– Interaction with DOE and community
– Feasibility based on prior VERDE expertise and
data-availability awareness
• Geospatial standards for situational energy data
– Web-feature service interfaces and
representation standards
– Leading to NGA data brokering (e.g., in GEOINT
Online)
• Enabling geospatial search and streaming-data
analysis capability
– Spatio-temporal queries relating to energy and
infrastructure (including HIFLD data sets, etc.)
– Determine “How does one pose such
questions?”
Managed by UT-Battelle
for the Department of Energy
EARSS: Hurricane Irene (8/26, 1625)
Managed by UT-Battelle
for the Department of Energy
ORNL Support for FEMA: Hurricane Sandy
•
96 hours before landfall published results from analytic
models that predicted:
•
areas where power will be out,
•
how many people may be impacted,
•
forecasts of number of days it may take for power to be
restored.
•
The restoration and outage information fed to the
FEMA Geo-Portal and re-published to deployed
personnel and analysts at DHS, to DOD’s Northcom,
Health and Human Services, and DOE
•
Power outage projections were updated with each
advisory from the National Hurricane Center based on
the projected wind speed, debris, precipitation, and
wind gusts
•
FEMA chaired a National Power Task Force to
coordinate the common operating picture for power
outage and restoration and re-published look ahead
forecasts to the responding community:
Managed by UT-Battelle
for the Department of Energy
Managed by UT-Battelle
for the Department of Energy