Computer models and big data enable environmental modeling at unprecedented scales. Big data provides detailed spatial, temporal, and radiometric resolution data for modeling. Cellular automata (CA) models like SLEUTH are well-suited for modeling complex urban systems. SLEUTH uses large datasets to calibrate transition rules and model land use change over time. It can generate scenarios by modifying parameters, transportation networks, and exclusion layers. Parallel and distributed computing allow SLEUTH to model vast areas quickly. Integrating models with big data and advanced computing removes barriers to simulating real-world systems.

1. What is big data?
Computer Models and Big Data: A loosely-defined term used to describe data sets so large and complex that
What can computation contribute? they become awkward to work with using on-hand database management
tools
Fed by large numbers of sensors, data collections means, images, satellites,
webcams, mobile devices, transactions, etc
Keith C. Clarke Petabytes to zettabytes (ZB, 1021 bytes) of data.
Professor, Department of Geography Science disciplines involved include meteorology, genomics, data fusion,
University of California, Santa Barbara image exploitation, geophysics, complex physics simulations, and biological
Santa Barbara and environmental research.
CA 93106-4060 Global per-capita capacity to store information has roughly doubled every 40
USA months since the 1980s, as of 2012, every day 2.5 quintillion (2.5×1018)
kclarke@geog.ucsb.edu bytes of data were created.
Big data is difficult to work with using relational databases and descriptive
statistics and visualization packages
Requires massively parallel software running on tens, hundreds, or even
thousands of servers
Taming big data Modeling is Enabled by Big Data
Business solutions have been toward cloud Environmental models have often been data
computing, scientific toward the grid hungry, and resolution and time sensitive
Cloud: aims at cost reduction, increased For example, the ecological fallacy or MAUP
flexibility, on-demand services makes analysis at once scale suspect, e.g.
Grid: aka cyberinfrastructure, aimed at scientific world climate change on a one degree grid
problem solving Superior data are now available, at all
Involves High Performance Computing and resolutions: radiometric, spatial and temporal
Parallel Processing Allows focus to change from analysis of states
Also includes server side management to analysis of dynamics
Modeling World Urbanization
Computational Simulation Models
Only option when the real system cannot be directly
controlled or when testing would be unethical
All good models simplify, but only as much as is necessary
to capture system behavior
Good models are simple, effective, can be reproduced,
give intuitively and statistically valid results, and are
tractable
Models have a vast array or tools, libraries, editing
systems, etc. to choose from
Yet most still run into tractability constraints
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2. Modeling Cities Computer modeling and the city
Rates of urbanization world wide are Many computer-based models of city growth,
unprecedented in human history, fastest rates services, and flows were developed during the
in China's Pearl River Delta 1970s based on the Forrester Systems
Urban expansion and land use change are Dynamics approach
good examples of complex systems Douglass B. Lee in 1973 published “Requiem
High degree of dependence on initial conditions for Large Scale Models” JAIP 39, 3, 162-178.
Multiple influences on change Seven Deadly sins: Hypercomprehensiveness,
grossness, hungriness, wrongheadedness,
Non-linear feedbacks complicatedness, mechanicalness, and
Phases and phase changes, boom and bust expensiveness.
A new generation of models Data for Modeling cities
Two new types of models emerged during the 1990s: Greatly facilitated by remote sensing
Cellular Automata and Agent Based Models
Resolutions have improved from 80m to 1m in
ABM best suited to hypothesis testing within cities and two decades (but makes cross time comparison
for demography. Appear difficult to apply
geocomputational methods
hard)
CA are ideal, strike down each of the seven sins New methods have been devised to accurately
map land use and detect what areas are urban
Simple to implement and understand, spatially explicit
and apparently accurate in modeling and forecasting RS data can be matched to local city-wide GIS
A perfect match to raster GIS and two dimensional data, management data bases and maps
arrays GIS enables layer matching, which must be
exact
The impact of resolution Many CA models
CA models consist of:
A set of existing conditions (Land use at some time
on the past)
A regular grid of cells (the framework)
A neighborhood over which the rules apply
A set of mutually exclusive and non-overlapping
states (e.g. urban, forest, water, agriculture)
100m 30m Rules governing transitions in each cell based on
5m the states of its neighbors
Almost all differences among models are in the
rules
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3. Elements of CA CA transition rules
Can be derived empirically if before and after
images are available (e.g. City in 1990 and
2010), but assumes rules do not change for a
Cell states
forecast in 2030
Can be devised by combinations of causative
factors
SLEUTH uses topographic slope, prior land
Kernel pixel, to which use, urban status, proximity to transportation
Rule is applied, e.g. if two
Neighborhood or more neighbors are and exclusions
Magenta, turn magenta
What is SLEUTH How does SLEUTH work?
A popular CA urban growth and land use change Assemble data in standard file naming
model convention
Open source for over 15 years Download and test model against supplied test
100+ applications data set, duplicate results
Source code in C, using gd graphics libraries with Unix Use in test mode to validate input data
or Linux. PC use possible under cygwin
Calibrate in three phases
Supported by NSF, USGS, and the USEPA
Many bug fixes, user for a, papers, on line
Using best calibration parameters, determine
documentation, etc output values at forecast start date
Parallel version uses MPI Run forecasts, examine statistics and graphics
1900 1925 1950 1975 2000 Behavior Rules
Slope
T0 T1
Land Cover
spreading road deltatron
spontaneous center organic influenced
Excluded
Urban
Transportation
Hillshade
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4. Spontaneous Growth Creation of new Spreading Centers
Some new urban settlements will become centers
of further growth.
urban settlements may occur anywhere on a landscape Others will remain isolated.
f (diffusion coefficient, slope resistance) f (spontaneous growth, breed coefficient,
slope resistance)
Organic Growth Road Influenced Growth
The most common type of development Urbanization has a tendency to follow lines
occurs at urban edges and as in-filling of transportation
f (spread coefficient, slope resistance) f (breed coefficient, road_gravity coefficient,
slope resistance, diffusion coefficient)
Deltatron Land Cover Model Land cover transitions
Phase 2: Perpetuate change
search for change in
the neighborhood find associated
land cover transitions
delta space
Transition Probability
Matrix
YEL ORN GRN
YEL 0.9 0.05 0.05
ORN 0.05 0.9 0.05
create
deltatrons
Age or
kill
deltatrons impose
change in
land cover
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5. Deltatrons at work Behavior Rules
T0 T1
spreading road
spontaneous organic deltatron
center influenced
f (slope f (slope f (slope f (slope resistance,
resistance, resistanc resistanc diffusion coefficient,
diffusion e, breed e, spread breed coefficient,
coefficient) coefficien coefficien road gravity)
t) t)
For i time periods
(years)
Calibr
The Method past
ation
Predicting the present
“Brute force calibration” from the past
Phased exploration of parameter space
Start with coarse parameter steps and coarsened
spatial data (no longer necessary)
Step to finer and finer data as calibration proceeds
For n
“Good” rather than best solution Monte Carlo
iterations
5 parameters 0-100 = 101^5 permutations
Initial runs in the late 1990s ran for 5000 hours For n
coefficient
Application in 2010 ran for 6 CPU months sets
“present”
Prediction (the future from the present)
SLEUTH in parallel Probability Images
Monte Carlo iteration and time steps are embarrassingly
parallel!
Massive speed-up attained
Have tested with clusters, Beowulf groups, Alternate Scenarios (Exclusion, roads)
supercomputers, etc.
Entire eastern USA modeled at 100m in 1 Cray hour
pSLEUTH uses pRPL, plans for USA at 30m Land Cover Uncertainty
Code modifications and optimization allow use even on a
PC under Windows/cygwin
Also explored genetic algorithms (80% reduction)
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6. A decade of SLEUTHing SLEUTH and Scenarios
Approximately 100 papers on applications Urban pattern in the future
Used on every continent except Antarctica Transportation network
Applied at scales from 1m to 1km Exclusion layer
Many lessons learned: three review papers now in Change parameters “Cross-breeding”
print Can couple with other models
Some applications as examples follow Starting to integrate policy: At first land protection,
e.g. Lisbon, now MCE and differential
assessment (CA Williamson Act)
Future Scenarios
Santa Barbara
Tulare Land 2003 Part 2: Input Images
Tulare excluded. Wac. (Used for the Williamson Act Excluded Layer)
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7. Scenario 1. Business As Usual (Current Administration) Model integration Westernport Project: DPI
Parkville Conceptual Framework
Stakeholders
Define a problem Evaluate Solutions
User Interface (Maps, Tables and Graphics)
Output
Input
MSE
Model Management System
Scenario
Management Terrestrial Component Marine Multi-criteria
Model Model
Land Use change Hydrological
Marine Models
Model (SLEUTH) Model
(Spatial) Database Management System (GIS-based)
Land Topography (Slope, Vegetation (EVC – Species (Animal Climate (Rainfall, Socio-economic
Soil Attributes
Use Elevation, Orientation) Native Plantation) Habitat) Temperature) characteristics
Study Area (Source: Claudia Pelizaro)
Scenario 2
• Land
development is
not controlled
by any statutory
regulation.
• Land use
change follows
past trends
• Google Earth
Leão, S., Bishop, I. and Evans, D. 2004. Spatial-temporal model for demand
SLEUTH Model Output allocation of waste landfills in growing urban regions. Computers Environment
and Urban Systems 28: 353-385.
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8. Conclusion
Al-Awadhi, T. (2007), Monitoring and Modeling Urban Expansion Using GIS & RS: Case
Study from Muscat, Oman, 2007 Urban Remote Sensing Joint Event, ©2007 IEEE.
•Cyberinfrastructure, the grid, and HPC have removed many
computational barriers to big data scale simulation modeling
•SLEUTH urban growth and land use change model was used as
an example of the most successful model type (CA) show how
advanced computing techniques have advanced modeling by
increasing tractability
•Most promise for scientific modeling lies in parallelization, for
which CA is a natural
•However an issue remains: few bother to learn parallel
programming or how to use grid tools
•Geoportals to HPC tools may be the best option
Thank you for your time
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