Geospatial Modeling
Maps and Animated Geography
E. Lynn Usery
Professor, University of Georgia
Research Geographer, U.S. Geological Survey
Models
• Scale - Differs from reality only in size
– Iconic - Miniature copies of reality
– Analog - Alter size, some properties - glacier model
with clay
• Conceptual -- Diagrammatic process model
– Usually with boxes and arrows, i.e., flowchart
• Mathematical - Allows prediction
– Probabilistic - Assumes components are related in
random fashion -Subject to chance, express initial
assumptions as set of probabilities and use
probability theory.
– Deterministic - Behavior controlled by natural laws.
Geospatial Models
Definition and Classification
• A geospatial model is a simplified representation
of geographic reality.
• Model Types
– Spatial – Generally static, model distributions
• Examples include maps, GIS databases, and cartographic
models (based on Map Algebra)
– Process – Static or dynamic, model processes
• Growth or accumulation
– urban growth, climate change, sea level rise
• Flows
– spatial interaction, gravity model, location-allocation
Spatial Models -- Maps
• Scale models, i.e., generalized representations
of geographic phenomena
• No map is accurate; all contain three types of
errors from transformations
– Spherical to plane
– Three-dimensions to two-dimensions
– Generalization
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Selection
Simplification
Symbolization
Induction
Global Landcover – Mollweide Projection
Spatial Models--Cartographic Models
• Map themes again geographically registered but
combined with a sequence of operations (map
algebra) that generate a desired result from a
set of basic input data layers
• Map layers become variables in map algebra
with operators on and between variables
• Operators include point, neighborhood, and
global
• Most commonly implemented with raster data
layers
Cartographic Model for
Profitability
Cartographic Model of Human
Effects on Animal Activity
• Measure animal activity over different time
periods
• Determine change over time
• Determine human activities over
samespace and time
• Compare the two activity levels to
determine effects
Spatial Models-- GIS Databases
• Map model placed in computer representation
• Includes all error inherent in the map model
• Usually include multiple maps of individual
themes registered to a common spheroid,
datum, projection, and coordinate system with
associated attributes linked to geographic object
(point, line, area) identifiers commonly stored in
a relational database
Entity Model
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What is it – attributes, theme
Where is it – location, space
When is it – time
What is its relation to other entities –
proximity, connectivity (topology)
Classes of Operations for
Entities
• Attribute operations
• Distance/location operations
• Topological operations
Attribute Operations
• Ui = f(A,B,C,D,…)
– Where Ui is the derived attribute
– A,B,C,D,… are attributes combined to derive Ui
– F ( ) is a function of one or more of:
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Logical (Boolean)
Arithmetical
Univariate statistics
Multivariate statistics
Multicriteria methods
Land Suitability Model
• Soil mapping units of texture and pH
• A is set of mapping units of Oregon Loam
• B is set of mapping units for pH >= 7.0, then
– X = A AND B finds all occurrences of Oregon Loam
with pH >= 7.0.
– X = A OR B finds all occurrences of Oregon Loam and
all mapping units with pH >=7.0.
– X = A XOR B finds all units that are either Oregon
Loam or have a pH >= 7.0, nut not in combination
– X = A NOT B finds all mapping units that are Oregon
Loam where the pH is less than 7.0.
Retrieving Entities with Only
Attributes
Retrieval and Recode
Reclassification
Deriving New Attributes
• Empirical Regression Models
– Temperature as function of elevation
– T = 5.697 – 0.00443*E
• where, T is temperature in degrees Celsius
• and E is elevation in meters
• Multivariate clustering
Polygon Overlay – Sliver
Problem
Distance Operators
Spatial Buffering
• Determine the number of fast food restaurants
within 5 km of the White House.
• Investigate the potential for water pollution in
terms of proximity of filling stations to natural
waterways.
• Compute the total value of the houses lying
within 200 m of the proposed route for a new
road.
• Compute the proportion of the world popultaion
lying within 100 km of the sea.
Spatial Buffering
Connectivity Operators
Geospatial Process Models
• Often use results of GIS Databases as
steps in a process
• Non-point Source Pollution -- AGNPS
• Sea Level Rise
• Urban Growth -- SLEUTH
AGNPS
• Agricultural Non-Point Pollution Source
Introduction -- AGNPS
• Operates on a cell basis and is a
distributed parameter, event-based model
• Requires 22 input parameters
• Elevation, land cover, and soils data are the
base for extraction of input parameters
Input Parameter Generation
• 22 parameters; varying degrees of
computational development
– Simple, straightforward, complex
Input Parameter Generation
Details on Generation of
Parameters
• Cell Number
• Receiving Cell Number
• SCS Curve Number
– Uses both soil and land cover to resolve curve number
Details on Generation of
Parameters
• Slope Shape Factor
Extraction Methods
• Used object-oriented programming and
macro languages
– C/ C++ and EML
• Manipulated the raster GIS databases with
Imagine
• Extracted parameters for each resolution for
both boundaries using AGNPS Data
Generator
Creating AGNPS Output
• AGNPS creates a nonpoint source (“.nps”)
file
• ASCII file like the input; tabular, numerical
form
AGNPS
Output
• AGNPS
Output
Creating AGNPS Output
Images
• Output Image Creation
– Combined “.nps” file with Parameter 1 to
create multidimensional images
– Users can graphically display AGNPS output
– Process: create image with “x” layers, fill
layers with AGNPS output data, set projection
and stats for image
– Multi-layered (bands) images per model event
Creating AGNPS Output Images
Creating AGNPS Images
Model of Sea Level Rise
• Data inputs
– Elevation – Gtopo 30
– Population -- Landscan
– Land Cover – Global Land Cover
• 30 arc-sec resolution cells (approximately
1 km at the Equator)
• Most accurate global data available
• Model for eastern North America only
Urban Growth -- SLEUTH
• Model of converting land to urban from
other uses
• Cellular Automata model based on
probabilities from Monte Carlo stochastic
simulation
• Model begins with an existing urban base
(i.e, some cells are urban and others nonurban based on historical land cover data)
Urban Growth -- SLEUTH
• Non-urban cells change to urban based on 7
controlling variables (GIS layers) and user
specified parameters controlling growth
• Variables: Slope, Land Cover, Elevation, Urban,
Transportation, Hillshade
• Types of growth:
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Spontaneous Growth
New Spreading Centers
Edge Growth
Road-Influenced Growth