Geospatial Modeling Maps and Animated Geography E. Lynn Usery Professor, University of Georgia...
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Transcript of Geospatial Modeling Maps and Animated Geography E. Lynn Usery Professor, University of Georgia...
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 ModelsDefinition 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
• 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
• 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:• 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 OperatorsSpatial 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
Flood_5m.gif
flood_30m.gif
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 non-urban 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: – Spontaneous Growth– New Spreading Centers– Edge Growth– Road-Influenced Growth