geospatial modeling maps and animated geography
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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