geospatial modeling maps and animated geography

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