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ISPRS Congress 2000
Multidimensional Representation of Geographic Features
E. Lynn Usery
Research Geographer
U.S. Geological Survey
ISPRS Congress 2000
Outline
• Introduction• Objectives• Background• Approach
– Theoretical Basis
– Implementation Strategy
• Application – DLG-F usage • Conclusions
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Introduction
• Need for geoinformation theory– UCGIS Research Priority on “Geographic
Representation”; proposed theme on ontology.– Need to handle 3 dimensions and time– Need to interface to geographic process models
• Climate models• Growth models• Biologic models• Watershed/water quality models
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Introduction
• Geographic reality consists of entities and processes
• We represent entities as objects and processes as models– Mathematical (process)– Data driven (map, spatial, or GIS)– Combinations
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Objectives
• Advance development of theory of geographic information supporting multiple representations.
• Validate theory in multiple applications.• Develop implementation around specific
application for feasibility testing.• Use current GIScience knowledge as base
from which to extend representation ideas.
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Background
• Significant work toward a theory– Peuquet, 1988; Molenaar, 1991; Mark, 1993;
Usery, 1996; Frank, 1998.– Geography
• Place, attribute, time as fundamental basis for spatial analysis from Berry (1964), basis of current GIS
• Region theory
– Cartography• Abstraction and generalization concepts
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Background
• Cognitive psychology– Basic level of categorization exists– For geography, that level is geographic entities
or features• Roads• Streams• Buildings• Watersheds• …
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Problems
• How to advance theory of geoinformation?• Limits of commercial GIS software systems
– Map model of reality– Geometry (raster or vector) based objects with
attached attributes
• Needs to advance ,,Z,t or X,Y,Z,t coordinates for entities– Motion and process
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Feature Approach
• Feature is geographic entity and object representation
• One feature, many objects– Multiple resolutions– Multiple geometries– Access from single identity
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Feature - A set of phenomena with common attributes and relationships. The concept of feature encompasses both entity and object.
Entity - A real-world phenomenon that cannot be subdivided into phenomena of the samekind.
Object - A digital representation of all or a part of an entity.
Attribute - Characteristic of a feature or of an attribute value.
Relationship - Linkage between features or objects.
Feature instance - An occurrence of a feature defined by a unique set of attributes andrelationships.
Definitions
ISPRS Congress 2000
Dimensions, Attributes, and Relationships of Geographic Phenomena
Space Theme Time-----------------------------------------------------------------------------------------------------------Attributes φ,λ,Z color, size, date, duration
point, line, area, shape, ph, ... period, ...surface, volume, pixel, voxel, ...
-----------------------------------------------------------------------------------------------------------Relationships topology, topology, topology,
direction, is_a, kind_of, is_a, was_a,distance, ... part_of, ... will_be ...
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Requirements to Move from Theoretical Concepts to
Implementation
• Theory of sufficient completeness to support application needs
• Transition framework from theoretical concepts to a data model
• Implementation methodology from the data model
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Theoretical Completeness
• Components of theory available– Feature concepts– Human understanding
• Category theory• Metaphor• Algebraic formalisms
• Missing links– Feature to feature relations
• Some work on topological relations
– Thematic, temporal relations
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Transition Framework
• Dimensions
• Concepts
• Data Models
• Data Structures
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Feature
Dimensions Space Theme Time
Concepts Image Schemata Experiential Continuous flowLanguage Formal Time slicesMetaphor Category theory Dynamic events
Data Models Geometry Relations RelationsTopology Objects Predictive formulas
Data Structures Vector lis ts Tables TablesRaster matrices Frames EquationsArc/node tables Semantic nets Semantic nets
Features Knowledgebase
ISPRS Congress 2000
Implementation Methodology
• Feature processing system– Create, select, manipulate, analyze features– Use existing databases
• Spatial, thematic, temporal attributes and relationships
• Vector geometry (,,Z,t lists)
• Raster geometry (pixel matrices)
– Heuristics, procedures, models
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User Interface
Feature ListFeature Processors
Feature Feature Feature Feature Manipulation Creator Selector Analyzer
Databases
Feature
Heuristics,Procedures,Models
Spatial Thematic Temporal φ,λ,Z,t lists Attributes and Relationships Pixel matrices
Image and map store
Features Knowledgebase
ISPRS Congress 2000
Application of the Framework
• Watershed/water quality modeling application• Test site in Little River, Georgia, USA
– 340 sq. km.– Traditional data layers
• Soils, land cover, elevation, precipitation
– Derived information • Slope, aspect, flow directions, flow paths, flow planes
– Multiple geometries and resolutions• Vector• Raster at 3, 30, 60, 120, 210, 240, 480, 960, 1920 m cells
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Watershed Features Modeled from Raster Geometry
Entity Object Attributes Relationships------------------------------------------------------------------------------------------------------Gaging station Pixel Table of heights Subwatershed area
Sampling station Pixel Table of wq values Subwatershed areaStream Line of pixels Name Connects to: streams
Table of flow Flow from: flowplanesFlowplane Pixel Slope (avg) Flows to: stream
aggregationSubwatershed Pixel Area Composed of: flowplanes
Aggregation Contains: streamPart of: subwatershed
ISPRS Congress 2000
Feature Attributes Relationships
Watershed
Spatial attributes Spatial RelationshipsBounding coordinates Adjacent watershedsPour point Containing watersheds
Contained watersheds
Thematic Attributes Thematic RelationshipsElevation (DEM matrix) Composed of subwatershedsSlope (Raster matrix) Contains drainage networkAspect (Raster matrix)Land cover (Raster matrix)Soils (Raster matrix)Name
Temporal Attributes Temporal RelationshipsDate State compared to time tn
ISPRS Congress 2000
Feature Attributes Relationships
Sampling station
Spatial attributes Spatial relationshipsX,Y,Z location Pour point for watershed
Pour point for sampling stations ...
Thematic attributes Thematic RelationshipsWater volume Date/TimeChemical content (time tn)
PhPNH4
Biotic content (time tn)E. Coli
Name
Temporal attributes Temporal relationshipsDate Time of thematic values
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Feature Attributes Relationships
Flow plane
Spatial attributes Spatial RelationshipsBounding coordinates Opposite flowplanePour point Bounding stream
Bounding ridgeContaining flowplanesContained flowplanes
Thematic Attributes Thematic RelationshipsElevation (DEM matrix) Composed of subwatershedsSlope (Raster matrix) Contains drainage networkAspect (Raster matrix)Land cover (Raster matrix)Soils (Raster matrix)Name
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Feature Attributes Relationships
Drainage network
Spatial attributes Spatial RelationshipsCenterline coordinates Flows intoPour point Containing watersheds
Contained watersheds
Thematic AttributesName
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Feature Attributes Relationships
Stream
Spatial attributes Spatial RelationshipsCenterline coordinates Part of networkPour point Flows from
Flows to
Thematic AttributesName
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Implementation of Watershed Features
• Use USGS DLG-F structures
• Apply to raster geometry
• Build attributes and relations specific to defined features
• Develop parameters for water models
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Conclusions
• Conceptual framework (addition to theory) supporting multiple geometries and multidimensional representation developed.
• Geographic feature is unique entity;basis of theory– Feature has multiple object representations
• Transition framework from concepts to data model developed
• Data model to data structure transition developed
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Conclusions
• Framework being implemented for watershed/water quality modeling
• Features developed
• Data structures for features developed from USGS DLG-F and are being implemented against raster geometry.
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