chapter 3 sections 3.5 – 3.7. vector data representation object-based “discrete objects”
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Vector Data Concepts
objects represented by points lines polygons
topology relationship of objects without respect to coordinates
Representation of Vector Data
coordinatesforms point: single coordinate line: string of coordinates with start
and end nodes polygon: closed loop of coordinates
node vs. vertex
Vector Data Model - concepts
spaghetti data model fig p. 85 no identities graphical elements
graphical entities requires feature identifier ArcView - shapefiles
main file index file database table
Vector Data Model - representation
cartographic representation number of arcs and nodes needed to
represent data may vary with scale affects accuracy & precision as scale
changes
cartographic symbolization appropriate form may vary with scale polygon vs point
Vector Data Model
numerical format determined by programmer double-precision, floating-point is best
Topological Data Model
uses relationships between vector data of the same form arc-node
used for line and polygon dataarcs and nodes are shared uses less storage space simplifies analyses
Topological Data
point: unique coordinatesline from & to nodes, intermediate vertices has unique ID # may share nodes with other lines (connectivity) may cross without sharing a node
polygon comprised of arcs (lines) and their nodes has unique ID # always minimum of two polys: inside and
outside
Topological Relationships
properties of geometric figures that do not change when the shape changeselements adjacency containment connectivity
Topological Relationships
point to point: no relationshipline to line may share nodes with other lines
(connectivity, adjacency) may cross without sharing a node
Topological Relationships
polygon may share nodes (connectivity,
adjacency) may share arcs (lines)
(connectivity, adjacency) right and left polygons
may contain another polygon (connectivity, adjacency, containment) shared arc polys are right and left
Use of Topology
data input spaghetti digitizing remove topological errors polygons identified very important for later use
spatial searches look for shared nodes and arcs
Georelational Data Model
ArcViewpoints, lines & polygons stored separatelyentities stored separatelyattribute data stored separately
Object-Oriented Data Model
specially designed softwareuser-specificbased on the data objects considered
Relationship Between Representation & Analysis
Raster less compact data
structure simple data model analysis of spatial
variability analysis of spatial
relationships of environmental data
Vector compact data structure complex data model analysis of distribution
and location of individual objects
works well with topological relationships (ie. land parcels & roads)
difficult overlay processing
Data Quality
“fitness for use”varies with intended use scale method of collection
quality of product may only be as good as the lowest quality data used to produce it
Data Quality
need for metadata: includes records relevant to data qualityneed for standards: define acceptable qualityneed for training in all areas
Measures of data quality
reliabilityaccuracycurrencyrelevancetimelinessintelligibility
completenessknown precisionconciseintelligibilityconvenienceintegrity
Accuracy
how closely the data represent the real worldlimited by data collection equipment and
technique intended use cost
Precision
exactness of representationnumerical data number of significant digits does not imply accuracy need varies with scale
categorical data level of detail number of categories residential vs type of residential
Error Sources
table p. 107original source materialdata collectiondata automation and compilationdata processing and analysisinherent & operational
Uncertainty
degree of doubtaccuracy and precision are not knownerror is not known (but may be large)greater when data from multiple sources & scales are mixedimportance of metadata!!!
Components of data quality
lineage (data history): list p. 109positional accuracy “one line width” varies with scale tables p. 109 & 110
attribute accuracy numerical categorical
Components of data quality
logical consistency with real world within model & system between data sets & files
boundary errors layering errors
completeness spatial thematic
Components of data quality
temporal accuracy precision of temporal measurements age of data
semantic accuracy labeling
Using components of data quality
level of quality desired will vary with scale intended application
transferring data from one application or scale to another may not be appropriatemust examine the metadata
Assessment of data quality
positional accuracy random sample root mean square
error (RMSE) fig p. 113 examine results
for patterns & concentrations
attribute accuracy random sample error matrix fig p. 114 errors of inclusion
& exclusion percent correctly
classified Kappa Index of
Agreement (p. 116)
Assessment of data quality
considerations data checks: field vs. reference file more precision, less accuracy
(sometimes) sample size & scheme (p. 118)
original & reference varies with data needs and real-world
structure of data to be collected
Error Management
QA/QCSOPs standardized methodology designed to avoid common errors
important error sources digitizing coordinate transformation
Error Propagation
end product accumulates errors of source datafig p. 120 (overly simplified)complexity error characteristics differ overlay operations differ in type of influence data set contributions to final product differ
may attempt to reduce at each stage via examination of product
Error Management
sensitivity analysis vary input layers & note effect on results helps in system design helps focus input data quality efforts may use in analyses (create varying scenarios)
reporting data quality numerical measures error matrices shadow map (p. 123)
Data Standards
reference document that provides rules, guidelines & proceduresallows interaction between entities benchmark for variation
types de facto (by popular use) de jure (developed by organization) regulatory
table p 124
Data standard components
standard data productsdata transfer standardsdata quality standardsmetadata standards
Standards
International ISO current, proposed & developing
National Spatial Data Transfer Standard (table
p. 129)
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