Chapter 3 Sections 3.5 – 3.7. Vector Data Representation object-based “discrete objects”

Chapter 3

Sections 3.5 – 3.7

Vector Data Representation

object-based“discrete objects”

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 in ArcView

Must choose form for themeCannot mix forms in single theme

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

Complex Spatial Objects

holes/islands/enclaves contained poly

multiple polys common identifier

Topological Errors

fig p. 92interfere with analysismust be corrected

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

Chapter 4: Data Quality & Data Standards

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

More considerations

projectionscaleclassification schemecartographic qualitymetadatatransfer format

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

deviation, variation, & discrpeancylack of accuracy & precisiontypes gross sytematic random

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)

Standards and GIS Development

interoperabilitydata infrastructure