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 coordinates forms 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 theme Cannot 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 data arcs and nodes are shared uses less storage space simplifies analyses

Topological Data point: unique coordinates line 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 changes elements adjacency containment connectivity

Topological Relationships point to point: no relationship line 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. 92 interfere with analysis must be corrected

Georelational Data Model ArcView points, lines & polygons stored separately entities stored separately attribute data stored separately

Object-Oriented Data Model specially designed software user-specific based 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 quality need for standards: define acceptable quality need for training in all areas

Measures of data quality reliability accuracy currency relevance timeliness intelligibility completeness known precision concise intelligibility convenience integrity

More considerations projection scale classification scheme cartographic quality metadata transfer format

Accuracy how closely the data represent the real world limited by data collection equipment and technique intended use cost

Precision exactness of representation numerical 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, & discrpeancy lack of accuracy & precision types gross sytematic random

Error Sources table p. 107 original source material data collection data automation and compilation data processing and analysis inherent & operational

Uncertainty degree of doubt accuracy and precision are not known error is not known (but may be large) greater when data from multiple sources & scales are mixed importance of metadata!!!

Components of data quality lineage (data history): list p. 109 positional 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 appropriate must 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/QC SOPs standardized methodology designed to avoid common errors important error sources digitizing coordinate transformation

Error Propagation end product accumulates errors of source data fig 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 & procedures allows 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 products data transfer standards data quality standards metadata standards

Standards International ISO current, proposed & developing National Spatial Data Transfer Standard (table p. 129)

Standards and GIS Development interoperability data infrastructure