Data Quality

1
Data Quality

• GiGo garbage in, garbage out
• Cos its in the computer, dont mean its right

Its not the things you dont know that matter,
its the things you know that arent
so. Will Rogers Famous Okie GI
specialist
But there are also unknown unknowns the ones we
don't know we don't know. Donald Rumsfeld
2
Murphys Laws of Mapmaking

• Cardinal Postulates
• area desired by the user has not yet been mapped.
• if mapped, area straddles zone boundaries--or at
  least map sheets
• if on one sheet, sheet is scheduled for update
  next year last update was 1901
• Corollary for GIS
• area desired by user is still in paper-map form
• if in GIS, recorded with X-Y coordinates and
  straddles zone boundaries--or at least map tiles
• if one tile, projection unknown and no
  information on date of creation and/or last
  update
• Conclusion GIS is not a panacea!

3
Horwoods Short Laws on Data

• Dr. Edgar Horwood, founder of the Urban and
  Regional Information Systems Association (URISA)
  and Professor of Civil Engineering and Urban
  Planning at the University of Washington was an
  early pioneer of computer mapping in the early
  1960s.
• Good data are the data you already have.
• Bad data drives out good.
• The data you have for the present crisis was
  collected to relate to the previous one.
• The respectability of existing data grows with
  elapsed time and distance from the source of the
  data.
• Data can be moved from one office to another but
  cannot be created or destroyed.
• If you have the right data, you have the wrong
  problem and vice versa.
• The important thing is not what you do but how
  you measure it.
• In complex systems there is no relationship
  between the information gathered and the decision
  made.
• The acquisition of knowledge from experience is
  an exception.
• Knowledge grows at half the rate at which
  academic courses proliferate.

For more information, go to http//urisa.org/pre
v/GIS_Hall_of_Fame/halloffame.htm
4
Data Quality How good is your data?

• Scale
• ratio of distance on a map to the equivalent
  distance on the earth's surface
• Primarily an output issue at what scale do I
  wish to display?
• Precision or Resolution
• the exactness of measurement or description
• Determined by input can output at lower (but not
  higher) resolution
• Accuracy
• the degree of correspondence between data and the
  real world
• Fundamentally controlled by the quality of the
  input
• Lineage
• The original sources for the data and the
  processing steps it has undergone
• Currency
• the degree to which data represents the world at
  the present moment in time
• Documentation or Metadata
• data about data recording all of the above
• Standards
• Common or agreed-to ways of doing things
• Data built to standards is more valuable since
  its more easily shareable

5
Scale

• ratio of distance on a map, to the equivalent
  distance on the earth's surface.
• Large scale --gtlarge detail, small area covered
  (1200 or 12,400)
• Small scale --gtsmall detail, large area
  (1250,000)
• A given object (e.g. land parcel) appears larger
  on a large scale map
• scale can never be constant everywhere on a map
  cos of map projection
• problem is worst for small scale maps certain
  projections (e.g. mercator)
• can be true from a single point to everywhere
• can be true along a line , or a set of lines
• on large scale maps, adjustments often made to
  achieve close to true scale everywhere (e.g
  State Plane and UTM systems)
• scale representation
• Verbal (good for interpretation.) 0ne inch each
  equals one statute mile
• representative fraction (RF) 1 63,360(good for
  measurement)(smaller fractionsmaller scale
• 12,000,000 smaller than 12,000)
• scale bar(good if enlarged/reduced)

use them all on a map!
6
Scale Examples

• Common Scales
• 1200 (116.8ft)
• 12,000 (156 yards 1cm20m)
• 120,000 (5cm1km)
• 124,000 (12,000ft)
• 125,000 (1cm.5km)
• 150,000 (2cm1km)
• 162,500 (1.6cm1km 1.986mi)
• 163,360 (11mile 1cm.634km)
• 1100,000 (11.58mi 1cm1km)
• 1500,000 (17.9mi 1cm5km)
• 11,000,000(115.8mi 1cm10km)
• 17,500,000(1118mi) 1cm750km)

• Large versus Small
• large above 112,500
• medium 113,000 - 1126,720
• small 1130,000 - 11,000,000
• very small below 11,000,000
• ( really, relative to whats available for a
  given area Maling 1989)
• Map sheet examples
• 124,000 7.5 minute USGS Quads
• (17 by 22 inches 6 by 8 miles)
• 17,500,000 US wall map
• (26 by 16 inches)
• 120,000,000 US 8.5 X 11

7
Scale, Resolution Accuracy in GIS Systems

• On paper maps, scale is hard to change, thus it
  generally determines resolution and accuracy--and
  consistent decisions are made for these.
• A GIS is scale independent since output can be
  produced at any scale, irrespective of the
  characteristics of the input data at least in
  theory
• in practice, an implicit range of scales or
  maximum scale for anticipated output should be
  chosen and used to determine
• what features to show
• manholes only on large scale maps
• how features will be represented
• manhole a polygon at 150 cities a point at
  11,000,000
• appropriate levels for accuracy and precision
• Larger scale generally requires greater
  resolution
• Larger scale necessitates a higher level of
  accuracy
• GIS also helps with the the generalization
  problem implicit in paper maps
• A road drawn with 0.5 mm wide line (the smallest
  for decent visibility)
• At 124,000 implies the road is 12 meters (36
  feet) wide
• At 1250,000 implies the road is 125 meters (375
  feet) wide
• At least in a GIS you can store the true road
  width, but be careful with plots!

8
Precision or Resolution its not the same as
scale or accuracy!

• Precision the exactness of measurement or
  description
• the size of the smallest feature which can be
  displayed, recognized, or described
• Can apply to space, time (e.g. daily versus
  annual), or attribute (douglas fir v. conifer)
• for raster data, it is the size of the pixel
  (resolution)
• e.g. for NTGISC digital orthos is 1.6ft (half
  meter)
• raster data can be resampled by combining
  adjacent cells
• this decreases resolution but saves storage
• eg 1.6 ft to 3.2 ft (1/4 storage) to 6.4 ft
  (1/16 storage)
• resolution and scale
• generally, increasing to larger scale allows
  features to be observed better and requires
  higher resolution
• but, because of the human eyes ability to
  recognize patterns, features in a lower
  resolution data set can sometimes be observed
  better by decreasing the scale (6.4 ft
  resolution shown at 1400 rather than 1200)
• resolution and positional accuracy
• you can see a feature (resolution), but it may
  not be in the right place (accuracy)
• higher accuracy generally costs much more to
  obtain than higher resolution
• accuracy cannot be greater (but may be much less)
  than resolution (e.g. if pixel size is one meter,
  then best accuracy possible is one meter)

9
Accuracy rests on at least four legs, not one!

• Positional Accuracy (sometimes called
  Quantitative accuracy)
• Spatial
• horizontal accuracy distance from true location
• vertical accuracy difference from true height
• Temporal
• Difference from actual time and/or date
• Attribute Accuracy or Consistency-- the validity
  concept in experimental design/stat. inf.
• a feature is what the GIS/map purports it to be
• a railroad is a railroad, and not a road
• A soil sample agrees with the type mapped
• Completeness--the reliability concept from
  experimental design/stat. inf.
• Are all instances of a feature the GIS/map claims
  to include, in fact, there?
• Partially a function of the criteria for
  including features when does a road become a
  track?
• Simply put, how much data is missing?
• Logical Consistency The presence of
  contradictory relationships in the database
• Non-Spatial
• Some crimes recorded at place of occurrence,
  others at place where report taken
• Data for one country is for 2000, for another its
  for 2001
• Annual data series not taken on same day/month
  etc. (sometimes called lineage error)

10
Sources of ErrorError is the inverse of
accuracy. It is a discrepancy between the coded
and actual values.

• Sources
• Inherent instability of the phenomena itself
• E.g. Random variation of most phenomena (e.g.
  leaf size)
• Measurement
• E.g. surveyor or instrument error
• Model used to represent data
• E.g. choice of spheroid, or classification
  systems
• Data encoding and entry
• E.g. keying or digitizing errors
• Data processing
• E.g. single versus double precision algorithms
  used
• Propagation or cascading from one data set to
  another
• E.g. using inaccurate layer as source for another
  layer

• Example for Positional Accuracy
• choice of spheroid and datum
• choice of map projection and its parameters
• accuracy of measured locations (surveying) of
  features on earth
• media stability (stretching ,folding, wrinkling
  of maps, photos)
• human drafting, digitizing or interpretation
  error
• resolution /or accuracy of drafting/digitizing
  equipment
• Thinnest visible line 0.1-0.2 millimeters
• At scale of 120,000 6.5 - 12.8 feet
• (20,000 x 0.2 4,000mm 4m 12.8 feet)
• registration accuracy of tics
• machine precision coordinate rounding error in
  storage and manipulation
• other unknown

11
Measurement of Positional Accuracy

• usually measured by root mean square error the
  square root of the average squared errors
• Usually expressed as a probability that no more
  than P of points will be further than S distance
  from their true location.
• Loosely we say that the rmse tells us how far
  recorded points in the GIS are from their true
  location on the ground, on average.
• More correctly, based on the normal distribution
  of errors, 68 of points will be rmse distance or
  less from their true location, 95 will be no
  more than twice this distance, providing the
  errors are random and not systematic (i.e. the
  mean of the errors is zero)
• e.g. for NTGISC digital orthos RMSE is 3.2 feet
  (one meter)
• for USGS Digital Ortho Quads RMSE spec. is
  approx. 33 feet or 10 meters (but in reality
  much better)
• -- with GPS, height is 2 or 3 times less
  accurate in practice at high precisionthan
  horizontal (officially the spec is 1.5, but data
  collection errors affect vertical the most)

12
Positional Accuracy
13
National Map Accuracy Standards 1941/47

• established in 1941 by the US Bureau of the
  Budget (now OMB) for use with US Geological
  Survey maps (Maling, 1989, p. 146)
• horizontal accuracy not more than 10 of tested,
  well defined points shall be more than the
  following distances from their true location
• 162,500 1/50th of an inch (.02)
• 124,000 1/40th of an inch (amended to
  1/50.02 in 1947)
• 112,000 1/30 of an inch (.033)
• Thus, on maps with a scale of 163,360 (11
  mile) 90
• of points should be within 105.6 feet (63360 X
  .02)/12) of their true location.
• on USGS quads with a scale of 124,000
  (12,000ft) 90 of points should be within 40
  feet (24,000 X .02)/12 of their true location.
• on a map with a scale of 112,000 (11,000ft),
  90 of points should be within 33 feet (1,000 X
  .033), approx. 10 meters
• gives rise to the loose, but often used,
  statement that the NMAS is 10 meters
• Inadequate for the computer age
• how many points? how select?
• how determine their true location
• what about attribute completeness?
• Unfortunately, the new standard doesnt
  address all these issues either

14
National Standard for Spatial Data Accuracy
(NSSDA)1998

• Geospatial Positioning Accuracy Standard
  (FGDC-STD-007)
• Part 3, National Standard for Spatial Data
  Accuracy FGDC-STD-007.3-1998
• replacement for National Map Accuracy Standard
  of 1941/47
• specifies a statistic and testing methodology
  for positional (horizontal and vertical) accuracy
  of maps and digital data
• no single threshold metric to achieve (as with
  old Standard), but users encouraged to establish
  thresholds for specific applications
• accuracy reported in ground units (not map units
  as in 1941 standard 1/30th inch)
• testing method compares data set point coordinate
  values with coordinate values from a higher
  accuracy source for readily visible or
  recoverable ground points
• altho. uses points, principles apply to all
  geospatial data including point, vector and
  raster objects
• other standards for data content will adopt NSSDA
  for particular spatial objects
• copies of the standard available at
  http//www.fgdc.gov
• Accuracy Standard has 7 parts, of which parts 4-7
  apply to specific data types

15
GPS and Positional Accuracy

• Global Positioning System satellite positioning
  with WAAS (wide area augmentation system)
  adjustment gives positional accuracy within about
  3 meters (10ft).
• This is more accurate than most printed maps and
  nautical charts!
• It is also more accurate than most digital maps
  and charts since these often derive from paper
  maps and surveys conducted prior to GPS
• Your integrated GPS/digital chart can show you
  nicely heading down the center of a channel, but
  positional inaccuracy in the chart can leave you
  grounded!

16
SummaryResolution, Scale, Accuracy
Storageillustrating the relationship
Largest (maximum) scale for given pixel
size. Storage is for USGS 7.5 quad. area (in
Texas, USGS quad is about 7 mi x 8.5 mi60 sq.
miles--16 quads for Dallas County) Source
GPS Technology Corporation
17
Examples of Accuracy

• Go to quality_graphics.ppt

18
Lineage

• identifies the original sources from which the
  data was derived
• details the processing steps through which the
  data has gone to reach its current form
• Both impact its accuracy
• Both should be in the metadata, and are required
  by the Content Standard for Metadata (see below)
• Michael Goodchild ( the guru of GIS) advocates
• Measurement-based GIS, in which how data
  collected and how measurements made are a part of
  the record (as in surveying)
• Coordinate-based GIS, is the current approach,
  and it tracks none of this.
• (see Shi, Fisher and Goodchild Spatial Data
  Quality London Taylor and Frances, 2002)

19
Currency Is my data up-to-date?

• data is always relative to a specific point in
  time, which must be documented.
• there are important applications for historical
  data (e.g. analyzing trends), so dont
  necessarily trash old data
• current data requires a specific plan for
  on-going maintenance
• may be continuous, or at pre-defined points in
  time.
• otherwise, data becomes outdated very quickly
• currency is not really an independent quality
  dimension it is simply a factor contributing to
  lack of accuracy regarding
• consistency some GIS features do not match
  those in the real world today
• completeness some real world features are
  missing from the GIS database

Many organizations spend substantial amounts
acquiring a data set without giving any thought
to how it will be maintained.
20
Standards common agreed-to ways of doing
things

• May exist for
• Data itself including process (the way its
  produced) and product (the outcome)
• Utilities Data Content Standard,
  FGDC-STD-010-2000 
• Accuracy of data
• Geospatial Positioning Accuracy Standard, Part 3,
  National Standard for Spatial Data Accuracy,
  FGDC-STD-007.3-1998 
• Documentation about the data (metadata)
• Content Standard for Digital Geospatial Metadata
  (version 2.0), FGDC-STD-001-1998 
• Transfer of data and its documentation
• Spatial Data Transfer Standard (SDTS),
  FGDC-STD-002
• For symbology and presentation
• Digital Geologic Map Symbolization  
• May address
• Content (what is recorded)
• Format (how its recorded file format, .tif,
  shapefile, etc)
• May be a product of
• An organizations internal actions private or
  organization standards
• An external government body (Federal Geographic
  Data Committee) or third sector body (Open GIS
  Consortium) public or de jure standards
• Laissez-faire market-place-forces leading to one
  dominant approach e.g. Wintel standard
  industry or de facto standards

http//www.fgdc.gov/standards/standards.html
21
Who Sets Public Standards ?

• Federal Geographic Data Committee
• Sets standards for geospatial data which all
  federal agencies are required to follow
• Has representatives from most federal agencies
• National Institute for Standards and Technology
  (NIST) sets federal gov. standards for other
  things (e.g. IT in general)
• national standards bodies
• American National Standards Institute (ANSI)
• has the USs single vote at ISO
• United States InterNational Committee on
  Information Technology Standards (INCITS) handles
  IT standards for ANSI
• Several FGDC standards been submitted for
  approval
• Most countries in the world have their equivalent
  to ANSI
• international standards bodies
• ISO (International Organization for
  Standardization)
• other assorted vendor groups, professional
  associations, trade associations, and consortia
• Open GIS Consortium (OGC) is the main player in
  GIS

22
The Process for Setting de jure standards!
Source URISA News Issue 197, Sept/Oct. 2003
Go to the following web site for excellent
overview of standard making process http//www.fg
dc.gov/publications/documents/standards/geospatial
_standards_part1.html
23
Adopting Standards What you should do

• Data quality achieved by adoption and use of
  standards Do it!
• Common ways of doing things essential for using
  sharing data internally and externally
• only federal agencies required to use FGDC
  standards, its optional for any others (e.g.
  state, local)
• power of feds often results in adoption by
  everybody, although there are some noted failures
  (e.g.the OSI, GOSIP, POSIX standards in
  computing in the 1980s failed and were withdrawn)
• FGDC or ISO standards provide excellent starting
  point for local standards, and should be adopted
  unless there are compelling reasons otherwise
• Standards for metadata (documenting your data)
  are the most important and should be first
  priority.
• Content Standard for Digital Geospatial Metadata
  (version 2.0), FGDC-STD-001-1998
• ISO Document 19115 Geographic Information-Metadata
  (content) and 19139, Geographic
  InformationMetadataImplementation
  Specification, (format for storing ISO 19115
  metadata in XML format)
• If not one of these standard for metadata, adopt
  some standard!

24
Content Standards for Digital Geospatial
MetadataWhat and Why?

• Metadata describes the content, quality,
  format, source and other characteristics of data.
• Allows you and others to
• Locate data (find, discover)
• Evaluate data (quality, restrictions, reputation)
• Extract (order, download, pay)
• Employ (apply, use)
• and automate this process.

25
Main Sections of the US FederalContent Standard
for Digital Geospatial Metadata

• Identification
• Title? Area covered? Themes? Currency?
  Restrictions?
• Data Quality (5 aspects)
• Positional Attribute Accuracy? Completeness?
  Logical Consistency? Lineage?
• Spatial Data Organization
• Indirect? Vector? Raster? Type of elements?
  Number?
• Spatial Reference
• Projection? Grid system? Datum? Coordinate
  system?
• Entity and Attribute Information
• Features? Attributes? Attribute values?
• Distribution
• Distributor? Formats? Media? Online? Price?
• Metadata Reference
• Metadata currency? Responsible party?
• For more info, go to http//www.fgdc.gov/metadata
  /contstan.html

By law (Executive Order 12906, 1994), all federal
agencies must document their data according
to Content Standard for Digital Geospatial
Metadata (version 2.0), FGDC-STD-001-1998 
26
Traditional Minimum Documentation Requirements
for Maps/GIS

• geodetic datum name (e.g NAD27)--which implies
• ellipsoid/spheroid name (earth model) e.g. Clark
  1866
• point of origin (ties ellipsoid to earth) e.g
  Meades Ranch
• required for all GIS data bases and maps
• projection name and its parameters and its
  measurement units
• (see terrestrial lecture for exact details)
• Required for all maps since 2-D by nature
• Required for GIS if data is in X-Y projected
  form
• Source information
• accuracy standard(s) to which built
• author/publisher/creator name and/or data source
• date(s) of data collection/update, and of map/gis
  creation
• Cartographers demand all maps have
• north arrow
• map scale
• graticule indication
• at least four latitude/longitude tic marks, with
  values in degrees
• at least four X-Y tic marks, with values and
  units of measurement (feet, meters, etc.)

If GIS data in lat/long, must know datum. If GIS
data in XY, must know datum and projection info)
27
Texas Standardshttp//www.dir.state.tx.us/tgic/pu
bs/pubs.htm

• Standards for digital spatial data (raster and
  vector) for State agencies in Texas were
  established in 1992
• http//www.dir.state.tx.us/tgic/pubs/gis-standards
  .htm
• Currently (2004), being reviewed by the Texas
  Geographic Information Council (TGIC) for
  possible update
• Apply to map scales of 124,000 and smaller
  (e.g., 1100,000 1250,000).
• Cover variety of issues including data layers,
  datum, projections, accuracy, metadata, etc..
• Two major planning reports on GIS in state gov.
  in Texas are
• Digital Texas 2002 Biennial Report on Geographic
  Information Systems Technology
• http//www.dir.state.tx.us/tgic/pubs/gift99-small.
  pdf
• Geographic Information Framework for Texas (1999)
• http//www.dir.state.tx.us/tgic/pubs/digtex-lowres
  .pdf

28
Importance of Standards

• Great Baltimore Fire of 1904 - fire engines from
  different regions responded only to be found
  useless since they had different hose coupling
  sizes that did not fit Baltimore hydrants - fire
  burned over 30 hours, resulted in destruction of
  1526 building covering 17 city blocks.
• Fire 1923 - Fall River, MA saved when over 20
  neighboring fire department responded to a town
  fire since they had standardized on hydrants and
  hose couplings sizes.
• 9/11 Response in NY and DC severely hampered by
• incompatibilities between GIS data sets, and
  lack of data
• Also, incompatibilities between communications
  systems
• The most important standard?
• Railroad track gauge - adopted by US, UK, Canada,
  and much of Europe.
• South America still hampered by differing
  railroad gauges between countries.

29
The Best Time to Adopt a Standard?
Now?
Now?
Before!
30
Appendix

• FGDC Standards
• (status as of March 2004)
• For latest, go to
• http//www.fgdc.gov/standards/standards.html

31
FGDC Metadata Standards

• Metadata
• Content Standard for Digital Geospatial Metadata
  (version 2.0) FGDC-STD-001-1998
• Content Standard for Digital Geospatial Metadata,
  Part 1 Biological Data Profile
  FGDC-STD-001.1-1999
• Metadata Profile for Shoreline Data
  (FGDC-STD-001.2-2001)
• Content Standard for Digital Geospatial Metadata
  extension for remote sensing data
  (FGDC-STD-0012-2002)
• Encoding Standard for Geospatial Metadata (Draft)
• Metadata Profile for Cultural and Demographic
  Data (dropped)

Current thrust is to integrate FGDC Metadata
standards (and other FGDC standards eventually)
into International Standards Organization (ISO)
standards.
32
FGDC Data Accuracy Standard

• Geospatial Positioning Accuracy Standard
  (FGDC-STD-007)
• Part 1, Reporting Methodology FGDC-STD-007.1-1998
• Part 2, Geodetic Control Networks
  FGDC-STD-007.2-1998
• Part 3, National Standard for Spatial Data
  Accuracy FGDC-STD-007.3-1998
• Part 4 Architecture, Engineering Construction,
  and Facilities Management (FGDC-STD-007.4-2002),
• Part 5 Standard for Hydrographic Surveys and
  Nautical Charts (Review)

• An umbrella incorporating several accuracy
  standards.
• Part 3 is the general standard.
• It essentially updates the National Map Accuracy
  Standard of 1941/47

33
FGDC Data Content Standards

• Facility ID Data Standard, (Review)
• Address Content Standard (Review)
• US National Grid (FGDC-STD-0011-2001)
• Earth Cover Classification System, (draft)
• Geologic Data Model, (Draft)
• Governmental Unit Boundary Data Content Standard,
  (Draft)
• Biological Nomenclature and Taxonomy Data
  Standard (draft)
• National Hydrography Framework Geospatial Data
  Content Standard (proposal)
• Environmental Hazards Geospatial Data Content
  Standard, (dropped)
• NSDI Framework Data layers (under Reviewsee
  next slide)

• Cadastral Data Content Standard FGDC-STD-003
• Classification of Wetlands and Deep Water
  Habitats FGDC-STD-004
• Vegetation Classification Standard FGDC-STD-005
• Soils Geographic Data Standard, FGDC-STD-006
• Content Standard for Digital Orthoimagery,
  (FGDC-STD-008-1999)
• Content Standard for Remote Sensing Swath Data,
  (FGDC-STD-009-1999)
• Utilities Data Content Standard,
  (FGDC-STD-010-2000)
• NSDI Framework Transportation Identification
  Standard, (Review)
• Hydrographic Data Content Standard for Coastal
  and Inland Waterways, (Review)
• Content Standard for Framework Land Elevation
  Data, (Review)

34
FGDC Framework Data Standards

• establish data content requirements for the seven
  layers of geospatial data that comprise the
  National Spatial Data Infrastructure (NSDI), the
  base layers needed for any geographic area

• geodetic control,
• elevation,
• Orthoimagery
• Hydrography (water)

• Transportation
• Cadastral (landownership)
• governmental unit boundaries

• Goals are to
• Facilitate and promote exchange of framework
  layers between producers, consumers, and vendors
  thru a common content and way of describing that
  content
• Lower the cost of data for everyone
• For each layer, specifies an integrated
  application schema in Unified Modeling Language
  (UML) including feature types, attribute types,
  attribute domain, feature relationships, spatial
  representation, data organization, and metadata
• no standard specified for data format, but an
  appendix describes a possible implementation
  using the Geography Markup Language (GML) Version
  3.0, developed through the Open GIS Consortium,
  Inc. (OGC).

35
FGDC Data Transfer Standards

• Spatial Data Transfer Standard (SDTS)
  FGDC-STD-002
• SDTS, Part 1 Logical Specification (FIPSPUB
  173-1, July 1994)
• SDTS, Part 2 Spatial Features (FIPSPUB 173-1,
  July 1994)
• SDTS, Part 3 ISO 8211 Encoding (FIPSPUB 173-1,
  July 1994)
• SDTS, Part 4 Topological Vector Encoding (FIPSPUB
  173-1, July 1994)
• SDTS, Part 5 Raster Profile and Extensions
  (FGDC-STD-002.5, 2000)
• SDTS, Part 6 Point Profile, FGDC-STD-002.6, 2000
• SDTS Part 7 Computer-Aided Design and Drafting
  (CADD) Profile (FGDC-STD-002.7, 2000)

• One of the first of the FGDC standards (along
  with metadata).
• Intended to facilitate transfers between
  different GIS systems.
• Competitive pressures plus internal weaknesses
  hindered adoption.

36
FGDC Data Symbology and Presentation Standards

• Digital Geologic Map Symbolization, (Review)