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1. Fundamentals of GIS
Chapter 4
DIGITIZATION, EDITING AND
STRUCTURING OF
MAP DATA

2. Fundamentals of GIS
Part 1:
•Data input methods: Digitizing
and Geocoding
------Using GIS--

3. Fundamentals of GIS
1. Geocoding
o is a computational process of transforming a
description of a location, such as an address
or place name, into geographic coordinates

4. Fundamentals of GIS
What is Geocoding?
•Is the process of transforming a description of a
location
 such as a pair of coordinates, an address, or a
name of a place to a location on the earth's
surface.
•Address matching is a type of geocoding using a
street address database, created from a streets layer.

5. Fundamentals of GIS
Geocoding

6. Fundamentals of GIS
Geocoding Service
• Geocoding styles are necessary because
– Reference layers come in many forms and formats.

7. Fundamentals of GIS
Materials by Austin Troy © 2006
Geocoding in Action
Mapping hazard zone
properties in L.A. to
see effects on
property values

8. Fundamentals of GIS
XY Geocoding
We can also create points from a table by their latitude and longitude
Do this by clicking:
CA haz. waste sites
•Then we specify the lat and
long fields
•Lat and Long should be in
decimal degrees

9. Fundamentals of GIS
2. Digitizing
Methods of data acquisition

10. Fundamentals of GIS
Digitizing
•This is generally the process of
converting data from analog to digital
, but usually refers to the process of
using a device, such as a digitizing
tablet or mouse to create new vector
features

11. Fundamentals of GIS
Digitizing
•Table digitizing involves use of a digitizing tablet or
table
•A digitizing table is a big table with an electronic
mesh that can sense the position of a digitizing cursor
•Usually have accuracy of .001 inch
•Transmits x and y coordinates of each mouse/cursor
click to the computer and usually joins those with lines

12. Fundamentals of GIS
Digitizing
•Notice how it is attached with tape
•If it moves, the
map will be
inaccurate,
because it’s
recording
position relative
to the tablet, not
the map

13. Fundamentals of GIS
Digitizing
•Many GIS packages have a built-in module to handle
manual digitizing data
•The highest quality way to do it is to use a Computer
Aided Design program (CAD) like AutoCAD, which is
what engineers use
•Multiple layers can be digitized from the same map in
CAD by activating a different “levels” for each layer
•However, that file will have no topology
•Topology, if desired, will have to be built in Arc/Info

14. Fundamentals of GIS
Digitizing
•Snapping: Arc will also snap closed any unsnapped
lines or polygons and will crop dangling lines, based
on user-defined tolerances
•
Snap tolerance: won’t snap together
Snap tolerance: will snap together
Dangling arc Snapped to other arc

15. Fundamentals of GIS
Digitizing
•Digitizing on a tablet requires defining “control
points” which allow the conversion of the digitized
map to real world coordinates.
•Usually, a corner point on the map of known
geographic location is digitized first and its coordinates
are assigned in some sort of header file; this way the
computer knows where the map is location, what the
scale is and what the relative location of all features is

16. Fundamentals of GIS
• Steps to digitize from photographic or map
images:
• Acquire the Image.
• Georeference the Image.
• Create a New Feature Class.
• Digitize the Features.
• Publish the Features.
• Save Your Project Package.

17. Fundamentals of GIS
Map data structure
The map data structure is typically implemented as
an associative array or hash table, with each key-
value pair assigned a unique index using a hash
function.
The value associated with that key is then stored
and retrieved using this index.

18. Fundamentals of GIS

19. Fundamentals of GIS
Materials by Austin Troy © 2006
Accuracy
• “the degree to which information on a map or in a
digital database matches true or accepted values.”
• From Kenneth E. Foote and Donald J. Huebner
http://www.colorado.edu/geography/gcraft/notes/error/error_f.html
• Reflection of how close a measurement represent
the actual quantity measured and of the number and
severity of errors in a dataset or map.

20. Fundamentals of GIS
Materials by Austin Troy © 2006
Precision
• Intensity or level of preciseness, or exactitude in
measurements. The more precise a measurement is,
the smaller the unit which you intend to measure
• Hence, a measurement down to a fraction of a cm is
more precise than a measurement to a cm
• However, data with a high level of precision can still
be inaccurate—this is due to errors
• Each application requires a different level of precision

21. Fundamentals of GIS
Materials by Austin Troy © 2006
Random and Systematic error
•Error can be systematic or random
•Systematic error can be rectified if discovered,
because its source is understood
•A common example is where an remote sensing
instrument consistently measures data erroneously
because of bad calibration—if the problem in
calibration can be understood and accounted for,
then that error is called systematic
•Another example: projecting map data using the
wrong zone would result in consistently wrong data

22. Fundamentals of GIS
Materials by Austin Troy © 2006
Random and Systematic error
•Systematic errors affect accuracy, but are usually
independent of precision; data can use highly precise
methods but still be inaccurate due to systematic error
Accurate and
precise: no
systematic , little
random error
inaccurate and
precise: little
random error but
significant
systematic error
Accurate and imprecise:
no systematic , but
considerable random
error
inaccurate and
imprecise: both
types of error

23. Fundamentals of GIS
Materials by Austin Troy © 2006
Error propagation
•Where one error leads to another
•Example: if a key reference point was mis-digitized in
layer A and that point was used to “register” layer B to
layer A, then the error is propagated in layer B and all
subsequent layers based on either of them; this error
can propagate additively or multiplicatively

24. Fundamentals of GIS
Materials by Austin Troy © 2006
Error cascading
•Refers to when errors are allowed to propagate
unchecked from one layer to the next and on to the
final set of products or recommendations
•Can be managed to a certain extent by conducting
“sensitivity analysis”
•Can occur with positional as well as with attribute
errors

25. Fundamentals of GIS
Materials by Austin Troy © 2006
Positional Accuracy
• Positional accuracy standards specify that
acceptable positional error varies with scale
• Data can have high level of precision but still be
positionally inaccurate
• Positional error is inversely related to precision and
to amount of processing

26. Fundamentals of GIS
Materials by Austin Troy © 2006
Measurement of Positional Accuracy
Often stated as confidence interval: e.g. 104.2 cm +/-
.01 = true value lies between 104.21 and 104.19
Root mean squared error (MSE); equals squared
difference between observed and expected value for
observation i divided by total number of
observations, summed across each observation i
This is just a standardized measure of error—how
close the predicted measure is to observed

27. Fundamentals of GIS
Materials by Austin Troy © 2006
Positional Error
• Different agencies have different standards for
positional error
• Example: USGS horizontal positional requirements
state that 90% of all points must be within 1/30th of
an inch for maps at a scale of 1:20,000 or larger,
and 1/50th of an inch for maps at scales smaller
than 1:20,000

28. Fundamentals of GIS
Materials by Austin Troy © 2006
Positional Error
• USGS Accuracy standards on the ground:
1:4,800 ± 13.33 feet
1:10,000 ± 27.78 feet
1:12,000 ± 33.33 feet
1:24,000 ± 40.00 feet
1:63,360 ± 105.60 feet
1:100,000 ± 166.67 feet
See image from U. Colorado
showing accuracy standards
visually
Hence, a point on a map
represents the center of a spatial
probability distribution of its
possible locations
Thanks to Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project,
Department of Geography, The University of Colorado at Boulder for links

29. Fundamentals of GIS
Materials by Austin Troy © 2006
Positional Error-some examples

30. Fundamentals of GIS
Materials by Austin Troy © 2006
Positional Error-some examples
Notice that the small scale map has much
less detail (less precision) and hence is less
accurate locally; here accuracy is a function
of precision; from a distance, however, this
is less apparent

31. Fundamentals of GIS
Materials by Austin Troy © 2006
Positional Error-some examples

32. Fundamentals of GIS
Materials by Austin Troy © 2006
Positional Error-some examples
Notice the same pattern between medium
and large scale

33. Fundamentals of GIS
Materials by Austin Troy © 2006
Positional Error-some examples

34. Fundamentals of GIS
Materials by Austin Troy © 2006
Positional Error-some examples
Here two layers derived from the same
scale data (1:100,000) have different
positions: the blue has less error because it
has a local projection, while the thicker
line has a regional projection; here
positional error is not due to precision, but
to processing

35. Fundamentals of GIS
Materials by Austin Troy © 2006
Attribute Precision
• Precision for a database means lots of details—lots
of attributes about a given record,
• Precision for a record means a high level of
numeric precision—that is, lots of digits, so does
not apply to categorical data
• Example: recording income down to cents, rather
than just dollars

36. Fundamentals of GIS
Materials by Austin Troy © 2006
Attribute Accuracy
• Continuous (numeric) attributes are often treated
like geo-spatial data in terms of accuracy; these
errors often arise from mis-measurement
• Conceptual Accuracy
• Conceptual Precision

37. Fundamentals of GIS
Materials by Austin Troy © 2006
Conceptual Precision
• The accuracy of your classifications will depend on
the precision you are using.
• The less precise you need your classifications to be,
the less likely there will be errors
• If just classifying as “land and water”, that is not
very precise, and not likely to result in an error

38. Fundamentals of GIS
Materials by Austin Troy © 2006
Other measures of data quality
• Logical consistency
• Completeness
• Data currency/timeliness
• Accessibility
• These apply to both attribute and positional data

39. Fundamentals of GIS
Materials by Austin Troy © 2006
Logical Consistency
• Do data follow rules of logic?
• Attribute Example: is something classified as both
water and as commercially zoned land?
• Geospatial example: Do lines intersect when they
should not (eg. With power lines)? Do polygons not
close on themselves

40. Fundamentals of GIS
Materials by Austin Troy © 2006
Completeness
• Is a data layer complete or lacking in coverage?
• Examples: does a layer on roads leave out some
roads? If so, does it do so systematically or
randomly? Does a database of buildings in a city
leave out some buildings?
• Examples where completeness is crucial: a database
of houses used to notify neighbors when a noxious
facility is proposed?

41. Fundamentals of GIS
Materials by Austin Troy © 2006
Completeness
• Completeness also describes completeness in coding
of features.
• If two databases are linked (say one has attributes
and the other has geometry) and one has features
added without the other being updated, one will be
incomplete and result in link inconsistencies
• This happens when several agencies each maintain
different parts of a commonly used meta-database

42. Fundamentals of GIS
Materials by Austin Troy © 2006
Currency and Timeliness
• Since some things change faster than others, the
importance of timeliness in data depends on what is
being displayed

43. Fundamentals of GIS
Materials by Austin Troy © 2006
Currency and Timeliness

44. Fundamentals of GIS
Materials by Austin Troy © 2006
Currency and Timeliness
• Streets are another data set where
currency is important; blue
represents all the additional streets
built between 1990 and 2000

45. Fundamentals of GIS
Materials by Austin Troy © 2006
Conflation
• When one layer is better in one way and another is
better in another and you wish to get the best of both
• Way of reconciling best geometric and attribute
features from two layers into a new one
• Very commonly used for case where one layer has
better attribute accuracy or completeness and another
has better geometric accuracy or resolution
• Also used where newer layer is produced for some
theme but is has lower resolution than older one

46. Fundamentals of GIS
Materials by Austin Troy © 2006
Two general types of Conflation
• Attribute conflation: transferring attributes from
an attribute rich layer to features in an attribute
poor layer
• Feature conflation: improvement of features in
one layer based on coordinates and shapes in
another, often called rubber sheeting. User either
transforms all features or specifies certain features
to be kept fixed

47. Fundamentals of GIS
Materials by Austin Troy © 2006
Conflation layers
• More spatially accurate layer is referred to as the
base, coordinate or target layer
• Layer with more accurate attribution is referred to
as the reference, or non-base layer

48. Fundamentals of GIS
Materials by Austin Troy © 2006
Conflation examples
• TIGER line files: good attribution, poor accuracy;
USGS DLGs: opposite. Attribute conflation is
frequently used by third party vendors to assign the
rich attribute data of TIGER to the positionally
accurate DLGs. Nodes are matched by iteratively
rubber sheeting the reference layer to the base layer
until matching nodes fall within certain tolerance.
Then line features are matched up.

49. Fundamentals of GIS
Materials by Austin Troy © 2006
Conflation examples
Source: Stanley Dalal, GIS cafe

50. Fundamentals of GIS
Materials by Austin Troy © 2006
Documentation and Metadata
•To avoid many of these errors, good documentation of
source data is needed
•Metadata is data documentation, or “data about data”
•Ideally, the metadata describes the data according to
federally recognized standards of accuracy
•Almost all state, local and federal agencies are
required to provide metadata with geodata they make

51. Fundamentals of GIS
Materials by Austin Troy © 2006
Documentation and Metadata
•The federal geographic data committee (FGDC) is
a federal entity that developed a “Content Standard
for Digital Geospatial Metadata” in 1998, which is
a model for all spatial data users to follow
•Purpose is: “to provide a common set of
terminology and definitions for the documentation
of digital geospatial data.”
•All federal agencies are required to use these
standards

52. Fundamentals of GIS
Materials by Austin Troy © 2006
Documentation and Metadata
• Some roles of metadata
1. Information retrieval, cataloguing, querying and
searching for data electronically.
2. Describing fitness for use and documenting the usability
and quality of data.
3. Describing how to transfer, access or process data
4. Documenting all relevant characteristics of data needed
to use it

53. Fundamentals of GIS
Materials by Austin Troy © 2006
Documentation and Metadata
•Critical components usually break down into:
•Dataset identification, overview
•Data quality
•Spatial reference information
•Data definition
•Administrative information
•Meta metadata

54. Fundamentals of GIS
Materials by Austin Troy © 2006
Documentation and Metadata
•Data identification, overview and administrative info:
•General info: name and brief ID of dataset and
owner organization, geographic domain, general
description/ summary of content, data model used to
represent spatial features, intent of production,
language used , reference to more detailed
documents, if applicable
•Constraints on access and use
•This is usually where info on currency is found

55. Fundamentals of GIS
Materials by Austin Troy © 2006
Documentation and Metadata
•Spatial reference should include:
• horizontal coordinate system (e.g. State Plane)
•Includes projection used, scale factors,
longitude of central meridian, latitude of
projection origin, distance units
•Geodetic model (e.g. NAD 83), ellipsoid, semi-
major axis

56. Fundamentals of GIS
Materials by Austin Troy © 2006
Documentation and Metadata
•Data definition, also known as “Entity and
Attribute Information,” should include:
•Entity types (e.g. polygon, raster)
•Information about each attribute, including
label, definition, domain of values
•Sometimes will include a data dictionary, or
description of attribute codes, while sometimes
it will reference a documents with those codes if
they are too long and complex

57. Fundamentals of GIS
Materials by Austin Troy © 2006
Documentation and Metadata
•Data distribution info usually includes:
•Name, address, phone, email of contact person
and organization
•Liability information
•Ordering information, including online and
ordering by other media; usually includes fees

58. Fundamentals of GIS
Materials by Austin Troy © 2006
Documentation and Metadata
•Metadata reference, or meta-metadata
•This is data about the metadata
•Contains information on
•When metadata updated
•Who made it
•What standard was used
•What constraints apply to the metadata

59. Fundamentals of GIS
Materials by Austin Troy © 2006
Metadata in Arc GIS
•Arc GIS allows you to display, import and export
metadata in and to a variety of Metadata formats:
•
•It defaults to FGDC ESRI which looks like:

60. Fundamentals of GIS
Materials by Austin Troy © 2006
Metadata in Arc GIS
•XML is the most flexible form because its tag
structure allows it to be used in programming; tags
can be called as variables or can be created through
form interfaces; allows for compatibility across
platforms and programs

61. Fundamentals of GIS
Materials by Austin Troy © 2006
Metadata in Arc GIS
•In the past, complete metadata was only available as
text; you had to create most embedded metadata tags
yourself. Today many state and nationwide datasets
come with complete embedded metadata including full
attribute codes
•E.g. NEDs, NLCD,
all VCGI data

62. Fundamentals of GIS
Materials by Austin Troy © 2006
Metadata in Arc GIS
•Can edit, import, edit and export metadata in multiple
formats allowing helping with proper sharing of data.