Mapping
Theory
Features and Datatypes
Which Error can Be Removed and How Much?
Mostly Removed
Atmospheric
Error
0%
Multi-Path Error
0%
Human Error
With all of this error, what can we expect?
In an open field (no multi-path), stated accuracies
Under canopy, double the stated accuracies
How Error is Measured: DOP (Dilution of Precision)
•The geometry of the satellite constellation can affect
the accuracy of the GPS positions.
VDOP
•DOP is an indicator of quality of the constellation at
any given time.
HDOP
•Lower the DOP, the better the geometry of the
constellation and the more accurate the GPS
positions.
Mapping Theory
•Static Data vs. Dynamic Data
•Different Feature Types
•Definition of Feature File
•Planning the Mapping Project
Static Data vs. Dynamic Data
Static Features: the process of averaging GPS positions taken successively over a period of time with
a stationary antenna to increase accuracy.
i.e. Property Corners, Stand Points, Log
Decks, Gates, etc.
Blue dots: Individual observations
Red Dot: Average of all individual observations
Dynamic Features: the process of collecting GPS data while the GPS antenna is in motion. Often
associated with Line or Area Features.
i.e. Roads, SMZs, Creeks, Meandering
Property Lines, etc.
The 3 Different Feature Types
Point:
Always a Static feature
i.e. Property Corners, Stand Points, Log
Decks, Gates, etc.
Line:
Can be Static, Dynamic, or Both
i.e. Roads, Creeks, Trails, etc.
Area:
Can Be Static, Dynamic, or Both
i.e. Stands, Tracts, Fields, etc.
Definition of Feature Files
Feature: the object which is being mapped with a GPS system. Features
may be points, lines or areas.
Attribute: a characteristic which describes a Feature. Attributes can be
thought of as questions which are asked about the Feature, i.e. Type,
Number, Condition, Name.
Value: descriptive information about a Feature. Values can be thought
of as the answers to the questions posed by Attributes, i.e. Dirt, Rd. #
322, Fair, Johnson Rd., respectively.
Importance of Feature Files
Why Describe the Features Being Collected?
Feature Files are the Beginning of a GIS Database
Allows the Researcher to Make Better Decisions
Easily Produce Professional Maps Very Quickly
Easier to Manage Large Amounts of Data
Geographic Data Types
There are two types of Geographic Data
1. Discrete Dataset
2. Continuous Dataset
Geographic Data Types
Discrete Data
• Data occupies one POINT, LINE or Polygon ( including a
RASTER cell) at a given time of measurement
• North Pole, Amazon River or Palo Duro State Park.
• Can have multiple values attached to a point, line,
polygon or cell
Geographic Data Types
Continuous Data
• Variables that can be measured at any point across the
landscape
• Elevation (Contour Lines)
• Temperature
Raw Data Forms
Data comes in two forms
1. Qualitative Data
2. Quantitative Data
Raw Data Forms
Qualitative Data
• Observations
• Perceptions
• Rankings
• Classes (range of values)
• Fuzzy Logic
• Warm/Cool/Cold – Just right
Raw Data Forms
Quantitative Data
• Measurements
• Recognized Standard Units
• Repeatability
• Calibration Important
• Accuracy –
• Precision –
How right is the measurement?
Finest of measurement
Geographic Data Measures
Levels of Measurement are used to classify data into one of
four categories:
• Nominal
• Ordinal
• Interval
• Ratio
Geographic Data Measures
Nominal
• Named features or objects
• Functions
• Frequency counts
• No other spatial statistical functions can be applied
Nominal Geographic Values
Substation
Bridge
Building
Water Plant
Residence
Building
Examples also include classifications types like gender,
nationality, ethnicity, language, genre, style, biological species,
and form
Geographic Data Measures
ORDINAL
• Qualitative Rankings
–**May involve numbering**
• Qualitative Terms or Relationships
–Beware of Misinterpretations
• Functions
–Data Values can be compared
–Limited statistical manipulation
Ordinal Geographic Values
• 1st
GOOD
Big
Community
• 2nd
FAIR
Medium
Town
• 3Rd
BAD
Small
City
Geographic Data Measures
INTERVAL
Interval data is quantative data that is measured on a physical scale that has equal intervals without
percentages or ratios, but where the 0 is arbitrary or does not have a true meaning
One example is temperature measured in degrees Fahrenheit. The degrees are measured on a scale that
has equal distance between the intervals (0 to 1, 1 to 2, etc) and the zero point of 0 degrees Fahrenheit is
not a true zero. This is because 0 degrees Fahrenheit does not mean there is no heat. A ratio between
these two numbers does not yield useful data. Because the zero is not a true zero it is not meaningful on
the interval scale, and ratio interpretations are not possible. For example 80 degrees Fahrenheit is not
twice as hot as 40 degrees Fahrenheit. When looking at numerical data most will be put into the Ratio
classification, it is difficult to think of examples for interval data other than the Fahrenheit scale.
• Measure features using calibrated STANDARD instruments
• Functions
• Data Values can be compared more precisely using several accepted estimates of difference
• Values have to be understood in context of the number line used to develop the measurement
scale
Geographic Data Measures
Ratio
Ratio data is quantitative data in which ratios between two values
have definite meaning (eg, 2 is half of 4 and twice as many as 1),
and unlike Interval data will have a meaningful zero. The normal
numbers (0, 1, 2, 3, etc) is a common ratio data set. Another
example is temperature measured in degrees Kelvin. Unlike
Fahrenheit, 0 degrees on the Kelvin scale actually means the
complete absence of thermal (kinetic) energy, and 100 degrees
Kelvin has twice as much thermal (kinetic) energy as 50 degrees
Kelvin. Meaning that unlike in Fahrenheit, 100 degrees Kelvin is
twice as hot as 50 degrees Kelvin.