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Global Positioning System
A three-dimensional measurement system operates using radio signals transmitted from satellites
orbiting the Earth Created and maintained by the U.S. Dept. of Defense and
the U.S. Air Force Russia and a European consortium are implementing
similar systems System as a whole consists of three components:
satellites (space segment) receivers (user segment) ground stations (control segment)
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• Fully operational in 1994
• 21 satellites, 3 spares
• Not as available during times of conflict
• 6 Orbital Planes
• 20,200 km orbit
• ~ 12 hour orbital period
• Each visible for ~ 5 hours
Global Positioning Systems (GPS)
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Satellites (Space Segment)
24 NAVSTAR satellites (21 operational and 3 spares) orbit the Earth every 12 hours ~11,000 miles altitude positioned in 6 orbital planes orbital period/planes designed to keep 4-6 above
the horizon at any time controlled by five ground stations around the
globe
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Satellites (Space Segment)
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Receivers (User Segment)
Ground-based devices can read and interpret the radio signal from several of the
NAVSTAR satellites at once. Use timing of radio signals to calculate position on the Earth's
surface Calculations result in varying degrees of accuracy -- depending
on: quality of the receiver user operation of the receiver local & atmospheric conditions current status of system
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Garmin
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Garmin’s Outdoor GPS Receivers:etrex series
Basic GPS
eTrex® eTrex Camo®eTrex Summit®
eTrex Venture
GPS 76 GPS 72 GPS 12 GPS 12XL
Geko™ 101 Geko 201 Geko 301
Foretrex™ 101 Foretrex 201
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How It Works – Part 1
Start by determining distance Start by determining distance between a GPS satellite and between a GPS satellite and your positionyour position
Adding more distance Adding more distance measurements to satellites measurements to satellites narrows down your possible narrows down your possible positionspositions
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How it Works – Part 2
Three distances = two pointsThree distances = two points
Four distances = one pointFour distances = one point
Note: Note: • 4th measurement not needed4th measurement not needed• Used for Used for timingtiming purposes instead purposes instead
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How it Works – Part 3
Distance between satellites and receivers determined by timing how long it takes the signal to travel
from satellite to receiver
How?
Radio signals travel at speed of light: 186,000 miles/second
Satellites and receivers generate exactly the same signal at exactly the same time
Signal travel time = delay of satellite signal relative to the receiver signal
Distance from satellite to receiver =
signal travel time * 186,000 miles/second
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How it Works – Part 4
How do we know that satellites and receivers generate the same signal at the same time? satellites have atomic clocks, so we know they are accurate
Receivers don't -- so can we ensure they are exactly accurate? No! But if the receiver's timing is off, the location in 3-D space will be
off slightly...
So: Use 4th satellite to resolve any signal timing error instead determine a correction factor using 4th satellite (like solving multiple equations...will only be one solution that
satisfies all equations)
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How it Works – Part 5
In order to make use of the distance measurements from the satellites, we must know their exact locations.
satellites are placed into high orbits -- makes their orbits very predictable
receivers have almanacs that tell them where satellites should be
minor variations in orbit are monitored -- correction factors transmitted along with the signals
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Selective Availability (SA)
intentional error introduced by the military for national security reasons
Pres. Clinton cancelled May 2, 2000.
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Selective Availability (SA)
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Error Sources
Satellite errors
satellite position error
atomic clock error
Ionospheric/tropospheric effects on signal
GPS signal can be slowed slightly in the atmosphere
(atmospheric modeling & dual-frequency receivers help alleviate this)
Multi-path distortion
signal may "bounce" off structures near the receiver
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Error Sources - More
Receiver errors
rounding errors in calculations, etc.
Position dilution of precision (PDOP): position of satellites in the sky has a "multiplier effect" on other errors
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Error Budget (typical case)
satellite clock: satellite orbit: ionosphere/troposphere: multipath distortion: receiver errors:
1.5 meters
2.5 meters
5.5 meters
0.6 meters
0.3 meters
and… PDOP will have a multiplier effect on total error.
- End result: position calculations can have significant error! - Original statement from Department of Defense (Standard Positioning Service): accurate within 100 meters of true position 95% percent of the time; this is being updated to reflect post-SA conditions- Current estimates: within 10-20 meters of true position
- in practice, even better results are frequently seen
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Error Reduction StrategiesElevation masking
ensure satellites are at least a specified angle about the horizon reduces ionospheric/tropospheric error
Point averaging position error varies with time collect points for a period of time & average them idea is to cancel out some error
Differential correction technique to remove much of the error from Standard Positioning Service can apply differential correction in "real time" or after the fact (post-
processing) basic idea:
Any errors in a GPS signal are likely to be the same among all receivers within 300 miles of each other.
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Error Reduction Strategies - More
Differential correction (cont’d.)
How it works: use a base station at a known position base station calculates its own position & compares to its
known position determines correction factors that can be applied to receiver-
calculated positions
Differential correction will reduce horizontal position error to 1 - 3 meters with standard receiver much GPS fieldwork for GIS/mapping purposes will require
differential correction!
National Differential GPS Network (NDGPS) being created
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GPS Applications…
generating mapped data for GIS databases “traditional” GIS analysts & data developers travel to field and capture locational & attribute information cheaply
others: E911/firefighter/police/ambulance dispatch car navigation roadside assistance business vehicle/fleet management mineral/resource exploration wildlife tracking boat navigation recreational
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Introduction
Outputs (maps and tables/graphs) are the pinnacle of GIS projects
Two main types of output Maps Visualizations
Maps are good at summarizing and communicating
Cartography is the art, science and techniques of making maps & communicating about data and patterns
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Maps and Cartography
Map – ‘digital or analog output from a GIS showing information using well established cartographic conventions’
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GIS Processing Transformations
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Characteristics of Map
Two main types Topographic Thematic
Some map problems Can miss-communicate Each map is just one of all possible
maps Complex maps can be difficult to
understand
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Map Design
Maps are a means of communication and organization of thought, created to transmit some type of spatial information to the map reader.
» Success or failure depends on whether or not the map communicates the intended information.
Cartography as a communication system: "How do I say what to whom?"
cartographer = I map reader/audience = whom map design & production methods = how subject & goals of map = what
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Models of Cartographic & GIS Information Communication
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3D ViewResolution Merge Ikonos Imagery (1m) Draped Over 10m DEM
Avalanche Path “Shed 7”Avalanche Path “Shed 10”Avalanche Path “I-Beam”
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Thematic vs. Reference Maps Our emphasis is on thematic maps (as opposed to
reference maps)
thematic maps: portray information about the quantity and distribution of a particular spatial phenomenon (or several phenomena)
reference maps: general purpose “base” maps, e.g.: topographic maps, tax/cadastral maps, or navigation charts
Why? Differing roles of... individual cartographers & GIS analysts
vs. large mapping organizations
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Thematic Mapping and GIS
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“Rules” of Cartography e.g. “maps should always have a scale bar” ”Rules” should be considered in terms of what the
cartographer is trying to communicate via the map e.g., A series of page-sized maps of the entire U.S.
displaying different population variables by state?
» a scale bar may not be important for these maps
Readers are likely to already be familiar with the size and shape of the U.S.
The purpose of these maps has nothing to do with measuring distances.
But, some guidelines...
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Map Elements
Most common: map/spatial data title legend scale north arrow inset(s) textual information (incl. spatial metadata) border, neatlines coordinate grid
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TitleLegend
Projection
Grid
Data Source
Inset map
Map Body
Author
North Arrow
Scale
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Titles Largest and most noticeable text on the map Be precise! no superfluous words Can be label, or can be a sentence No unvarying information in titles for maps in a series No "Map of..." in the title -- we know it's a map
Scale Only graphic scales will survive enlargement or reduction during reproduction Some thematic maps may not need a scale bar
Map Elements
Legends Almost always required on thematic maps Symbolization in the legend must exactly match symbolization used on the map No "Legend" in the title for the legend
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Textual information Common uses: author/publisher date of production date of map information projection type and coordinate system information data sources brief information on how the map was produced
Borders, Neatlines Provide a graphic "container” Almost essential when an unclosed portion of a geographic area is
being shown
More Map Elements
Insets locator maps detail maps
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Coordinate grid
Map must also state the coordinate system used
More Map Elements
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Map layout - visual balance (left) based on graphic or visual weight relative to the visual center
Design Guidelines
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Visual Contrast
Variation and contrast will improve legibility Can be expressed with size, intensity, and shape of map
elements and symbolization.
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Figure/Ground Relationships
Figure: eye settles on and sees clearly Ground: amorphous area around the figure that map readers will
not perceive as readily Figure/ground relationships used to focus reader’s attention
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Legibility/Clarity Must consider the final production medium!
book poster-sized map computer display web page
Reduction factor: will the map be reduced for the final version?
Simplicity Economy of expression less is frequently more only include elements for which you have a defensible reason for their
presence Compose maps as if they were essays in freshman composition course
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Data Symbolization
Variety of issues for symbolization…
discrete vs. continuous geographic phenomena
point, line, and area symbols
recognize and understand nominal, ordinal, and interval/ratio thematic data types
The cartographer/map designer must make decisions about how to display thematic data in a m generally decisions about what symbols to use to represent real-world geographic phenomena
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Sample Households & Survey Sectors
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Geographic Phenomena
...can be considered discrete or continuous discrete
phenomenon to be mapped is inherently spatially bounded, e.g: well location road voting district
continuous phenomenon to be mapped is without any inherent spatial
boundaries, e.g.: rainfall population density elevation
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Discrete Phenomena We conceptualize discrete geographic phenomena as individual
locations, linear features, and area features this corresponds to how we represent them in the vector data
model and to how we generally symbolize them in maps: points, lines,
polygons But…choosing symbols to represent discrete phenomena is scale-
dependent! e.g., Symbolize a city street…
at 1:50,000 clearly a linear feature can represent using a 1-D symbol
at 1:400 an area feature represent using a 2-D symbol?
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Continuous Phenomena
We can think of continuous geographic phenomena as “surfaces” this conceptualization stems from mapping terrain
Symbolizing continuous phenomena
…variety of ways to simulate a continuous surface contour lines shading polygons continuous variation in shading of raster
(Remember that we never truly have continuous data…only discrete data that approximates continuous data.)
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Levels of Measurement Nominal
distinguish among data values based on qualitative differences
Ordinal
distinguish among data on the basis of order, but without measurable differences between data values
Interval
distinguish among ordered data values with measurable differences between them, but with an arbitrary origin
Ratio
distinguish among ordered data values with measurable differences between them, and a non-arbitrary origin
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Visual Variables Size
difference in geometric dimensions (e.g. length, height, diameter) of symbols
useful for ordinal & interval/ratio data; bad for nominal
convention: larger size = greater quantity or importance
Shape
differences in forms of symbols can be abstract and "geometric", or iconographic
useful for nominal data; bad for for ordinal, interval/ratio
too many different shapes = cluttered & difficult for the map reader to discriminate
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Visual Variables
Color Hue (color) differences in wavelengths of light reflected (or emitted, in
the case of computer monitors) useful for nominal data, can be used for ordinal &
interval/ratio data but is tricky perceptual difficulties for some map readers is a problem
(e.g. 6-8% males color-blind)
Color Value (lightness/darkness, intensity) relative lightness or darkness of symbols (can also be
thought of as intensity) useful for representing ordinal & interval/ratio data convention: darker = higher numerical values difficult for map readers to perceive more than four or five
values
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Visual Variables
Arrangement
configuration (random vs. systematic)
useful for nominal, bad for ordinal & interval/ratio
convention: random often used to symbolize natural phenomena, systematic used to symbolize human-made phenomena
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Map Design - Thematic Mapping
Our objectives: consider three very common thematic map types
choropleth proportional symbol dot density
understand decisions involved in classifying quantitative data in thematic maps
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Choropleth Maps Greek: choros (place) + plethos (filled)
Used to map categorical and quantitative data over defined areas polygonal enumeration units e.g. Census tract, county, watershed
Polygon data values are generally classified into ranges allow map reader to more readily interpret the map
Polygons can produce misleading impressions area/size of polygon vs. quantity of thematic data value also -- Modifiable Areal Unit Problem
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Choropleth Maps
Color ramp, non-overlapping ranges
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Proportional Symbol Maps
Size of symbol is proportional to size of data value also called graduated symbol maps
Especially good for representing count data without running into area distortions
Frequently used for mapping points can also be used to map areas avoid distortions due to area size seen in choropleth
maps
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Proportional Symbol Map
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Dot Density Maps
Provide immediate picture of density over area 1 dot = ??? quantity of data value
e.g. 1 dot = 500 persons quantity usually still associated with polygon enumeration unit but, avoids some distortions seen in choropleth maps
Placement of dots within polygon enumeration units can be an issue depends on the scale of the map vs. the scale of the
enumeration units
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Dot Density Map
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Ecological Fallacies
Ecological & individualistic fallacies exemplify problems associated with data aggregation or the Modifiable Areal Unit Problem Relationships that exist at an aggregate level don’t
necessarily exist at a disaggregate level Causes found at lower levels may not be the same
as those operating at higher levels Need for compatibility of data scales across
thematic domains, particularly, as data are linked to place through remote sensing land cover data
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Thematic Mapping: Modifiable Areal Unit Problem
Assumption: Mapped phenomena are uniformly spatially distributed within
each polygon unit True…?
Usually not! Boundaries of enumeration units are frequently unrelated to the
spatial distribution of the phenomena being mapped Change the boundaries, and the distribution portrayed also
changes This issue is always present when dealing with data
collected/aggregated by polygon units
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Classifying Thematic Data Data values are classed into ranges for many thematic
maps (especially choropleth) aids reader’s interpretation of map
Trade-off: presenting the underlying data accurately
vs. generalizing data using classes
Goal is to meaningfully class the data group features with similar values assign them the same symbol
But how to meaningfully class the data?
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Creating Classes
How many classes? too few - obscures patterns too many - confuses map reader
difficult to recognize more than seven classes How do we create the classes? - methods:
assign classes manually equal intervals natural breaks quantiles standard deviation
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Choropleth Class Schemes
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Manually Create Classes
Examples income tax brackets population above/below poverty level precipitation above/below drought level
Good for… mapping data with external, meaningful breaks
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Equal Intervals
Classes have equal ranges of values
(difference between low & high is same for each class)
Totally ignores underlying data distribution if there are clusters, some classes may be empty
Good for… creating classes that are readily recognized, and
appear logical (though they may not be)
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Optimization/Natural Breaks
classes broken into “natural” groups based on data distribution
algorithms minimize within-class variation and maximize between-class variation
Good for… mapping data with uneven distribution making sure each class is statistically meaningful
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Quantiles (Percentiles)
classes have equal number of observations e.g., 50 states = 5 classes w/ 10 states in each
common: quartiles (4 classes), quintiles (5 classes) Good for…
mapping data with even distribution showing relative positions of features being mapped
e.g. population below poverty level, 4 classes: top 25% of states 25% of states above middle 25% of states below middle bottom 25% of states
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Standard Deviation
classes centered around the mean data value classes based on distance from the mean & squared
Variations/related methods: nested means – class subdivisions based on a hierarchy of means box & whisker – based on mean, std. dev., and outlier (extreme)
values
Good for… clearly showing above- & below-average values representing underlying data distributions in some cases
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Choropleth Class Schemes
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Limitations of Paper Maps
Fixed scale Fixed extent Static view Flat and hence limited for 3D visualization Only presents ‘complete’ world view Map producer-centric
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Conclusions
Cartography is both an art and a science Maps are fundamental to GIS projects Modern advances in cartography make it
easy to produce good and bad maps New technology and especially the
Internet has change the content and techniques of GIS-based cartography
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GeoVisualization: Overview
How GIS affects visual communication User interfaces and spatial query How GIS-based representations may be
transformed How 3-D geovisualization and VR help us
to understand the world
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GeoVisualization & GIS
Maps are important decision support tools E.g. GIS and geopolitics Historic role of paper mapping
GIS and geovisualization: ViSc, catrography, image analysis, etc. The ICA Commission on Visualization
and Virtual Environments
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Spatial Query
Improved ability to explore, synthesize, present, and analyze
The WIMP interface: pointing, clicking, and dragging windows and icons
Dynamic updates
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Transformation
Cartograms distort area or distance in order to achieve a specific objective
Dasymetric maps use the intersection of two datasets (or layers in the same dataset) to obtain more precise estimates of a spatial distribution
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3-D GIS and VR Systems
Facilities to: Take different views Fly-Throughs Reposition or rearrange Interact as avatars in virtual worlds Develop new representations Create immersive and semi-immersive
VR systems
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Conclusions
ViSc in simulation and decision-making The medium and the message Clarifying or obscuring the message
Data quality must be up to the applications task
‘Seeing is believing’ Is it? Should it be?