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)

• 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)

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

Satellites (Space Segment)

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

Garmin

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    

                            

           

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

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

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

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)

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

Selective Availability (SA)

intentional error introduced by the military for national security reasons

Pres. Clinton cancelled May 2, 2000.

Selective Availability (SA)

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

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

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

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.

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

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

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

Maps and Cartography

Map – ‘digital or analog output from a GIS showing information using well established cartographic conventions’

GIS Processing Transformations

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

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

Models of Cartographic & GIS Information Communication

3D ViewResolution Merge Ikonos Imagery (1m) Draped Over 10m DEM

Avalanche Path “Shed 7”Avalanche Path “Shed 10”Avalanche Path “I-Beam”

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

Thematic Mapping and GIS

“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...

Map Elements

Most common: map/spatial data title legend scale north arrow inset(s) textual information (incl. spatial metadata) border, neatlines coordinate grid

TitleLegend

Projection

Grid

Data Source

Inset map

Map Body

Author

North Arrow

Scale

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

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

Coordinate grid

Map must also state the coordinate system used

More Map Elements

Map layout - visual balance (left) based on graphic or visual weight relative to the visual center

Design Guidelines

Visual Contrast

Variation and contrast will improve legibility Can be expressed with size, intensity, and shape of map

elements and symbolization.

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

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

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

Sample Households & Survey Sectors

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

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?

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.)

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

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

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

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

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

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

Choropleth Maps

Color ramp, non-overlapping ranges

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

Proportional Symbol Map

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

Dot Density Map

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

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

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?

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

Choropleth Class Schemes

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

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)

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

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

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

Choropleth Class Schemes

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

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

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

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

Spatial Query

Improved ability to explore, synthesize, present, and analyze

The WIMP interface: pointing, clicking, and dragging windows and icons

Dynamic updates

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

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

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?