GIS 2735 Flashcards

Study of where things are and why they are there.

The study, science, and technology of using and understanding spatial data

GPS, GIS, and remote sensing

2.16M km²

9.98M km²

The study of the Earth's shape, orientation in space, and variations in gravity

Ellipsoid (Spheroid)

Geoid

A mathematical reference surface, or model, used for plotting locations. Can be either global or local in coverage.

a region of best fit

an imaginary ellipsoid that best regionally fits the Geoid.

A series of calculations that convert datums from one to another

WGS84 and NAD83

equator due to rotational forces

establish Geographic Coordinate systems (GCS)

A global reference system used for determining locations on an ellipsoid

imaginary lines on a globe running from pole to pole describing location from East to West

the origin, or zero degrees

Imaginary lines on a globe running from East to West describing location from North to South

The line of Origin or zero degrees

D + (M/60) + (S/3600) or D + ((M+S)/60)/60

Spheroid

42^o 9' 6.788" W

130^o 40' 44.4" N

A mathematical process of converting a 3d model of Earth into a 2d map of Earth

Azimuthal, Conical, and Cylindrical

Normal, Transverse, and oblique

LCCPs are not suitable for larger areas because it only minimizes distortion locally.

Conical

MMs are good for things like navigating by compass because it minimizes straight line distortion.

They sacrifice accuracy of depictions of area on a map to project straight lines more accurately.

A projection that most accurately depicts area on a map while maintaining minimum distortion

A coordinate on a flat 2d surface. The Surface has constant lengths, angles, and areas.

an international coordinate metric system

6 degrees of longitude per zone

above 84 degrees N and 80 degree S of latitude

East from the 180th Meridian

Meters

Distance N or S of the equator

the distance E or W from the central meridian or the False Easting

500,000m

Locations west of the central meridian

Zone Number

A system developed by Canada that makes UTM zones line up for easier usage.

West of Winnipeg, Manitoba/Sask Border, Moose Jaw Sask, Sask Alberta Border, Calgary (Barlow Trail), and Grand Prairie

Township (6 x 6 miles)

tiers of township

Defined as lines of longitude

Township numbers and range numbers

They start just North of the first baseline and increase going North

Range numbers

Manitoba

36 sections

4 quarter sections or 16 Legal Subdivisions (LSDs)

Technology that broadcasts satellite signals for navigation and position determination on Earth

tracking for military and commercial sea vessels

a GPS system developed by the US that implemented a navigating system that had timing and ranging.

the first four satellites were launched

The soviet union shoots down North Korean air lines flight 007 after it flew off course.

The US makes it's GPS system globally available.

first usages of selective accessability

the 24th satellite is launched and is fully operational by 1995

Differential GPS services make selective accessibility less effective

Overall term for technology that uses satellite signals to find locations on Earth.

Space, Control, User

24 Satellites

orbit altitude of 20,200km, six orbital planes separated by 60 degrees

Information about the satellite's status, orbit, and precise location information

Signal containing Satellite position and its precise time of transmission

Pseudo-Random code

L1 frequency information that is available for all GPS users

Navigation information (time and position)

L1 and L2 frequency information available to military recievers

Measures atmospheric interference

encrypted version of P-code intended for military use

GPS receivers on the ground that pick up satellite signals

The number of Channels

12 Satellites

Receivers that only use the L1 frequency

Receivers that use both L1 and L2 frequencies

A process of finding a position based on its distance from three or more other known points

term for finding a point on the Earth's Ellipsoid surface using Trilateration

The distance between a GPS receiver and satellite

PR= c x Transmission time

A fourth GPS Satellite

Atomic Clocks

Quartz Clocks

TDOP, PDOP, atmospheric interference, multipath signals, and selective availability

• Error introduced due to the geometric position of satellites
• A wide distribution of satellites results in higher position accuracy
• GPS receivers can select satellites based on position
• Some receivers will calculate the range of PDOP
• Other receivers may allow users to select satellites

have Poor Geometry, are less accurate

have Good Geometry, are More accurate

Refraction and slowing of GPS signals can cause roughly a 5m error in pseudo range

refractions can cause up to 0.5m errors in pseudo range

Error caused by the reflection of GPS signals from surrounding surfaces. effect can be combatted by raising the height of the receiver antenna or dish.

Sometimes intentional degradation of the timing and location of GPS satellite information which can limit C/A code accuracy to about 100m

A method of using ground-based corrections in addition to satellite signals. They work best when they are closer to receivers. reduces error to ~5m

Combination of GPS signals and a base station to provide real-time corrections. Commonly used for mobile data connections

Combination of GPS signals and a base station to correct location information after data collection

A network of ground stations that measure variation in GPS signal. reduces error to ~3m

Generalization, Simplification, and Symbology

represents spatial data that provides a reader with information. They can be abstract representations of the real world. A complex model of reality.

The simplification of representing items on a map. often controlled by a scale

curved lines are simplified based on a set of defined points

describes how features can be moved slightly to increase clarity. (smoothing or enhancement)

Line simplification, reduction of spatial complexity, symbology

The real-world size or area of a feature. Larger objects on the ground have a larger geographic scale.

A value representing the number of units on a map relative to the number of the same units on the ground

The number of units on a map Vs. the number of the same units on the ground. These are unitless values.

Using relatable units on both sides of the relation

A graphic representation of the map scale

Maps showing a small geographic region with a large RF value.

Maps showing a large geographic region with a small RF value

The degree to which information in a map or a digital database matches true values (refers to data quality and the number of errors in a dataset)

The level of measurement exactness or repeatability of a dataset. (no. of significant digits used.)

The area of uncertainty increases as scale increases.

A map that shows where geographic features are in relation to each other

A map designed to convey information about a single topic

These maps have strict rules about how they are made

The set of conventions or rules that define how geographic features are
represented with symbols on a map

Symbology method where all features draw in the same color and symbol

Symbology method where features draw differently based on category or type attribute

Symbology Method where features are placed in classes based on numeric values

Symbology method where features are placed in classes based on numeric values and symbol size reflects class value

Features are divided by numeric values into classes. Has a large range of classifying methods. Only used together with Graduated Symbology.

Determines classes based on the natural grouping of the data

Another name for natural breaks

Each class contains an equal number of features

Divides the range of values into equally sized subranges

• Good for mapping uneven distributions

• Not good for comparing data

• Difficult to determine the proper number of classes

• Provides an understanding of relative position

• Similar features may end up in different classes

• Wide range of values may end up in the same class

• Best for familiar values such as percentages or temperature

• Prone to issues with clustering

• Not ideal for uneven distributions

• Doing this to data creates a ratio map
• Allows for comparison between different areas

1. Title
2. Data frame
3. Scale
4. Legend
5. Descriptive text
6. North arrow
7. Sources
8. Name, date, class number
9. Neatline

1. Title
2. Data frame
3. Scale
4. Legend
5. Descriptive text
6. North arrow

• Data portion of a map
• Consider the purpose of your map when selecting what and how much you show

A legend on a map

Mapped the locations of outbreaks in London and examined the relationship between outbreak locations and things like road networks, neighborhoods, and water sources.

• “Night air” or “Bad air”
• Belief that disease was called by smell
• Cesspools were emptied into the river
• Cholera outbreaks increased

• Microorganisms can affect diseases
• Proposed in the 1500s, accepted in the 1880s

Baby Lewis

The outbreak was centered around the Broad Street water pump. Dr. Snow convinced the Parish Board of Governors to remove the pump handle.

The first epidemiologist

Ian McHarg

• Author on landscape architecture and regional planning
using natural systems.
• Pioneered the concept of ecological planning with his
book Design with Nature (1969).
• Argued that humans should integrate with nature and
strongly opposed the idea of subjugating nature.
• Fundamental in forming the basic concepts used in
geographic information systems.

Analysis of an area based on layers made up of certain features that can be removed or added to show their relationships

Roger Tomlinson

• Created the Canadian Geographic System (CGIS) in 1962
• The first operational GIS
• CGIS used a layered approach to mapping
• Used to store geospatial data for the Canada Land Inventory

Created SYMAP; one of the first computer mapping software's in 1964

Harvard Laboratory for Computer Graphics

Environmental Systems Research Institute (ESRI)

An institution that applies mapping and spatial analysis to help land resource managers make decisions.

The first commercial GIS product first released in 1981

• Geolocation data collected from portable technology
• Contributions to OpenSteeetMap, geotagged images, business tracking

•Increased operational efficiency
• Development of two-way data communication
•Analyze large datasets

• Computer-based hardware and software used to capture, analyze, manipulate, and visualize geospatial data.
• The ability to handle spatial data separates GIS from other software.

Toolbox, Database, Organization

• Any data with spatial coordinates
• Points, lines, polygons, rasters

• Databases and data integration
• Non-spatial data (e.g., Income data, average revenue, population, age…)

• Integration of data and tools
• Hardware, software, toolboxes, printers, and users

1. Ask
2. Acquire
3. Examine
4. Analyze
5. Act

• data collection
• storage and management
• Data retrieval
• Data conversion
• Analysis
• Modeling
• Display

Descriptive information about a data file

• Identification numbers
• Data quality and accuracy
• Spatial organization (vector or raster)
• Spatial reference data
• Description of each attribute
• Where data can be found
• Citations
• Contact information

Single folder that can hold numerous files with almost unlimited space

Single data layer (point, line, or polygon). Also stores raster, CAD files, tables, etc

Grouping of multiple feature classes. Effective way of storing and sharing data

allows you to view, create, and manage items in your project

• .cpg – Characters used to display text
• .dbf – Stores attributes
• .prj – Stores coordinate system information
• .shp – The main shapefile
• .shx – The index of the feature geometry
• .ovr – The compression and quality of
rasters
• .rrd – reduced-resolution of rasters

• Shares just one layer
• Includes properties and data for a layer

• Shares an entire map
• Includes properties and data for layers in a map

• Share the entire project
• Includes properties and data for layers in all maps
• Stores toolboxes, databases, styles, models, and more

• Shares data layers in a map as web layers

• Shares an entire map and creates a web map

Representing the world with a series of separate objects.

• Points: A simple set of coordinates
• Lines: A one-dimensional object that connects starting and endpoints
• Polygons: A two-dimensional object that forms an area from a set of lines

Viewing the world as items that vary across the Earth’s surface as constant fields

Spatial model that uses an array of equally sized cells arranged in rows and columns

• Maximum of 13 characters
• Cannot start with a number
• No spaces
• Underscore is the only character that can be used
• File path cannot be more than 128 characters

• No generalization
• Aesthetically pleasing
• Accurate geographic locations
• Can store many attributes

• The location of each vertex is stored explicitly
• Not effective for continuous data
• Spatial analysis within a polygon is not possible

• The location of each cell is implied by its location in the grid
• One attribute per cell is ideal for mathematical modelling
• Represent continuous data

• Cell size can result in block images
• Poorly represents linear features
• Files can be large
• Spatial inaccuracies

Non-spatial data associated with a spatial location. Attributes are stored in an attribute table.

Many can be assigned (Numerous)

a method of linking two (or more) attribute tables
•Attribute tables must share a common field
•Your “join table” will be added to your “input table” based on the common field
• Joins may be removed once created

• Defines a relationship between two or more tables but does not attach or move data
• Requires a common field
• Can be a preferred method if working with one-to-many relationships or numerous tables
• Relates can be undone

Used when layers do not have a common attribute field

A Join Operation that summarizes the joining information with each feature in the target layer

A Join Operation where If multiple join features overlay the target feature, the output will contain multiple copies of the target feature.

•Interactive selection
• Used on a map or attribute table
• Database query “Select By Attributes”
•Spatial query “Select By Location”
• Use the clear button to remove selections

Computer language with defined syntax used for accessing data from databases

Structured Query Language (SQL)

<Field_Name><Operator><Value or String>

• Text variables must be in ‘ ‘
• Enter the Boolean operator if multiple criteria are required

A query used to make selections based on multiple criteria.

AND

OR

NOT

XOR

Selecting features or information based on a spatial relationship

Selects features in the input layer that completely or partially overlap the selecting features

• Creates a search area from the selecting feature
•Selects input features that fall within that search area
•Example: Select buildings within 1000 m of a railroad

Selects input features that are located completely or partially within the selecting
feature.

•Selects the input feature if it does not share a boundary with the selecting feature.
•Alberta is within Canada
•Alberta is not completely within Canada

•Selects the input feature that has the selecting feature within it.
•Inverse of within
• The United States contains Texas

• The selecting feature must be completely within the input feature
•Input must be a polygon
•Inverse of completely within
• The United States contains Texas
• The United States completely contains Kansas

•Selects the input if it touches the boundary of the selecting feature
•Input and selecting features must be lines or polygons
• The United States, Guatemala, and Belize touch the boundary of Mexico

• Copies but does not save the new shapefile
• Right-click on the layer → Selection → Make a layer from selected features
• Copy features tool (Data Management Tools)

• Converts a shapefile to a new shapefile based on the selection
• Allows you to output the data
• Right-click on the layer → Data → Export data

Process of creating points, lines, or polygons which represent features from a map or image. Errors can propagate during digitizing

Obsolete method of digitizing

• Digitizing tablet
• Hardcopy map

Newer method of digitizing

•On-screen
•Satellite images, air photos, or scanned maps

• Named based on the position of the user's head while digitizing
• Tablets use a grid of wires to generate a magnetic field which is detected by the cursor.
• Tablet accuracies are about 0.1 mm
• User accuracy is about 0.5 mm

• Digitizing features on a computer screen
• Digital files or scanned hardcopy maps
• Digital files must be georeferenced
• Zoom function reduces human error
• Digitizing can be done to create new or edit existing features

Heads Down Digitizing needs these 4 things

• Right-click on your database
• New → Feature class
•Provide a name
•Select the type of feature
•Provides the option to add fields to the attribute table

The user identifies the points to be captured by intentionally pressing a button

Points are captured at set time intervals. (about 10 points/second)

occur when digitized polygons overlay each other or gaps exist between the boundaries
• Unwanted small polygons
• Use the snapping tool

1. Create a new shapefile or select a shapefile to edit
2. Open the Editing tab and select Create
3. Choose the file you would like to edit
4. Start digitizing
5. SAVE when done!

The process of aligning an unreferenced dataset to one that has a spatial reference system.

Satellite, aerial images, and CAD files

Scanned maps

• Unreferenced data
• A dataset with real-world coordinates
• Identifiable locations in both datasets

Locations that are identifiable and have known coordinates. Used to 'tie' unreferenced data to a dataset with real-world coordinates.

• Road intersections
• Corners of buildings
• Boulders
• Mountain peaks

• Tops of buildings
• Center of a field
• Trees
• Shorelines

1. Compare datasets with known and unknown coordinates
2. Identify locations that can be used for Ground Control Points (GCPs)
3. Add control points by clicking the GCP in the unknown image first
4. Choose the corresponding location on the image or map with known coordinates
5. Add and remove GCPs
6. Transform the image

Ground Control Point

Requires 1

shifts the map, no change in scale or rotation

can shift, scale, and rotate a map

requires 3

• 1 for a zero-order shift (shifts the map, no change in scale or rotation)
• 3 for a first-order affine (can shift, scale, and rotate )
• 6 for a second-order (can “bend” the image)
• 10 for a third-order (can “twist” the image)

• Calculated when a transformation is applied
• Difference between where the georeferenced point is and the specified location.
• Assessment of the transformation accuracy.T

The difference between the user-defined (observed) point and the modelled
(predicted) point

the square root of the mean value of all the squared errors (residuals)

Minimum of 4

The quality of GCPs

Causes a higher derived RMSE value

Shows the error in the same units as the data frame

Shows you the error in pixel units

Measure of overall accuracy measured by pixels

• During transformation, an empty cell matrix is computed
• Each cell is then given a new value based on its location

• Nearest neighbor
• Bilinear interpolation
• Cubic convolution

• Does not alter original values
• Adopts the value of the nearest pixel
• Best for discrete data (Land use, zoning, roads…)

• Some values may be duplicated or lost
• May result in blocky/disjointed images

• Weighted average of four pixels in the original grid nearest the new pixel
• Creates a new pixel value in the output
• Used for continuous data (Elevation, precipitation…)

• Calculates a distance-weighted average of 16 pixels from the original grid that surrounds the new output pixel.
• Creates a new pixel value in the output
• Used for continuous data
• Elevation, precipitation…

Bilinear Interpolation and Cubic Convolution

They produce sharper image quality and are preferred for remote-sensing data

Describes how features are spatially related to one another

Selections and queries to identify features that meet certain criteria

How close one feature is to another feature

• What is the shortest route to a location?
• How large of an area can a location serve?

Are nearby features similar to one another?

a map that shows the area around a point that is closer to that point than any other point

1. Point data
2. Connect points with thin lines
3. Mark the center point of each line
4. Draw perpendicular lines
5. Erase your thin lines

• A spatial proximity built around a point, line, or polygon
• Everything that falls within a buffer is within the set distance

• Buffer uses Euclidean distance
• Straight line
• Ignores networks such as roads

• Measured Manhattan Distance
• Analyze routes
• Analyze a service area
• Use distance or time

• Distance between two points on a grid
• Requires a network (typically, road)

• Near features can be points, lines, or polygons
• Measures the distance between input features and near features
• Distance is stored in the input feature

• The Near Tool will add a new attribute field called “near distance”
• Users set a search distance
• No changes in the visual output

•Kernel Density (KDE) calculates the density of point features around each output raster cell
• Creates an output raster and calculates the density of points around each raster cell

Point and Line Features

House density, crime reports, roads, wildlife habitat, etc

Counts the number of points within it to determine the density.

Integers, Real Numbers, or Null (NODATA)

baseline used for measuring elevation
• Based on mean sea level determined by the shape of the geoid

contour lines

It must be Georeferenced and Digitized

Stereo pairs used to calculate elevation manually or digitally

•Emits a laser pulse to the Earth’s surface and measures the return
•Satellite, aircraft, or drone-based
•Accuracy ranges from 3 to 30 cm

Emits a radio wave to measure the Earth's surface

Representation of the surface of the Earth

• Bare Earth model
• Does not include features on the surface
• Raster-based approach with one value

• Vector-based approach to creating Digital Elevation Models
•Allows for non-equally spaced elevation points
•Adjacent points are connected by lines to create a network of nonoverlapping triangles
• Calculate interpolated values between points using trigonometry

• Accepts randomly sampled data
• Displays linear features such as contours and break lines
• Accepts point features (peaks)
• Can vary the density of points according to terrain

• Accepts data directly from a matrix of cells
• Less complex and faster processing

• Data intense and longer processing time
• Each vertex stores x, y, z coordinates

• Must be resampled if irregular data is used
• May miss complex topography
• May include redundant data in low-relief areas

• A measurements of ground elevation heights as well as the objects on the ground.
• May be thought of as a full 3-D model of the surface.

• DEMS are used to delineate watersheds, calculate flow accumulation and direction.
•Impacts political agreements, downstream agriculture, and communities.

Using measurements at a set of locations to predict values in locations that were not measured.

Interpolate and/or Extrapolate

is the process of predicting values between known points

predicts values outside of known sample points

Creates a surface that passes through all known points

Creates a surface that may vary from known values

Use spatially defined data subsets

Use all data in the study area

• Inverse Distance Weighting (IDW)
• Natural Neighbor
• Spline
• Trend

• IDW predicts values using a weighted combination of sample points
• Weight decreases with distance from the grid cell
• Follows an inverse power function
• The Power controls the significance of points based on their distance.
• Increased power puts more emphasis on the nearest points (Default = 2)

“Everything is related to everything else, but near things are more related than
distant things."

• There is a known influence of proximity
• Uniform distribution of points
• Can control the smoothness

• Doesn’t handle sharp changes in data
• Can create a bullseye pattern around points
• Does not extrapolate

Fixed search radius will remain constant unless a minimum number of points is not met

Variable search radius will change to include a minimum number of sample points

• Breaklines that limit the search for samples
• Cliff or ridge line

• Finds the nearest input samples to a grid cell and weights them based on proportionate areas overlapping the grid cell area.
• Local interpolation
• Exact interpolation

• Ideal for irregularly spaced data
• Resistant to cluster bias or overrepresentation

• Does not represent peaks, ridges or valleys
• Computationally intensive
• Does not extrapolate

• Minimizes the curvature to create a smooth surface
• Local interpolation
• Exact interpolation that exceeds the minimum and maximum values

• Users can control the number of points used to calculate each interpolated cell value.
• More points = smoother surface

• Estimates beyond the max & min
• Captures subtle variations
• Best for gently varying surfaces
• Extrapolates based on the last trend

• Can miss sharp changes (cliffs, fault lines…)
• Can create unrealistic values
• Not ideal for dense points with extreme differences

allows users to adjust the weight parameter to smooth the surface.
• Higher weight = smoother surface

allows users to adjust the weight parameter to stiffen the surface.
• Creates a less smooth surface constrained by the sample points.

A layer that reveals more information about an underlying map

• Global polynomial interpolation method used to capture coarse-scale patterns
• Global interpolation
• Approximate interpolation
• Passing a piece of paper through raised points
• Mathematical formulas can increase the “bending” of the s

linear

one bend

two bends

• Large-scale pattern recognition
• Extrapolates data

• Oversimplifies data
• Miss local variability
• Inaccurate for small-scale analysis