Geographic Information Sciences (GIS)
Computer-based mapping and storage for a spatial database
Remote Sensing
Acquisition of data and imagery from satellites or aircraft. (aerial photos, and more)
Global Positioning System (GPS)
Acquisition of real-time location information from satellites.
Geospatial Data
Any piece of data with a location associated with it
Volunteered Geographic Information (VGI)
Crowd Sourced or Citizen Science
Citizen Science
Training citizens to participate in data collection/analysis
Crowd Sourcing
Open call for anyone to submit data
Geolocation
Determining where something is (smart devices finding current location)
Geotag
Putting location on some piece of media (post, tweet, SMS, etc.)
Visual Hierarchy
Used to highlight importance of features relative to each other
Geographic Scale
Real-world size/extent of an area
Map Scale
Metric showing the relationship between a measurement made on a map and the real world
Representative Factor (RF)
Ratio or fraction showing scale relationship
Different from map scale
Applies to all units
Small-Scale Maps
Show larger geographic area
small on detail
Smaller RF (Larger denominator)
Large-Scale Maps
Show smaller geographic area
large on detail
Larger RF (Smaller denominator)
Reference Maps
Atlas, Google Maps, Campus Maps
Used to give location information of specific features
No associated data
Thematic maps
Conveys a piece of information about data
Population map
Usually has numbers associated with it
Choropleth Maps
Type of thematic map where the theme is divided into categories based on geographic boundaries
Quantile Classification
Put an equal or nearly equal # of data values (objects) into each category
Divide # of data values by # of categories
3201 counties / 3 categories = 1067 counties per category
Equal Interval Classification
Selects breaks by taking the total range of values and dividing by the number of desired categories
Example:
Range: Highest - Lowest. 56.8% - 27.9% = 28.9%
28.9%/3 = 9.6%
Start at lowest value and add 9.6%
27.9 - 37.5, 37.6 - 47.2, 47.3 - 56.8
Given data, intervals may not be entirely even.
Why is it hard to model the earth?
3D → 2D Creates Distortion
3 things to model the earth
Geodetic Datum (model of the earth)
Geographic Coordinate System (location on the earth)
Projected Coordinate System (location on map)
Geodetic Datum
Model of the earth
Associated with measurements of earth
What shape/size the earth is
Standard used for taking measurements from geographic surveying
Geographic Coordinate System
location on earth
on the globe given a particular geodetic datum
Projected Coordinate System
Location on map
reference point after a projection
on the map given a particular projection
Geographic Coordinate System (GCS)
A global reference system for determining the exact position of a point on earth
Types of Distortion
Shape
Area
Distance
Direction
Types of map projections
cylindrical
conical
planar (flat)
Cylindrical Projections
Cylinder wrapped around the entire globe
Touches (tangent) at the equator
Least distorted at the equator
Representation of the entire world
latitude and longitude perpendicular
Conical Projections
Cone sitting on part of the globe
tangent to one or two lines of latitude
localized (ex. one hemisphere)
good for mid latitudes
Planar Projections (Azimuthal)
Plane tangent to one point on a globe
usually polar viewpoints
latitude forms concentric circles
longitude radiates from the center
distorts towards edges
Incantrix
Calculations and visualizations of distortion
Shape preserved by
Conformal Projections (Mercader)
Distance preserved by
Equidistant Projections
Direction preserved by
Azimuthal projections
Area preserved by
Equal-area projections
Winkel tripel
Preserves distance, direction, and area
Projection Coordinate System
Similar to GCS, but based on a projection model
Universal Transverse Mercator
Grid system similar to lat/long
world divided into 60 vertical zones
Degrees, minutes, seconds
Northing and Easting instead of NSEW
Dr. Roger Tomlinson
“Father” of GIS
Views of the real world
Object and Field
Object View
objects
boundaries and dimensions
Discrete Object View
The world is made of objects that:
have fixed locations
have fixed boundaries
Three types of objects
Points, Lines, Polygons
Points
Zero dimensional objects
Represent towns, buildings, trees
Lines
One-dimensional object
Dimension: Length
Represent streets, rivers, boundaries
Polygon
Two-dimensional objects
Lakes, states, countries, buildings
Vectors
Measure discrete objects
Consists of points, lines, polygons
Topology
A quality of data
How vector objects relate to each other geometrically
Adjacency, Connectivity, and Containment
Independent of coordinates
Continuous Field View
Represents data without concrete boundaries
precipitation, temperature, elevation
Data represented by a set of evenly distributed, square gridlines
Modeled as Raster data
Satellite data always raster
Raster Data Model
comprised of grid squares (cells)
Resolution of the cell = length of one side
Range from 10 ft to several km
Each cell:
has the same resolution
contain a single value
Values can be codes
Nonspatial Data in Vector Data
Represented in attribute tables
Attribute
Any piece of nonspatial data associated with a spatial location
Fields
Columns in an attribute table
Represent categories of attributes
Records
Rows in an attribute table
Represent an object
Each is a separate polygon
Shapefiles
Vector ONLY
File format that stores geometric location and attribute information
One file made up of at least 3 other files
Shapefile Key
Method for automatically transferring data to shapefiles
Requires an identical field in both tables
Raster Attribute Tables
Only ever have one file
Made of two main items
Value in the cell
Count showing # of cells within that value
Can’t add data to raster files
Metadata
Data about data
Information can be:
projection info
origin
meaning/units of attribute
time period
data quality
Nominal Data
Categorical/Qualitative Data
Has no meaningful order
May be numbers, but they don’t represent a numerical value
Examples
hair color, ID number, athlete number
Ordinal Data
Categorical/Qualitative Data
Order IS meaningful
Differences between values
Not known
Don’t matter
Examples
place in finishing a race
yelp reviews
Interval Data
Quantitative Data
Meaningful numeric units
Values and differences between them are significant
No meaningful zero
Examples
temperature scales
years
Ratio Data
Quantitative
Meaningful numeric value
Meaningful zero
Examples
car price
elevation
person’s height
distance to ___
Spatial Analysis Involves
The statistical recognition of a spatial pattern
Visualization of a spatial pattern and its variables
The association of one or more variables with that spatial pattern
Equal Operator
=
Find an exact match to the query
Not Equal Operator
<>
excludes values matching the query
Greater Than Query
>
Greater Than or Equal to Query
>=
Less Than Query
<
Less Than or Equal to Query
<=
Compound Queries
Allows for selections using multiple criteria
Uses a Boolean Operator
Boolean Operator
Combines queries
And, Or, Not, Xor
AND
Both criteria of the query must be met
Intersection between both parts of the query
only overlap
OR
One or both criteria must be met
Union of the two parts of the query
includes overlap
NOT
excludes a record from the selection
negation
XOR
leaves out what two criteria have in common
Opposite of and
excludes the overlap