GIS 205 Exam One

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