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Basic Geographic Concepts. GEOG 370 Instructor: Christine Erlien. Basic Geographic Concepts. Real World  Digital Environment How are real world objects recorded in digital format? Directly (by instruments on the ground) Remotely (by satellites hundreds of miles above the earth’s surface)

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basic geographic concepts

Basic Geographic Concepts

GEOG 370

Instructor: Christine Erlien

basic geographic concepts1
Basic Geographic Concepts

Real World  Digital Environment

How are real world objects recorded in digital format?

  • Directly (by instruments on the ground)
  • Remotely (by satellites hundreds of miles above the earth’s surface)
  • Collected by census takers
  • Extracted from documents or maps
from real world objects to cartographic objects
From Real World Objects to Cartographic Objects
  • Real world objects differ in:
    • Size
    • Shape
    • Color
    • Pattern
  • These differences affect how these objects are represented digitally
real world cartographic objects description
Real World Cartographic Objects: Description
  • Attributes
    • Information about object (e.g., characteristics)
  • Location/Spatial information
    • Coordinates
    • May contain elevation information
  • Time
    • When collected/created
    • Why? Objects may have different attributes over time
generalizing real world objects
Generalizing Real World Objects
  • Point: Location only
  • Line
    • 1-D: length
    • Made up of a connected sequence of points
  • Polygon
    • 2-D: length & width
    • Enclosed area
  • Surface
    • 3-D: length, width, height
    • Incorporates elevation data

Scale affects how an object is generalized

Close-up (large scale)  houses appear to have length & width

Small-scale  houses appear as points

generalizing spatial objects cont
Generalizing Spatial Objects (Cont.)
  • Representing an object as point? line? polygon?
    • Depends on
      • Scale (small or large area)
      • Data
      • Purpose of your research
    • Example: House
      • Point (small scale mapping)
      • Polygon
      • 3D object (modeling a city block)
data continuous vs discrete
Data: Continuous vs. discrete
  • Continuous
    • Data values distributed across a surface w/out interruption
    • Examples: elevation, temperature
  • Discrete
    • Occurs at a given point in space; at a given spot, the feature is present or not
    • Examples
      • Points: Town, power pole
      • Lines: Highway, stream
      • Areas: U.S. Counties, national parks
continuous discrete
Continuous & discrete?
  • Some data types may be presented as either discrete or continuous
    • Example
      • Population at a point (discrete)
      • Population density surface for an area (continuous)
selection of world s largest cities
Selection of world’s largest cities

  • Continuous data
    • Raster
  • Discrete data
    • Vector
spatial measurement levels
Spatial Measurement Levels

Three levels of spatial measurement:

  • Nominal scale
  • Ordinal level
  • Interval/ratio
spatial measurement levels nominal
Spatial Measurement Levels: Nominal
  • Simplest/lowest level of measurement
  • Identification/labeling of data
  • Does not allow direct comparisons between one named object and another
    • Notes difference
spatial measurement levels ordinal
Spatial Measurement Levels: Ordinal
  • Data ranked based on a particular characteristic
  • Gives us insights into logical comparisons of spatial objects
  • Examples:
    • Large, small, medium sized cities
    • Interstate highway, US highway, State highway, Country road
spatial measurement levels interval
Spatial Measurement Levels: Interval
  • Numbers assigned to items measured
  • Measured on a relative scale rather than absolute scale
    • 0 point in scale is arbitrary
  • Data can be compared with more precise estimates of the differences than nominal or ordinal levels
  • Not very common
spatial measurement levels interval1
Spatial Measurement Levels: Interval
  • Example: Temperature
  • Zero temperature varies according to the unit of measurement (0 deg. C = 32 deg. F)
  • 0 deg. C is not the absence of heat  Absolute zero is identified by 0 Kelvin
spatial measurement levels interval2
Spatial Measurement Levels: Interval
  • The difference between values makes sense, but ratios of interval data do not
  • Ex.: A piece of metal at 300 degrees Fahrenheit is not twice as hot as a piece of metal at 150 degrees Fahrenheit
    • Why? the ratio of these values is different using Celsius

150 deg. F=66 C 300 deg. F.=149 deg. C


Spatial Measurement Levels: Ratio

  • Numbers assigned to items measured
  • Measured on an absolute scale (use true 0 point in scaling)
    • Measurements of length, volume, density, etc.
  • Data can be compared with more precise estimates of the differences than nominal or ordinal levels
spatial measurement levels ratio
Spatial Measurement Levels: Ratio
  • Examples
    • Locational coordinates in a standard system
    • Total precipitation
    • Population density
    • Volume of stream discharge
    • Areas of countries
measurement levels mathematical comparisons
Measurement Levels & Mathematical Comparisons
  • Nominal scale
    • Not possible
  • Ordinal scale
    • Compare in terms of greater than, less than, equal to
  • Interval/ratio scales
    • Mathematical operations
      • Interval: addition, subtraction
      • Ratio: add, subtract, multiply, divide

We’ve been talking about

  • Characterizing objects
    • How to generalize/represent real world objects?
    • Attributes
    • Continuous vs. discrete data types
    • Spatial measurement levels

We’re moving on to location

spatial location and reference
Spatial Location and Reference

Communicating the location of objects

  • Absolute location
    • Definitive, measurable, fixed point in space
    • Requires a reference system (e.g., grid system such as Latitude/Longitude)
  • Relative location
    • Location determined relative to other objects in geographic space
      • Giving directions
      • UTM

Spatial Location and Reference: Latitude / Longitude

  • Most commonly-used coordinate system
  • Lines of latitude are called parallels
  • Lines of longitude are called meridians

Latitude / Longitude

  • Prime Meridian & Equator are the reference points used to define latitude and longitude
spatial comparisons
Spatial Comparisons
  • Pattern analysis: An important way to understand spatial relationships between objects.
  • Three point distribution patterns:
    • Regular: Uniform
    • Clustered
    • Random: No apparent organization
describing spatial patterns
Describing Spatial Patterns
  • Proximity: Nearness
  • Orientation: Azimuthal direction (N,S,E,W) relating the spatial arrangement of objects
  • Diffusion: Objects move from one area to another through time
  • Density
relationships between sets of features
Relationships between sets of features
  • Association: Spatial relationship between different characteristics of the same location
    • Example: Vegetation-elevation
  • Correlation: Statistically significant relationship between objects that are associated spatially
collecting geographic data
Collecting Geographic Data
  • Small areas
    • Ground survey
    • Census
  • Large areas
    • Census (less oftenevery 10 years)
    • Remote sensing
    • GPS (e.g., collared animals)
collecting geographic data sampling sampling schemes
Collecting Geographic Data: Sampling & Sampling Schemes
  • Sampling: When a census isn’t practical
  • Types of sampling
    • Directed: Based on experience, accessibility, selection of particular study areas
    • Probability-based: For the total population of interest, each element has a known probability of being selected
sampling sampling schemes
Sampling & Sampling Schemes
  • Probabilistic sampling methods
    • Random: Each feature has same probability of selection
    • Systematic: Repeated pattern guides sample selection
    • Homogeneous
    • Stratified: Area divided based on particular characteristics, then features sampled w/in selected areas
samples making inferences
Samples: Making inferences
  • Why? Sampling leaves gaps in knowledge
    • What to do? Use models to predict missing values
  • Interpolation: Predicting unknown values using known values occurring at locations around the unknown value
  • Extrapolation: Predicting missing values using existing values that exist only on one side of the point in question
important concepts from ch 2
Important Concepts from Ch.2
  • How real world objects may be generalized in the digital environment
  • How the representation of real world objects may change based on the scale of observation
  • Discrete vs. continuous data
  • Measurement levels: nominal, ordinal, interval, ratio
important concepts from ch 21
Important Concepts from Ch.2
  • Lat/long
  • Absolute vs. relative location
  • Describing spatial patterns
  • Collecting geographic data and how it might differ based on size of study area
  • Sampling & sampling methods