Basic Geographic Concepts GEOG 370 Instructor: Christine Erlien.
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Transcript of Basic Geographic Concepts GEOG 370 Instructor: Christine Erlien.
![Page 1: Basic Geographic Concepts GEOG 370 Instructor: Christine Erlien.](https://reader030.fdocuments.in/reader030/viewer/2022032605/56649e845503460f94b863ec/html5/thumbnails/1.jpg)
Basic Geographic Concepts
GEOG 370
Instructor: Christine Erlien
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Basic Geographic Concepts
Real World Digital EnvironmentHow 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
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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
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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
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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
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Scale affects how an object is generalized
Close-up (large scale) houses appear to have length & widthSmall-scale houses appear as points
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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)
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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
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http://weather.unisys.com/surface/sst.gif
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www.regional.org.au/au/asa/2003/i/6/walcott.htm
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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)
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http://www.citypopulation.de/World.html
Selection of world’s largest cities
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Generalities
Continuous data– Raster
Discrete data– Vector
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Spatial Measurement Levels
Three levels of spatial measurement: Nominal scale
Ordinal level
Interval/ratio
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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
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ESRI Mapbook 18
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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
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ESRI Mapbook 18
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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
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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: Interval
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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
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http://weather.unisys.com/surface/sst.gif
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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
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Spatial Measurement Levels: Ratio
Examples– Locational coordinates in a standard
system
– Total precipitation
– Population density
– Volume of stream discharge
– Areas of countries
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ESRI Mapbook 18
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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
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Summarizing
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
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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
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Spatial Location and Reference: Latitude / Longitude Most commonly-used coordinate system Lines of latitude are called parallels Lines of longitude are called meridians
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Latitude / Longitude
Prime Meridian & Equator are the reference points used to define latitude and longitude
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Spatial Comparisons
Pattern analysis: An important way to understand spatial relationships between objects.
Three point distribution patterns:– Regular: Uniform
– Clustered
– Random: No apparent organization
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http://en.wikipedia.org/wiki/Image:Snow-cholera-map.jpg
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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
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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
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Collecting Geographic Data
Small areas– Ground survey
– Census Large areas
– Census (less oftenevery 10 years)
– Remote sensing
– GPS (e.g., collared animals)
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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
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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
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Probabilistic sampling methods
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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
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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
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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