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

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)

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

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

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

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

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

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