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## Basic Geographic Concepts

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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

- Real world objects differ in:
- Size
- Shape
- Color
- Pattern
- These differences affect how these objects are represented digitally

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

- 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.)

- 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

- 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?

- 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

http://www.citypopulation.de/World.html

Generalities

- Continuous data
- Raster
- Discrete data
- Vector

Spatial Measurement Levels

Three levels of spatial measurement:

- Nominal scale
- Ordinal level
- Interval/ratio

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

- 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

- 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: 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: 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

- Examples
- Locational coordinates in a standard system
- Total precipitation
- Population density
- Volume of stream discharge
- Areas of countries

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

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

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

- Prime Meridian & Equator are the reference points used to define latitude and longitude

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

- 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

- 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

- Small areas
- Ground survey
- Census
- Large areas
- Census (less oftenevery 10 years)
- Remote sensing
- GPS (e.g., collared animals)

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

- 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

- 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

- 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.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

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