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Basic Geographic Concepts

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

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  1. Basic Geographic Concepts GEOG 370 Instructor: Christine Erlien

  2. 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

  3. 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

  4. 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

  5. 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

  6. Scale affects how an object is generalized Close-up (large scale)  houses appear to have length & width Small-scale  houses appear as points

  7. 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)

  8. 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

  9. http://weather.unisys.com/surface/sst.gif

  10. www.regional.org.au/au/asa/2003/i/6/walcott.htm

  11. 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)

  12. Selection of world’s largest cities http://www.citypopulation.de/World.html

  13. http://sedac.ciesin.columbia.edu/gpw/

  14. Generalities • Continuous data • Raster • Discrete data • Vector

  15. Spatial Measurement Levels Three levels of spatial measurement: • Nominal scale • Ordinal level • Interval/ratio

  16. 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

  17. ESRI Mapbook 18

  18. 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

  19. ESRI Mapbook 18

  20. 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

  21. 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

  22. 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

  23. http://weather.unisys.com/surface/sst.gif

  24. 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

  25. Spatial Measurement Levels: Ratio • Examples • Locational coordinates in a standard system • Total precipitation • Population density • Volume of stream discharge • Areas of countries

  26. ESRI Mapbook 18

  27. 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

  28. 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

  29. 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

  30. Spatial Location and Reference: Latitude / Longitude • Most commonly-used coordinate system • Lines of latitude are called parallels • Lines of longitude are called meridians

  31. Latitude / Longitude • Prime Meridian & Equator are the reference points used to define latitude and longitude

  32. Spatial Comparisons • Pattern analysis: An important way to understand spatial relationships between objects. • Three point distribution patterns: • Regular: Uniform • Clustered • Random: No apparent organization

  33. http://en.wikipedia.org/wiki/Image:Snow-cholera-map.jpg

  34. 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

  35. 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

  36. Collecting Geographic Data • Small areas • Ground survey • Census • Large areas • Census (less oftenevery 10 years) • Remote sensing • GPS (e.g., collared animals)

  37. 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

  38. 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

  39. Probabilistic sampling methods

  40. 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

  41. 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

  42. 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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