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

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

8 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)

9 Data: Continuous vs. discrete Continuous –Data values distributed across a surface w/out interruption –Examples: elevation, temperatureelevationtemperature 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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13 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

16 Generalities Continuous data –Raster Discrete data –Vector

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

18 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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20 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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22 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

23 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

24 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 C300 deg. F.=149 deg. C

26 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

27 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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31 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

32 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

33 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

34 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

37 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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40 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

41 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

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

43 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

44 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

45 Probabilistic sampling methods

46 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

47 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

48 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