Basic Geographic ConceptsSlide 2From Real World Objects to Cartographic ObjectsReal World Cartographic Objects: DescriptionGeneralizing Real World ObjectsSlide 6Slide 7Generalizing Spatial Objects (Cont.)Data: Continuous vs. discreteSlide 10Slide 11Slide 12Continuous & discrete?Selection of world’s largest citiesSlide 15GeneralitiesSpatial Measurement LevelsSpatial Measurement Levels: NominalSlide 19Spatial Measurement Levels: OrdinalSlide 21Spatial Measurement Levels: IntervalSlide 23Slide 24Slide 25Slide 26Spatial Measurement Levels: RatioSlide 28Slide 29Slide 30Measurement Levels & Mathematical ComparisonsSummarizingSpatial Location and ReferenceSlide 34Slide 35Slide 36Spatial ComparisonsSlide 38Slide 39Describing Spatial PatternsRelationships between sets of featuresCollecting Geographic DataCollecting Geographic Data: Sampling & Sampling SchemesSampling & Sampling SchemesProbabilistic sampling methodsSamples: Making inferencesImportant Concepts from Ch.2Slide 48Basic Geographic ConceptsGEOG 370Instructor: Christine Erlien2Basic Geographic ConceptsReal 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 maps3From Real World Objects to Cartographic ObjectsReal world objects differ in:–Size–Shape–Color–PatternThese differences affect how these objects are represented digitally4Real 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 time5Generalizing 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 data6Scale affects how an object is generalizedClose-up (large scale)  houses appear to have length & widthSmall-scale  houses appear as points8Generalizing 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)9Data: 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 parks10http://weather.unisys.com/surface/sst.gifwww.regional.org.au/au/asa/2003/i/6/walcott.htm13Continuous & 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.htmlSelection of world’s largest cities16GeneralitiesContinuous data–RasterDiscrete data–Vector17Spatial Measurement LevelsThree levels of spatial measurement:Nominal scaleOrdinal levelInterval/ratio18Spatial Measurement Levels: NominalSimplest/lowest level of measurementIdentification/labeling of data Does not allow direct comparisons between one named object and another–Notes differenceESRI Mapbook 1820Spatial 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 roadESRI Mapbook 1822Spatial 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 common23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 KelvinSpatial Measurement Levels: Interval24Spatial 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 Celsius150 deg. F=66 C 300 deg. F.=149 deg. Chttp://weather.unisys.com/surface/sst.gif26Spatial 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 levels27Spatial Measurement Levels: RatioExamples–Locational coordinates in a standard system–Total precipitation–Population density–Volume of stream discharge–Areas of countriesESRI Mapbook 1831Measurement 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, divide32SummarizingWe’ve been talking aboutCharacterizing objects–How to generalize/represent real world objects?–Attributes–Continuous vs. discrete data types–Spatial measurement levelsWe’re moving on to location33Spatial Location and ReferenceCommunicating 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•UTM34Spatial Location and Reference: Latitude / LongitudeMost commonly-used coordinate system Lines of latitude are called parallelsLines of longitude are called meridiansLatitude / LongitudePrime Meridian & Equator are the reference points used to define latitude and longitude37Spatial ComparisonsPattern analysis: An important way to understand spatial relationships between objects. Three point distribution patterns:–Regular: