1. Cartographic abstraction Summary session GEO381/550 October 5 th, 2004

2. Outlines Basics Basics Geographic phenomenon Geographic phenomenon Describing data distribution Describing data distribution Components of cartographic abstraction Components of cartographic abstraction Data classification Data classification Quantitative classification methods Quantitative classification methods Simplification Simplification Map symbolization Map symbolization Visual variables by measurement scale Visual variables by measurement scale Map types by the behavior of geographic phenomenon Map types by the behavior of geographic phenomenon

3. Basics Geographic phenomenon Measurement scale Data distribution

4. Geographic phenomenon Location, Scale Location, Scale Spatial dimension Spatial dimension Continuous vs. discrete Continuous vs. discrete Q. number, Mars, human organ Q. number, Mars, human organ Q. Tornado path, elevation Q. Tornado path, elevation Q. Temperature, cold/hot, population, population density Q. Temperature, cold/hot, population, population density

5. Measurement scale of geographic phenomenon NominalOrdinal Interval/Ra tio Concept Type, category Result of ranking Result of measuring Example Male/female, agricultural region Mega/large/ medium/sma ll city Temperature, Mortality rate Year, land use, elevation, strongly agree/strongly disagree, religion, coffee consumption, national income, occupation

6. Describing data distribution NominalOrdinal Interval/Rat io Central tendency Mode: most frequently occurring value Median: value exactly in half when ranked Mean:  = Σx / N Dispersion Variation ratio Quartile deviation Standard deviation Σ (x-  ) 2 / N

7. Histogram and descriptive statistics

8. Components of cartographic abstraction SelectionClassificationSimplificationSymbolization

9. Selectionpreliminary steps Selectionpreliminary steps Classification Classification Simplificationdata processing Simplificationdata processing Symbolizationchoosing symbols Symbolizationchoosing symbols

10. Classification Group values into class such that geographic pattern can be better revealed Group values into class such that geographic pattern can be better revealed

11. How do you determine class boundary? Equal interval Equal interval put any number of values into class with the same interval put any number of values into class with the same interval Quantile Quantile put the same number of values into class put the same number of values into class Natural break Natural break marginal change in values marginal change in values Standard deviation Standard deviation how much deviated from the mean? how much deviated from the mean?

12. Data classification method  σ l1l1 l2l2 l3l3 l4l4 l5l5 a1a1 a2a2 a5a5 a4a4 a5a5 Equal intervalQuantile Natural break Standard deviation

13. Simplification Alter geometry such that relevant details are pronounced while irrelevant details are suppressed Alter geometry such that relevant details are pronounced while irrelevant details are suppressed Line simplificationArea dissolution

14. Criteria for symbolization Measurement scale  visual variables Measurement scale  visual variables Use ordering visual var. for quantitative scale Use ordering visual var. for quantitative scale Use distinguishing visual var. for qualitative scale Use distinguishing visual var. for qualitative scale The behavior of phenomenon  map types The behavior of phenomenon  map types Observed in a discrete/continuous scale & in a abrupt/smooth frequency Observed in a discrete/continuous scale & in a abrupt/smooth frequency Maps sometimes reflect the way data collected rather than phenomenon. (e.g. crime is reported in the unit of jurisdiction) Maps sometimes reflect the way data collected rather than phenomenon. (e.g. crime is reported in the unit of jurisdiction)

15. Appropriate use of visual variables - measurement scale - qualitativequantitative pointShapeSize line Shape, Hue Size area Hue, Arrangement Value, Texture

16. Appropriate choice of map types - behavior of phenomenon - abruptsmooth discrete Graduated symbol map Dot density map Chorodot continuous Choropleth map Isopleth map Because of the discrepancy between phenomenon and data, we need to process data by manipulating spatial scale…. Handling GIS data well is an essential skill for advanced map-making!