# Geog 416 Final Exam

 Isolines
 [image] We Will Write A Custom Essay Sample On ANY TOPIC SPECIFICALLYFOR YOU For Only \$13.90/page order now vector representations of continuous data helpful in 3 dimensions
 Isoline Creations
 created infinite numbers of points line drawn on interpolated values between points once drawn smooth   when points are few interpolate additional point value then interpolate isolines
 Types of Isolines
 types of isolines = quantitative data types can be collected as points   many as specific names   Examples: isobath isocline isohips
 Isoline GIS
 each line = 1 meter context lines = multiples of isolines
 Rasters
 [image] spreadsheets row & columns = forms cells grid = rows columns = tessellations data unit = spatial (cell) — x,y = implicit entity (object) info = explicity encoded each cells must have numeral value   all about numbers analytical modeling
 Natural Surfaces   Raster
 natural world = continuous data ideal spatial model to illustrate these surface
 Natural Surfaces Examples   Raster
 [image] Elevation Contours Rasters Surfaces symbolized low & high using color ramps elevation ramp colors – rising & lowering elevation
 Color Ramps & Continuous Surfaces
 Color Ramps = stretched data stretched data organized values into 256 classes
 Color Ramps & Continuous Surfaces Examples
 [image] Hillshades = create classic cartographic product a shaded relief map   use as reference layer, to help ppl orient themselves within map
 Pseudo 3D Surfaces
 [image] flat surface appear 3D Seldom Digital animated to different perspectives
 Data Scale
 nominal, ordinal, interval, ratio   numbers = data scale
 Nominal Scale   Data Scale
 telephone numbers establish identity race = individuals have numbers to identify him not order & value
 Ordinal Data Scale   Data Scale
 establish order 1st place, 2nd place, 3rd place   phone number is NOT ordinal
 Interval Scale   Data Scale
 no absolute zero negative values   degrees 100oC –50oC
 Ratio Scale Data   Data Scale
 has absolute zero no negative values   weight = 50 kg direct composition
 Why we should care?   Data Scale
 different types of analysis different cartographic symbols different inappropriate values
 nominal or categorical data   {data classifications}
 qualitative: ordered but without a measurable range   no absolute values
 ordinal data   {data classifications}
 relative NOT absolute value deals with quantitative but without a measurable range using numbers label ordinal data often confusing
 interval data   {data classificitions}
 quantitative: has NO absolute zero subtraction works NOT division class range = absolute zero negative numbers
 ratio scale data   {data classifications}
 quantitative: absolute zero so both subtraction & division work no negative values in classification
 Data Classification
 sorting or arranging entities into groups or categories   number of classes usually between 5 & 10, more likely 5 than 10   classifcation methods vary depending on data   ArcGIS = # of classification
 Equal Interval   {data classification}
 [image] constant interval between classes # of observations will be different from class to class   Good = direct comparisons between different choropleth maps
 Calculating Equal Interval
 subtract minimum from maximum divide result by # of classes result = width of each class add value with minimum value for first class   repeat until done
 Quantile   {data classification}
 [image] equal # of observation per class same class = interval between classes = different
 Calculating Quantile
 divide count of features by # of classes arrange features least to greatest divide into classes = matches result of division equation
 Jenks – Natural Breaks   {data classification}
 [image] minimizes variance within a class by dividing classes in areas different sized class & different # of observations
 Mean & Standard Deviations   {data classification}
 [image] classes = mean & deviations from the mean best if data displays a nominal distribution
 Calculating Mean and Standard Deviation
 Calculate the mean of data Calculate the standard deviation of data Arrange your first class to straddle[stand] mean Then add classes at intervals of standard deviation both above and below the mean class
 Quantile – 5 Classes using 7,1,18,20,6,14,19,13,21,25,2,23,1,15
 using 3 observation: (1,1,1) (2,2,6) (7,13,14) (15,18,19) (20,23,25)
 Equal Interval – 5 classes using 7,1,18,20,6,14,19,13,21,25,2,23,1,15
 (1,1,1,2,2) (6,7) (13,14,15) (18,19,20) (23,25)
 Cartogram
 diagram or abstract geographical to distorted proportionally value of an attribute NOT that useful Trade off between Area error and shape error   Hard to make a real shapes Do not use cartograms to show average values, per capita, values, etc People look what’s on the map but comparing to what’s in their head CANNOT show mean, average [image]
 World Population {Cartogram}
 [image] Area scale accurately represents selected variable   Contiguity is maintained   Shapes should remain recognized   World population is useful but not as continuity
 Bush and Kerry Cartogram
 [image]Look cool and artistic but hard to read   Election Count by counties NOT states
 Types of Cartogram
 Contiguous Non – Contiguous
 Contiguous {Cartogram}
 Maintained Recognized = shapes Area scale accurately selected   Advantages: Easy to read   Disadvantage: Distortion can confuse reader Shapes of internal numeration may recognition impossible Difficult to produce through commercial GIS software The circle population = contiguity [image]
 Non – Contiguous {Cartogram}
 Not maintained Area scale accurately recognized selected   Advantage: Easy to construct GIS True shapes of enumeration units   Disadvantage: Separated No compact White spaces Do not convey continuous nature of geographical space Trade off between maintaining relative positive of enumeration unit and not overlapping [image]
 Restrictions on Cartogram
 Shape quality: cartograms useless? some approximation of true shape can be achieved Each enumeration unit needs size, shape, orientation and contiguity… Least important = communication
 Data Limitation Cartogram
 Data must be ratio Positive values in data sets with large range are problem. Negative values cannot be mapped Zero values eliminate the enumeration unit, creating map
 Creating Cartogram
 Calculate total value for enumeration units based on single attribute   Compute proportional area of enumeration unit’s base on attribute value for each divided by total value   Draw, calculate and conform shapes and values