Geog 416 Final Exam



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


  1. when points are few
  2. interpolate additional point value
  3. then interpolate isolines

Types of Isolines

types of isolines = quantitative data types can be collected as points


many as specific names






Isoline GIS

each line = 1 meter

context lines = multiples of isolines




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



natural world = continuous data

ideal spatial model to illustrate these surface


Natural Surfaces Examples




  • Elevation
    • Contours
    • Rasters
  • Surfaces symbolized low & high using color ramps
  • elevation
    • ramp colorsrising & lowering elevation

Color Ramps & Continuous Surfaces

Color Ramps = stretched data

stretched data organized values into 256 classes

Color Ramps & Continuous Surfaces



Hillshades = create classic cartographic product a shaded relief map


use as reference layer, to help ppl orient themselves within map

Pseudo 3D Surfaces


flat surface

appear 3D



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





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}


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



{data classification}


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}


minimizes variance within a class

by dividing classes in areas

different sized class &

different # of observations

Mean & Standard Deviations


{data classification}


classes = mean & deviations from the mean

best if data displays a nominal distribution

Calculating Mean and Standard Deviation
  1. Calculate the mean of data
  2. Calculate the standard deviation of data
  3. Arrange your first class to straddle[stand] mean
  4. 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)
  1. diagram or abstract geographical to distorted proportionally value of an attribute
  2. NOT that useful
  3. 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


World Population



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
  1. Contiguous
  2. Non – Contiguous



Maintained Recognized = shapes

Area scale accurately selected



Easy to read



Distortion can confuse reader

Shapes of internal numeration may recognition impossible

Difficult to produce through commercial GIS software

The circle population = contiguity


Non – Contiguous


Not maintained

Area scale accurately recognized selected



Easy to construct GIS

True shapes of enumeration units




No compact

White spaces

Do not convey continuous nature of geographical space

Trade off between maintaining relative positive of enumeration unit and not overlapping


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

Leave a Reply

Your email address will not be published. Required fields are marked *