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Geogra ph i cal Data. Type s , relati ons , m easures , classificati on s, dimensi on , aggregati on. To be seen on maps. urban. grass. water. te x t (nam e , e levation ). dik e. Topogra ph ic map. C lassif ied isolin e map. To be seen on maps. Choropleth map:

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Geogra ph i cal data

Geographical Data

Types, relations, measures, classifications, dimension, aggregation


To be seen on maps
To be seen on maps

urban

grass

water

text

(name,elevation)

dike

Topographic map

Classified isoline map


To be seen on maps1
To be seen on maps

Choropleth map:

Map with administrative

boundarieswhich shows per region a value by a color or shade

Useofpesticide 1_3_D

per county


Maps show
Maps show ...

  • Relation of place (geographic location) to a value (here 780 mm precipitation) or name (here is Minnesota).

  • An abstraction (model, simplification) of reality

  • A combinationof themes (different sorts of data)

  • Connections (subway maps)

Tokyo subway map


Scales of measurement
Scales of measurement

  • Nominal scale

  • Ordinal scale

  • Interval scale

  • Ratio scale

  • ( Angle/direction, vector, … )

Classificationoftypes of data by statisticalproperties (Stevens, 1946)


Nominal scale
Nominal scale

  • Administrative map (namesofthe countries)

  • Landuse map (namesof landuse: urban, grass, forest, water, …)

  • Geologicalmap (namesofsoil types: sand, clay, rock, …)

Finite number ofclasses, each with a name.

Testingis possible for equivalenceof name.


Ordinal scale
Ordinal scale

  • School type (VMBO, HAVO, VWO)

  • Wind force on schaleof Beaufort (0=no wind, ... 6=heavy wind, …, 9=storm, ...)

  • Questionnaire-answers (disagree, partly disagree, neutral, partly agree, agree)

Finite number of classes, each with a name

Testingforequivalenceof nameandfor order


Interval scal e
Interval scale

  • Temperature in degrees Celsius or Fahrenheit

  • Time/year on Christian calendar

Unboundednumber ofclasses, each with a value

Testingfor equivalence, for orderandfordifference(aunit distance exists)


Ratio scale
Ratio scale

  • Measurements: concentrationof lead in soil

  • Counts: population, number ofairports

  • Percentages: unemployment percentage, percent of landuse type forest

Unboundednumber ofclasses, eachwith a value

Testing forequivalence, for order, fordifferenceandforratio (a naturalzero exists)



Over view
Overview

collection

twodata


Other scales
Otherscales

  • Angle (wind direction, direction of spreading)

  • Vector:angleandvalue (primary wind direction and speed)

  • Categorical scaleswith partial membership (fuzzy sets; points onindeterminate boundarybetween “plains” and “mountains”; location of coast line: tide)



Classificati on schem e s
Classification schemes

Data on nominal scale: hierarchical classification schemes

houses

living

flats

urban

working

agriculture

cattle

landuse

fruit

plants

nature

water


Classificati on schem e s1
Classification schemes

  • Data on interval and ratio scales

  • Fixed intervals

  • Fixed intervals based onspread

  • Quantiles: equalrepresentatives

  • “Natural” boundaries

4, 5, 5, 8, 12, 14, 17, 23, 27

[1-10], [11-20], [21-30]

[4-11], [12-19], [20-27]

[4-5], [8-14], [17-27]

[4-5], [8-17], [23-27]


Classificati on schem e s cont d
Classification schemes, cont’d

  • Statisticalboundaries: average , standarddeviation , then e.g. boundaries - 2,  - , ,  + ,  + 2

  • Arbitrary


Tw o classificati on s
Two classifications

Counties of Arizona, total population

Quartiles

Four equal intervals


Why is choice of classification important
Why is choice of classification important?

  • Visualization often needs classification

  • Choice of class intervals influences interpretationThink of a report that addresses air pollution due to a factory made by the board of the factory or by an environmental organization


Data object and field view
Data: object and field view

  • Object view: discrete objects in the real world

    • road

    • telephone pole

    • lake

  • Field view: geographic variable has a “value” at every location in the real world

    • elevation

    • temperature

    • soil type

    • land cover


Referen c e system
Referencesystem

  • Data according to the scales of measurement are attribute values in areferencesystem

  • A geographical reference system is spatial, temporal or both

At 12 noon ofAugust 26, 1999 , a temperature of 17.6 degrees Celsius is measured at 5 degrees longitude and 53 degrees latitude


Spati al object s
Spatial objects

  • Points; 0-dimensional, e.g.measurement point

  • (Polygonal) line; 1-dimensional, e.g. borderbetweenBoliviaandPeru

  • Polygons; 2-dimensional, e.g. Switzerland

  • Sets of points, e.g. locations of accidents

  • Systems of lines (trees, graphs), e.g. streetnetwork

  • Sets of polygons, subdivisions, e.g. islandgroup, provinces of Nederland


Dependency of dimensi on
Dependencyof dimension

  • Dimensionof an object can be scale dependent: Rhineriver atscale 1 on 25.000 is 2-dim.; Rhineat scale 1 on 1.000.000 is 1-dim.

  • Dimensionofan object can be application dependent: Rhine as transportroute is 1-dim.(length is relevant; not the surface area); Rhine as land cover in Nederland is 2-dim.


The third dimensi on
The third dimension

  • Elevationcan be considered an attribute onthe ratio (!?) scaleat (x,y)-coordinates

  • For civil engineering:crossing ofstreet and railroad can be at the same level, or one above the other

  • Data onsubsurface layersand their thickness


The time component
The time component

  • Same region, same themes, different dates: Allows computation of change

  • Trajectories give the locations at certain times for moving objects


Level of aggregati on
Level of aggregation

Income ofan individual

Average income in amunicipality

Average income in a province

Average income in acountry

Higherlevel of aggregation


Various aggregati on s in the netherlands
Various aggregations in the Netherlands

  • Prinvines (12)

  • Municipalities (441)

  • COROP regions (40)

  • Water districts (39)

  • Economic-geographic regions (129)

  • 2- and 4-number postal codes

  • Macro-regions (4 of 5; provinces joined)

  • Labor exchange district (127), planning region (43), nodal region (80), ...


Aggregati on dangers
Aggregation: dangers

  • MAUP: modifiable areal unit problem

Located occurrencesof a raredisease

0 - 1

2 - 4

5 -

clustering?


Aggregati on dangers1
Aggregation: dangers

  • MAUP: modifiable areal unit problem

Located occurrencesof a raredisease

0 - 1

2 - 4

5 -

Aggregation boundarieshave got nothing to do with mapped theme

clustering?


Aggregati on dangers2
Aggregation: dangers

  • Not enough aggregation: privacy violations(e.g. AIDS-cases with complete postal code)

  • Correctionfor population spreadis necessaryin case of data on people

0 - 1

Locatedoccurrences of a rare disease

2 - 4

5 -

clustering?



Summary
Summary

  • Data is geometry, attribute, and time

  • Data is coded in a reference system

  • Attribute data is usually on one of the standard scales of measurement

  • Classification of interval and ratio data is needed for mapping (isoline or choropleth) and histograms

  • The object view and field view exist

  • Geometric data has a dimension (point, line, area), but this may depend on scale and application

  • Data is often spatially aggregated


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