GIS

1
GIS

• September 27, 2005

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Announcements

• Next lecture is on October 18th (read chapters 9
  and 10)

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Uncertainty
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• Error (differences between observers or between
  measuring instruments)
• Accuracy (difference between reality and our
  representation of reality)
• Precision (the repeatability of measurements)
• Quality (attribute accuracy, positional accuracy,
  logical consistency, completelness, and lineage)

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U1 Conception

• Spatial uncertainty
• Do Natural geographic units exist?
• Scales for bivariate/multivariate analyses?
• Discrete objects more reliant on natural units
• Vagueness (in boundaries, membership)
• Statistical, cartographic, cognitive
• Ambiguity
• Different labels by different national or
  cultural groups, language (GIS is not
  value-neutral!!)

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Indicators

• Direct - clear correspondence with mapped
  phenomenon)
• Indirect (or proxy) best available measure
• Selection of indicators is subjective
• Differences in definitions are a major impediment
  to integration of geographic data over wide areas

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Fuzzy Approaches to Uncertainty

• In fuzzy set theory, it is possible to have
  partial membership in a set
• membership can vary, e.g. from 0 to 1
• this adds a third option to classification yes,
  no, and maybe
• Fuzzy approaches have been applied to the mapping
  of soils, vegetation cover, land use, and
  vulnerability

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Scale Geographic Individuals

• Regions
• Uniformity (internal homogeneity)
• Functional zones (boundaries as breakpoints)
• Relationships typically grow stronger when based
  on larger geographic units

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Scale and Spatial Autocorrelation

• No. of geographic Correlation
• areas
• 48 .2189
• 24 .2963
• 12 .5757
• 6 .7649
• 3 .9902

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U2 Measurement/representation

• Representational models filter reality
  differently
• Vector (requires a priori conceptualization of
  geographic features as discrete objects)
• Raster (boundaries seldom resemble natural
  features, but convenient and efficient)

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0.9 1.0
0.5 0.9
0.1 0.5
0.0 0.1
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Other issues

• Measurements only accurate to a limited extent
• Continuous scales are in practice discrete
• Discrete isopleth/choropleth map display
• Choropleth mapping in multivariate cases
• Box 4.3 explains the difference!

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Spatially Intensive versus Extensive Variables

• Choropleth maps use values describing properties
  of non-overlapping areas (municipalities, states,
  countries)
• Extensive variables values true for the entire
  area are the same color E.g. Total population
• Intensive variables values could potentially be
  true for every part of the area (an average).
  E.g. Population density.

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Measurement Error

• Digitizing errors
• Automated solutions
• Conflation of adjacent map sheets

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Data Integration and Lineage

• Concatenation
• E.g. polygon overlay
• Conflation
• E.g. rubber sheeting
• Persistent error indicates shared lineage
• Errors tend to exhibit strong positive spatial
  autocorrelation

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U3 Analysis

• Can good spatial analysis develop on uncertain
  foundations?
• Can rarely correct source
• More usually tackle operation (internal
  validation)
• Conflation/concatenation allows external
  validation of zonal averaging effects
• Error propagation measures impacts of uncertainty
  in data on the results

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Ecological Fallacy

• Inappropriate inference from aggregate data about
  the characteristics of individuals
• Fundamental difference between geography and
  other scientific disciplines is that definitions
  of objects of study is almost always ambiguous.

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Modifiable Areal Unit Problem (MAUP)

• Scale aggregation MAUP
• can be investigated through simulation of large
  numbers of alternative zoning schemes
• Apparent spatial distributions which are
  unrepresentative of the scale and configuration
  of real-world geographic phenomena (example
  urban density)

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Summary

• Uncertainty is more than error
• Richer representations can create uncertainty!
• Need for a priori understanding of data and
  sensitivity analysis
• Spatial analysis is often context-sensitive (you
  need to know your data and place!)

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Living with Uncertainty

• Acknowledge that uncertainty is inevitable
• Data should never be taken as truth (assess
  whether it is suitable)
• Uncertainties in outputs may exceed uncertainties
  in inputs because many GIS processes are highly
  non-linear
• Rely on multiple sources of data
• Be honest and informative in reporting the
  results of GIS analysis.