Julie Sungsoon Hwang

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Empirical study on location indeterminacy of
localities

• Julie Sungsoon Hwang Jean-Claude Thill
• Department of Geography
• State University of New York at Buffalo
• U.S.A.
• August 24, 2004
• 11th Intl Symposium of Spatial Data Handling

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Research question

• How can we represent vague concepts of spatial
  object in a (discrete) computing environment
  (e.g. GIS)?
• Nearness in localities
• Mental maps of localities
• Indeterminate boundaries of localities

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Research scope

• Mental maps
• Generals f (distance, relation, scale)
• Specifics f (preferences, experience, )
• Localities
• Official recognition eg. administrative unit
• Unofficial recognition eg. vernacular region

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Research objective 1

• Building the model of locality boundary using
  fuzzy regions (egg-yolk model) and some rules
  regarding nearness

1
B
A
0
B
A
2-Dimensional Geographic Space
x 1-Dimensional Geographic Space Y Degree of
Membership
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Research objective 2

• Examining any difference in location
  indeterminacy between urban and rural settings

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Example identifying localities
Which city?
Accident location?
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Task 1 theoretical

• Building the model of locality boundary using
  fuzzy region and rules of nearness

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Fuzzy regions
Core
Exterior
Boundary
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Nearness
Near Syracuse?
Fuzzy set membership of belonging to Syracuse

• What determines the fuzzy set membership
  function value?
• Euclidean distance
• Spatial qualitative relation
• Scale-dependent

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Locality as a fuzzy region
1stOrderGr
2ndOrderGr
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Computing fuzzy set membership value in GIS work
steps
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Computing fuzzy set membership value in GIS
results
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Comparison to other proximity measures
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Task 2 empirical

• Examining any difference in location
  indeterminacy between urban and rural settings

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Georeferencing traffic accident data
We considered 5460 out of 8631 cases from NYS
96-01
Of these, 246 urban, and 298 rural localities are
compared
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Computing location indeterminacy index of
localities
?i 1 - (S?i)/n
78 sure
95 sure
58 sure
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Comparing location indeterminacy index of urban
versus rural localities

• Average number of fatal crashes in rural areas is
  2 whereas those in urban areas is 16
• To work around small number problem, we compute
  Bayesian estimates of both groups adjusted for
  within-group distributions

People are 94 (or somewhere between 93 and 95)
sure in identifying urban localities while they
are 88 (or somewhere between 86 and 90) sure
in identifying rural localities
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ANOVA
Analysis of variance conducted on Bayesian
estimates of location indeterminacy confirms the
difference between urban versus rural locality is
significant in terms of location indeterminacy
Neighborhood types may affect the degree of
certainty to which the boundary of locality is
perceived
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Interpretation of results

• Mental maps of urban settings may be less
  error-prone than those of rural settings
• Spatial knowledge acquisition city provides more
  landmark or route upon which judgment on
  indeterminate boundaries of localities can be
  based
• Scale factor dense urban settings provide a
  reasonable scale in which humans can
  conceptualize localities without much difficulty

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Conclusions

• Fuzzy set theory provides a reasonable mechanics
  to represent vague concept of geospatial objects
• Neighborhood types affect the way humans acquire
  spatial knowledge and forge mental
  representations of it