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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 – PowerPoint PPT presentation

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Title: Julie Sungsoon Hwang


1
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

2
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

3
Research scope
  • Mental maps
  • Generals f (distance, relation, scale)
  • Specifics f (preferences, experience, )
  • Localities
  • Official recognition eg. administrative unit
  • Unofficial recognition eg. vernacular region

4
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
5
Research objective 2
  • Examining any difference in location
    indeterminacy between urban and rural settings

6
Example identifying localities
Which city?
Accident location?
7
Task 1 theoretical
  • Building the model of locality boundary using
    fuzzy region and rules of nearness

8
Fuzzy regions
Core
Exterior
Boundary
9
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

10
Locality as a fuzzy region
1stOrderGr
2ndOrderGr
11
Computing fuzzy set membership value in GIS work
steps
12
Computing fuzzy set membership value in GIS
results
13
Comparison to other proximity measures
14
Task 2 empirical
  • Examining any difference in location
    indeterminacy between urban and rural settings

15
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
16
Computing location indeterminacy index of
localities
?i 1 - (S?i)/n
78 sure
95 sure
58 sure
17
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
18
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
19
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

20
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
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