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6th International Symposium on Web and Wireless Geographical Information SystemsDecember 4-5, 2006, Hong Kong, China
Web-based Cluster Analysis for theTime-series Signature of Local Spatial Association
Jae-Seong Ahn1, Yang-Won Lee2, and Key-Ho Park1
Department of Geography, Seoul National University, KoreaCenter for Spatial Information Science, The University of Tokyo, Japan
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Outline
Introduction
– Background, Research objective
Proposed method to formalize temporal contexture of local spatial
association
System architecture based on XML Web Service
Conclusion
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Introduction - Background
Recent trends in Web-based GIS
– Beyond the emphases on map delivery, cartographic presentation, and providing of
geographic information
– Implementation of spatial analytical functionality on the Web
Including exploratory spatial data analysis and spatial modeling
Moreover, analytical functionalities allowing for client-provided dataset as well
as server-provided dataset are necessary
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Introduction - Objective
To propose a method for modeling time-series of spatial
association in geographical phenomena– To formalize the time-series of local spatial association by employing Moran
scatterplot
– To group together similar time-series into clusters by using the similarity measures of “state sequence” and “clustering transition”.
To implement a prototype system for Web-based spatial data
analysis with the functionality of modeling time-series of
spatial association
– Generating similarity matrices and producing a clustered classification map
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Formalizing Time-series of Local Spatial Association
measures of local spatial association– To examine the spatial dependence among subset regions focusing on the
variation within a study area.– LISA (Local Indicator of Spatial Association : eg. Local Moran’s I), local Gi
statistic
Visualization effect of Local measure of spatial associaton2005
20052005
Raw Data(Choropleth map)
Local Moran’s I Local Gi*
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Quadrant of Moran Scatter Plot
Moran scatterplot– Four quadrants of Moran scatterplot show a local pattern of spatial
association, by using local Moran’s I
∑ −−
= )(2 xxwS
xxI jij
ii
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Proposed Method (1)
Time-series signature of local spatial association– Temporal changes of local spatial association– State change
e.g., LL LH HH– Clustering transition
e.g., UC (upward clustering): LH HH, HL HH
DC (downward clustering): e.g., LH LL, HL LL
UD (upward declustering): e.g., HH HL, LL HL
DD (downward declustering): e.g., HH LH, LL LH
JU (joint upward clustering): e.g., LL HH
JD (joint downward clustering): e.g., HH LL
O : no change
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Proposed Method (2)
Similarity measures for time-series signature of local spatial associationLevenshtein metric– The minimum number of edit operation (insertion/deletion/substitution) needed to
transform one sequence into the other– Application
To find similar sequence of nuclei acids in DNA or amino acids in proteinsTo measure similarity of spatiotemporal trajectories etc.
Algorithm and example– Region A : ⅢⅢⅡⅠⅠⅠ
– Region B : ⅢⅡⅡⅠⅠⅣ
– Levenshtein metric : 2
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Proposed Method (3)
Clustered classification of changing regions– To classify changing regions in terms of “state sequence” and
“clustering transition” by using Ward method– To generate a clustered classification map
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Feasibility Test of the Proposed Method
Change of Index of Aging from 1998 to 2005
Cluster Map based on
similarity of local spatial association
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System Architecture Based on XML Web Services
Client-side Web pages are composed in ASP.NET linked with the code-behind in C#.Net – Server-side business logics are composed in XML Web Services
Implemented modules of server-side business logic– Data handler– Spatial analyzer
cluster analysis for the time-series signature of local spatial association
– Graphics handler
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Experimental Analysis
State sequence Clustering transition
10 years’ data of the elderly population ratio of 65 administrative units in Seoul Metropolitan Area, 1995 to 2004.
Client-provided dataset can be accommodated in the form of a zipped Shapefile.
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Concluding Remarks
We proposed a method of cluster analysis on the temporal context of local spatial association– By developing the time-series signature of Moran scatterplot
– Covering “state sequence” and “clustering transition” of local spatial association
We implemented analytical functionalities for the time-series cluster analysis on the Web– Using XML Web Services
– Accommodating client-provided dataset