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

Outline

Introduction

– Background, Research objective

Proposed method to formalize temporal contexture of local spatial

association

System architecture based on XML Web Service

Conclusion

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

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

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*

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

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

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

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

Feasibility Test of the Proposed Method

Change of Index of Aging from 1998 to 2005

Cluster Map based on

similarity of local spatial association

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

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.

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