1SPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS", Bangkok, May 23-25, 2001 
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MINING SEQUENTIAL PATTERN FROM GEOSPATIAL DATA 
Yin SHAN 
Joint Lab. For Geoinformation Science & Department of Geography 
The Chinese University of Hong Kong 
shanyin@cuhk.edu.hk 
Tao CHENG 
Department of Geography, University of Leicester, Leicester LE1 7RH, United Kingdom 
tcheng@le.ac.uk 
Hui LIN 
Joint Lab. For Geoinformation Science, The Chinese University of Hong Kong 
huilin@cuhk.edu.hk 
KEYWORDS ¡Spatio-Temporal Data Mining, Sequential Pattern, GIS 
ABSTRACT 
Recently spatio-temporal data mining begins to be introduced into Geographical Information Systems (GIS) community. We present a 
method to mine sequential pattern from geographical data in this paper. WINEPI algorithm is employed to discovery frequent episodes 
in spatio-temporal data. 
1. INTRODUCTION 
Since our capability of gathering and managing data improved 
considerably in recent years, traditional data analysis cannot 
make sense of this huge volume of data. Data mining, as a 
term which appeared in late 80s, is now an active research 
field to facilitate extracting novel, interesting and potentially 
useful knowledge from these massive datasets. Data mining, 
which is originated for extracting knowledge from transaction 
database, is now extended for processing spatial, temporal, 
even spatio-temporal data. 
Recently spatio-temporal data mining begins to be introduced 
into Geographical Information Systems (GIS) community 
(koperski 1999; Buttenfield, Gahegan et al. 2000). In fact, 
because of its multidimensional and dynamic characteristics, 
GIS as a science and collection of techniques for handling 
geo-referenced data, is not only a satisfying application 
domain for data mining, but also can propose fresh questions 
for data mining community. More importantly, GIS as a 
powerful tool to manage geographical data, have already had 
so much data stored in it. Since geographical data are 
collected with high cost, it is more necessary and emergent to 
make the most of these data. Data mining, especially spatio- 
temproal data mining, should play a notable role in this 
process. 
Basically, the tasks of data mining include segmentation, 
dependency analysis, deviation and outlier analysis, trend 
detection, generalization and so on. Its main techniques 
consist of cluster analysis, Bayesian classification, decision 
tree, neural networks, association rules, outlier detection, 
attribute-oriented induction, etc. (Miller and Han 2001) 
We present a method to mine sequential pattern from 
geographical data in this paper. The discovery of frequent 
sequences is important domain of temporal mining. 
Correlations are discovered among events ordered on the time 
axis. In many applications, sequence mining is used to assess 
after which events an interesting event is expected to occur. In 
the context of GIS, the sequential pattern of spatial changes of 
geographical objects depicts the correlation, maybe potential 
causal relation, among changes. These correlation can not 
only be used for prediction, but also motivate scientist to look 
for explanation and mechanism behind the correlation, which 
will enrich our knowledge in the end. 
The rest of this paper is organized as follows. In the next 
section our method for mining sequential pattern from 
geospatial data is presented. Three sections that follow 
discuss the three steps of our method separately. At last, 
conclusion and future research issues are given. 
2. A METHOD FOR MINING SEQUENTIAL PATTERN 
FROM GEOSPATIAL DATA 
Generally speaking, spatio-temporal data mining is an active 
but immature field. Spatial generalisation, spatial clustering 
and spatial associations can be extended to deal with temporal 
information (Abraham and Roddick 1998). In order to be able 
to handle time as well as space, some new rule types, such as 
meta-rule and evolution rule are also proposed. (Abraham 
1999) In this paper, we propose a different method. We 
introduce WINEPI algorithm (Mannila, Toivonen et al. 1997) to 
discovery frequent episodes in spatio-temporal data. 
Abstractly, data with temporal attributes can be viewed as a 
sequence of events, where each event has an associated time 
of occurrence. One basic problem in analyzing such a 
sequence is to find frequent episodes, i.e., collections of 
events occurring frequently together (Mannila, Toivonen et al. 
1997). We believe this analysis could be introduced into 
mining geospatial data to find the sequential pattern among 
spatial changes.