=Paper=
{{Paper
|id=Vol-1743/ks2
|storemode=property
|title=Spatio-Temporal Data Mining: From Big Data to Patterns
|pdfUrl=https://ceur-ws.org/Vol-1743/ks2.pdf
|volume=Vol-1743
|authors=Maguelonne Teisseire
|dblpUrl=https://dblp.org/rec/conf/simbig/Teisseire16
}}
==Spatio-Temporal Data Mining: From Big Data to Patterns==
https://ceur-ws.org/Vol-1743/ks2.pdf
Spatio-Temporal Data Mining: From Big Data to Patterns
Maguelonne Teisseire
UMR TETIS (Cirad, Irstea, AgroParisTech, CNRS) – France
maguelonne.teisseire@irstea.fr
Web: www.textmining.biz
Abstract With the dramatic growth of spatial informa-
tion and Geographic Information Systems (GIS),
Technological advances in terms of data many studies have been carried out in the context
acquisition enable to better monitor dy- of spatiotemporal patterns mining. Early work in
namic phenomena in various domains (ar- this area has dealt with spatial and temporal di-
eas, fields) including environment. The mensions separately. Extraction of temporal se-
collected data is more and more complex quences aims at identifying features frequent over
- spatial, temporal, heterogeneous and time without taking into account spatial relation-
multi-scale. Exploiting this data requires ships. Colocation mining methods extract set of
new data analysis and knowledge discov- features which frequently appear in close objects
ery methods. In that context, approaches without taking into account the temporal aspect.
aimed at discovering spatio-temporal pat- More recently, these works have been extended
terns are particularly relevant. This paper1 to simultaneously integrate spatial and temporal
focuses on spatio-temporal data and asso- dimensions. Examples include the detection of
ciated data mining methods. sequences of located events and trajectory min-
ing. A review has been published by the consor-
1 Spatio-temporal Data tium GeoPKDD (Giannotti and Pedreschi, 2008).
In recent years, technological advances in data ac- However, in those approaches, the mined patterns
quisition (satellite images, sensors, etc.) have en- do not match the spatial complexity encountered
abled numerous applications in surveillance and when dealing with sattelite images. Similarly, the
environmental monitoring: detection of abrupt primitive constraints usually used (typically mini-
changes (natural disasters, etc.), evolution track- mum frequency) are not sufficient to express crite-
ing of natural phenomena (coastal erosion, deser- ria of interest for experts, such as geologists.
tification, wildfires, etc.) or development of mod- A spatiotemporal database contains information
els (hydrology, agriculture, etc.). The collected characterized by a spatial and a temporal dimen-
data is usually heterogeneous, multiscale, spa- sions. Two types of spatiotemporal databases are
tial and temporal (time series of satellite images, mainly considered: databases containing trajecto-
aerial or terrestrial photos, digital terrain models, ries of moving objects located in both space and
physical ground measurements, qualitative obser- time (e.g. bird or aircraft trajectories); databases
vations, etc. ). This data is used to understand and storing spatial and temporal dynamics of events
predict phenomena generated by processes that are (e.g. erosion evolution in a region or epidemic
complex and of multidisciplinary origin (climatic, spread in a city).
geological, etc.). Exploitation by experts of those
2 Mining moving object trajectories
huge volume of complex data (big data) requires
not only to structure it to the best but also and The emergence of new mobile technologies has
mainly to design data analysis and knowledge dis- facilitated the collection of large amounts of spa-
covery methods. In that context, approaches in- tiotemporal data, dedicated to the localization of
volving pattern mining are particularly relevant. mobile objects in space and time (Perera et al.,
1
2015). These new databases provide opportunities
The content of the paper was prepared in collaboration
with H. Alatrista Salas, S. Bringay, F. Flouvat, and N. Sel- for new applications. The project GeoPKDD (Gi-
maoui. annotti and Pedreschi, 2008), for example, studied
17
the development of traffic planning in large cities for those patterns would further facilitate their in-
according to vehicle- flows. Other application do- terpretation. Many application areas remain to
mains include socio-economic geography, sports be explored as for example image-mining where
(e.g. football players), fishing control and weather large amounts of data are available but few effec-
forecast- (e.g. hurricanes). In most of these appli- tive and scalable methods have been developed so
cations, the number of paths is high. One of the far. Finally, there is a real need for collaboration
objectives of trajectory analysis is to find the most between domain experts and data mining experts.
relevant paths according to the targeted problem Collaboration is the key to success for the knowl-
(e.g. the most frequent, the most unexpected, peri- edge extraction process.
odic, etc.). Several approaches have been recently
proposed in the literature, for instance (Orakzai et
al., 2015). References
Hugo Alatrista-Salas, Sandra Bringay, Frédéric Flou-
3 Spatial patterns and spatiotemporal vat, Nazha Selmaoui-Folcher, and Maguelonne Teis-
patterns for located event- mining seire. 2016. Spatio-sequential patterns mining: Be-
yond the boundaries. Intell. Data Anal., 20(2):293–
The extraction of spatial and spatiotemporal pat- 316.
terns has been studied extensively in recent years Mete Celik, Shashi Shekhar, James P. Rogers, and
in geographic data and GIS. There are two families James A. Shine. 2008. Mixed-drove spatiotem-
of approaches: colocations (Shekhar and Huang, poral co-occurrence pattern mining. IEEE TKDE,
20(10):1322–1335.
2001) that identify events that are frequently close;
and spatiotemporal patterns that identify the evo- Fosca Giannotti and Dino Pedreschi, editors. 2008.
lution of events in both space and time (Alatrista- Mobility, Data Mining and Privacy - Geographic
Knowledge Discovery. Springer.
Salas et al., 2016). Sequences and more gener-
ally graphs have often been used and extended to Christian S. Jensen, Markus Schneider, Bernhard
Seeger, and Vassilis J. Tsotras, editors. 2001. Ad-
the spatiotemporal context in order to represent the
vances in Spatial and Temporal Databases, 7th
propagation of phenomena in space and time. Col- International Symposium, SSTD 2001, Redondo
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Celik et al., 2008). Springer.
Faisal Orakzai, Thomas Devogele, and Toon Calders.
4 Conclusion 2015. Towards distributed convoy pattern mining.
In Proceedings of the 23rd SIGSPATIAL Interna-
The challenges associated with spatial and spatio- tional Conference on Advances in Geographic Infor-
temporal databases are numerous. Firstly, the se- mation Systems, Bellevue, WA, USA, November 3-6,
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China, August 3-9, 2013, pages 1642–1648.
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terpretation. A definition of relevant visualizations
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