Guest editorial: special issue on Environmental and Geospatial Data Analytics
International Journal of Data Science and Analytics
Guest editorial: special issue on Environmental and Geospatial Data Analytics
Diana Inkpen 0 1 2 3
Mathieu Roche 0 1 2 3
Maguelonne Teisseire 0 1 2 3
Diana Inkpen 0 1 2 3
0 French Agricultural Research Centre for International Development (Cirad), TETIS, Univ. Montpellier , APT, Cirad, Cnrs, Irstea, Montpellier , France
1 School of Electrical Engineering and Computer Science, University of Ottawa , Ottawa, ON , Canada
2 Maguelonne Teisseire
3 National Research Institute of Science and Technology for Environment and Agriculture (Irstea), TETIS, Univ. Montpellier , APT, Cirad, Cnrs, Irstea, Montpellier , France
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Environmental and more generally geospatial information
is now provided by crowdsourcing but also by public
administrations in the context of the open data policies. Analyses of
such data are still challenging, because of their
heterogeneity (structural, semantic, spatial, and temporal) and because
of the difficulty in choosing the “best” knowledge discovery
process to apply, according to the needs of the experts in the
field. Challenges about data science deal with creation,
storage, search, sharing, modeling, analysis, and visualization of
data, information, and knowledge. In the data science
context, spatiotemporal aspects are crucial in order to manage
and mine data, to index and retrieve information, and finally
to discover and visualize knowledge. By taking into account
these spatiotemporal aspects, original methods have to be
proposed for processing real and complex data from
different domains, e.g., environment, agriculture, health, urban,
and so forth.
This special issue of the International Journal of Data
Science and Analytics Environmental and Geospatial Data
Analytics contains a collection of seven papers and provides
high-quality research covering part of the challenges
mentioned above, from a theoretical or experimental point of
view. It includes extended papers from the EnGeoData
sessions of DSAA 2015 and DSAA 2016 (IEEE International
Conference on Data Science and Advanced Analytics) that
have been invited to submit.
The EnGeoData sessions bring together researchers
interested by pre- and post-processing of environmental data,
geographical information retrieval, spatial data mining and
spatial data warehousing, knowledge discovery use-cases
dedicated to environmental data, spatial text mining,
spatial ontology, spatial recommendations and personalization,
visual analytics for geospatial data, and dedicated
applications.
Geospatial data can be processed by different kind of
data mining approaches. The paper of Sujing Wang et al. (A
Data Mining Framework for Environmental and Geospatial
Data Analysis) deals with a data mining framework, which
includes preprocessing of environmental and geospatial data,
geospatial data mining techniques, and visual analysis of
environmental and geospatial data. The work of Mark P.
Wachowiak et al. (Visual analytics of high-frequency lake
monitoring data: A case study of multiple stressors on a large
inland lake system) focuses on visual analytics techniques.
A visual analytics system (i.e., web-based tools) is described
that leverages humans’ innate capability for pattern
recognition and feature detection. The proposed visualizations
facilitate community-based participatory research among
scientists, government agencies, and community
stakeholders.
To extract patterns, considering the complexity of
geospatial data, there is a crucial need of specific techniques. In
this context, the work of Andrej Dobrkovic et al. (Maritime
pattern extraction and route reconstruction from incomplete
AIS data) adapts genetic algorithms. The results by
comparing with known inland water routes highlight the strengths
and weaknesses of the proposed approaches. Following the
same way of mining patterns, the paper of Mohomed Shazan
Mohomed Jabbar et al. (discovering co-location patterns
with aggregated spatial transactions and dependency rules)
proposes an original approach to transform spatial data to
transaction data by using statistically significant dependency
rule searching methods to find co-location rules. The
applications in environmental health highlight potential associations
between air pollution and adverse birth outcomes in Canada.
Finally, data from multi-sources have to be taken into
account for analysis of environmental and geospatial data.
For instance, a key challenge today consists of handling the
domain-specific image streams most efficiently and
effectively. In this context, the paper of Keke Chen et al. addresses
this issue (SPIN: Cleaning, Monitoring, and Querying Image
Streams Generated by Ground-Based Telescopes for Space
Situational Awareness). Moreover, other types of data have
to be considered like GPS data for trajectory mining. In this
context, the paper of Mohamed Quafafou et al. (Detecting
Behavior Types of Moving Object Trajectories) computes
a formal concept lattice encoding optim (...truncated)