Nowadays, the existence of vast amount of environmental-oriented data -along with the daily collection of such data from a various set of sources- raise a set of opportunities to environmental scientists for realising advanced analysis and providing answers to issues that were hard to be tackled in the past. Towards this direction, the European Union has introduced the INSPIRE Directive which requires public authorities across Europe to provide access to their environmental datasets through the adoption of a common framework.
However, the effective exploitation of opportunities that arise from the creation and maintenance of environmental oriented linked data is highly related with the need to overcome data quality, management and analysis challenges. Such challenges stem from the need for appropriate representation of data in the available datasets, the existence of high-quality data that are considered valuable for further analysis and the support of the overall process from data scientists that are aware of the environmental domain peculiarities.
The need for interpretation of spatial data in many cases as well as the need for interlinking of data among national and international data sources is considered as “business as usual” scenario. Through proper interlinking of the available data, the environmental scientists are able to examine the characteristics of phenomena in various locations, validate their assumptions or studies in multiple cases as well as realize analysis that necessitates the interconnection of parameters collected in diverse datasets. Such interconnection/interlinking of data provides multiple degrees of freedom to environmental scientists to realize analysis with significantly reduced data management and maintenance overhead.
Besides the creation of the datasets and the establishment of a maintenance/update procedure, another challenge relates to the availability of user friendly tools facilitating the processing of the datasets and the realisation of analysis by scientists and employees in the environmental sector (e.g. public sector environmental agencies, NGOs) that do not have a deep expertise in Semantic Web and Linked Data technologies. Interlinking the analysis results with the initial datasets used can be also considered very helpful within such communities, since it supports the extension of available analysis as well as the comparison of existing and currently produced results.
When it comes to the linked data analytics, the extraction of analytics that take into account spatial descriptions of the data as well as the capacity for identification of trends and detection of anomalies in data series are the most common scenarios that, if automated, are going to facilitate the daily operations of environmental scientists.
It should be noted that most of the above-mentioned challenges can be handled via the use of the LinDA ecosystem, while meaningful results are also envisaged to be produced via the execution of an environmental analytics pilot related to the examination of the correlation among the pollution in several areas in Italy and its impact on the health of the citizens.