Environmental data and models are uncertain by nature. The lack of knowledge about, for example, the magnitude of potential measurement errors may lead to unforeseen consequences. This makes it di cult to assess the data's or model's usefulness for critical applications. We present an approach for the visualization of uncertainty coming from in-situ environmental sensors. The visualization component is part of a Web-enabled environmental modelling platform which also supports the speci cation of processing work ows. The concept of uncertainty and means for its encoding as part of the environmental data are introduced. The individual components in the processing work ows propagate and update the uncertainty information. We also explain how the uncertainty in the original sensor data has been identi ed, and how the visualization component has been implemented.
The on-going implementation of the European INSPIRE directive facilitated the
access to geographic information on the Web. Embedded in Spatial Data
Infrastructures (SDI), geospatial Web services provide means to access, process, and
visualize spatial data. Interoperability between di erent Web services is to a
certain degree ensured by the standards set by the Open Geospatial Consortium.
The standards also enable the direct integration into local geospatial
applications. But there has been only limited uptake by the environmental modelling
community. The concept of a Shared Environmental Information System (SEIS)
has been recently introduced to address this issue [
Environmental information is traditionally resulting from environmental computer models. Simple computer models could perform atomic computations, such as interpolating the elevation within the DEM (Digital Elevation Model). To solve complex problems such as predicting the impact of an oil spill on the local wildlife, the chaining of individual simple models might be required. In a rst step, a weather forecasting service encapsulated as an OGC Web Processing Service takes real-time weather data from an OGC Sensor Observation Service to predict future weather conditions. The result is taken as input for the oil drift model, which computes the dispersion and weathering of an oil slick for the forecasting period. As nal step, this distribution may be used to assess the impact of the oil slick on the local wildlife (e.g. using ESI2 maps). Each of these models are implemented as WPS, the coupling with other Spatial Data Services (SDS) serving the input is done with the Business Process Modelling Language (BPEL). Standard BPEL engines can then execute these work ows. 3
Results from the work ows help domain experts in their decision-making tasks,
e.g. for assessing response measures for an oil spill. Oil spills could be fought
by distributing detergents, skimming, or even burning. Which method to apply
depends on the results of the oil spill model. Taking the wrong decisions here
could potentially have devastating e ects on the environment; having correct
and highly certain results is therefore crucial. Much of the data that forms the
input for the computer models contains some sort of uncertainty. Environmental
information is uncertain by nature. "You are uncertain, to varying degrees, about
everything in the future; much of the past is hidden from you; and there is a
lot of the present about which you do not have full information. Uncertainty is
everywhere and you cannot escape from it" [
Uncertainty is an expression of con dence about our knowledge [
generate new data. Uncertainty is part - and therefore an important aspect
of GI throughout the complete services work ow, starting with the creation of
the data until its visualisation on a map [
Processing uncertain environmental data propagates the uncertainty often
unpredictably [
The implementation of this project is divided into the following sub-tasks: (1) Identi cation of existence data quality parameters, (2) extending the standard data format for sensor data with uncertainty information, and (3) visualizing uncertainty in a graph. 4.1
Information about data quality can be either qualitative (e.g. adding information about the data provider to address issues such as trust) or, in most cases, quantitative (e.g. completeness, accuracy, scale, and more). Data quality has been acknowledged to play an important role for geographic information, and OGC and ISO published standards for representing data quality parameters. This work is focussing on the accuracy of sensor measurements. A piezometric
sensor system is used for ENVISION to monitor underground water levels. The sensed data can be accessed through an OGC SOS4 interface. The accuracy of the measured data from these piezometers is found to be around 1% of the measured value5. 4.2
Sensed data always comes with some sort of error. From a conceptual
perspective all data should be considered to be uncertain. Even though most data lacks
information about uncertainty, some data sets may have descriptive
information about it in its metadata (following the ISO 19013 standard for data quality
metadata). This global de nition of uncertainty is many cases insu cient for
the visualization and assessment of the data set's usefulness for critical
application. The Uncertainty Markup Language (UncertML) has been introduced as
extension to the OGC Geography Markup Language to address this issue. It
is focussed on an XML encoding for the transport and storage of uncertainty
information [
Request parameters and examples are available at: http://sosades.brgm.fr/ 5 As reported by the service providers BRGM, the French geological survey
The implementation adopts the visualization through the charts. The component expects the element Gaussian Distribution and computes the according uncertainty intervals. A time-series chart displays the maximum, minimum value of the uncertainty and the mean value of the incoming observations. The following screen-shots includes the chart viewer on the left side, and the map showing the according sensor positions on the right side. They belong to a set of components developed in the ENVISION project. The individual modules are implemented as Portlets (compliant to the Java Portlet Speci cation 2866), which can be best described as pluggable user interface components for the Web. By simply selecting one of the sensors displayed in the map, the according time series can be visualized in the chart. The red line represents the actually observed values (the mean), while the blue lines represent the according boundaries of the uncertainty intervals. The chart is based on a JavaScript library7 which supports rich interaction with the graph. The user can hover over the chart to see the individual values at di erent points in time. The focus of this work is on the visualization of uncertainty resulting from measurement errors and the processing of the environmental data. It relies on the encoding of the uncertainty using the UncertML standard. How to come up with 6 More information available at: http://www.jcp.org/en/jsr/detail?id=286 7 More information available at: http://www.highcharts.com/ this uncertainty information - and how to propagate uncertainty in geospatial work ows - has not been subject of this research. This is investigated in the research project UncertWeb. Hence, future work will focus on integrating the other features supported by UncertML in the visualization components. This includes research on the usability, i.e. how can we best communicate uncertainty to support the end-users in the decision making process. In ENVISION the execution of the geospatial work- ow is handled by a distributed execution infrastructure. It includes techniques for the optimization of the work- ows, and the ad-hoc adaptation of execution paths according to certain context parameters. Future work will also investigate how uncertainty information may contribute to this adaptation process. 6
This work has been funded by the European research project ENVISION (FP7249170, see http://www.envision-project.eu)