## 4 Real-Time Risk Estimation  
Integration is not limited to mere data collection, but the semantics of the data is analyzed to offer decision support services, as explained in this section. Indeed, the Marine Information System (MIS) has been endowed with methods for the provision of real-time risk assessment, in order to produce a snapshot of risk in the region of interest, which, in turn, can either be (i) automatically exploited by proactive services (as explained in the next section) or (ii) directly analyzed by stakeholders helping them to keep the threat to safe navigation and the natural environment as low as reasonably practicable. Conventionally, risk assessment regards (i) estimation of hazard potential and (ii) vulnerability analysis [26], which do not have a single universally-accepted definition, but that are usually considered to rely on a number of static and time-varying features, including the presence of harbors, the density of marine traffic and weather and hydrodynamic conditions. In addition, proximity to the coast or the presence of endangered biological species favor the increase of hazard potential. In the past few decades, several frameworks for hazard potential have been described  
Figure 4: A report displayed through the Marine Information System (MIS) web interface of an alert produced by a volunteer: data, identifier of the volunteer (if registered), severity, wind speed, remarks, latitude and longitude are displayed. in the literature [7; 27; 28]. Vulnerability analysis addresses instead the impact of the actual hazard or the set of conditions/processes resulting from biophysical, environmental and socio-economic factors that decrease resilience of an area [29]. In particular, in the context of marine pollution, the presence of a pervasive monitoring system and of an effective risk management chain increases resilience to hazardous events. Extending these ideas, we aimed at devising a method for the computation of a local risk estimate for each point in a geographical region of interest. The computed pointwise estimates can then be visualized in a thematic map of risk for the given region, similarly to Environmental Sensitivity Index (ESI) maps [30]. In our framework, global hazard potential is derived by multiplying the likelihood of occurrence of adverse consequences by the magnitude of each consequence. In addition, the monitoring efficiency and coverage are quantitatively translated into numerical features representing the vulnerability of the monitoring system. To this end, among the data integrated into the MIS, a selection has been made to extract those parts of information that might be relevant in risk analysis for oil spills and other pollution events. In particular, it has been decided to encompass: (i) maritime traffic data; (ii) the oil spill reports produced by remote sensing; (iii) all the in situ observations including volunteer reports; and (iv) an index expressing the goodness of local monitoring coverage. A dynamic risk map is automatically recalculated at regular time intervals and whenever new information coming from a connected data source deserves an update. This is the case when the MIS receives a report from a volunteer. Figure 5 shows an example of the risk map modification upon the reception of an oil spill report from a WhiteBox device. Having the general risk computational framework being described in [8; 31], we briefly describe here how the contribution of volunteered data is incorporated. The available reports generated by volunteers contribute to the risk in location qat time t by the following amount:  
\[R_{\text{volunteer}}(q,t)=\sum_{r}w_{r}(\tau*\chi_{r})(q)\exp(-\lambda|t-T_{r}|) \tag{1}\]  
Figure 5: Marine Information System (MIS) interface showing dynamic risk map modification produced after reception of an alert from a volunteer.