Our lives are affected by myriads of events happening daily all over the world. For efficient planning and management of complex systems composed of various components, understanding relationships between an event and the reactive behaviour of involved components is vital. Analysing these complex relations demands a spatiotemporal event-based model, in which the event plays a central role. In this article we develop a framework which provides the possibility of mapping and storing event-related information on the OpenStreetMap (OSM) platform by volunteers. The study is divided into two different phases: first, mapping the event elements by adding new attributes adequately designed to encode spatiotemporal and semantic event information; and, second, representing the event-related information on a map by developing a Web application, offering a volunteered location-based service. To facilitate the event-mapping procedure, a Java OpenStreetMap (JOSM) plug-in was developed for volunteers. The plug-in was developed based on the notion of an event to adequately store and manipulate the semantic information of events in the OSM structure. The tool was used by more than 100 volunteers in Munich for the years 2012 to 2014. In addition to manual collection of event-related information by volunteers, a crawling framework was also developed to automatically collect freely available event information from various Web pages on the Internet. The framework extracts the same event elements as the plug-in. But the framework crawls each Web page according to some pre-defined rules and follows a post-processing step, if necessary. The manually collected events along with the crawled event information are visualized in a Web application. The study revealed that adding the possibility of event-oriented mapping to OSM empowers volunteers to collect a higher level of information (event information) for city maps. This information can furthermore be used for strategy development and service planning by decision-makers.

The issue of temporal and spatial variation in soil salinity is considered as a fundamental element in salinity monitoring. The aim of this study is to develop a framework which integrates image mining techniques with Fuzzy logic methodology to improve the evaluation of spatio-temporal variation of soil salinity in areas with lack of available ground observation. Intensity and duration of salinity was characterized in space by the deviation of the current NDVI at each location from its corresponding temporal mean value. Landsat and ASTER images data was used to provide frequent Normalized Difference Vegetation Index (NDVI) in cultivation phase for a period of 22 years. Evolution of salinity condition before planting season was assessed by applying stepwise regression method on image data for two available dataset. The regression equation was obtained between reflectance value of band three and the measured soil Electrical Conductivity (EC) from field. Validation of the developed algorithm was done by comparing the obtained outputs with 50 ground observations, available salinity reports, and previous soil salinity maps. The result revealed that the proposed framework can be considered as a cost and time effective tool for proper assessment of the spatio-temporal variation of soil salinity.

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