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Recent events (WTC attacks, Southeast Asia Tsunamis, Hurricane Katrina, London bombings) have illustrated the need for accurate and timely situational awareness tools in emergency response. Developing effective situational awareness (SA) systems has the potential to radically improve decision support in crises by improving the accuracy and reliability of the information available to the decision-makers. In an evolving crisis, raw situational information comes from a variety of sources in the form of situational reports, live radio transcripts, sensor data, video streams. Much of the data resides (or can be converted) in the form of free text, from which events of interest are extracted. Spatial or location information is one of the fundamental attributes of the events, and is useful for a variety of situational awareness (SA) tasks. [ABSTRACT FROM AUTHOR]

Location-based services like reminders, electronic graffiti, and tourist guides normally require a custom, location-sensitive database that must be custom-tailored for the application at hand. This deployment cost reduces the initial appeal of such services. However, there is much location-tagged data already available on the Web which can be easily used to create compelling location-aware applications with almost no deployment cost. Such tagged data can be used directly in applications as well as to provide evidence in models of activity. We describe three applications that take advantage of existing Web data combined with location measurements from a GPS receiver. The first application, "Pinpoint Search", finds web pages of nearby places based on GPS coordinates, queries from a Web mapping service, and general Web searches. The second application, "XRay", uses the mapping service to find businesses in a building by pointing a GPS-equipped electronic compass at the building. The third application is called "Travelogue", and it builds a map and clickable points of interest to help automatically annotate a trip based on GPS coordinates. Finally, we discuss the use of Web-based data as rich sources of evidence for probabilistic models of a user's activity, including a means for interpreting the explanation for the loss of Web signals as users enter structures. [ABSTRACT FROM AUTHOR]

A datatype with increasing importance in GIS is what we call the location history-a record of an entity's location in geographical space over an interval of time. This paper proposes a number of rigorously defined data structures and algorithms for analyzing and generating location histories. Stays are instances where a subject has spent some time at a single location, and destinations are clusters of stays. Using stays and destinations, we then propose two methods for modeling location histories probabilistically. Experiments show the value of these data structures, as well as the possible applications of probabilistic models of location histories. [ABSTRACT FROM AUTHOR]