Ambient Intelligence and Immersive Geospatial Visual Analytics

Ambient Intelligence and Immersive Geospatial Visual Analytics

Raffaele De Amicis (Fondazione Graphitech, Italy) and Giuseppe Conti (Fondazione Graphitech, Italy)
DOI: 10.4018/978-1-61692-857-5.ch028


The chapter finally concludes by highlighting a number of open challenges brought by the convergence between GVA and AmI which need to be addressed by the research community in the next future.
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Boundary-Free Spatial Domain For Ambient Intelligence

In recent years the focus of many research efforts in the field of Ambient Intelligence (AmI) has brought to several anticipatory, context-aware and custom-tailored IT solutions capable to respond to a number of case studies often limited in terms of spatial domain as home or office. Extensive use of hardware technologies such as RFID, sensor networks, wireless communications as well as software techniques including software agents, affective computing paradigms or human-centered interfaces have been mostly confined to indoor scenarios or to well-controlled outdoor contexts.

However the ubiquitous adoption of geospatial technologies has made it possible to expand the domain of Ambient Intelligence from the boundaries of enclosed spaces and from scenarios such as home automation, health care and elderly assistance, to a much wider perspective which is embracing the environment that surrounds us.

In a perhaps provocative and certainly challenging claim, the world itself has become the domain of Ambient Intelligence (AmI). In fact the convergence between Geospatial Technologies and AmI opens up radically new scenarios of globalised intelligent applications capable to benefit from heterogeneous distributed information on the spatial environment surrounding us. These include data from spatial databases, web-based repositories, a large variety of real-time information continuously delivered by ground sensors and by satellite-born technologies and, last but not least, an increasingly large amount of geo-referenced information generated by user communities which are available on the web.

This information-rich scenario is at the base of a number of intelligent services essential to numerous daily activities. In fact, in our everyday lives, we already make extensive use of Geospatial Technologies and of Geographic Information (GI) in a number of applications which today are regarded as a commodity, including:

  • Personal navigation systems;

  • Media-rich web 2.0 “mash-ups”, using a variety of mapping APIs (Application Programming Interfaces) as components of larger modular web 2.0 applications;

  • 3D web-based applications, known as 3D Geobrowsers or “spinning globes”, the most famous of which being perhaps Google Earth, Microsoft Virtual Earth or NASA WorldWind.

In our daily practice we use a number of web-based applications delivering real-time geo-referenced information for instance on road traffic and congestion, on localized weather forecast, on pollution distribution. We constantly use geographical information and personal satellite navigation technologies for pedestrian and vehicle navigation and routing systems, to find the closest shop or to track the path of a user playing outdoor sports1.

More specifically the use of geospatial technologies is extremely important in the context of environmental management and planning. Operators need to be able to access data on the ecosystem, to perform spatial analysis of environmental data, to extract environmental indicators of interest. For this reason it is essential to provide them with the most effective tools, based on geospatial technologies, to ensure the best possible management of the environment as well as to ensure the wellbeing of those living within it.

Within a wider context it is therefore important to provide operators with technologies to access, manage, process GI from basic cartographic data, to information on wildlife, population and pollutant distribution, to classification of industrial sites as well as localization of other anthropogenic activities. The type of data being managed may include among other, high-resolution areal or satellite imagery, digital raster or vector maps, alphanumerical information on economic, social, demographic indicators, real-time sensor data on pollution to name but a few.

Within this context the adoption of an Ambient Intelligence-oriented methodology can significantly improve analysis and monitoring of ecosystem services. This is extremely important as planning or management of environmental resources can have severe long-lasting consequences both in terms of public health and more generally in terms of people’s quality of live and welfare.

Key Terms in this Chapter

GIS: Geographical Information Systems

GI: Geographic Information

ESB: Enterprise Service Bus

SOA: Service Oriented Architecture

SDI: Spatial Data Infrastructure

GVA: GeoVisual Analytics

OGC: Open Geospatial Consortium, Inc.

API: Application Programming Interface

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