Statistical modelling to predict events based on georeferenced data
A Spanish university has developed a technology for modeling complex phenomena and predicting their behavior with the aim of making important decisions. It allows the utilization of large amounts of georeferenced data that could not be used until now due to the difficulty of processing them with conventional techniques. Researchers are seeking partners to reach commercial agreements with technical assistance or develop R&D projects.
- Type of partner sought: Companies, public administrations or R&D institutions - Specific area of activity of the partner: insurance companies, public administrations, emergency services, etc. - Task to be performed: use of georeferenced data to make decisions, statistical analysis of georeferenced data The Spanish research group is interested in reaching commercial agreements with technical assistance with companies or public administrations. The developed technology can be implemented on a non-exclusive basis in the potential partner systems and the technology transfer can be supported with the provision of additional support services. In addition, researchers are interested in reaching research cooperation agreements to develop R&D&i projects on demand and to carry out further developments and adaptations.
The complexity of many human and environmental phenomena makes it difficult to correctly understand and predict their development in the future. A Spanish research group has developed space-time statistical techniques capable of modeling complex phenomena and predicting their behavior with the aim of drawing some useful conclusions or making important decisions. These techniques are applied to georeferenced information, that is, on events that take place at a certain place and time. These multivariate statistical and data mining techniques allow offering public administration and companies solutions for prediction in fields such as public safety, environmental pollution, engineering or epidemiology. In the case of criminology, for example, the integrated analysis of large amounts of data of different types (topographic, meteorological, socioeconomic, from social networks, etc.) may help public administrations to identify areas which are especially vulnerable to crime. As a result, better use can be made of the resources destined to the upkeep of public security. Likewise, in the field of environmental management, the research group has designed a more complete model for mapping forest fire risk, which includes physical as well as socioeconomic variables with the aim of improving the existing simulation-based fire hazard maps. In this way, variables such as type of vegetation, nearby roads or conflicts over urban development are taken into account along with the temperature, accumulated rainfall or wind speed. All these variables allow fire prevention to be refined. The techniques developed can be applied in any situation in which georeferenced information is used; in other words, events that occur at a known place and time and whose statistical analysis allows relevant data to be inferred for decision-making or planning. ● Public administrations: in various applications, both in the field of big data exploitation (such as those collected in an emergency call centre) and in the generation of space-time statistic tools for epidemiological studies, the improvement of forest fire management, predicting the future evolution of air pollution in cities and crime prevention, among others. ● Insurance companies: multifactorial studies on the incidence of the road and other types of accidents; epidemiological analyses in health insurance companies, the development of predictive mortality maps, etc. ● Companies in general that want to exploit georeferenced information. The researchers are seeking companies or public institutions interested in applying the developed techniques through commercial agreements with technical assistance. They are opened to carry out further developments or adaptations to specific needs, through collaborations in R&D projects.
Advantages and innovations
The technical advantages offered by the methodologies developed in this project are the following: ● They allow the utilisation of large amounts of data that could not be used until now due to the difficulty of processing them with conventional techniques. ● They allow events to be predicted by modelling them and, consequently, decision-making. ● Once implemented, the modelling techniques can be used at a relatively low cost, meaning that the return on R&D&I investment is quick. The techniques offered can be adapted to any problem that arises, provided that it involves georeferenced information.
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