The geographic dimension of spatially distributed phenomena
The geographic dimension of any spatially distributed phenomenon is taken into account to complete any standard analytical process, and to contribute to a better territorial assessment and management. As a member of the Institute of Environmental Engineering (IIE), LASIG developed its research activities and specific competences in decision making support, human-computer interactions, and spatial analysis.
The different research topics addressed at LASIG are:
- GIS and decision-making support
- Roles, objectives and organization of GIS and geographical information in companies and administrations
- Methodological frameworks for land and urban planning, design and implementation of GIS
- Spatial analysis: this field highlights recurrent geographical patterns of any distributed phenomena (pollution, economic activities, unemployment, genetic diversity, etc.) on any given territory, mainly investigating the relationship between location and the variation of any variable under study. This discipline permits to analyse processes likely to govern these spatial structures through the medium of several basic concepts like distance, spatial (in)dependance, spatial interaction, etc. These elements are integrated within theories and models to characterize the functioning and the evolution of spatial or geographic systems.
- Exploratory Spatial data Analysis (ESDA) and Geovisualization (GVIS): nowadays, environmental scientists and engineers have to deal with gradually increasing amounts of data related to any research field or professional domain, most of which possess a geographical component. Visualization of such data belongs to the research area known as geographic visualization, or geovisualization (GVIS). GVIS is an important approach in the explorative stages of research. Its power is rooted in visualization, i.e. in the ability of the human eye-brain system to recognize patterns in the visual field.
GVIS encourages the creation and inspection of simultaneous multiple dynamic and interactive linked views of data sets, allowing researchers to search for patterns that might deserve further investigation, and providing different perspectives into the data. As spatial data often have a temporal component, their structure is complex and involves space, time, and a number of thematic attributes, which poses significant geovisualization challenges to be addressed.