27-31 Mar 2023 Tallinn (Estonia)


The Geographical Information Analytics(GIA) Track is a technical track at SAC 2023 -The 38th ACM/SIGAPP Symposium On Applied Computing and follows the KEGeoD Track organized in 2018 in Pau (France), and the GIA Track organized in 2019 in Limassol (Cyprus), in 2020 in Brno (Czech Republic), Virtually in 2021 and  in Brno (Czech Republic) in 2022.

The production and use of geo-referenced digital resources is expanding rapidly. In order to exploit their contents, the documents are annotated, indexed and analyzed according to data models dedicated to the description of particular domains. The multiple dimensions of data descriptors can be divided into three categories: location (spatial dimension), date/time (temporal dimension), and theme (thematic dimension). Data consisting of these multi-dimensional characteristics are considered geographical data.

In recent years, a variety of works have highlighted the potential of the extraction, analysis and retrieval of geographic information in corpora composed of textual documents, images and maps. A number of engines and services dedicated to the search for geographical information have been proposed: they mostly cover spatial information, but some include spatio-temporal and thematic information. This Track aims to bring together the growing community of professionals and researchers in the field of geographic information extraction, retrieval and analysis and of the corresponding applications. The GIA track is at the crossroads of several disciplines: of course, geomatics, but also Knowledge Engineering (KE), natural languages processing (NLP), data mining (DM), information extraction (IE) and data visualization (Dataviz).




How to effectively exploit the power of geographical information available on the Web, through the
thematic, spatial and temporal dimensions? How to use the complementarity of external knowledge
resources? These questions highlight a non-exhaustive list of themes considered for GIA track:

Preparation of Geographical Data
o Identification of resources and of data (texts, images, etc.);
o Data Modeling (spatial, temporal, spatio-temporal and thematic data);
o Considering specific characteristics: heterogeneity, volumetry, mono or multi-dimensionality.

Geographical knowledge Extraction
o Construction and Acquisition of spatial/temporal/thematic Knowledge;
o Qualitative approaches (expert annotation, etc.) and quantitative approaches (data
mining, NLP, etc.).
Geographical Data Analytics
o Single or multi-Dimensional Data Analysis (interpretation and data enrichment),
o Quality measurements on spatial and temporal data;
o Tools and techniques supporting the analysis of geospatial data through the use of
interactive visualization;

Evaluation of spatial and temporal tools, resources and knowledge;

Uses, methods and issues in Humanities and Social Sciences topics: geography, health,
history, archaeology, sociology, etc.

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