The Geographical Information Analytics (GIA) track is a technical track at the ACM/SIGAPP Symposium on Applied Computing 2025, which is running in its 40th iteration in 2025. The symposium is held March 31 - April 4, 2025 in Sicily, Italy.

The proliferation of geo-referenced digital resources continues to accelerate, presenting new opportunities and challenges for researchers and professionals alike. To harness the potential of these resources, it is essential to annotate, index, and analyze them according to data models tailored to specific domains. Geographic data, characterized by spatial (location), temporal (date/time), and thematic (theme) dimensions, are increasingly crucial in various fields.

In recent years, significant advances have been made in the extraction, analysis, and retrieval of geographic information from diverse sources such as textual documents, images, satellite data  and maps. Numerous engines and services have been developed to search for geographical information, often incorporating spatial, spatio-temporal, and thematic data.

The GIA track aims to unite a diverse community of professionals and researchers focused on geographic information extraction, retrieval, and analysis, alongside relevant applications. This track intersects several disciplines, including geomatics, knowledge engineering (KE), natural language processing (NLP), data mining (DM), information extraction (IE), and data visualization (Dataviz).

The submission deadline for the ACM SIGAPP GIA track is in line with the main conference track. All submissions are expected by September 20, 2024.

The GIA track invites contributions that explore innovative methods for exploiting geographic information across thematic, spatial, and temporal dimensions, and leveraging external knowledge resources. Key themes include:

• Preparation of Geographical Data
  • Identification of resources (texts, images, sensors, etc.)
  • Data modeling (spatial, temporal, spatio-temporal, thematic)
  • Addressing specific characteristics: heterogeneity, volume, multi-dimensionality
• Geographical Knowledge Extraction
  • Construction and acquisition of spatial/temporal/thematic knowledge
  • Qualitative (expert annotation) and quantitative (data mining, NLP) approaches
• Geographical Data Analytics
  • Single or multi-dimensional data analysis (interpretation, enrichment)
  • Quality measurements on spatial and temporal data
• Geovisualization
• Tools and techniques for interactive visualization of geospatial data
• Evaluation of spatial and temporal tools, resources, and knowledge
• Applications in Humanities and Social Sciences: geography, health, history, archaeology, sociology, etc.

Original papers from the above-mentioned or other related areas will be considered. This includes three categories of submissions:

1. Original and unpublished research;
2. Reports of innovative computing applications;
3. Reports of successful technology transfer to new problem domains.

Each submitted paper will undergo a (double) blind review process and will be refereed by at least three referees. To ease blind review, you shall remove author names and any information that might divulge authors' identity from your paper before submitting it.

Accepted papers in all categories will be published in the ACM SAC 2025 proceedings.

The camera-ready version of the accepted paper should be prepared using the ACM format (guidelines are given on the SAC 2025 web site).
Accepted full papers should not exceed 8 pages in a double column format (with the option to add more pages at extra charge).

Relevant Dates:

• Sept 20, 2024, 11:59PM (UTC+0.00): Submission of regular papers and SRC research abstracts.
• October 30, 2024: Notification of papers, posters, and SRC research abstracts.
• November 29, 2024: Camera-ready copies of accepted paper.
• December 6, 2024: Authors registration due.

• Bianca Schoen-Phelan, School of Computer Science, TU Dublin, Ireland.
• Gavin McArdle, School of Computer Science, University College Dublin, Ireland.
• Christian Sallaberry, LIUPPA Lab, France.
• Eric Kergosien, Assistant Professor of Information Sciences, GERiiCO Lab, Lille University, France.