Emerging Spatial Analysis Methods in Geographic Information Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: 20 April 2026 | Viewed by 63
Special Issue Editors
Interests: geographical information systems; spatial databases; medical informatics; tele-rehabilitation systems; virtual and augmented reality
Special Issues, Collections and Topics in MDPI journals
Interests: spatial entropy (Shannon-based and alternatives) for mapping heterogeneity; Kullback–Leibler (KL) divergence to compare spatial/temporal distributions and settlement patterns; demography: segregation, concentration, and diversity metrics across spatial scales
Special Issue Information
Dear Colleagues,
We are delighted to announce this call for papers for a Special Issue titled "Emerging Spatial Analysis Methods in Geographic Information Systems." The significance of spatial granularity in energy planning, urban development, and various scientific domains has markedly increased in recent years, creating a demand for sophisticated analytical approaches. This Special Issue aims to explore cutting-edge methodologies that are transforming how we understand and interact with geographic information systems (GIS).
The integration of advanced artificial intelligence (AI), machine learning (ML), deep learning (DL), and large language models (LLMs) with GIS is opening new frontiers, enabling the processing of vast and complex geospatial datasets with unprecedented efficiency. These emerging methods address challenges such as computational performance, data heterogeneity, and scalability, while enhancing accessibility for non-expert users.
We invite contributions on topics including, but not limited to, spatial clustering techniques (e.g., k-means, max-p), natural language processing (NLP) for geospatial queries, deep transfer learning (DTL) applications, and innovative GIS-based approaches for renewable energy assessment, urban scenario simulations, infrastructure design, disaster management, and public health studies. We welcome research presenting novel methods, practical applications, discussions of benefits and challenges, and future research avenues in this rapidly evolving field.
Dr. Andrea Nucita
Dr. Massimo Mucciardi
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- spatial analysis
- geographic information systems (GIS)
- artificial intelligence (AI)
- machine learning (ML)
- deep learning (DL)
- large language models (LLMs)
- natural language processing (NLP)
- spatial clustering
- urban planning
- energy systems modelling
- disaster management
- remote sensing
- GeoAI
- data aggregation
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