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ACM Transactions on Spatial Algorithms and Systems | 2024 Impact Factor:1.6 | Cite Score:3.2 | Q3

ACM Transactions on Spatial Algorithms and Systems Journal With Cite Score

Cite Score and Journal Rank of ACM Transactions on Spatial Algorithms and Systems

  • About: ACM Transactions on Spatial Algorithms and Systems (TSAS) is a peer-reviewed journal that focuses on the development and application of spatial algorithms and systems. It covers a broad range of topics related to spatial data, spatial analysis, computational geometry, and algorithms for processing and managing spatial information. The journal aims to provide a comprehensive platform for significant advances in spatial algorithms, systems, and their applications in various domains such as geographic information systems (GIS), computer graphics, robotics, and data analysis.
  • Objective:
    The primary objective of TSAS is to advance the field of spatial algorithms and systems by disseminating high-quality research findings and innovative methodologies. The journal seeks to provide a venue for researchers, practitioners, and educators to share their work on algorithms, systems, and applications related to spatial data and analysis. TSAS aims to support the development of effective and efficient spatial algorithms and systems that address complex challenges and enhance the processing and utilization of spatial information.
  • Interdisciplinary Approach:
    TSAS embraces an interdisciplinary approach, encouraging contributions from various fields such as computer science, mathematics, geography, and engineering. This approach ensures a holistic exploration of spatial algorithms and systems, integrating diverse perspectives and methodologies. By promoting interdisciplinary research, the journal aims to address complex spatial problems and develop integrated solutions that advance the state of knowledge and practice in spatial data processing and analysis.
  • Impact:
    The journal has a significant impact on both academic research and practical applications in the field of spatial algorithms and systems. It is widely cited by researchers, practitioners, and industry professionals interested in the latest developments in spatial data processing and analysis. The research published in TSAS contributes to the development of new algorithms, systems, and techniques that enhance the efficiency and effectiveness of spatial information management. The journal serves as a valuable resource for professionals involved in spatial data analysis, computational geometry, and related fields.
  • Significance:
    TSAS plays a crucial role in advancing the study and practice of spatial algorithms and systems by providing a platform for high-quality research and practical insights. Its contributions support the development of innovative algorithms and systems that address current and future challenges in spatial data processing and analysis. The journals commitment to excellence and interdisciplinary focus make it an essential resource for anyone involved in research, development, and application in the field of spatial algorithms and systems. Through its rigorous scholarship and broad coverage, TSAS helps shape the future of spatial data processing, driving progress in the effective utilization of spatial information.

  • Editor-in-Chief:  Walid G. Aref

  • Scope: The ACM Transactions on Spatial Algorithms and Systems (TSAS) journal focuses on the development and application of spatial algorithms and systems. Its scope includes, but is not limited to:
  • Spatial Data Structures: Research on data structures designed to efficiently store, manage, and query spatial data, including spatial indexing structures like R-trees, KD-trees, and quad-trees.
  • Spatial Algorithms: Exploration of algorithms for solving spatial problems, including algorithms for spatial search, spatial clustering, and spatial optimization.
  • Geometric Computing: Studies on computational geometry techniques for processing and analyzing geometric data, including algorithms for convex hulls, triangulations, and geometric intersections.
  • Spatial Database Systems: Research on database systems that manage spatial data, including spatial query processing, spatial data management, and spatial database design.
  • Geographic Information Systems (GIS): Exploration of technologies and methodologies for GIS, including spatial data analysis, map visualization, and geospatial data integration.
  • Spatial Data Mining: Research on techniques for extracting patterns and knowledge from spatial data, including spatial data mining algorithms and applications.
  • Spatial Computing: Studies on computational methods for spatial analysis and modeling, including spatial simulations and spatial algorithms for scientific computing.
  • Computer Vision and Image Analysis: Research on techniques for spatial analysis in computer vision and image processing, including object detection, image segmentation, and spatial feature extraction.
  • Spatial Optimization: Exploration of optimization techniques for spatial problems, including facility location, routing, and resource allocation.
  • Spatial Statistics: Research on statistical methods for analyzing spatial data, including spatial autocorrelation, spatial regression, and geostatistics.
  • Spatial Computing for Big Data: Studies on the challenges and solutions for processing and analyzing large-scale spatial data, including spatial big data analytics and cloud-based spatial computing.
  • Latest Research Topics for PhD in Computer Science
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  • Print ISSN:  23740353

    Electronic ISSN:   23740361

  • Abstracting and Indexing:  Scopus

  • Imapct Factor 2024:  1.6

  • Subject Area and Category:   Computer Science, Computer Science Applications, Information Systems, Signal Processing, Mathematics, Discrete Mathematics and Combinatorics, Geometry and Topology, Modeling and Simulation

  • Publication Frequency:  

  • H Index:  20

  • Best Quartile:

    Q1:  

    Q2:  

    Q3:  Computer Science Applications

    Q4:  

  • Cite Score:  3.2

  • SNIP:  0.797

  • Journal Rank(SJR):  0.338