Research Topic

Big Spatial Data

About this Research Topic

The recent explosion in the amount of spatial data has motivated significant research efforts on managing and analyzing big spatial data. This research includes specialized scalable algorithms and systems that index, query, mine, and visualize big spatial data for different application domains, such as agriculture, public safety, climate analysis, transportation and traffic applications, scientific applications, and more. Consequently, research on big spatial data is getting growing attention worldwide with more research centers established in top universities and specialized graduate degrees offered in spatial data science. Research challenges include not only the volume of spatial data, but also the variety of enriched spatial data such as geo-social and geo-multimedia data, the velocity of changes in spatio-temporal data, and the inherent uncertainty in spatial data. In addition, several significant spatial analysis techniques, such as spatial data mining and spatial statistical analysis, are challenging to scale even for medium-sized datasets. Such expensive operations are crucial in revealing abundant knowledge from spatial datasets.


Keywords: spatial data, gis, spatial data management, spatial data mining


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The recent explosion in the amount of spatial data has motivated significant research efforts on managing and analyzing big spatial data. This research includes specialized scalable algorithms and systems that index, query, mine, and visualize big spatial data for different application domains, such as agriculture, public safety, climate analysis, transportation and traffic applications, scientific applications, and more. Consequently, research on big spatial data is getting growing attention worldwide with more research centers established in top universities and specialized graduate degrees offered in spatial data science. Research challenges include not only the volume of spatial data, but also the variety of enriched spatial data such as geo-social and geo-multimedia data, the velocity of changes in spatio-temporal data, and the inherent uncertainty in spatial data. In addition, several significant spatial analysis techniques, such as spatial data mining and spatial statistical analysis, are challenging to scale even for medium-sized datasets. Such expensive operations are crucial in revealing abundant knowledge from spatial datasets.


Keywords: spatial data, gis, spatial data management, spatial data mining


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

15 May 2019 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

15 May 2019 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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