Spatiotemporal Data Mining

Spatiotemporal Data Mining is a subfield of data mining that focuses on discovering patterns, relationships, and anomalies in datasets that explicitly incorporate both spatial and temporal dimensions, distinguishing it from purely spatial or temporal data mining by integrating the interdependent analysis of location-based and time-evolving phenomena.

470

Publications

21.9K

Citations

1.5K

Authors

559

Institutions

Publications per year

2017–2026

192

Authors

1.5K

  1. IL
    Ickjai Lee

    James Cook University, Australia

    12 publications · 443 citations

    11

    h-index

  2. YZ
    Yu Zheng

    Microsoft Research Asia (China), China

    10 publications · 848 citations

    10

    h-index

  3. BA
    Berkay Aydin

    Georgia State University, United States

    10 publications · 174 citations

    10

    h-index

  4. RA
    Rafal A. Angryk

    Georgia State University, United States

    11 publications · 214 citations

    10

    h-index

  5. SS
    Shashi Shekhar

    University of Minnesota, United States

    9 publications · 538 citations

    9

    h-index

1–5 of 1.5K

Venues

  1. 13

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 12 publications · 1.5K citations

    12

    h-index

  3. IEEE Access

    13 publications · 556 citations

    12

    h-index

  4. ISPRS International Journal of Geo-Information

    IJGI · 12 publications · 716 citations

    12

    h-index

  5. Expert Systems with Applications

    9 publications · 497 citations

    9

    h-index

1–5