02056nam 2200373 450 991051046800332120230830141528.0(CKB)5470000000736688(NjHacI)995470000000736688(EXLCZ)99547000000073668820230830d2020 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierProceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data /editors, Varun Chandola, Ranga Raju Vatsavai, Ashwin ShashidharanNew York :Association for Computing Machinery,2020.1 online resource (68 pages) illustrationsACM Conferences1-4503-8162-6 Big data is an important area of research for data researchers and scientists. Within the realm of big data, spatial and spatio-temporal data are among the fastest growing types of data. With advances in remote sensors, sensor networks, and the proliferation of location sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatiotemporal data has exploded in recent years. In addition, significant progress in ground, air and space-borne sensor technologies has led to an unprecedented access to earth science data for scientists from different disciplines, interested in studying the complementary nature of different parameters. Analyzing this data poses a massive challenge to researchers.Neural networks (Computer science)Neural networks (Computer science)006.32Chandola VarunVatsavai Ranga RajuShashidharan AshwinNjHacINjHaclBOOK9910510468003321Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data2136001UNINA