Spatiotemporal data mining: A computational perspective
Explosive growth in geospatial and temporal data as well as the emergence of new
technologies emphasize the need for automated discovery of spatiotemporal knowledge …
technologies emphasize the need for automated discovery of spatiotemporal knowledge …
Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology
Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines
innovations in spatial science, artificial intelligence methods in machine learning (eg, deep …
innovations in spatial science, artificial intelligence methods in machine learning (eg, deep …
TRY–a global database of plant traits
Plant traits–the morphological, anatomical, physiological, biochemical and phenological
characteristics of plants and their organs–determine how primary producers respond to …
characteristics of plants and their organs–determine how primary producers respond to …
[图书][B] Geographic data mining and knowledge discovery
The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and
Spatiotemporal DatabasesSince the publication of the first edition of Geographic Data …
Spatiotemporal DatabasesSince the publication of the first edition of Geographic Data …
Spatial data mining and geographic knowledge discovery—An introduction
Voluminous geographic data have been, and continue to be, collected with modern data
acquisition techniques such as global positioning systems (GPS), high-resolution remote …
acquisition techniques such as global positioning systems (GPS), high-resolution remote …
A joinless approach for mining spatial colocation patterns
Spatial colocations represent the subsets of features which are frequently located together in
geographic space. Colocation pattern discovery presents challenges since spatial objects …
geographic space. Colocation pattern discovery presents challenges since spatial objects …
Machine learning models of groundwater arsenic spatial distribution in Bangladesh: influence of Holocene sediment depositional history
Z Tan, Q Yang, Y Zheng - Environmental Science & Technology, 2020 - ACS Publications
Recent advances in machine learning methods offer the opportunity to improve risk
assessment and to decipher factors influencing the spatial variability of groundwater arsenic …
assessment and to decipher factors influencing the spatial variability of groundwater arsenic …
A Big Data platform for smart meter data analytics
Smart grids have started generating an ever increasingly large volume of data. Extensive
research has been done in meter data analytics for small data sets of electrical grid and …
research has been done in meter data analytics for small data sets of electrical grid and …
A survey on spatial prediction methods
Z Jiang - IEEE transactions on knowledge and Data …, 2018 - ieeexplore.ieee.org
With the advancement of GPS and remote sensing technologies, large amounts of
geospatial data are being collected from various domains, driving the need for effective and …
geospatial data are being collected from various domains, driving the need for effective and …
A primer of knowledge management for smart city governance
R Laurini - Land Use Policy, 2021 - Elsevier
The concepts of smart city cannot be understood without examining their links with the
knowledge society. In this kind of society, knowledge must be considered as a capital …
knowledge society. In this kind of society, knowledge must be considered as a capital …