Search USGS⌕ Search

USGS · 70236535

GeoAI and the future of spatial analytics

Abstract

This chapter discusses the challenges of traditional spatial analytical methods in their limited capacity to handle big and messy data, as well as mining unknown or latent patterns. It then introduces a new form of spatial analytics—geospatial artificial intelligence (GeoAI)—and describes the advantages of this new strategy in big data analytics and data-driven discovery. Finally, a convergent spatial analytical framework is suggested as a potential future pathway for spatial analysis.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wenwen Li, Samantha T. Arundel. 2022-07-01. GeoAI and the future of spatial analytics. https://doi.org/10.1007/978-981-19-3816-0_17

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Ground failure resulting from earthquake ground motions

The ground failure resulting from an earthquake, at all scales, is ultimately controlled by the details of near-surface earthquake ground motions, the spatially distributed mechanical characteristics of surface materials and how the two interact. This chapter introduces the aspects of ground-motion seismology that are most relevant to the triggering of different types of ground failure, such as landslides and liquefaction. In particular, the chapter focuses on different sources of local modification (e.g. amplification) of earthquake ground motions in the shallow subsurface, known as site effects. The chapter ends with an overview of the controls on, general spatial patterns of, and approaches to regional modelling of the manifestation of ground failure that may be triggered by an earthquake.

Book chapter↗

Loma salmonae and related species

Loma salmonae is a microsporidium that infects Pacific salmon and causes a gill inflammatory syndrome known as microsporidial gill disease of salmon. This disease has been mostly associated with netpen-farmed Chinook salmon ( Oncorhynchus tshawytscha ) in British Columbia, Canada. Clinical, diagnostic, pathological aspects of disease, as well as approaches for disease avoidance in salmon aquaculture are discussed. A laboratory infection model in rainbow trout was used to determine life cycle-stages, transmission dynamics, pathophysiology, influence of temperature, and to test therapeutics applicable to aquaculture. This experimental model has been informative on various approaches of disease control, including the development of a promising vaccine. In addition to improving fish health in salmon farming in North America, this L. salmonae model will be applicable to other microsporidial diseases that may be encountered in emerging aquaculture regions.

Book chapter↗

Cumulative effects of multiple stressors on marine mammals: Elephant seals as a model system

Noise exposure is a potential stressor for free-ranging marine mammals and is often studied in the absence of other environmental factors. Here, a multi-investigator, interdisciplinary effort was undertaken to examine the response of elephant seals to multiple stressors. An integrated physiological and ecological approach was taken, including immunology, stress physiology, toxicology, animal behavior, population biology, and life history theory, to examine the cumulative effects of exposure to multiple stressors in elephant seals. While we measured the response of individual animals, a population response can be predicted by incorporating these results into the long-term data on elephant seal demographics.

Book chapter↗