A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States

Numerous organizations regularly produce enormous volumes of geospatial data due to the widespread use of sensors and location-based services. However, traditionally collecting, storing, managing, exploring, analyzing, and visualization of geospatial data has been a complex and time-consuming task. This study proposed a big data analytics approach to collect, store, manage, explore, process, and analyze massive amounts of geospatial data. A comprehensive literature review, various Python libraries for geospatial big data, challenges in geospatial big data analytics, and big data analytics techniques such as spatial clustering, spatial regression analysis, and spatial-temporal analysis, were presented. In addition, geospatial big data analytics algorithms like K-means clustering, ordinary least squares (OLS), geographically weighted regression (GWR), Spatio-temporal clustering algorithms, Spatio-temporal regression models, and others were discussed. Finally, case studies on performing geospatial big data analytics using Pyspark were addressed.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States

Cited by 2 publications

References 56 publications

Dissimilarity-Based Clustering with Soft Proximity Constraints

Dissimilarity-Based Clustering with Soft Proximity Constraints

Big Data Analytics for Geospatial Application Using Python

Contact Info

Product

Resources

About