Interactive analysis of big geospatial data with high‐performance computing: A case study of partisan segregation in the United States

Abstract

Increasingly, GIS workflows need to be able to support the processing and visualization of very large datasets.Massive geospatial datasets have become increasingly common, coming from satellite and ground sensors, commercial transactions, social media, online publications, and so forth (Goodchild, 2016). Desktop GIS systems have limited capacity for dealing with big data challenges. Examples of these challenges include the following: unusual data forms, the processing of streaming data, parallel computing, and interactive visualization (Yue & Jiang, 2014). This article focuses on developing approaches to interactively analyze geospatial big data and demonstrate with a case study. The case study involved using a detailed voter dataset to calculate partisan segregation down

A Big Data Grided Organization and Management Method for Cropland Quality Evaluation

“…Similarly, Kazemi et al [18] utilized a multicriteria decision analysis approach, implementing ArcGIS's weighted overlay analysis to superimpose digital layers for the evaluation of soil quality classes in northeastern Iran. However, as the number of indicators and the volume of data increase, the performance of data-processing capacity of traditional evaluation techniques experiences a significant decline [19,20].…”