Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective. (31st August 2020) - de Carvalho Castro, João Pedro; Chaves Carniel, Anderson; Dutra de Aguiar Ciferri, Cristina
Summary: Spatial analytics systems (SASs) represent a technology capable of managing huge volumes of spatial data using frameworks such as Apache Hadoop and Apache Spark. An increasing number of SASs have been proposed, requiring a comparison among them. However, existing comparisons in the literature provide a system‐centric view based on performance evaluations. Thus, there is a lack of comparisons based on the user‐centric view, that is, comparisons that help users to understand how the characteristics of SASs are useful to meet the specific requirements of their spatial applications. In this article, we provide a user‐centric comparison of the following SASs based on Hadoop and Spark: Hadoop‐GIS, SpatialHadoop, SpatialSpark, GeoSpark, GeoMesa Spark, SIMBA, LocationSpark, STARK, Magellan, SparkGIS, and Elcano. This comparison employs an extensive set of criteria related to the general characteristics of these systems, to the aspects of spatial data handling, and to the aspects inherent to distributed systems. Based on this comparison, we introduce guidelines to help users to choose an appropriate SAS. We also describe two case studies based on real‐world applications to illustrate the use of these guidelines. Finally, we discuss chronological tendencies related to SASs and identify limitations that SASs should address to improve user experience.