This is the solution for the zonal statistics operation of Map Algebra in, at least, ArcGIS and GRASS. For instance, the usual solution for queries that involve (together) raster and vector datasets is to transform the vector dataset into a raster dataset, and then to use a raster algorithm to solve the query. The two models are rarely handled together. Obviously, combining different data models becomes more difficult when dealing with large amounts of data.Īlthough there is a large body of research regarding the size, the analysis, and the heterogeneity of data, in the case of spatial data, in most cases, that research is focused either on the vector model or on the raster model separately. Nowadays, many application areas require the combination of data stored in different formats to run complex analysis. This big increase in the variety, richness, and amount of spatial data has also led to new information demands. ![]() Only taking into account the images acquired by satellites, several terabytes of data are generated each day, and it has been estimated that the archived amount of raster data will soon reach the zettabyte scale. The same phenomenon can be found in raster datasets, where the advances in hardware are responsible for an important increment of the size and the amount of available data. The advance of the digital society is providing a continuous growth of the amount of available vector data, but the appearance of cheap devices equipped with GPS, like smartphones, is responsible for a big data explosion, mainly of trajectories of moving objects. When dealing with spatial data, depending on the particular characteristics of the type of information, it may be more appropriate to represent that information (at the logical level) using either a raster or a vector data model. įunding: This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 690941 from the Ministerio de Ciencia, Innovación y Universidades (PGE and ERDF) grant numbers TIN2016-78011-C4-1-R TIN2016-77158 C4-3-R RTC-2017-5908-7 from Xunta de Galicia (co-founded with ERDF) grant numbers ED431C 2017/58 ED431G/01 IN852A 2018/14 and University of Bío-Bío grant numbers 192119 2/R 195119 GI/VC.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All files will be available from the following URL. ![]() Received: JanuAccepted: DecemPublished: January 10, 2020Ĭopyright: © 2020 Silva-Coira et al. University of Missouri Kansas City, UNITED STATES Citation: Silva-Coira F, Paramá JR, Ladra S, López JR, Gutiérrez G (2020) Efficient processing of raster and vector data.
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