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Three types of spatial dimensions: non-geometric, geometric and mixed spatial dimensions. After (Rivest, Bédard, Proulx & Nadeau, 2003).
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It is recognized that 80% of data have a spatial component (ex. street address, place name, geographic coordinates, map coordinates). Having the possibilities to display data on maps, to compare maps of different phenomena or epochs, and to combine maps with tables and statistical charts allows one to get more insights into spatial datasets. Furthe...
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Context 1
... members of the geometric and mixed spatial dimensions can be displayed on maps using visual variables that relates to the values of the different measures contained in the datacube being analyzed. Figure 3 presents examples of the three types of spatial dimensions. Two types of spatial measures can be defined ( Bédard et al., 2001;Han et al., 1998;Rivest et al., 2001;Stefanovik, 1997;Tchounikine et al., 2005). ...
Citations
... However, challenges still exist, like the substantial neglect of geographic aspects of data engineers and ensuring that there is an integration between specialists in the fields of research to reach a useful final product. GIS works to discover and select data from separate and different databases to arrange them for speed of work and spatial analysis, then the results are visualized to reach the ideal time and the best results [20]. GIS and Big Data play an important and effective role in a variety of areas, ranging from the rapid collection of large multi-source data to the rapid visualization of epidemiological data, spatial tracking, and regional transmission prediction, as well as determining the spatial distribution of risks and choosing the level of balance. ...
In the past few decades, the use of geographic information systems (GIS) was efficient with servers that could handle the amount of data used. However, as geographical big data grows in size and complexity, storing, managing, processing, analyzing, visualizing, and confirming data quality becomes more difficult. Academia, industry, government, and other institutions are increasingly interested in this information. It's known as Big Data. Since that kind of data recently became massive, there was a need to develop methods to deal with big data and analyze it to keep pace with development. In this paper, we review the previous studies that involve both Big Data and GIS in different applications. Moreover, we focus on the field of agriculture, which is considered one of the most important sources of the economy. Produced results in this research area help decision-makers to make sound executive steps to reach better production.
A tessellation-based methodology for interactively analyzing the spatiooral evolution of a dynamic phenomenon ' ice coverage and its characteristics ' using a spatial online analytical processing (OLAP) approach is proposed. The feasibility of the method was tested through a prototype developed in the context of the CanICE project using Canadian Ice Service data and the Egg Code, an international standard for characterizing sea and lake ice. By transforming the standard spatial OLAP vector-based point of view ' aggregating data from instances of evolving features ' into a tessellation-based point of view ' aggregating data from constant spaces with evolving properties ' the proposed solution makes it possible to meet the criteria of interactive multidimensional analysis for dynamic phenomena. The second innovative aspect of the methodology relates to the management of data quality with a spatial OLAP approach.
