Comparing to traditional cartography, big data geographic information processing is not a simple task at all, it requires special methods and methods. When existing geovisualization systems face millions of data, the zoom function and the dynamical data adding function usually cannot be satisfied at the same time. This research classify the existing methods of geovisualization, then analyze its functions and bottlenecks, analyze its applicability in the big data environment, and proposes a method that combines spatial data structure and iterative calculation on demand. It also proves that this method can effectively balance the performance of scaling and new data, and it is significantly better than the existing library in the time consumption of new data and scaling<br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12213344 |
| Date | 29 April 2020 |
| Creators | Siqi Gu (8779961) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Scalable_Dynamic_Big_Data_Geovisualization_With_Spatial_Data_Structure/12213344 |
Page generated in 0.0021 seconds