GeohashTile: Vector Geographic Data Display Method Based on Geohash

© 2020 MDPI AG. All rights reserved. In the development of geographic information-based applications for mobile devices, achieving better access speed and visual effects is the main research aim. In this paper, we propose a new geographic data display method based on Geohash, namely GeohashTile, to improve the performance of traditional geographic data display methods in data indexing, data compression, and the projection of different granularities. First, we use the Geohash encoding system to represent coordinates, as well as to partition and index large-scale geographic data. The data compression and tile encoding is accomplished by Geohash. Second, to realize a direct conversion between Geohash and screen-pixel coordinates, we adopt the relative position projection method. Finally, we improve the calculation and rendering efficiency by using the intermediate result caching method. To evaluate the GeohashTile method, we have implemented the client and the server of the GeohashTile system, which is also evaluated in a real-world environment. The results show that Geohash encoding can accurately represent latitude and longitude coordinates in vector maps, while the GeohashTile framework has obvious advantages when requesting data volume and average load time compared to the state-of-the-art GeoTile system

A geohash-based index for spatial data management in distributed memory

In Geographical Information System (GIS) fields, the performance of data access is critical for the whole performance of applications. But spatial data access becomes the performance bottleneck of high-tech research and development in GIS. In this study, features are mapped into a distributed memory through distributed hash functions. Then, the Geohash method is adopted to build a distributed spatial index for the distributed memory. At the end of this article, a contrast experiment is carried out between our method and a traditional spatial database. The results show that for complex data operation, our method is dozens faster. To sum it up, through the key technology above, the reading and writing performance of spatial data makes a great progress, which provides a solid technical foundation for high performance geographical computation.EICPCI-S(ISTP)