Abstract
Online visualization and query of massive geo-spatial data are facing increasing challenges with the explosive growth of location-based spatial datasets. In the practical scenario, online visualization is carried out in a progressive way, namely, a sketchy view map is first presented, and more detailed view maps are produced gradually as the viewport scale goes deeper. One approach is to use the multi-scale spatial index technique. However, it loses the original data attribute and cannot provide spatial statistics information. The paper is to provide an improved index structure, the Geo-Gap tree, which aims to enhance online interactive access to large spatial datasets, as well as enable one to compute statistical attributes like aggregation at the coarse level. Therefore, the first focus of Geo-Gap tree is improving the efficiency of tree building. For this purpose, an adaptive geohash coding is introduced to reduce the computing of neighboring objects. And, this phase can be improved in parallel once objects are partitioned. Compare to Gap tree, the cost of building the Geo-Gap tree can be greatly reduced. The second contribution is to choose data at different level based on sampling so that a sample for each level can be served as a progressive query result. The third contribution is an estimation of progressive query results, which ensure that progressive query accuracy can be controlled within the range of theoretical analysis. With the query continuing to execute, the query results become more and more accurate. The method is now integrated successfully into a high-performance geographic information system called HiGIS.
Highlights
The fast development of data acquisition tools led to the explosion in the data volume, making researchers and engineers heavily dependent on statistical analysis and visualization tools to investigate inner patterns of massive spatial information [1]
In order to provide both the multi-scale visualization ability for online mapping and real-time aggregation ability for online analysis, this paper proposes a novel index structure: the Geo-Gap tree, which is based on the gap tree and enhanced by the idea of geohash coding
DETAILED DESIGN OF THE GEO-GAP TREE The gap tree is a hierarchical structure that supports access to spatial datasets and realizes progressive visualization, but it suffers from some drawbacks
Summary
The fast development of data acquisition tools led to the explosion in the data volume, making researchers and engineers heavily dependent on statistical analysis and visualization tools to investigate inner patterns of massive spatial information [1]. Taking the typical current online application in China as an example, maximum location requests to Baidu Map in one day have been more than 23 billion times. Didi Chuxing, a famous mobile transportation platform in China, offers more than 1000 car requirements per second, 9 billion routing requests in a day. In this context, thousands of retrieval per second, such as traffic situation and available vehicles, place higher demands on the concurrent load and response time of spatial query processing [2]–[5]. The user experience of online query and analysis requires a faster response.
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