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Framework for Visualization of GeoSpatial Query Processing by Integrating MongoDB with Spark
Published in Qurban A. Memon, Shakeel Ahmed Khoja, Data Science, 2019
S. Vasavi, P. Vamsi Krishna, Anu A. Gokhale
GeoMongoSpark added geospatial query processing that works as an effective storage and retrieval system. Using shard key during storage and retrieval helped in faster data access. Integrating Spark and MongoDB made us to process spatial query data stored in MongoDB without having to move data into Spark environment. Geohash helped to shard data, thereby improving query performance. When the shard key is appended with a geospatial query, mongos routes the query to a subset of shards in the cluster. Three benchmark datasets are used for experimenting on a variety of queries and two sharding techniques. GeoMongoSpark performance is compared with GeoSpark, SpatialSpark and Stark. Performance is also compared for different k values (10, 20, 30) for k-NN and k-NN join query. Results of geospatial queries are visualized using tableau. Zone sharding proved to be better than Range sharding.
Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China
Published in Geomatics, Natural Hazards and Risk, 2023
Xinxin Guo, Benyong Wei, Guiwu Su, Wenhua Qi, Tengfei Zhang
The data in this study are minute-by-minute mobile signaling data from the GeoHash grid obtained by a third party. The GeoHash grid is a two-dimensional spatial latitude and longitude data grid encoded into a string using an address encoding method. The longer the string is, the more accurate the representation is. In this study, GeoHash encoding has 6-bit precision, indicating a rectangular area of 1.2 * 0.6 km. The mobile signaling data is a count of the number of mobile devices in each GeoHash square in the region range every minute, obtained using several location techniques for the service. If the number of devices in a GeoHash unit varies rapidly within a time slice, large-scale anomalies in the range and period may indicate that an earthquake or other abrupt calamity has happened in the region. And the data have been desensitized and do not affect personal privacy, as required by law.
A spatial multi-scale integer coding method and its application to three-dimensional model organization
Published in International Journal of Digital Earth, 2020
Guangling Lai, Xiaochong Tong, Yongsheng Zhang, Lu Ding, Yinling Sui, Yi Lei, Yong Zhang
2. Spatial code indexing. Spatial code indexing refers to the construction of sequential structures based on spatial grid divisions. These methods are applicable to managing spatial data since they are able to rapidly encode a subject and perform rapid access and relationship calculations for a subject based on similarities between the grid and target (Tong et al. 2013). Typical spatial code indexing methods include regular gridding (Cressie 2015; Freeman 1991), space-filling curves (Sagan 1994), and multilevel grids (Hackbusch 2013; Triebel, Pfaff, and Burgard 2006). Spatial code indexing methods can be further divided into two types according to their coding methods. The first type uses strings or arrays of variable length to encode grids. In this coding method, alphanumeric characters with different bits represent grids with different scales or levels, where the length of a character string or array is used to determine the division depth. However, this coding method requires array cycles and subscript operations, which results in low levels of efficiency when using large amounts of data. An example of this method is multilevel grids (Hackbusch 2013; Triebel, Pfaff, and Burgard 2006). The second type uses numbers with fixed lengths to encode a grid, where external data is used to encode depth or scale. The methods in this category include space-filling curves (Sagan 1994) and regular gridding (Cressie 2015; Freeman 1991). These methods use spatial sequences to determine the coding of single-scale grids while depth and scale information is added separately. Due to the separation of the scale information from code information, both factors must be considered simultaneously when querying spatial regions, which results in higher levels of computational complexity. In this method, the typical algorithms are GeoHash (Yi 2017) and Google S2 (Yi 2017). GeoHash mainly recursively divides one space into two and encodes the space region according to the rules of ‘odd latitude and even longitude.’ Spatial queries are mainly performed by comparing the coincidence degree of the coding prefixes, such that method is mainly applicable to spatial single-scale point queries, such as trajectories and points of interest (Niemeyer 2013; Xiang, Dehao, and Jianya 2017). Google S2 converts two-dimensional codes into one-dimensional codes by projecting Earth into a two-dimensional plane and encoding the two-dimensional space with a Hilbert curve, which finally connects the one-dimensional codes into a plane by covering different intervals. This method has a total of 30 levels and can effectively perform multi-scale organization and region queries. However, there is no three-dimensional form that is applicable to the organization and indexing of three-dimensional models (Perone 2015).