GeohashTile: Vector Geographic Data Display Method Based on Geohash

Zhou, Changjiu; Lu, Hanqing; Xiang, Yong; Wu, Jingbang; Wang, Rui

doi:10.3390/ijgi9070418

Cited by 15 publications

(6 citation statements)

References 23 publications

(38 reference statements)

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“…Geohash encoding converts latitude and longitude into a set of binary strings and then crosses the two sets of strings bit by bit to generate a new set of binary strings (Zhou et al, 2020), converting two‐dimensional spatial queries into one‐dimensional string matching. With this advantage, geohash can achieve fast queries with time complexity of O(1).…”

Section: Knn Calculation Methodsmentioning

confidence: 99%

Interactive analysis of big geospatial data with high‐performance computing: A case study of partisan segregation in the United States

Kakkar

Lewis

Guan

2022

Transactions in GIS

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Increasingly, GIS workflows need to be able to support the processing and visualization of very large datasets.Massive geospatial datasets have become increasingly common, coming from satellite and ground sensors, commercial transactions, social media, online publications, and so forth (Goodchild, 2016). Desktop GIS systems have limited capacity for dealing with big data challenges. Examples of these challenges include the following: unusual data forms, the processing of streaming data, parallel computing, and interactive visualization (Yue & Jiang, 2014). This article focuses on developing approaches to interactively analyze geospatial big data and demonstrate with a case study. The case study involved using a detailed voter dataset to calculate partisan segregation down

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Section: Knn Calculation Methodsmentioning

confidence: 99%

Interactive analysis of big geospatial data with high‐performance computing: A case study of partisan segregation in the United States

Kakkar

Lewis

Guan

2022

Transactions in GIS

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“…In the field of spatial data partitioning,Geohash encoding and space-filling curves are two effective methods for dimensionality reduction partitioning of data. The literature [2,3] used Geohash strings as partitioning keys to partition and index large-scale geographic data, which effectively improved the retrieval efficiency of data. The literature [4] constructs a multi-level spatio-temporal grid index based on Z-curves, but fails to take into account the sparse and uneven distribution of trajectory data.…”

Section: Big Data Storagementioning

confidence: 99%

Trajectory visualization based on multi-level spatio-temporal data organization model

Yao

Zhang

et al. 2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

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Current trajectory data grows rapidly in a dynamic and streaming form, and unreasonable data organization causes the problems of skewed data storage and high overhead, as well as slow retrieval speed and page lag during visualization. To achieve effective spatial data organization, this paper proposes a data storage model with multi-level spatio-temporal organization. Spatially, the trajectory data is partitioned based on Hilbert curve, combined with pre-partitioning mechanism to solve the storage skewing problem of distributed database HBase; temporally, borrowing from the organization of spatio-temporal cube, the spatio-temporal hybrid coding is constructed by using the method of slicing by day and minute system coding to solve the retrieval of trajectory data into maps. The experiment proves that the organization model can effectively improve the data storage and retrieval efficiency, enhance the overall effect of trajectory visualization, and provide effective technical support for data mining and analysis.

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“…Firstly, we unpack the map of the sea area that the fishing boat passes through into rectangular grids by using the Geohash algorithm [42,43], and the size of the divided rectangular grids is determined by the character coding length of Geohash. Based on the size of the fishing boat, we choose the character length of 7 to determine the grid area size, and the area size determined by the Geohash algorithm is 153 m × 153 m. The coded fishing ground area is composed of a series of numbered grids, and the trajectory points where fishing boats are composed of these numbers.…”

Section: Regional Gridding Of Fishing Ground and Vectorization Of Fis...mentioning

confidence: 99%

The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea

Xing

Zhang

Liu³

et al. 2023

JMSE

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The goal of this paper is to strengthen the supervision of fishing behavior in the East China Sea and effectively ensure the sustainable development of fishery resources. Based on AIS data, this paper analyzes three types of fishing boats (purse seine operation, gill net operation and trawl operation) and uses the cubic spline interpolation algorithm to optimize the ship trajectory and construct high-dimensional features. It proposes a new coding method for fishing boat trajectory sequences. This method uses the Geohash algorithm to divide the East China Sea into grids and generate corresponding numbers. Then, the ship trajectory is mapped to the grid, the fishing boat trajectory points are associated with the divided grid, and the ship trajectory ID is extracted from the corresponding grid. The extracted complete trajectory sequence passes through the CBOW (continuous bag of words) model, and the correlation of trajectory points is fully learned. Finally, the fishing boat trajectory is converted from coordinate sequence to trajectory vector, and the processed trajectory sequence is trained by the LightGBM algorithm. In order to obtain the optimal classification effect, the optimal superparameter combination is selected. We put forward a LightGBM algorithm based on the Bayesian optimization algorithm, and obtained the classification results of three kinds of fishing boats. The final result was evaluated using the F1_score. Experimental results show that the F1_score trained with the proposed trajectory vectorization method is the highest, with a training accuracy of 0.925. Compared to XgBoost and CatBoost, the F1_score increased by 1.8% and 1.2%, respectively. The results show that this algorithm demonstrates strong applicability and effectiveness in fishery area evaluations and is significant for strengthening fishery resource management.

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GeohashTile: Vector Geographic Data Display Method Based on Geohash

Cited by 15 publications

References 23 publications

Interactive analysis of big geospatial data with high‐performance computing: A case study of partisan segregation in the United States

Interactive analysis of big geospatial data with high‐performance computing: A case study of partisan segregation in the United States

Trajectory visualization based on multi-level spatio-temporal data organization model

The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea

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