Abstract
Arable land quality (ALQ) data are a foundational resource for national food security. With the rapid development of spatial information technologies, the annual acquisition and update of ALQ data covering the country have become more accurate and faster. ALQ data are mainly vector-based spatial big data in the ESRI (Environmental Systems Research Institute) shapefile format. Although the shapefile is the most common GIS vector data format, unfortunately, the usage of ALQ data is very constrained due to its massive size and the limited capabilities of traditional applications. To tackle the above issues, this paper introduces LandQv2, which is a MapReduce-based parallel processing system for ALQ big data. The core content of LandQv2 is composed of four key technologies including data preprocessing, the distributed R-tree index, the spatial range query, and the map tile pyramid model-based visualization. According to the functions in LandQv2, firstly, ALQ big data are transformed by a MapReduce-based parallel algorithm from the ESRI Shapefile format to the GeoCSV file format in HDFS (Hadoop Distributed File System), and then, the spatial coding-based partition and R-tree index are executed for the spatial range query operation. In addition, the visualization of ALQ big data with a GIS (Geographic Information System) web API (Application Programming Interface) uses the MapReduce program to generate a single image or pyramid tiles for big data display. Finally, a set of experiments running on a live system deployed on a cluster of machines shows the efficiency and scalability of the proposed system. All of these functions supported by LandQv2 are integrated into SpatialHadoop, and it is also able to efficiently support any other distributed spatial big data systems.
Highlights
Arable land is a foundational resource for national food security
The key technologies mentioned above are tested with the national arable land quality (ALQ) big data
This paper presents LandQv2, which is a MapReduce-based parallel processing system for arable land quality (ALQ) big data that uses the Hadoop cloud computing platform
Summary
Arable land is a foundational resource for national food security. Arable land quality (ALQ) data are about the quality evaluation of arable land by classification and gradation. In 2013, the government formed a relatively complete and large arable land quality dataset of approximately 2.51 TB in the ESRI shapefile format [3]. To make such an enormous amount of data readily available requires a management information system with high-performance computing that will play a crucial role in offering accessibility for the government to make scientific decisions and ensuring valuable spatial data safety, readily, and practically. LandQv2 (version 2) is a MapReduce-based parallel processing system that lays out the necessary infrastructure to store, index, query, and visualize the nation’s arable land quality (ALQ) big data. All these functions, supported by LandQv2, are integrated into SpatialHadoop [7], which is a very successful framework for spatial big data, and it is able to efficiently support any other distributed big spatial data systems
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