Next Article in Journal
Data Extraction Algorithm for Energy Performance Certificates (EPC) to Estimate the Maximum Economic Damage of Buildings for Economic Impact Assessment of Floods in Flanders, Belgium
Next Article in Special Issue
High-Performance Geospatial Big Data Processing System Based on MapReduce
Previous Article in Journal
Shared Execution Approach to ε-Distance Join Queries in Dynamic Road Networks
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessFeature PaperArticle

LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Authors to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(7), 271;
Received: 25 May 2018 / Revised: 24 June 2018 / Accepted: 6 July 2018 / Published: 10 July 2018
(This article belongs to the Special Issue Distributed and Parallel Architectures for Spatial Data)
PDF [8401 KB, uploaded 10 July 2018]


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. View Full-Text
Keywords: spatial big data; parallel processing; MapReduce; arable land quality (ALQ); GIS spatial big data; parallel processing; MapReduce; arable land quality (ALQ); GIS

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Yao, X.; Mokbel, M.F.; Ye, S.; Li, G.; Alarabi, L.; Eldawy, A.; Zhao, Z.; Zhao, L.; Zhu, D. LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data. ISPRS Int. J. Geo-Inf. 2018, 7, 271.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top