Next Article in Journal
Evaluating the Suitability of Urban Expansion Based on the Logic Minimum Cumulative Resistance Model: A Case Study from Leshan, China
Previous Article in Journal
A Low-Altitude Flight Conflict Detection Algorithm Based on a Multilevel Grid Spatiotemporal Index
Previous Article in Special Issue
Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge Representation
Open AccessArticle

High-Performance Overlay Analysis of Massive Geographic Polygons That Considers Shape Complexity in a Cloud Environment

State Key Laboratory for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Information Center, Department of Land and Resources of Yunnan Province, Kunming 650504, China
Department of Geoinformation Science, Kunming University of Science and Technology, Kunming 650504, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(7), 290;
Received: 24 March 2019 / Revised: 19 June 2019 / Accepted: 24 June 2019 / Published: 26 June 2019
(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)
PDF [4004 KB, uploaded 27 June 2019]
  |     |  


Overlay analysis is a common task in geographic computing that is widely used in geographic information systems, computer graphics, and computer science. With the breakthroughs in Earth observation technologies, particularly the emergence of high-resolution satellite remote-sensing technology, geographic data have demonstrated explosive growth. The overlay analysis of massive and complex geographic data has become a computationally intensive task. Distributed parallel processing in a cloud environment provides an efficient solution to this problem. The cloud computing paradigm represented by Spark has become the standard for massive data processing in the industry and academia due to its large-scale and low-latency characteristics. The cloud computing paradigm has attracted further attention for the purpose of solving the overlay analysis of massive data. These studies mainly focus on how to implement parallel overlay analysis in a cloud computing paradigm but pay less attention to the impact of spatial data graphics complexity on parallel computing efficiency, especially the data skew caused by the difference in the graphic complexity. Geographic polygons often have complex graphical structures, such as many vertices, composite structures including holes and islands. When the Spark paradigm is used to solve the overlay analysis of massive geographic polygons, its calculation efficiency is closely related to factors such as data organization and algorithm design. Considering the influence of the shape complexity of polygons on the performance of overlay analysis, we design and implement a parallel processing algorithm based on the Spark paradigm in this paper. Based on the analysis of the shape complexity of polygons, the overlay analysis speed is improved via reasonable data partition, distributed spatial index, a minimum boundary rectangular filter and other optimization processes, and the high speed and parallel efficiency are maintained. View Full-Text
Keywords: overlay analysis; shape complexity; massive data; cloud; parallel computing overlay analysis; shape complexity; massive data; cloud; parallel computing

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

Zhao, K.; Jin, B.; Fan, H.; Song, W.; Zhou, S.; Jiang, Y. High-Performance Overlay Analysis of Massive Geographic Polygons That Considers Shape Complexity in a Cloud Environment. ISPRS Int. J. Geo-Inf. 2019, 8, 290.

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