Figure 3- - uploaded by Junfu Fan
Content may be subject to copyright.
Source publication
On buffer zone construction, the rasterization-based dilation method inevitably introduces errors, and the double-sided parallel line method involves a series of complex operations. In this paper, we proposed a parallel buffer algorithm based on area merging and MPI (Message Passing Interface) to improve the performances of buffer analyses on proce...
Contexts in source publication
Context 1
... -For polygon object, a polygon denotes a plane-shaped area enclosed by a group of closed polylines. As shown in Figure 3-(a), an enclosed polyline is also called a ring, which can be divided into an interior ring and an exterior ring according to the strike of the points constituting the ring. A simple polygon only contains one exterior ring and several interior rings, and a polygon that contains several exterior rings is called a multi-polygon. ...
Context 2
... instance, in creation of a bilateral buffer, the rules are as follows: the exterior ring created from the input polygon's exterior ring is reserved; the interior ring created from the polygon's interior ring is reserved; and other rings are deleted. Figure 3-(b) shows that the input polygon was composed of an exterior ring (R 0 ) and an interior ring (R 1 ), and all of the rings were split up at the starting point/end point. A buffer was created for each ring by using the buffer creation algorithm for polyline resulting in 4 rings (R 0 ', R 0 '', R 1 ' and R 1 '') ( Figure 3- (c)). ...
Context 3
... 3-(b) shows that the input polygon was composed of an exterior ring (R 0 ) and an interior ring (R 1 ), and all of the rings were split up at the starting point/end point. A buffer was created for each ring by using the buffer creation algorithm for polyline resulting in 4 rings (R 0 ', R 0 '', R 1 ' and R 1 '') ( Figure 3- (c)). Based on the conservation rule for result buffer polygon rings, the R 0 ' exterior ring created from the R 0 exterior ring as well as the R 1 '' interior ring created from the R 1 interior ring were conserved, while R 0 '' and R 1 ' were deleted. ...
Context 4
... on the conservation rule for result buffer polygon rings, the R 0 ' exterior ring created from the R 0 exterior ring as well as the R 1 '' interior ring created from the R 1 interior ring were conserved, while R 0 '' and R 1 ' were deleted. Finally, a result polygon was created as indicted by the shadow-filled region enclosed by the real line ( Figure 3-(d)). The buffer of a multi-polygon can be created by dissolving the buffers of simple polygons. ...
Similar publications
p>Data mining is a combination technology for analyze a useful information from dataset using some technique such as classification, clustering, and etc. Clustering is one of the most used data mining technique these day. K-Means and K-Medoids is one of clustering algorithms that mostly used because it’s easy implementation, efficient, and also pre...
In recent times, geospatial datasets are growing in terms of size, complexity and heterogeneity. High performance systems are needed to analyze such data to produce actionable insights in an efficient manner. For polygonal a.k.a vector datasets, operations such as I/O, data partitioning, communication, and load balancing becomes challenging in a cl...
Citations
... Buffer is a zone around a map feature measured in units of distance or time (Fan et al., 2014). ...
Indicator of risk of gender-based violence in Medellin's road network is a project through which I hope to contribute to the protection of women in Medellin using geospatial, statistical and optimization techniques.
... This method has successfully been applied in three-dimensional (3D) digital earth. [18] proposed "a parallel buffer algorithm to improve the performances of buffer analyses on processing large datasets," which was based on area merging and massage passing interface. But, in the algorithm, the relationship of adjacent vector features was not considered. ...
... The mathematical models can be referenced to Eq. (16) -Eq. (18). The implementations can be carried out by: Step 1: Extraction of the polygon boundary, and initialization of the boundary line. ...
The existing buffers algorithms cannot effectively to meet the demands of high accuracy of buffer analysis in practice although many efforts have been made in the past 60 years. A generalized buffering algorithm (GBA) is presented, which considers the geometric distance and the attribute characteristics of all instances within buffer zone. The proposed algorithm includes three major steps: (1) select and initialize target instance; (2) determine buffer boundary points through mining homogeneous pattern; (3) “smoothly” connect buffer boundary points to generate the generalized buffer zone. The details for the generations of the generalized point buffer (GPIB) zone, the generalized line buffer (GLB) zone, and the generalized polygon buffer (GPLB) zone are discussed. Two dataset are used to validate the performances of the proposed GBA. Six parameters are applied as indexes to evaluate the proposed algorithm. The experimental results discovered that (1) the GBA is close to the tradition buffering algorithm (TBA) when the angle increment (Δφ) in GPIB, line increment (ΔL) in GLB, and arc length increment (ΔS) in GPLB approach to zero, respectively; (2) the proposed GBA can accurately reflect the real situation of the buffering zone, and improve the deficiency and accuracy of TBA in real application.
... A split-and-merge paradigm for spatial data processing was proposed by Guan, Wu, and Li (2012), and they also presented a robust parallel framework in a cluster environment to support this paradigm. Some researchers have proposed a parallel buffer algorithm based on area merging and MPI (Message Passing Interface) to improve the performance of buffer analysis on processing large datasets (Fan, Ji, Gu, & Sun, 2014). Fan also presented a rasterization computing-based parallel vector polygon overlay analysis algorithm using OpenMP and MPI, extended by a rasterization-based polygon clipping algorithm (Fan et al., 2018). ...
With the increasing volume of spatial data, conventional vector data buffer analysis algorithms cannot meet the demands of fast data processing, so parallel computing is introduced to accelerate vector data analysis. However, it is difficult for these GIS platforms to adopt parallel approaches to conduct buffer analysis due to their conventional data format and vector buffer analysis algorithms. To address the problem, a universal parallel scheduling approach to buffer analysis on conventional GIS platforms is proposed in this study. First, the key impacting factor of buffer analysis computing time is identified. The relationship between the impacting factor and the buffer analysis computing time is analyzed to generate computational intensity transformation functions. Then, computational intensity grids (CIGs) of polyline and polygon are constructed by partially computing the computational intensity of a lattice. Using the corresponding CIGs, a two‐stage adaptive spatial decomposition method for parallel buffer analysis is developed. Firstly, the whole domain of the spatial vector dataset is divided into sub‐domains, with the computational intensity of each sub‐domain being equal as far as possible; secondly, the features are evenly assigned within the sub‐domains into parallel buffer analysis tasks for load balance. The experiments demonstrate that the approach presented in this article can effectively evaluate and spatially represent the computational intensity of buffer analysis for polylines and polygons. Compared with typical spatial adaptive decomposition methods and regular domain decomposition methods, the new approach accomplishes greater balanced decomposition of computational intensity for parallel buffer analysis and achieves near‐linear speedups. The new approach achieves excellent performance on three conventional GIS platforms, indicating that our approach is an effective parallel scheduling approach to vector data buffer analysis for conventional GIS platforms.
... A split-and-merge paradigm for spatial data processing was proposed by Guan, Wu, and Li (2012), and they also presented a robust parallel framework in a cluster environment to support this paradigm. Some researchers have proposed a parallel buffer algorithm based on area merging and MPI (Message Passing Interface) to improve the performance of buffer analysis on processing large datasets (Fan, Ji, Gu, & Sun, 2014). Fan also presented a rasterization computing-based parallel vector polygon overlay analysis algorithm using OpenMP and MPI, extended by a rasterization-based polygon clipping algorithm (Fan et al., 2018). ...
With the increasing volume of spatial data, conventional vector data buffer analysis algorithms cannot meet the demands of fast data processing, so parallel computing is introduced to accelerate vector data analysis. However, it is difficult for these GIS platforms to adopt parallel approaches to conduct buffer analysis due to their conventional data format and vector buffer analysis algorithms. To address the problem, a universal parallel scheduling approach to buffer analysis on conventional GIS platforms is proposed in this study. First, the key impacting factor of buffer analysis computing time is identified. The relationship between the impacting factor and the buffer analysis computing time is analyzed to generate computational intensity transformation functions. Then, computational intensity grids (CIGs) of polyline and polygon are constructed by partially computing the computational intensity of a lattice. Using the corresponding CIGs, a two-stage adaptive spatial decomposition method for parallel buffer analysis is developed. Firstly, the whole domain of the spatial vector dataset is divided into sub-domains, with the computational intensity of each sub-domain being equal as far as possible; secondly, the features are evenly assigned within the sub-domains into parallel buffer analysis tasks for load balance. The experiments demonstrate that the approach presented in this article can effectively evaluate and spatially represent the computational intensity of
... As shown in line 1-8, for polygon edges we use the same solution adopted for linestring objects. For the areas inside polygon objects, we first use the TmpMBR to find the candidate polygons (line 9) and then judge the spatial relationship between the pixel and each candidate polygon, one by one, until we find the polygon which contains the pixel (line [11][12][13][14][15][16][17][18][19][20][21][22]. We apply the ray-casting algorithm [23] to determine whether a pixel is inside a polygon. ...
Buffer and overlay analysis are fundamental operations which are widely used in Geographic Information Systems (GIS) for resource allocation, land planning, and other relevant fields. Real-time buffer and overlay analysis for large-scale spatial data remains a challenging problem because the computational scales of conventional data-oriented methods expand rapidly with data volumes. In this paper, we present HiBO, a visualization-oriented buffer-overlay analysis model which is less sensitive to data volumes. In HiBO, the core task is to determine the value of pixels for display. Therefore, we introduce an efficient spatial-index-based buffer generation method and an effective set-transformation-based overlay optimization method. Moreover, we propose a fully optimized hybrid-parallel processing architecture to ensure the real-time capability of HiBO. Experiments on real-world datasets show that our approach is capable of handling ten-million-scale spatial data in real time. An online demonstration of HiBO is provided (http://www.higis.org.cn: 8080/hibo).
... Therefore, the performance is limited when processing large-scale spatial data. Developments in parallel computing technologies provide a prerequisite for high-performance buffer generation, and several parallel strategies have been proposed to solve the bottlenecks [5][6][7][8]. ...
... Huang [5] introduced parallel buffer algorithm which consists of a point-based partition method and a binary-union-tree-based buffer boundary-dissolving method. Fan [6] proposed a parallel buffer algorithm based on area merging to improve the performance of buffer analysis on processing large datasets. Wang [17] proposed a parallel buffer generation method based on the arc partition strategy, which achieves a better load balance performance than the point partition strategy. ...
... For each dataset, we generated 5000 tasks through a test program, which requested different buffer tiles at different zoom levels randomly. We analyzed the tile rendering logs, and the experimental results are shown in Figure 8. is based on three optimization methods: split-and-conquer buffer zone generation, vertices-based task decomposition, and tree-like merger [6]. b adopts strategies such as vertices-based task decomposition and tree-like merger [5]. ...
Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.
... Buffer analysis and spatial clipping method are the basic and common spatial operations. The parallel algorithms for buffering (Fan, Ji, Gu, & Sun, 2014) and clipping (Puri & Prasad, 2014 are usually adopted to improve computational efficiency in large-scale data. The Voronoi analysis is a spatial proximity analysis method. ...
Spatial vector data with high-precision and wide-coverage has exploded globally, such as land cover, social media, and other data-sets, which provides a good opportunity to enhance the national macroscopic decision-making, social supervision, public services, and emergency capabilities. Simultaneously, it also brings great challenges in management technology for big spatial vector data (BSVD). In recent years, a large number of new concepts, parallel algorithms, processing tools, platforms, and applications have been proposed and developed to improve the value of BSVD from both academia and industry. To better understand BSVD and take advantage of its value effectively, this paper presents a review that surveys recent studies and research work in the data management field for BSVD. In this paper, we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management. It aims to help interested readers to learn about the latest research advances and choose the most suitable big data technologies and approaches depending on their system architectures. To support them more fully, firstly, we identify new concepts and ideas from numerous scholars about geographic information system to focus on BSVD scope in the big data era. Then, we conclude systematically not only the most recent published literatures but also a global view of main spatial technologies of BSVD, including data storage and organization, spatial index, processing methods, and spatial analysis. Finally, based on the above commentary and related work, several opportunities and challenges are listed as the future research interests and directions for reference.
... Thus, several parallel strategies have been put forward to solve those bottleneck problems. The first strategy is based on the MPI (message passing interface) protocol, which supports the parallel execution of buffer analysis algorithms on HPC (high-performance computing) cluster computers [14][15][16][17] but suffers from weak extensibility when faced with the big spatial data scenarios. The second strategy comes from the distributed high-performance computing framework for big data, MapReduce and Spark, which have recently gained much attention and popularity in the field of geoanalysis. ...
... The solution was applied to a two-node self-build cluster with a dataset containing 200,000 features. Fan [17] proposed a parallel buffer algorithm based on area merging and vertex partitioning to improve the performances of buffer analyses when processing large datasets. Fan implemented the algorithm in the MPI programming model, which is the mainstream architecture in high-performance computing. ...
... We found that space-filling curves have good advantages in terms of the aggregation of neighboring spatial objects when compared with an ordinary grid [14]. Space-filling curves in parallel buffer analysis has not been addressed in recent research [16,17,32], though in his literature [17], Fan presented this topic as a future research direction. Therefore, we present a more efficient partitioning method based on space-filling curves, which essentially contains two steps. ...
The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario, the improvement in the stand-alone environment is limited when considering large-scale mass vector data. Nevertheless, recent parallel dissolved vector buffer algorithms suffer from scalability problems, leaving room for further optimization. At present, the prevailing in-memory cluster-computing framework—Spark—provides promising efficiency for computing-intensive analysis; however, it has seldom been researched for buffer analysis. On this basis, we propose a cluster-computing-oriented parallel dissolved vector buffer generating algorithm, called the HPBM, that contains a Hilbert-space-filling-curve-based data partition method, a data skew and cross-boundary objects processing strategy, and a depth-given tree-like merging method. Experiments are conducted in both stand-alone and cluster environments using real-world vector data that include points and roads. Compared with some existing parallel buffer algorithms, as well as various popular GIS software, the HPBM achieves a performance gain of more than 50%.
... Yao et al. [12] presented a parallel algorithm for buffer analysis based on grid computing that decomposed the computational tasks according to both the map layer and the geographic spatial area. Pang et al. [13] and Fernandez et al. [14] realized parallel computing by dividing and storing related data in the same computing node. ...
Caching can prepare data for computational tasks in advance by tracking the requirements and behaviors of distributed geographical information systems to reduce network latency and improve computational performance. This paper presents an enhanced method to actively cache data for data-intensive computations that considers both data relationships and the timeliness of those relationships. First, the access correlations, the correlation steps and the times of the correlations are computed based on the behaviors of the computational tasks. Because the influence of historically accessed records will decrease gradually over time, only recently accessed records are used. To track changes in the relationships and prevent cache waste problems, each record is given a different age-based weight. A conditional caching probability can then be computed based on the timeliness relationships, which can be used to find the appropriate data to compute simultaneously. Finally, we present several experiments that compare the proposed method with techniques that use other data placement strategies, active caching strategies and passive caching algorithms. The results show that the proposed model has better performance than other algorithms in all respects. In addition, the proposed model results in a lower cache replacement ratio. The experiments with different data sets on different data scales indicate that the proposed algorithm can also be used in large-scale distributed environments.
... Pang [57] adopted the division method of geometry object, dividing different objects to each computing node, but the corresponding overlap operation was not mentioned. Fan [19] also used the idea of data parallel and MPI model to improved the parallel efficiency of buffer analysis in each steps. ...
With the increasing interest in large-scale, high-resolution and real-time geographic information system (GIS) applications and spatial big data processing, traditional GIS is not efficient enough to handle the required loads due to limited computational capabilities.Various attempts have been made to adopt high performance computation techniques from different applications, such as designs of advanced architectures, strategies of data partition and direct parallelization method of spatial analysis algorithm, to address such challenges. This paper surveys the current state of parallel GIS with respect to parallel GIS architectures, parallel processing strategies, and relevant topics. We present the general evolution of the GIS architecture which includes main two parallel GIS architectures based on high performance computing cluster and Hadoop cluster. Then we summarize the current spatial data partition strategies, key methods to realize parallel GIS in the view of data decomposition and progress of the special parallel GIS algorithms. We use the parallel processing of GRASS as a case study. We also identify key problems and future potential research directions of parallel GIS.
