HiSpatialCluster: A novel high‐performance software tool for clustering massive spatial points

Chen Yiran; Huang Zhou; Pei Tao; Liu Yu

摘要

Abstract >In the era of big data, spatial clustering is a very important means for geo‐data analysis. When clustering big geo‐data such as social media check‐in data, geotagged photos, and taxi trajectory points, traditional spatial clustering algorithms are facing more challenges. On the one hand, existing spatial clustering tools cannot support the clustering of massive point sets; on the other hand, there is no perfect solution for self‐adaptive spatial clustering. In order to achieve clustering of millions or even billions of points adaptively, a new spatial clustering tool—HiSpatialCluster—was proposed, in which the CFSFDP (clustering by fast search and finding density peaks) idea to find cluster centers and the DBSCAN (density‐based spatial clustering of applications with noise) idea of density‐connect filtering for classification are introduced. The tool’s source codes and other resources have been released on Github, and experimental evaluation was performed through clustering massive taxi trajectory points and Flickr geotagged photos in Beijing, China. The spatial clustering results were compared with those through K‐means and DBSCAN as well. As a spatial clustering tool, HiSpatialCluster is expected to play a fundamental role in big geo‐data research. First, this tool enables clustering adaptively on massive point datasets with uneven spatial density distribution. Second, the density‐connect filter method is applied to generate homogeneous analysis units from geotagged data. Third, the tool is accelerated by both parallel CPU and GPU computing so that millions or even billions of points can be clustered efficiently. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“main”XML：ID =“TGIS12463-ABS-ABS-0001”XML：Lang =“en”> <标题类型=“main”>抽象</ title> >在大数据的时代，空间聚类是地理数据分析的一个非常重要的手段。当聚类大地理数据（如社交媒体检测数据，地理标记照片和出租车轨迹点）时，传统的空间聚类算法面临更多挑战。一方面，现有的空间聚类工具无法支持大量点集的聚类;另一方面，对自适应空间聚类没有完美的解决方案。为了自适应地实现数百万甚至数十亿个点的聚类，提出了一种新的空间聚类工具 - HispatialCluster - 其中CFSFDP（通过快速搜索和查找密度峰值群集的聚类）想法以查找集群中心和DBSCAN（密度 - 介绍了基于噪声的空间聚类）噪声的思想思想 - 连接滤波的分类。该工具的源代码和其他资源已在GitHub上发布，通过在北京，中国北京的大规模出租车轨迹点和Flickr地理标记照片进行实验评估。将空间聚类结果与通过K-means和DBSCAN进行比较。作为空间聚类工具，预计HispatialCluster将在大地理数据研究中发挥基本作用。首先，此工具使得能够自适应地在具有不均匀的空间密度分布的大点数据集上进行聚类。其次，施加密度连接滤波器方法以产生从地理标记数据的同质分析单元。第三，该工具通过并行CPU和GPU计算加速，因此可以有效地聚集数百万甚至数十亿个点。</ p> </ abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-31674/'>《Transactions in GIS: TG》</a> <b style="margin: 0 2px;">|</b><span>2018年第5期</span><b style="margin: 0 2px;">|</b><span>共24页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Chen Yiran&option=202" target="_blank" rel="nofollow">Chen Yiran;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Huang Zhou&option=202" target="_blank" rel="nofollow">Huang Zhou;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Pei Tao&option=202" target="_blank" rel="nofollow">Pei Tao;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Liu Yu&option=202" target="_blank" rel="nofollow">Liu Yu;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Institute of Remote Sensing and Geographical Information SystemsPeking UniversityBeijing China;</p> <p>Institute of Remote Sensing and Geographical Information SystemsPeking UniversityBeijing China;</p> <p>State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographical Sciences and Natural Resources Research Chinese Academy of SciencesBeijing China;</p> <p>Institute of Remote Sensing and Geographical Information SystemsPeking UniversityBeijing China;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/4940.html" title="测绘数据库与信息系统">测绘数据库与信息系统;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> </p> </li> </ul> </div> </div> <div class="literature cardcommon"> <div class="similarity "> <h3 class="all_title" id="enpatent66">相似文献</h3> <div class="similaritytab clearfix"> <ul> <li class="active" >外文文献</li> <li >中文文献</li> <li >专利</li> </ul> </div> <div class="similarity_details"> <ul > <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/journal-foreign-detail/0704024965737.html">HiSpatialCluster: A novel high‐performance software 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