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Inferring the home locations of Twitter users based on the spatiotemporal clustering of Twitter data

机译：基于推特数据的时空群集推断推特用户的家庭位置

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Abstract >Residential locations play an important role in understanding the form and function of urban systems. However, it is impossible to release this detailed information publicly, due to the issue of privacy. The rapid development of location‐based services and the prevalence of global position system (GPS)‐equipped devices provide an unprecedented opportunity to infer residential locations from user‐generated geographic information. This article compares different approaches for predicting Twitter users' home locations at a precise point level based on temporal and spatial features extracted from geo‐tagged tweets. Among the three deterministic approaches, the one that estimates the home location for each user by finding the weighted most frequently visited (WMFV) cluster of that user always provides the best performance when compared with the other two methods. The results of a fourth approach, based on the support vector machine (SVM), are severely affected by the threshold value for a cluster to be identified as the home. </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”> <标题类型=“main”>抽象</ title> >住宅位置在了解城市系统的形式和功能方面发挥着重要作用。但是，由于隐私问题，不可能公开发布此详细信息。基于位置的服务的快速发展和全球位置系统的普遍存在（GPS） - 易用的设备提供了前所未有的机会，可以从用户生成的地理信息推断住宅区。本文基于从地理标记推文中提取的时间和空间特征，比较了预测Twitter用户家庭位置的不同方法。在三种确定性方法中，通过查找该用户的加权最常见的（WMFV）群集估计每个用户的归属位置的归属位置始终提供最佳性能，而其他两种方法相比。基于支持向量机（SVM）的第四种方法的结果受到群集的阈值的严重影响，该群集被识别为家庭。</ 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1期</span><b style="margin: 0 2px;">|</b><span>共16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=Lin Jie&option=202" target="_blank" rel="nofollow">Lin Jie;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Cromley Robert G.&option=202" target="_blank" rel="nofollow">Cromley Robert G.;</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>School of Earth SciencesZhejiang UniversityHangzhou China;</p> <p>Department of GeographyUniversity of ConnecticutStorrs Connecticut;</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 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