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Exploring the effectiveness of location‐based social media in modeling user activity space: A case study of Weibo

机译：探索基于位置的社交媒体在建模用户活动空间中的有效性 - 以微博案例研究

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Abstract >Location‐based social media (LBSM) has been widely utilized to supplement traditional survey methods in modeling human activity patterns. However, there has not been sufficient study to assess the reliability of these data in deriving human movement. This research aims to evaluate how data collection duration and sample sizes affect the reliability of LBSM data in activity modeling based on two indicators: radius of gyration (ROG) and entropy. We use a linear regression model with logarithmic transformation to approximate how the magnitude of each indicator changes with different data collection durations—from 1 to 12 months. The results indicate that both ROG and entropy increase when the amount of data increases. However, the rate of increase slows down and approaches zero eventually. We also approximated the limit values and verified that with 12‐month data, we are at approximately 95% magnitude of the limit values for both indicators in all three cities. The clustering analysis also demonstrated that there are outlier users who exhibit distinct patterns. This case study focuses on three Chinese cities (Beijing, Shanghai, and Guangzhou) and provides a useful reference to explore the balance point between data effectiveness and an appropriate sample size from LBSM data. </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 =“TGIS12450-ABS-ABS-0001”XML：Lang =“en”> <标题类型=“main”>抽象</ title> >基于位置的社交媒体（LBSM）已广泛用于补充传统的调查方法在建模人类活动模式中。然而，还没有足够的研究来评估这些数据在衍生人类运动方面的可靠性。本研究旨在评估数据收集持续时间和样本大小如何影响基于两个指示器的活动建模中LBSM数据的可靠性：旋转半径（罗格）和熵。我们使用具有对数转换的线性回归模型来近似每个指示符的大小如何随不同的数据收集持续时间而变化 - 从1到12个月。结果表明，当数据量增加时，罗格和熵都会增加。然而，增加速率下降并最终接近零。我们还将极限值近似并验证，使用12个月的数据，我们大约是＆所有三个城市的指标的极限值为95％。聚类分析还证明存在具有不同模式的异常用户用户。本案例研究侧重于三个中国城市（北京，上海和广州），并提供有用的参考，探讨数据效率与LBSM数据的适当样本大小之间的平衡点。</ 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4期</span><b style="margin: 0 2px;">|</b><span>共28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=Yuan Yihong&option=202" target="_blank" rel="nofollow">Yuan Yihong;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Wang Xujiao&option=202" target="_blank" rel="nofollow">Wang Xujiao;</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>Department of GeographyTexas State UniversitySan Marcos Texas;</p> <p>Department of GeographyTexas State UniversitySan Marcos Texas;</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 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