Multi-scale analysis of residential behaviour based on UWB indoor positioning system-a case study of retired household in Beijing, China
Multi-scale analysis of residential behaviour based on UWB indoor positioning system-a case study of retired household in Beijing, China
复制标题
基于UWB室内定位系统的居住行为多尺度分析——以北京市退休家庭为例
DOI：
10.1080/13467581.2019.1682000
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发表时间：
2019-09
影响因子：
1.3
通讯作者：
Weixin Huang
中科院分区：
文献类型：
--
作者：
Lijing Yang;Weixin Huang
摘要本文提出了一种基于UWB室内定位系统的数据采集和分析流程和框架，以阐明居住者的日常居住行为。在不同的尺度上，即网格水平，凸多边形水平和房间水平的日常活动进行了分析。在第一层，计算每个1 m*1 m网格中居住者停留的时间，从而获得居住者停留时间较长的区域。在凸多边形层次上，通过分析各区域的属性及其连接权值，明确了空间网络的结构。虽然网络在用户和时间不同时会动态变化，但可以通过分析大量数据来捕获一般模式。在房间层面，这是共同的分析单位，我们得到的房间之间的运动和分析的家庭成员的房间为基础的行为网络。基于三个层次的调查结果，在回归模型中引入了新的变量，发现相互可见性会显著影响家庭的行为。本研究借由介绍高解析度室内定位系统与都市与社会研究中最常用的网路分析法，为居住者室内行为的资料搜集与分析提供一个新的前景。
ABSTRACT This paper proposes a data acquisition and analysis workflow and framework to clarify the daily residential behaviour of occupants based on the UWB indoor positioning system. The daily activities are analysed in different scales, namely grid level, convex polygon level and room level. At the first level, how long occupants stay in each 1 m*1 m grid was calculated, so that areas occupants stay longer was obtained. In the convex polygon level, the structure of the spatial network was clarified through analysis of the properties of each zone and weight of their connections. Although the network dynamically changes when the users and time differ, general patterns can be captured through analysis of large amounts of data. In the room level, which is the common analysing units, we get the movements between rooms and analysed the room-based behavioural networks of family members. Based on the findings in three levels, new variables were introduced in a regression model, and mutual visibility is found to significantly affect the household’s behaviour. By introducing the high-resolution indoor positioning system and the network analysis most used in the urban and social studies, this research provides a new prospect of data collecting and analysis of occupant indoor behaviour.
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DOI：
10.4018/978-1-7998-6713-5.ch008
发表时间：
2021
期刊：
Advances in Human Resources Management and Organizational Development
影响因子：
--
作者：
Yuh-Wen Chen
通讯作者：
Yuh-Wen Chen
影响因子：
1.6
作者：
C. Streeter;D. F. Gillespie
通讯作者：
C. Streeter;D. F. Gillespie
DOI：
10.1007/978-3-319-32010-6_187
发表时间：
2022-05
期刊：
Encyclopedia of Big Data
影响因子：
--
作者：
Magdalena Bielenia-Grajewska
通讯作者：
Magdalena Bielenia-Grajewska
DOI：
10.1249/mss.0b013e3181cea27a
发表时间：
2010-03-01
期刊：
MEDICINE AND SCIENCE IN SPORTS AND EXERCISE
影响因子：
--
作者：
Cosco, Nilda G.;Moore, Robin C.;Islam, Mohammed Z.
通讯作者：
Islam, Mohammed Z.
DOI：
10.1007/978-1-4614-6170-8_110108
发表时间：
2014-09
期刊：
--
影响因子：
--
作者：
Manuela Caiani
通讯作者：
Manuela Caiani