Towards Estimating Urban Population Distributions from Mobile Call Data

Kang, Chaogui; Liu, Yu; Ma, Xiaolong; Wu, Lun

doi:10.1080/10630732.2012.715479

Cited by 103 publications

(70 citation statements)

References 42 publications

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“…令数据研究城市人口分布 [7] 、空间结构 [8] 、商圈影响力、居民出行距离等 [9] ；利用出租车 GPS 数据预测拥堵地点 [10] ；利用居民活动 GPS 数据分析城郊居民日常活动时空特征 [11] ；利用社交网络位置数据和签到信息研究城市用地功能与混合度 [12] 、城市发展边界 [13] 、城市活动区域划分 [14] 、城市网络信息空间结构 [15] ；利用百度指数研究区域城市网络特征 [16] ；利用百度、高德迁徙数据研究城镇体系、居民黄金周旅游行为等 [17] ；利用大众点评数据研究餐饮业格局 [18] 、餐厅选址 [19] 等；利用全国 PM2.5 监测的在线数据研究 PM2.5 污染分布 …”

Section: 龙瀛刘伦unclassified

Four Transformations of Chinese Quantitative Urban Research in the New Data Environment

Long¹,

Liu²

2017

UPI

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Section: 龙瀛刘伦unclassified

Four Transformations of Chinese Quantitative Urban Research in the New Data Environment

Long¹,

Liu²

2017

UPI

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“…Netflow was used as an indicator of human convergence and divergence in a grid cell during time slot Tj. Compared to the call activity of CDRs, which reflects activity intensity, netflow reflects the difference in inflow and outflow, which indicates the change in the number of people in a cell during a time slot [42]. A positive netflow indicates that the number of people in the grid cell increased during the time slot, i.e., convergence, and a negative netflow indicates a decreasing number of people, i.e., divergence.…”

Section: Classification Of Human Convergence and Divergence Using Quamentioning

confidence: 99%

Understanding Spatiotemporal Patterns of Human Convergence and Divergence Using Mobile Phone Location Data

Yang

Fang

et al. 2016

IJGI

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Investigating human mobility patterns can help researchers and agencies understand the driving forces of human movement, with potential benefits for urban planning and traffic management. Recent advances in location-aware technologies have provided many new data sources (e.g., mobile phone and social media data) for studying human space-time behavioral regularity. Although existing studies have utilized these new datasets to characterize human mobility patterns from various aspects, such as predicting human mobility and monitoring urban dynamics, few studies have focused on human convergence and divergence patterns within a city. This study aims to explore human spatial convergence and divergence and their evolutions over time using large-scale mobile phone location data. Using a dataset from Shenzhen, China, we developed a method to identify spatiotemporal patterns of human convergence and divergence. Eight distinct patterns were extracted, and the spatial distributions of these patterns are discussed in the context of urban functional regions. Thus, this study investigates urban human convergence and divergence patterns and their relationships with the urban functional environment, which is helpful for urban policy development, urban planning and traffic management.

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“…Similarly, Toole et al (2012) used such data to discover urban land uses, while Kang et al (2012) explored the correlation between mobile phone activity and resident population. A critical view on these concepts can be found in Resch et al (2012).…”

Section: Urban Dynamicsmentioning

confidence: 99%

Mobile Phone Data and Urban Analysis: An Exploratory Space Time Study

Tranos¹,

Steenbruggen²,

Nijkamp³

2013

SSRN Journal

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Towards Estimating Urban Population Distributions from Mobile Call Data

Cited by 103 publications

References 42 publications

Four Transformations of Chinese Quantitative Urban Research in the New Data Environment

Four Transformations of Chinese Quantitative Urban Research in the New Data Environment

Understanding Spatiotemporal Patterns of Human Convergence and Divergence Using Mobile Phone Location Data

Mobile Phone Data and Urban Analysis: An Exploratory Space Time Study

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