Portraying the spatial dynamics of urban vibrancy using multisource urban big data

Tu, Wei; Zhu, Tingting; Xia, Jizhe; Zhou, Yulun; Lai, Yani; Jiang, Jincheng; Li, Qingquan

doi:10.1016/j.compenvurbsys.2019.101428

Cited by 158 publications

(111 citation statements)

References 57 publications

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“…Recently, urban vibrancy studies have attracted attention not only from urban designers but also from geographers and sociologists [ 7 ]. Researchers from multiple disciplines are commonly focusing on the inner mechanism of urban vibrancy and how to portray vibrancy [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, there are still many questions in urban vibrancy studies that need to be settled. Two main issues have been raised: urban vibrancy portrayal and exploration of determinants [ 7 , 10 ]. Urban vibrancy has rich connotations, and different researchers hold different views on the subject.…”

Section: Introductionmentioning

confidence: 99%

“…Such a situation has created difficulty in determining a good proxy to represent vibrancy. Previous studies have used various spatiotemporally tagged data, including field observations and interview data [ 13 , 14 ], Hand-held GPS tracking data [ 8 ], cellular signaling data [ 7 , 12 ], small catering business data [ 2 , 10 ] and point-of-interest (POI) data [ 15 ], to portray vibrancy. In fact, some of these data are extremely costly or limited by spatial scale, and few of them can clearly depict urban vibrancy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Relationship between Urban Vibrancy and Built Environment: An Empirical Study from an Emerging City in an Arid Region

Zhang

Yang

et al. 2021

IJERPH

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Creating a vital and lively urban environment is an inherent requirement of urban sustainable development, and understanding urban vibrancy is helpful for urban development policy making. The urban vibrancy theory needs more empirical supplementation and more evidence for the effect of the built environment on urban vibrancy. We use multisource urban spatial information data, including real-time population distribution (RPD) data and small catering business (SCB) data; quantitatively measure urban vibrancy; and build a comparative framework to explore the effect of the built environment on the urban vibrancy of a northwestern emerging city in China. The results demonstrate that the two urban vibrancy metrics present a spatial distribution pattern that is high in the south and low in the north areas of the city with significant spatial aggregation. Land-use intensity and diversity have strong positive effects on urban vibrancy but present a different pattern of effects on the two vibrancy measures. The influences on urban vibrancy of distance to the district center and distance to the nearest commercial complex are spatially complementary in the study area, and the effect of accessibility factors is weak. Our findings suggest that a somewhat cautious approach is required in the application of these classical planning theories to Urumqi.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Relationship between Urban Vibrancy and Built Environment: An Empirical Study from an Emerging City in an Arid Region

Zhang

Yang

et al. 2021

IJERPH

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“…5G is mooted to revolutionize urban environments by providing higher capacity communications with lower latency. Mobile connectivity has already changed how we interact with our environment, and the resulting novel, often big, datasets have enabled advances in urban analytics and the science of cities (Chin, Huang, Horn, Kasanicky, & Weibel, 2019;Fan et al, 2018;Li, Gao, Lu, & Zhang, 2019;Li & Goldberg, 2018;Ríos & Muñoz, 2017;Semanjski, Gautama, Ahas, & Witlox, 2017;Tu et al, 2019;Wan et al, 2018;Yuan, Raubal, & Liu, 2012;Zhai, Wu, Fan, & Wang, 2018). Yet, there is a relative lack of analysis of the infrastructure which enables this data collection.…”

Section: Introductionmentioning

confidence: 99%

The importance of spatio-temporal infrastructure assessment: Evidence for 5G from the Oxford–Cambridge Arc

Oughton

Russell

2020

Computers, Environment and Urban Systems

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The roll-out of 5G infrastructure can provide enhanced high capacity, low latency communications enabling a range of new use cases. However, to deliver the improvements 5G promises, we need to understand how to enhance capacity and coverage, at reasonable cost, across space and over time. In this paper, we take a spatiotemporal simulation modeling approach, using industry-standard engineering models of 5G wireless networks, to test how different infrastructure strategies perform under scenarios of uncertain future demand. We use coupled open-source models to analyze a UK growth corridor, a system-of-cities comprising 7 urban areas, known as the Oxford-Cambridge Arc. We find that population growth has a marginal impact on total demand for 5G (up to 15%), as the main factor driving demand is the increase in per user data consumption resulting mainly from video. Additionally, the results suggest only limited justification for deploying 5G based purely on the need for more capacity. Strategies which reuse existing brownfield Macro Cell sites are enough to meet future demand for Enhanced Mobile Broadband, except in the densest urban areas. While spatio-temporal analysis of infrastructure is common in some sectors (e.g. transport, energy and water), there has been a lack of open analysis of digital infrastructure. This study makes a novel contribution by providing an open and reproducible spatiotemporal assessment of different 5G technologies at a time when 5G is starting to roll-out around the world.

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“…Recently, the rapid development of big data technology and mobile information has greatly increased the available methods for urban studies, especially in the area concerning the dynamic spatial and temporal relationships of human behavior and environmental studies [30][31][32]. Mobile phone data are of the most popular datasets among newly available data, as it can provide precise spatial and temporal information, which can be used with precision in urban studies, such as urban structure research [33][34][35], research on accessibility to service facilities [36], and identification of urban functional areas [37].…”

Section: Introductionmentioning

confidence: 99%

Revealing the Correlation between Population Density and the Spatial Distribution of Urban Public Service Facilities with Mobile Phone Data

Shi

Yang

Shen

2020

IJGI

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Some studies have confirmed the association between urban public services and population density; however, other studies using census data, for example, have arrived at the opposite conclusion. Mobile signaling data provide new technological tools to investigate the subject. Based on the data of 20 million 2G mobile phone users in downtown Shanghai and the land use data of urban public service facilities, this study explores the spatiotemporal correlation between population density and public service facilities’ locations in downtown Shanghai and its variation laws. The correlation between individual population density at day vs. night and urban public service facilities distribution was also examined from a dynamic perspective. The results show a correlation between service facilities’ locations and urban population density at different times of the day. As a result, the average population density observed over a long period of time (day-time periodicity or longer) with census data or remote sensing data does not directly correlation with the distribution of public service facilities despite its correlation with public service facilities distribution. Among them, there is a significant spatial correlation between public service facilities and daytime population density and a significant spatial correlation between non-public service facilities and night-time population density. The spatial and temporal changes in the relationship between urban population density and service facilities is due to changing crowd behavior; however, the density of specific types of behavior is the real factor that affects the layout of urban public service facilities. The results show that mobile signaling data and land use data of service facilities are of great value for studying the spatiotemporal correlations between urban population density and service facilities.

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Portraying the spatial dynamics of urban vibrancy using multisource urban big data

Cited by 158 publications

References 57 publications

The Relationship between Urban Vibrancy and Built Environment: An Empirical Study from an Emerging City in an Arid Region

The Relationship between Urban Vibrancy and Built Environment: An Empirical Study from an Emerging City in an Arid Region

The importance of spatio-temporal infrastructure assessment: Evidence for 5G from the Oxford–Cambridge Arc

Revealing the Correlation between Population Density and the Spatial Distribution of Urban Public Service Facilities with Mobile Phone Data

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