scholarly journals Exploring the Influence of Urban Form on Urban Vibrancy in Shenzhen Based on Mobile Phone Data

2018 ◽  
Vol 10 (12) ◽  
pp. 4565 ◽  
Author(s):  
Lingjun Tang ◽  
Yu Lin ◽  
Sijia Li ◽  
Sheng Li ◽  
Jingyi Li ◽  
...  
Keyword(s):  
Big Data ◽  
Mobile Phone ◽  
Urban Form ◽  
City Planning ◽  
Human Perception ◽  
Urban Morphology ◽  
Mobile Phone Data ◽  
Important Indicator ◽  
Spatial And Temporal Scales ◽  
Functional Zones

Urban vibrancy is an important indicator of the attractiveness of a city and its potential for comprehensive, healthy and sustainable development in all aspects. With the development of big data, an increasing number of datasets can be used to analyse urban vibrancy on fine spatial and temporal scales from the perspective of human perception. In this study, we applied mobile phone data as a proxy for local vibrancy in Shenzhen and constructed a comprehensive framework for the factors that influence urban vibrancy, especially in terms of urban morphology and space syntax. In addition, the popular geographically and temporally weighted regression (GTWR) method was used to explore the spatiotemporal relationships between vibrancy and its influencing factors. The spatial and temporal coefficients are presented through maps. The conclusions of this attempt to study urban vibrancy with urban big data have significant implications for helping urban planners and policy makers optimize the spatial layouts of urban functional zones and perform high-quality city planning.

Privacy will test development actors using 'big data'

2019 ◽  
Keyword(s):  
Big Data ◽  
Mobile Phone ◽  
Test Development ◽  
Data Gathering ◽  
Mobile Phone Data ◽  
Development Projects ◽  
Content Type ◽  
Multiple Challenges ◽  
The Government

Subject Use of 'big data' for welfare projects. Significance Development actors within and outside the government are harnessing ‘big data’ for welfare projects but they face multiple challenges. Impacts Development projects will continue to rely heavily on mobile phone data. Traditional on-the-ground data gathering and surveys remain important. More advanced uses of big data require greater coordination between owners of individual tranches of information.

scholarly journals Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data

2020 ◽  
Vol 9 (11) ◽  
pp. 666
Author(s):  
Chengming Li ◽  
Jiaxi Hu ◽  
Zhaoxin Dai ◽  
Zixian Fan ◽  
Zheng Wu
Keyword(s):  
Big Data ◽  
Mobile Phone ◽  
Smart Cities ◽  
Sampling Rate ◽  
Individual Characteristics ◽  
Mobile Phone Data ◽  
Mobility Pattern ◽  
Individual Mobility ◽  
Short Period ◽  
The Individual

With the arrival of the big data era, mobile phone data have attracted increasing attention due to their rich information and high sampling rate. Currently, researchers have conducted various studies using mobile phone data. However, most existing studies have focused on macroscopic analysis, such as urban hot spot detection and crowd behavior analysis over a short period. With the development of the smart city, personal service and management have become very important, so microscopic portraiture research and mobility pattern of an individual based on big data is necessary. Therefore, this paper first proposes a method to depict the individual mobility pattern, and based on the long-term mobile phone data (from 2007 to 2012) of volunteers from Beijing as part of project Geolife conducted by Microsoft Research Asia, more detailed individual portrait depiction analysis is performed. The conclusions are as follows: (1) Based on high-density cluster identification, the behavior trajectories of volunteers are generalized into three types, and among them, the two-point-one-line trajectory and evenly distributed behavior trajectory were more prevalent in Beijing. (2) By integrating with Google Maps data, five volunteers’ behavior trajectories and the activity patterns of individuals were analyzed in detail, and a portrait depiction method for individual characteristics comprehensively considering their attributes, such as occupation and hobbies, is proposed. (3) Based on analysis of the individual characteristics of some volunteers, it is discovered that two-point-one-line individuals are generally white-collar workers working in enterprises or institutions, and the situation of a single cluster mainly exists among college students and home freelancer. The findings of this study are important for individual classification and prediction in the big data era and can also provide useful guidance for targeted services and individualized management of smart cities.

The Emerging Role of Citizen Science and Geospatial Big Data in Supporting the SDGs

2020 ◽  
Author(s):  
Steffen Fritz
Keyword(s):  
Sustainable Development ◽  
Big Data ◽  
Data Collection ◽  
Mobile Phone ◽  
Citizen Science ◽  
National Level ◽  
Well Being ◽  
Data Sources ◽  
Mobile Phone Data ◽  
Development Goals

<p>In September 2015, the United Nations ratified the 17 Sustainable Development Goals (SDGs), which are comprised of a further 169 targets and 232 indicators for monitoring progress on poverty, well-being and major environmental and socio-economic problems, both nationally and globally. Much of the data used for SDG monitoring comes from censuses, surveys and other administrative data provided by national statistical offices, government agencies and international organizations. However, traditional data collection can be costly and infrequent, and the information can become outdated very quickly. Moreover, reporting is generally at the national level, so spatial variations of indicators within a country are not often available, yet this information is critical for effective spatial planning. Without knowing where issues are occurring in space, we cannot implement targeted solutions. Hence, there is currently a lack of data needed for effective monitoring and implementation of the SDGs.</p><p>Non-traditional data sources such as those arising from citizen science and geospatial big data, e.g., satellite imagery, mobile phone data, social media, etc. are part of the current ‘data revolution’, all of which have potential use in SDG monitoring and implementation. This lecture will provide an overview of these new and emerging non-traditional data sources in monitoring the SDGs, providing a range of examples from citizen science, Earth Observation (including the work of the Group on Earth Observations) and mobile phone data, among others. Where relevant, we will touch upon disaster risk reduction. Finally, actions will be presented that are currently happening to promote the data revolution for sustainable development and what is still needed to make tangible progress on SDG implementation using these new data sources as well as how the engagement of citizens in data collection can trigger transformative and behavioral change.</p>

scholarly journals Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

2019 ◽  
Vol 26 (3) ◽  
Author(s):  
Shengjie Lai ◽  
Andrea Farnham ◽  
Nick W Ruktanonchai ◽  
Andrew J Tatem
Keyword(s):  
Health Risk ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Spatial Scales ◽  
Human Mobility ◽  
Travel Medicine ◽  
Mobile Phone Data ◽  
Individual Risk ◽  
Spatial And Temporal Scales ◽  
Human Movements

Abstract Rationale for review The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data. Key findings Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel. Conclusions Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin–destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.

scholarly journals Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology

2019 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Urban Form ◽  
Data Science ◽  
Spatial Information ◽  
Smart Cities ◽  
Urban Morphology ◽  
Urban Fabric ◽  
Form Analysis ◽  
Study Sites

Urban planning and morphology have relied on analytical cartography and visual communication tools for centuries to illustrate spatial patterns, propose designs, compare alternatives, and engage the public. Classic urban form visualizations – from Giambattista Nolli’s ichnographic maps of Rome to Allan Jacobs’s figure-ground diagrams of city streets – have compressed physical urban complexity into easily comprehensible information artifacts. Today we can enhance these traditional workflows through the Smart Cities paradigm of understanding cities via user-generated content and harvested data in an information management context. New spatial technology platforms and big data offer new lenses to understand, evaluate, monitor, and manage urban form and evolution. This paper builds on the theoretical framework of visual cultures in urban planning and morphology to introduce and situate computational data science processes for exploring urban fabric patterns and spatial order. It demonstrates these workflows with OSMnx and data from OpenStreetMap, a collaborative spatial information system and mapping platform, to examine street network patterns, orientations, and configurations in different study sites around the world, considering what these reveal about the urban fabric. The age of ubiquitous urban data and computational toolkits opens up a new era of worldwide urban form analysis from integrated quantitative and qualitative perspectives.

scholarly journals Positioning Error in Mobile Phone Tracking Data with Consideration of Geographic Environment Factors

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Xiaoqing Song ◽  
Yi Long ◽  
Ling Zhang
Keyword(s):  
Big Data ◽  
Mobile Phone ◽  
Machine Learning Algorithms ◽  
Mobile Phone Data ◽  
Distribution Model ◽  
Positioning Error ◽  
Environment Factors ◽  
Gps Positioning ◽  
Spatial Big Data ◽  
Personal Travel

<p><strong>Abstract.</strong> Summary. This research contributes to both theory and application. This is an important supplement to the research on the quality and uncertainty of spatial big data and it will help mobile phone data play a more effective application value in future research. In recent years, the rapid development of data acquisition means has led to the rapid accumulation of spatial data and generated spatial big data with geographic location information. However, the uncertainty in spatial big data leads to the quality problem of spatial big data, which is one of the main bottlenecks restricting the effective utilization of spatial big data. As an important part of spatial big data, it is necessary to study the quality and uncertainty of mobile phone data. In view of this, we delve into the key geographical environment factors that affect the positioning error of mobile phone data, extract error evaluation indexes and construct the spatial distribution model of positioning error with machine learning algorithms. To explore the distribution law of mobile phone data positioning error under different geographical scenarios. In addition, considering the high accuracy of personal travel GPS positioning, this study takes personal travel GPS positioning data as the comparative object of mobile phone data for analysis.</p>

scholarly journals Urban Morphology and Sustainability: towards a shared design methodology.

2017 ◽  
Author(s):  
Marco Maretto ◽  
Barbara Gherri ◽  
Greta Pitanti ◽  
Francesco Scattino
Keyword(s):  
Urban Form ◽  
Design Methodology ◽  
City Planning ◽  
Environmental Control ◽  
Urban Morphology ◽  
Smart Devices ◽  
Low Carbon ◽  
Design Strategies ◽  
Large Area ◽  
Daylight Performance

The information revolution is radically transforming the very foundation of the ‘fossil city’. A ‘virtual’ macro-urbanism will intersect with an ‘actual’ micro-urbanism, physical and concrete, determining the form of the new urban environment.  Within the binomial of macro- and micro- urbanism, urban morphology identifies an interesting socio-building scale that can serve as the basic strategy for sustainable city planning in the twenty-first century. Morphology thus becomes the necessary ‘plug-in’ for registering the different ‘networks’ that characterize the contemporary city – from IT and ‘smart’ devices to energy and environmental systems - translating these networks into building practices, into ‘fabrics’, for the physical city. At this purpose an Urban Design methodology has been developed in order to combine the Urban Morphology tools with those of Sustainability giving particular attention to the topics of the comfort outdoor and the passive environmental control systems. The methodology has then been applied in the Sant Adrià De Besos Waterfront Regeneration Project in Barcelona. Neighbourhood’s size, complexity and localisation, between the sea and a large area of brown fields at the northern gateway of the Catalan capital, has set up an interesting testing bench. A sequence of consecutive steps characterizes the methodology in which morphology, architecture and sustainability intersect one another within a single design process.     References   Gherri B. (2015) Assessment of Daylight Performance in Buildings: Methods and Design Strategies, (WIT Press, Boston). Gherri, B. (2016) ‘Environmental Analysis Towards Low Carbon Urban Retrofitting For Public Spaces’, Proceedings of HERITAGE 2016 – 5th International Conference on Heritage and Sustainable Development,Vol. 1, p. 499-508. Marat-Mendes, T. (2013) ‘Sustainability and the study of urban form’, Urban Morphology 17, 123-4. Maretto, M. (2014) ‘Sustainable Urbanism: the role of urban morphology’, Urban Morphology 18(2), 163-74. Maretto, M. (2013) Ecocities. Il progetto urbano tra morfologia e sostenibilità (Franco Angeli, Roma).

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