Optimization Big Data Real-time Analytics Using Mobile Phone Data in Origin Destination National Transportation (ATTN) Survey

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.

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Real-time people movement estimation in large disasters from several kinds of mobile phone data

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct - UbiComp '16 ◽

10.1145/2968219.2968421 ◽

2016 ◽

Cited By ~ 9

Author(s):

Yoshihide Sekimoto ◽

Akihito Sudo ◽

Takehiro Kashiyama ◽

Toshikazu Seto ◽

Hideki Hayashi ◽

...

Keyword(s):

Mobile Phone ◽

Real Time ◽

Mobile Phone Data ◽

People Movement

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Research on the Traffic Simulation Platform Based on the Real-Time Mobile Phone Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.253-255.1365 ◽

2012 ◽

Vol 253-255 ◽

pp. 1365-1368

Author(s):

Ge Qi Qi ◽

Jian Ping Wu ◽

Yi Man Du

Keyword(s):

Mobile Phone ◽

Real Time ◽

Traffic Simulation ◽

Rapid Development ◽

Mobile Phone Data ◽

Simulation Platform ◽

Traffic Data ◽

The Real ◽

Real Time Traffic ◽

Wide Range

With the rapid development of the society, the transportation system has become more complicated and vulnerable. For simulating the real-time traffic condition of the whole city, a wide range of OD matrix data are needed which are hard to collect in whole based on the present conventional methods. The paper raises a feasible design of the traffic simulation platform based on the real-time mobile phone data. The popularity and development of mobile phones make the vast amounts of real-time traffic data can be collected and usable. With the help of the GIS module, dynamic OD traffic generation module and other related modules, the real-time mobile phone data will be converted to the valuable traffic data and applied to the traffic simulation platform.

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Implementation of Mobile Phone Data Collection in the Conduct EPI Comprehensive Review in East and Southern African Countries

Journal of Immunological Sciences ◽

10.29245/2578-3009/2021/s2.1108 ◽

2021 ◽

Vol Special Issue (2) ◽

pp. 55-62

Author(s):

Isah Mohammed Bello ◽

Abubakar Sadiq Umar ◽

Godwin Ubong Akpan ◽

Joseph Okeibunor ◽

Chukwudi Shibeshi ◽

...

Keyword(s):

Data Collection ◽

Mobile Phone ◽

Real Time ◽

Mobile Phones ◽

Health Sector ◽

Mobile Phone Data ◽

Data Detection ◽

Time Data ◽

African Countries ◽

Comprehensive Review

Mobile phone data collection tools are increasingly becoming very usable collecting, collating and analysing data in the health sector. In this paper, we documented the experiences with mobile phone data collection, collation and analysis in 5 countries of the East and Southern African, using Open Data Kit (ODK), where questionnaires were designed and coded on an XML form, uploaded and data collected using Android-Based mobile phones, with a web-based system to monitor data in real-time during EPI comprehensive review. The ODK interface supports in real-time monitoring of the flow of data, detection of missing or incomplete data, coordinate location of all locations visited, embedded charts for basic analysis. It also minimized data quality errors at entry level with the use of validation codes and constraint developed into the checklist. These benefits, combined with the improvement that mobile phones offer over paper-based in terms of timeliness, data loss, collation, and real-time data collection, analysis and uploading difficulties, make mobile phone data collection a feasible method of data collection that needs to be further explored in the conduct of all surveys in the organization.

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Privacy will test development actors using 'big data'

Emerald Expert Briefings ◽

10.1108/oxan-db241416 ◽

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.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9110666 ◽

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.

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Real-Time Monitoring and Forecast of Active Population Density Using Mobile Phone Data

Communications in Computer and Information Science - Big Data Technology and Applications ◽

10.1007/978-981-10-0457-5_12 ◽

2016 ◽

pp. 116-129 ◽

Cited By ~ 1

Author(s):

Qi Li ◽

Bin Xu ◽

Yukun Ma ◽

Tonglee Chung

Keyword(s):

Population Density ◽

Mobile Phone ◽

Real Time ◽

Mobile Phone Data ◽

Real Time Monitoring ◽

Active Population

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

10.5194/egusphere-egu2020-16829 ◽

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 &#8216;data revolution&#8217;, 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>

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