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.

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.

METHOD FOR EVALUATING INDIVIDUAL CHARACTERISTICS OF MILK EJECTION OF COWS WHEN USING DIFFERENT MILKING TECHNOLOGIES

2021 ◽  
pp. 66-78
Author(s):  
V.P. MESHCHERYAKOV ◽  
◽  
YU.G. IVANOV ◽  
T.N. PIMKINA ◽  
E.V. ERMOSHINA
Keyword(s):  
Latent Period ◽  
Average Duration ◽  
Individual Characteristics ◽  
Maximum Intensity ◽  
Milk Ejection ◽  
Short Period ◽  
Robotic Milking ◽  
Automatic Milking Systems ◽  
Milking Machine ◽  
The Individual

The aim of the research is to study the possibility of using a latent period of the ejection of the first portion of milk in order to evaluate the individual characteristics of the milk ejection features of cows using the technology of bucket milking and robotic milking. Two experiments were conducted on cows of Black-Motley breed. Under the first experiment, the individual characteristics of the milk ejection were shown using the technology of bucket milking. Under the second experiment, they were determined for the technology of robotic milking. The first experiment was conducted on 12 mature cows. They were milked with a serial milking machine. The process of lactation was recorded by means of a bucket counter. The parameters of milk ejection were defined by analyzing the curve of lactation and making calculations. The second experiment was conducted on 30 first-calf heifers. Cows were milked on robotic installation the Astronaut A4 of Lely Company (the Netherlands). The data of the information system of herd management Lely T4C have been used for the analysis. Depending on the indicator of a latent period of the first milk portion ejection in both experiments three groups of cows (I–III) have been isolated. The ability of milk ejection in the first group was identified as high, in the second group – average and in the third group – low. Both experiments showed that the value of a latent period of the first milk portion ejection determined the milk ejection ability of cows. The increase in the period of the first milk portion ejection has been found among cows as their milk ejection ability decreses. The currently used milking technology shows that the reduced milk ejection among cows leads to the decrease in the indicators of the average and maximum intensity of milk ejection, the first two minutes of milking and also it leads to longer duration of milking. Using the robotic milking, the authors found that the first-calf heifers with the short period of the first milk portion ejection are characterized by the shortest duration of treating the teats and staying in the milking parlor, the average duration of milk ejection from the each quarter of the udder, as well as high values of the average and maximum intensity of milk ejection. The first-calf heifers with slow milking capacity are characterized by the longest duration of treating the teats and staying in the milking parlor, the average duration of milk ejection from the each quarter of the udder, as well as the lowest values of the average and maximum intensity of milk ejection. This suggests that the selection of first-calf heifers with high milk ejection ability will help to increase the productivity of automatic milking systems during the milking process. It is proposed to use the value of a latent period of the first milk portion ejection in the breeding activities.

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.

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 A Novel Trip Coverage Index for Transit Accessibility Assessment Using Mobile Phone Data

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Zhengyi Cai ◽  
Dianhai Wang ◽  
Xiqun (Michael) Chen
Keyword(s):  
Mobile Phone ◽  
Assessment Tool ◽  
Base Stations ◽  
Mobile Phone Data ◽  
Transit System ◽  
Transit Systems ◽  
The Public ◽  
Transit Accessibility ◽  
Transit Services ◽  
The Individual

Transit accessibility is an important measure on the service performance of transit systems. To assess whether the public transit service is well accessible for trips of specific origins, destinations, and origin-destination (OD) pairs, a novel measure, the Trip Coverage Index (TCI), is proposed in this paper. TCI considers both the transit trip coverage and spatial distribution of individual travel demands. Massive trips between cellular base stations are estimated by using over four-million mobile phone users. An easy-to-implement method is also developed to extract the transit information and driving routes for millions of requests. Then the trip coverage of each OD pair is calculated. For demonstrative purposes, TCI is applied to the transit network of Hangzhou, China. The results show that TCI represents the better transit trip coverage and provides a more powerful assessment tool of transit quality of service. Since the calculation is based on trips of all modes, but not only the transit trips, TCI offers an overall accessibility for the transit system performance. It enables decision makers to assess transit accessibility in a finer-grained manner on the individual trip level and can be well transformed to measure transit services of other cities.

Exploring Individual Activity-Travel Patterns Based on Geolocation Data from Mobile Phones

2021 ◽  
pp. 036119812110312
Author(s):  
Biao Yin ◽  
Fabien Leurent
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Travel Patterns ◽  
Mobility Patterns ◽  
Individual Activity ◽  
Data Set ◽  
Individual Mobility ◽  
Daily Mobility ◽  
Home Based ◽  
The Individual

Data mining techniques can extract useful activity and travel information from large-scale data sources such as mobile phone geolocation data. This paper aims to explore individual activity-travel patterns from samples of mobile phone users using a two-week geolocation data set from the Paris region in France. After filtering the data set, we propose techniques to identify individual stays and activity places. Typical activity places such as the primary anchor place and the secondary place are detected. The daily timeline (i.e., activity-travel program) is reconstructed with the detected activity places and the trips in-between. Based on user-day timelines, a three-stage clustering method is proposed for mobility pattern analysis. In the method framework, activity types are first identified by clustering analysis. In the second stage, daily mobility patterns are obtained after clustering the daily mobility features. Activity-travel topologies are statistically investigated to support the interpretation of daily mobility patterns. In the last stage, we analyze statistically the individual mobility patterns for all samples over 14 days, measured by the number of days for all kinds of daily mobility patterns. All individual samples are divided into several groups where people have similar travel behaviors. A kmeans++ algorithm is applied to obtain the appropriate number of patterns in each stage. Finally, we interpret the individual mobility patterns with statistical descriptions and reveal home-based differences in spatial distribution for the grouped individuals.

scholarly journals Evaluation of home detection algorithms on mobile phone data using individual-level ground truth

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Luca Pappalardo ◽  
Leo Ferres ◽  
Manuel Sacasa ◽  
Ciro Cattuto ◽  
Loreto Bravo
Keyword(s):  
Mobile Phone ◽  
Ground Truth ◽  
Mobile Phone Data ◽  
Data Generation ◽  
Individual Level ◽  
Detection Algorithms ◽  
Home Location ◽  
Residence Location ◽  
The Individual ◽  
And Control

AbstractInferring mobile phone users’ home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location.

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