Impact of the spatial context on human communication activity

Technology development produces terabytes of data generated by hu- man activity in space and time. This enormous amount of data often called big data becomes crucial for delivering new insights to decision makers. It contains behavioral information on different types of human activity influenced by many external factors such as geographic infor- mation and weather forecast. Early recognition and prediction of those human behaviors are of great importance in many societal applications like health-care, risk management and urban planning, etc. In this pa- per, we investigate relevant geographical areas based on their categories of human activities (i.e., working and shopping) which identified from ge- ographic information (i.e., Openstreetmap). We use spectral clustering followed by k-means clustering algorithm based on TF/IDF cosine simi- larity metric. We evaluate the quality of those observed clusters with the use of silhouette coefficients which are estimated based on the similari- ties of the mobile communication activity temporal patterns. The area clusters are further used to explain typical or exceptional communication activities. We demonstrate the study using a real dataset containing 1 million Call Detailed Records. This type of analysis and its application are important for analyzing the dependency of human behaviors from the external factors and hidden relationships and unknown correlations and other useful information that can support decision-making.Comment: 12 pages, 11 figure

Dynamic Ridesharing Recommendation Method for Commuting Private Vehicles Based on Cross-domain Urban Data Fusion

城市基础设施建设极大地便利了人们的日常交通出行，同时，城市化进程在交通拥堵、能源消耗和空气污染等方面也带来了诸多问题和挑战。因此，如何利用大数据分析技术，融合挖掘城市中产生的海量交通数据，为城市交通拥堵治理提供必要的数据决策支持，已逐渐成为交通大数据应用研究领域的热点研究问题之一。随着基于位置服务和移动互联网技术的飞速发展，拼车服务逐渐成为缓解城市交通拥堵的共享经济新模式之一，然而现有拼车方法存在如下两个问题：1、现有算法大多以满足乘客的实时拼车需求为单一目标，即如何减少乘客的等待时间，而尚未有效解决拼车驾驶员之间因同业竞争而增加私家车使用量的问题，因此，在早晚高峰时段的现有拼车服务有可能造成...城市基础设施建设极大地便利了人们的日常交通出行,同时,城市化进程在交通拥堵、能源消耗和空气污染等方面也带来了诸多问题和挑战。因此,如何利用大数据分析技术,融合挖掘城市中产生的海量交通数据,为城市交通拥堵治理提供必要的数据决策支持,已逐渐成为交通大数据应用研究领域的热点研究问题之一。随着基于位置服务和移动互联网技术的飞速发展,拼车服务逐渐成为缓解城市交通拥堵的共享经济新模式之一,然而现有拼车方法存在如下两个问题：1、现有算法大多以满足乘客的实时拼车需求为单一目标,即如何减少乘客的等待时间,而尚未有效解决拼车驾驶员之间因同业竞争而增加私家车使用量的问题,因此,在早晚高峰时段的现有拼车服务有可能造成更加严重的道路拥堵问题；2、现有拼车方案大多依赖于GPS轨迹和手机信令等大规模人群移动数据,轨迹丰富但语义信息缺失,且尚未有效考虑天气、节假日和交通事故等动态因素对拼车方案的重要影响。因而难以为用户提...学位：工学硕士院系专业：信息科学与技术学院_工程硕士(计算机技术)学号：2302014115319

Geo-located Twitter as the proxy for global mobility patterns

In the advent of a pervasive presence of location sharing services researchers gained an unprecedented access to the direct records of human activity in space and time. This paper analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012 we estimate volumes of international travelers in respect to their country of residence. We examine mobility profiles of different nations looking at the characteristics such as mobility rate, radius of gyration, diversity of destinations and a balance of the inflows and outflows. The temporal patterns disclose the universal seasons of increased international mobility and the peculiar national nature of overseen travels. Our analysis of the community structure of the Twitter mobility network, obtained with the iterative network partitioning, reveals spatially cohesive regions that follow the regional division of the world. Finally, we validate our result with the global tourism statistics and mobility models provided by other authors, and argue that Twitter is a viable source to understand and quantify global mobility patterns.Comment: 17 pages, 13 figure

Extraction and Classification of Twitter Messages to Apply in Business Intelligence

Maury Jean Siffrein, abbé, Rewbell Jean François. Discussion sur le rapport de M. de Menou sur l'affaire d'Avignon, lors de la séance du 23 mai 1791. In: Archives Parlementaires de 1787 à 1860 - Première série (1787-1799) Tome XXVI - Du 12 mai au 5 juin 1791. Paris : Librairie Administrative P. Dupont, 1887. pp. 314-315

Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics

The impact of micro-level people’s activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants. Therefore, understanding the interdependence and mutual reinforcement between micro- and macro-level factors is critical to defining effective public policies. The increasing availability of digital data sources, such as social media and mobile phones, has opened up new opportunities for the quantitative study of this interdependency. This paper aims to detect meaningful city clusters on the basis of a detailed analysis of the spatiotemporal activity patterns for each city. The study is carried out on a worldwide city dataset of spatiotemporal activity patterns obtained from geotagged social media data. Clustering features are obtained from unsupervised topic analyses of activity patterns. Our study compares state-of-the-art clustering models, selecting the model achieving a 2.7% greater Silhouette Score than the next-best model. Three well-separated city clusters are identified. Additionally, the study of the distribution of the City Innovation Index over these three city clusters shows discrimination of low performing from high performing cities relative to innovation. Low performing cities are identified in one well-separated cluster. Therefore, it is possible to correlate micro-scale individual-level activities to large-scale urban characteristics.This work would not have been accomplished without the financial support of CONICYT-PFCHA/DOCTORADO BECAS CHILE/2019-21190345. The last author received research funds from the Basque Government as the head of the Grupo de Inteligencia Computacional, Universidad del Pais Vasco, UPV/EHU, from 2007 until 2025. The current code for the grant is IT1689-22. Additionally, the author participates in Elkartek projects KK-2022/00051 and KK-2021/00070. The Spanish MCIN has also granted the author a research project under code PID2020-116346GB-I00

Assessing the Potential of Ride-Sharing Using Mobile and Social Data

Ride-sharing on the daily home-work-home commute can help individuals save on gasoline and other car-related costs, while at the same time it can reduce traffic and pollution. This paper assesses the potential of ride-sharing for reducing traffic in a city, based on mobility data extracted from 3G Call Description Records (CDRs, for the cities of Barcelona and Madrid) and from Online Social Networks (Twitter, collected for the cities of New York and Los Angeles). We first analyze these data sets to understand mobility patterns, home and work locations, and social ties between users. We then develop an efficient algorithm for matching users with similar mobility patterns, considering a range of constraints. The solution provides an upper bound to the potential reduction of cars in a city that can be achieved by ride-sharing. We use our framework to understand the different constraints and city characteristics on this potential benefit. For example, our study shows that traffic in the city of Madrid can be reduced by 59% if users are willing to share a ride with people who live and work within 1 km; if they can only accept a pick-up and drop-off delay up to 10 minutes, this potential benefit drops to 24%; if drivers also pick up passengers along the way, this number increases to 53%. If users are willing to ride only with people they know ("friends" in the CDR and OSN data sets), the potential of ride-sharing becomes negligible; if they are willing to ride with friends of friends, the potential reduction is up to 31%.Comment: 11 page

MobiGroup: Enabling Lifecycle Support to Social Activity Organization and Suggestion with Mobile Crowd Sensing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial-temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs

A study on Analysis and Utilization of Crowd-sourced Spatio-temporal Contexts from Social Media

兵庫県立大学大学院201

USING SOCIALLY SENSED BIG DATA TO MODEL PATTERNS AND GEOGRAPHIC CONTEXT OF HUMAN ACTIVITIES IN CITIES

Understanding dynamic interactions between human activities and land-use structure in a city is a key lens to explore the city as a complex system. This dissertation contributes to understanding the complexity of urban dynamics by gaining knowledge of the interactions between human activities and city land-use structures by utilizing free-accessible socially sensed data sources, and building upon recent research trend and technologies in geographical information science, urban study, and computer science. This dissertation addresses three main questions related to human dynamics: 1) how human activities in an urban environment are shaped by socioeconomic status and the intra-city land-use structure, and how in turn, the knowledge of socioeconomic status-activity relationships can contribute to understanding the social landscape of a city; 2) how different types of activities are located in space and time in three U.S. cities and how the spatiotemporal activity patterns in these cities characterize the activity profile of different neighborhoods in the cities; and 3) how recent socially sensed information on human activities can be integrated with widely-used remotely sensed geographical data to create a novel approach for discovering patterns of land use in cities that are otherwise lacking in up to date land use information. This dissertation models the associations between socioeconomics and mobility in the Washington, D.C. metropolitan area as a case study and applies the learned associations for inferring geographical patterns of socioeconomic status (SES) solely using the socially sensed data. This dissertation also implements a semi-automated workflow to retrieve activity details from socially sensed Twitter data in Washington, D.C., the City of Baltimore, and New York City. The dissertation integrates remotely-sensed imagery and socially sensed data to model the dynamics associated with changing land-use types in the Washington, D.C.-Baltimore metropolitan area over time