Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data

Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. Defining urban boundaries that consider socio-economic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, non-overlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.Comment: 32 pages, 7 figures, International Journal of Geographic Information Scienc

Estimating Movement from Mobile Telephony Data

Mobile enabled devices are ubiquitous in modern society. The information gathered by their normal service operations has become one of the primary data sources used in the understanding of human mobility, social connection and information transfer. This thesis investigates techniques that can extract useful information from anonymised call detail records (CDR). CDR consist of mobile subscriber data related to people in connection with the network operators, the nature of their communication activity (voice, SMS, data, etc.), duration of the activity and starting time of the activity and servicing cell identification numbers of both the sender and the receiver when available. The main contributions of the research are a methodology for distance measurements which enables the identification of mobile subscriber travel paths and a methodology for population density estimation based on significant mobile subscriber regions of interest. In addition, insights are given into how a mobile network operator may use geographically located subscriber data to create new revenue streams and improved network performance. A range of novel algorithms and techniques underpin the development of these methodologies. These include, among others, techniques for CDR feature extraction, data visualisation and CDR data cleansing. The primary data source used in this body of work was the CDR of Meteor, a mobile network operator in the Republic of Ireland. The Meteor network under investigation has just over 1 million customers, which represents approximately a quarter of the country’s 4.6 million inhabitants, and operates using both 2G and 3G cellular telephony technologies. Results show that the steady state vector analysis of modified Markov chain mobility models can return population density estimates comparable to population estimates obtained through a census. Evaluated using a test dataset, results of travel path identification showed that developed distance measurements achieved greater accuracy when classifying the routes CDR journey trajectories took compared to traditional trajectory distance measurements. Results from subscriber segmentation indicate that subscribers who have perceived similar relationships to geographical features can be grouped based on weighted steady state mobility vectors. Overall, this thesis proposes novel algorithms and techniques for the estimation of movement from mobile telephony data addressing practical issues related to sampling, privacy and spatial uncertainty

Identifying the use of a park based on clusters of visitors' movements from mobile phone data

none6noPlanning urban parks is a burdensome task, requiring knowledge of countless variables that are impossible to consider all at the same time. One of these variables is the set of people who use the parks. Despite information and communication technologies being a valuable source of data, a standardized method which enables landscape planners to use such information to design urban parks is still broadly missing. The objective of this study is to design an approach that can identify how an urban green park is used by its visitors in order to provide planners and the managing authorities with a standardized method. The investigation was conducted by exploiting tracking data from an existing mobile application developed for Cardeto Park, an urban green area in the heart of the old town of Ancona, Italy. A trajectory clustering algorithm is used to infer the most common trajectories of visitors, exploiting global positioning system and sensor-based tracks. The data used are made publicly available in an open dataset, which is the first one based on real data in this field. On the basis of these user-generated data, the proposed datadriven approach can determine the mission of the park by processing visitors' trajectories whilst using a mobile application specifically designed for this purpose. The reliability of the clustering method has also been confirmed by an additional statistical analysis. This investigation reveals other important user behavioral patterns or trends.openPierdicca R.; Paolanti M.; Vaira R.; Marcheggiani E.; Malinverni E.S.; Frontoni E.Pierdicca, R.; Paolanti, M.; Vaira, R.; Marcheggiani, E.; Malinverni, E. S.; Frontoni, E

Identifying the use of a park based on clusters of visitors\u27 movements from mobile phone data

Planning urban parks is a burdensome task, requiring knowledge of countless variables that are impossible to consider all at the same time. One of these variables is the set of people who use the parks. Despite information and communication technologies being a valuable source of data, a standardized method which enables landscape planners to use such information to design urban parks is still broadly missing. The objective of this study is to design an approach that can identify how an urban green park is used by its visitors in order to provide planners and the managing authorities with a standardized method. The investigation was conducted by exploiting tracking data from an existing mobile application developed for Cardeto Park, an urban green area in the heart of the old town of Ancona, Italy. A trajectory clustering algorithm is used to infer the most common trajectories of visitors, exploiting global positioning system and sensor-based tracks. The data used are made publicly available in an open dataset, which is the first one based on real data in this field. On the basis of these user-generated data, the proposed data-driven approach can determine the mission of the park by processing visitors\u27 trajectories whilst using a mobile application specifically designed for this purpose. The reliability of the clustering method has also been confirmed by an additional statistical analysis. This investigation reveals other important user behavioral patterns or trends

Applications of Internet of Things

This book introduces the Special Issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al

Intelligent Development Research on Job-Housing Space in Chinese Metropolitan Area under the Background of Rapid Urbanization

Under the impact of regional integration and rapid urbanization, Chinese metropolitan area is confronted with the pressure brought by further massiveness, high density and continuous development. The existing layout of job-housing space balance in cities has been further spread and aggravated, which leads to a series of problems including traffic jams and air pollution, etc. This thesis excavates, analyzes and integrates the city residents’ action trajectory data in various heterogeneous cities through the intelligent transportation data platform of metropolitan area. Furthermore, the research also extracts the intelligent knowledge on the aspect of urban job-housing space, identifies and analyzes its characteristics effectively. This thesis takes Beijing-Tianjin-Hebei metropolitan area as the research object to carry out intelligent analysis on working and residential space in main cities. We can identify residents' commuting behaviors with multi-source location perception data. Firstly, the GPS trajectory data of large-scale taxi will be utilized, and the transportation behaviors and characteristics of taxi will be assumed as the urban residents’ trip behaviors. Then the research of urban space-time structure and residents’ activities hot spots will be carried out from the macro perspective. Secondly, a residents’ trip survey method combining mobile phone location and internet feedback will be put forward. Aiming at the location Microblog data, the characteristics of residents’ workplaces and residences could be identified with fuzzy mathematical method. During the identification process, the individual behavior patterns obtained from the resident trip survey data will be used as the recognition feature. Through the analysis, We discovered that the data mining method of the residents’ action trajectory is feasible for the study of job-housing space. The study shows that the key factor influencing the job-housing balance in metropolitan area is the improvement of disperse urbanization life-style which takes family as a single unit. It also puts forwards the future ternary development mode of “employment-residence-public service” of job-housing balance in Chinese metropolitan area. The research also discovers a measurement method of excess commuting to develop the commuting efficiency in job-housing space. Furthermore, through the research on excess commuting degree of main cities in Beijing-Tianjin-Hebei metropolitan area by utilizing the commuting behaviors extraction result of Microsoft data, the correlation factor of characteristic attributes and job-housing separation phenomenon in urban community could be found. Finally, the intelligent development characteristics of job-housing space in metropolitan area will be discussed by combining the geographical visualization method and taxi trajectory mining result

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Placement and Movement Episodes Detection using Mobile Trajectories Data

Teostatud töö eesmärgiks on tuvastada asukohaandmetest seisu- ning liikumisepisoode kasutades selleks trajektoori ülekattuvusmaatriksit. Antud töös kasutatud andmed on väga hajusad nii ajalises kui ka geograafilises mõttes. Seetõttu on antud ülesanne suur väljakutse. Välja pakutud lahenduse raames teostati andmeanalüüs mille raames tuvastati kasutajatele tähtsad asukohad ning pakuti välja algoritm, mille abil tuvastda seisu- ning liikumisepisoodid. Andmete analüüsimiseks ning visualiseerimiseks kasutati R-i.This thesis presents a trajectory episode matrix to enable the detection of placement and movement episodes from mobile location data. The data used in this work is very sparse in time and space. Therefore, the estimation of user’s placement and movement patterns poses a big challenge. The presented approach performs data analysis to find meaningful locations and introduces an algorithm to detect movement and placement episodes. To perform the analysis and visualize the results a statistical analysis tool was developed with R. The work done as a result of this thesis can be used to improve the identification of the meaningful locations and to help predicting the semantic meanings of mobile user’s patterns

MOBILITY AND ACTIVITY SPACE: UNDERSTANDING HUMAN DYNAMICS FROM MOBILE PHONE LOCATION DATA

Studying human mobility patterns and people’s use of space has been a major focus in geographic research for ages. Recent advancements of location-aware technologies have produced large collections of individual tracking datasets. Mobile phone location data, as one of the many emerging data sources, provide new opportunities to understand how people move around at a relatively low cost and unprecedented scale. However, the increasing data volume, issue of data sparsity, and lack of supplementary information introduce additional challenges when such data are used for human behavioral research. Effective analytical methods are needed to meet the challenges to gain an improved understanding of individual mobility and collective behavioral patterns. This dissertation proposes several approaches for analyzing two types of mobile phone location data (Call Detail Records and Actively Tracked Mobile Phone Location Data) to uncover important characteristics of human mobility patterns and activity spaces. First, it introduces a home-based approach to understanding the spatial extent of individual activity space and the geographic patterns of aggregate activity space characteristics. Second, this study proposes an analytical framework which is capable of examining multiple determinants of individual activity space simultaneously. Third, the study introduces an anchor-point based trajectory segmentation method to uncover potential demand of bicycle trips in a city. The major contributions of this dissertation include: (1) introducing an activity space measure that can be used to evaluate how individuals use urban space around where they live; (2) proposing an analytical framework with three individual mobility indicators that can be used to summarize and compare human activity spaces systematically across different population groups or geographic regions; (3) developing analytical methods for uncovering the spatiotemporal dynamics of travel demand that can be potentially served by bicycles in a city, and providing suggestions for the locations and daily operation of bike sharing stations