Political management of emigration from Bangladesh in the framework of a social network theory

The subject of the study is the social network theory for the management of international migration. The theory suggests that migration from the society of origin to the hosting society can occur if links exist between these two societies, and that the flow of migrants follows the established links. The theoretical prediction which one can make is that, if the political administration wishes to establish a flow of migrants between any two societies, a link between these two societies must be established first. The author tested the theory on the case of managing the emigration of workers by the government of Bangladesh. The paper found that, firstly, the links between the origin society and the host society were created artificially, however, in contrast to the theoretical forecast, the author observed that such links usually do not correspond to the geographical distribution of maximum proximity to origin or destination society. Instead, the study revealed, that the closeness of communication between two societies is generated by the proximity between political administrations of the same societies, which contradicts the theoretical expectations. In this regard, the author proposed to expand the theory of international migration in the social network, suggesting that the proximity between two political administrations, and not between two societies as a whole, is a condition necessary for international migration. This, in turn, allows us to fill a theoretical gap that is associated with the relationship between social network theory and the management of international migration. The paper concludes that it is possible to generate arbitrary migration flows, creating appropriate links between any two societies

Delineating Geographical Regions with Networks of Human Interactions in an Extensive Set of Countries

Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions.National Science Foundation (U.S.)Singapore-MIT Alliance for Research and Technolog

Exploring the potential of phone call data to characterize the relationship between social network and travel behavior

[EN] Social network contacts have significant influence on individual travel behavior. However, transport models rarely consider social interaction. One of the reasons is the difficulty to properly model social influence based on the limited data available. Non-conventional, passively collected data sources, such as Twitter, Facebook or mobile phones, provide large amounts of data containing both social interaction and spatiotemporal information. The analysis of such data opens an opportunity to better understand the influence of social networks on travel behavior. The main objective of this paper is to examine the relationship between travel behavior and social networks using mobile phone data. A huge dataset containing billions of registers has been used for this study. The paper analyzes the nature of co-location events and frequent locations shared by social network contacts, aiming not only to provide understanding on why users share certain locations, but also to quantify the degree in which the different types of locations are shared. Locations have been classified as frequent (home, work and other) and non-frequent. A novel approach to identify co-location events based on the intersection of users' mobility models has been proposed. Results show that other locations different from home and work are frequently associated to social interaction. Additionally, the importance of non-frequent locations in co-location events is shown. Finally, the potential application of the data analysis results to improve activity-based transport models and assess transport policies is discussed.The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no 318367 (EUNOIA project) and no 611307 (INSIGHT project). The work of ML has been funded under the PD/004/2013 project, from the Conselleria de Educacion, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013-2017.Picornell Tronch, M.; Ruiz Sánchez, T.; Lenormand, M.; Ramasco, JJ.; Dubernet, T.; Frías-Martínez, E. (2015). Exploring the potential of phone call data to characterize the relationship between social network and travel behavior. Transportation. 42(4):647-668. https://doi.org/10.1007/s11116-015-9594-1S647668424Ahas, R., Aasa, A., Silm, S., Tiru, M.: Daily rhythms of suburban commuters’ movements in the tallinn metropolitan area: case study with mobile positioning data. Transp. Res. 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Data from mobile phone operators: A tool for smarter cities?

Abstract The use of mobile phone data provides new spatio-temporal tools for improving urban planning, and for reducing inefficiencies in present-day urban systems. Data from mobile phones, originally intended as a communication tool, are increasingly used as innovative tools in geography and social sciences research. Empirical studies on complex city systems from human-centred and urban dynamics perspectives provide new insights to develop promising applications for supporting smart city initiatives. This paper provides a comprehensive review and a typology of spatial studies on mobile phone data, and highlights the applicability of such digital data to develop innovative applications for enhanced urban management

Digital geographies of transnational spaces: a mixed-method study of Mexico-US migration

textThe central objective of this thesis is to explore how sophisticated information and communication technologies (ICTs) impact Mexico-US migration. In particular, it attends to those ICTs that enable Mexican immigrants in the United States to stay “in touch” with their loved ones in Mexico. Rather than pursue the impacts of these technologies through a singular methodology or theoretical framework, this study employs an array of approaches in order to examine the geography transnational communication across multiple scales. At the level of the individual, I examine how Mexican immigrants living in Austin, TX, incorporate communication technologies into their daily lives. Informed by a series of semi-structured and in-depth interviews, I argue that cellular phone calls, text messaging, and social media platforms enable a passive, routinized transnationalism that allows migrants to maintain a degree of presence both “here” and “there.” I subsequently scale up my analysis in order to trace the emergence of digital social media—Facebook, in particular—as a communication tool for dispersed Mexican immigrant communities, and I interrogate the ways in which digital social media engender transnational social networks. Using place as a guiding conceptual theme, I demonstrate how senses of and attachments to place form the basis of communal social interactions online, and I identify the many different places, both in the US and Mexico, that are involved in particular transnational social networks and migration flows. This study concludes by drawing on recent critical GIS scholarship and volunteered geographic information (VGI) in order to visualize the digital, place-to-place connections between Mexican migrants living in United States and their friends and family members living in Mexico and elsewhere.Geography and the Environmen

Learning from Structured Data with High Dimensional Structured Input and Output Domain

Structured data is accumulated rapidly in many applications, e.g. Bioinformatics, Cheminformatics, social network analysis, natural language processing and text mining. Designing and analyzing algorithms for handling these large collections of structured data has received significant interests in data mining and machine learning communities, both in the input and output domain. However, it is nontrivial to adopt traditional machine learning algorithms, e.g. SVM, linear regression to structured data. For one thing, the structural information in the input domain and output domain is ignored if applying the normal algorithms to structured data. For another, the major challenge in learning from many high-dimensional structured data is that input/output domain can contain tens of thousands even larger number of features and labels. With the high dimensional structured input space and/or structured output space, learning a low dimensional and consistent structured predictive function is important for both robustness and interpretability of the model. In this dissertation, we will present a few machine learning models that learn from the data with structured input features and structured output tasks. For learning from the data with structured input features, I have developed structured sparse boosting for graph classification, structured joint sparse PCA for anomaly detection and localization. Besides learning from structured input, I also investigated the interplay between structured input and output under the context of multi-task learning. In particular, I designed a multi-task learning algorithms that performs structured feature selection & task relationship Inference. We will demonstrate the applications of these structured models on subgraph based graph classification, networked data stream anomaly detection/localization, multiple cancer type prediction, neuron activity prediction and social behavior prediction. Finally, through my intern work at IBM T.J. Watson Research, I will demonstrate how to leverage structural information from mobile data (e.g. call detail record and GPS data) to derive important places from people's daily life for transit optimization and urban planning

METODOLOGÍA PARA LA EXTRACCIÓN DE PATRONES DE MOVILIDAD URBANA MEDIANTE EL ANÁLISIS DE REGISTROS DE ACTIVIDAD TELEFÓNICA (CALL DETAIL RECORD)

In the last century, Europe has seen a strong migration from rural to urban areas. Urban mobility is key to the economic and social development of cities, but at the same time it generates a significant number of negative effects such as congestion or air pollution. Understanding urban mobility patterns is essential to evaluate which are the most appropriate policies and measures to achieve sustainable urban development. Most of the empirical studies on urban mobility are based on surveys, since they provide detailed information about population mobility patterns and a large amount of socio-demographic information. However, surveys have several practical limitations (Ortúzar & Willumsen, 2011) such as their high costs and long lead times. The pervasive used of mobile devices opens the opportunity of gather large amounts of anonymised, passively-collected geolocation data overcoming some of the limitations of traditional surveys. Mobile phone data are probably one of the best data sources from which extract population mobility patterns at city scale because of their advantages (large samples, wide spatial coverage, low data collection costs, etc.). The main objective of this research is to contribute to the recent advances in the analysis of mobile phone data by developing and validating a new methodology to extract population activity and mobility patterns in urban areas. The methodology developed present several improvements with respect to previous studies, such as the identification of frequent locations different from home and work, better trip time estimations, sample selection and expansion procedures and improvements on population density estimations. The methodology developed has been tested in three different case studies: (1) estimation of mobility statistics and origin-destination matrices, (2) analysis of the relationship between social network and travel behaviour and (3) evaluation of population exposure to air pollution taking into account population activity and mobility patterns. The results show the potential of mobile phone data to extract information about mobility patterns in urban areas, to better understand the relationship between social network and travel behaviour and to improve population exposure assessment to air pollutants. Despite the potential of mobile phone data to provide rich information about activity and mobility patterns, a number of drawbacks and limitations shall be taken into account. Limitations are mainly related to the spatio-temporal resolution of the data and the limited socio-demographic information available. The results of this research are of great interest for transport planning studies, social network and transport modelling, and population exposure assessments.En el último siglo, Europa ha vivido una fuerte migración del ámbito rural al urbano. La movilidad urbana es fundamental para el desarrollo económico y social de las ciudades pero al mismo tiempo conlleva a una serie de importantes efectos negativos, tales como la congestión o la contaminación del aire. El entendimiento de los patrones de movilidad urbana de los ciudadanos es esencial para que los gestores puedan evaluar cuáles son las políticas y medidas más adecuadas para conseguir un desarrollo urbano sostenible. La mayoría de los estudios empíricos sobre movilidad urbana se apoyan en encuestas. Sin embargo, las encuestas presentan una serie de limitaciones prácticas importantes (Ortúzar & Willumsen, 2011) tales como sus elevados costes económicos o sus largos plazos de ejecución. El uso generalizado de dispositivos móviles por parte de la población proporciona la posibilidad de recoger de manera anónima y pasiva una gran cantidad de información espacio-temporal de una gran muestra de usuarios, superando algunas de las limitaciones de los actuales métodos de recogida de información. En concreto, los datos de la red de telefonía móvil presentan una serie de ventajas que los posicionan como una de las mejores fuentes de datos para el estudio de la movilidad general de grandes núcleos de población (bajos costes de extracción de los datos, gran tamaño de muestra, amplía cobertura espacial, etc.). El objetivo principal de esta investigación es contribuir a los recientes avances en el campo del análisis de los datos de telefonía móvil mediante el desarrollo y validación de una metodología que permita extraer información de patrones de actividad y movilidad de la población en ámbitos urbanos. La metodología desarrollada presenta una serie de mejoras relevantes con respecto a estudios previos, como la estimación de localizaciones frecuentes distintas de casa y trabajo, la mejora en la estimación de la hora del viaje, procedimientos para la selección y expansión de la muestra o la mejora en la estimación del número de personas en un área específica a partir de los patrones de actividad y movilidad de las mismas. Esta metodología ha sido aplicada en tres casos de uso para: (1) la obtención de estadísticas básicas de movilidad y matrices origen-destino en ámbitos urbanos, (2) el análisis de la influencia de la red social en la movilidad y (3) el estudio de la exposición de la población a la contaminación. Los resultados obtenidos demuestran el potencial de los datos de telefonía móvil para extraer información sobre patrones de movilidad en ámbitos urbanos, entender mejor la influencia de la red social en la movilidad y mejorar las estimaciones de exposición de la población a la contaminación. A pesar de las ventajas que proporcionan los datos de telefonía móvil, también se han observado limitaciones relevantes en los distintos estudios realizados, derivadas principalmente de la resolución espacio-temporal de los datos y de la limitada información socio-demográfica disponible. Los resultados de esta investigación son de gran relevancia para estudios de planificación y gestión del transporte, para el desarrollo de nuevos modelos de transporte que tengan en consideración la influencia de la red social en la movilidad y en estudios de evaluación de la exposición de la población a la contaminación.En l'últim segle, Europa ha viscut una forta migració de l'àmbit rural a l'urbà. La mobilitat urbana és fonamental per al desenvolupament econòmic i social de les ciutats però al mateix temps comporta a una sèrie d'importants efectes negatius, com ara la congestió o la contaminació de l'aire. L'entesa dels patrons de mobilitat urbana dels ciutadans és essencial perquè els gestors puguin avaluar quines són les polítiques i mesures més adequades per aconseguir un desenvolupament urbà sostenible. La majoria dels estudis empírics sobre mobilitat urbana es recolzen en enquestes. No obstant això, les enquestes presenten una sèrie de limitacions pràctiques importants (Ortúzar & Willumsen, 2011) com ara els seus elevats costos econòmics o els seus llargs terminis d'execució. L'ús generalitzat de dispositius mòbils per part de la població proporciona la possibilitat de recollir de manera anònima i passiva una gran quantitat d'informació espai-temporal d'una gran mostra d'usuaris, superant algunes de les limitacions dels actuals mètodes de recollida d'informació. En concret, les dades de la xarxa de telefonia mòbil presenten una sèrie d'avantatges que els posicionen com una de les millors fonts de dades per a l'estudi de la mobilitat general de grans nuclis de població (baixos costos d'extracció de les dades, grans dimensions de mostra, amplia cobertura espacial, etc.). L'objectiu principal d'aquesta investigació és contribuir als recents avenços en el camp de l'anàlisi de les dades de telefonia mòbil mitjançant el desenvolupament i validació d'una metodologia que permeti extreure informació de patrons d'activitat i mobilitat de la població en àmbits urbans. La metodologia desenvolupada presenta una sèrie de millores rellevants pel que fa a estudis previs, com l'estimació de localitzacions freqüents diferents de casa i treball, la millora en l'estimació de l'hora del viatge, procediments per a la selecció i expansió de la mostra o la millora en l'estimació del nombre de persones en una àrea específica a partir dels patrons d'activitat i mobilitat de les mateixes. Aquesta metodologia ha estat aplicada en tres casos d'ús per a: (1) l'obtenció d'estadístiques bàsiques de mobilitat i matrius origen-destinació en àmbits urbans, (2) l'anàlisi de la influència de la xarxa social en la mobilitat i (3) l'estudi de l'exposició de la població a la contaminació. Els resultats obtinguts demostren el potencial de les dades de telefonia mòbil per extreure informació sobre patrons de mobilitat en àmbits urbans, entendre millor la influència de la xarxa social en la mobilitat i millorar les estimacions d'exposició de la població a la contaminació. Tot i els avantatges que proporcionen les dades de telefonia mòbil, també s'han observat limitacions rellevants en els diferents estudis realitzats, derivades principalment de la resolució espai-temporal de les dades i de la limitada informació sociodemogràfica disponible. Els resultats d'aquesta investigació són de gran rellevància per a estudis de planificació i gestió del transport, per al desenvolupament de nous models de transport que tinguin en consideració la influència de la xarxa social en la mobilitat i en estudis d'avaluació de l'exposició de la població a la contaminació.Picornell Tronch, M. (2017). METODOLOGÍA PARA LA EXTRACCIÓN DE PATRONES DE MOVILIDAD URBANA MEDIANTE EL ANÁLISIS DE REGISTROS DE ACTIVIDAD TELEFÓNICA (CALL DETAIL RECORD) [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/88397TESI

Understanding Geo-Social Network Patterns: Computation, Visualization, and Usability

Geo-social networks are formed by flows of physical entities (e.g., humans, vehicles, sensors, animals), and communication (e.g., information, ideas, innovation) that connect places to places and individuals to individuals. Several major problems remain to be addressed for understanding the complex patterns in geo-social networks. This dissertation makes the following contributions to the theory and methodologies that aim at understanding complex geo-social data by integrating methods of computation, visualization and usability evaluation. Chapter 2 introduces a novel network-based smoothing approach that addresses the size-difference and small area problem in calculating and mapping locational (graph) measures in spatial interaction networks. The new approach is a generic framework that can be used to smooth various graph measures which help examine multi-space and multi-scale characteristics of geo-social data. Chapter 3 introduces a space-time visualization approach to discover spatial, temporal and relational patterns in a dynamic geo-social network embedded in space and time. By developing and visualizing a measure of connectedness across space and time, the new approach facilitates the discovery of hot spots (hubs, where connectedness is strong) and the changing patterns of such spots across space and time. Chapter 4 introduces a series of user evaluations to obtain knowledge on how map readers perceive information presented with flow maps, and how design factors such as flow line style (curved or straight) and layout characteristics may affect flow map perception and users’ performance in addressing different tasks for pattern exploration. The findings of this study have significant implications for iterative design, interaction strategies and further user experiments on flow mapping

Migration management in Nigeria : a case study of Edo State

Thesis (PhD (Public Policy))--University of Pretoria, 2023.Nigerian, African, and global migration have received political and foreign policy attention in recent years, with follow-up actions by global and continental frameworks. The United Nations Global Compact on Safe, Orderly and Regular Migration, the 2015 European Union (EU) border hotspot externalisation regime and the African Union (AU) Migration Policy and Development Framework 2006 are few examples of such frameworks. However, Nigerian migrants are on the receiving end of migration policies, which restrict movement and are focused on intensive securitisation and protectionism rather than managing migration. The study used Edo State in Nigeria, a major migration hotspot as a case study that involved in-depth interviews and multiple focus group discussions to arrive at its findings. Using a thematic analysis approach and ATLAS.ti 9 social statistical software for analysis and interpretation, five themes were developed to include a fair, orderly, predictable and explicable migration management framework for Nigerian migrants. The themes highlight international collaborations, synergy, international networks, strategic alliances and linkages; financial management and reporting; global best practices in migration management, legal frontiers of migration, robust migration policy formulation, implementation and post-implementation. The research contributes to beneficial migration science by designing a long-term composite framework which incorporates a mixture of regulating, enhancing, or controlling migration. The theoretical frameworks include the theory of social network, the theory of basic human needs, state fragility theory and the functional theory of human value and social equity. The research concludes by making policy recommendations to migration authorities and non-governmental organisations (NGOs), scholars and technocrats along with potential and returnee migrants on the importance of soft communication and networking skills, policy implementation coherence and matching, information management and ethics, training and re-training, and continuous monitoring of the migration policy and implementation process.Partnership for African Social and Governance Research DPP Grant.School of Public Management and Administration (SPMA)PhD (Public Policy)UnrestrictedFaculty of Economic And Management SciencesSDG-01: No povertySDG-03: Good health and well-beingSDG-10: Reduces inequalitiesSDG-17: Partnerships for the goal