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Data science for migration and mobility / edited by Albert A. Salah, Emre E. Korkmaz, and Tuba Bircan.
- Format:
- Book
- Series:
- Proceedings of the British Academy ; 251.
- British Academy scholarship online.
- Proceedings of the British Academy ; 251
- British Academy scholarship online
- Language:
- English
- Subjects (All):
- Emigration and immigration--Research--Methodology.
- Emigration and immigration.
- Social mobility--Research--Methodology.
- Social mobility.
- Data mining.
- Physical Description:
- 1 online resource (xxxiv, 428 pages) : illustrations (black and white, and colour), maps (black and white, and colour).
- Edition:
- First edition.
- Place of Publication:
- Oxford : Published for The British Academy by Oxford University Press, [2023]
- Summary:
- 'Data Science for Migration and Mobility' provides an interdisciplinary introduction to the usage of new data sources in migration and mobility research, including mobile phone records, social media content, satellite images, event and financial databases.
- Contents:
- Cover
- Half-title
- Title page
- Copyright information
- Dedication
- Table of contents
- List of Figures
- Colour Plates
- List of Tables
- Notes on Contributors
- Preface
- Part I: Introduction
- 1 New Data Sources and Computational Approaches to Migration and Human Mobility
- Introduction
- Conceptual groundwork
- Migration and mobility
- The data perspective
- Approaches to empirical modelling
- Why new data sources for migration?
- Limitations of traditional data sources
- Challenges for new data sources
- Big data and migration research in practice
- Data sharing practices
- Stakeholders and collaborators
- Researchers
- Data collectors and providers
- Policyand decision-makers
- Migrants
- Found data and private data
- A case study on risks: Digital identity, big data, and the power of corporations
- The identification problem
- Stakeholders and suppliers
- Conclusions
- Acknowledgements
- References
- 2 Ethical and Legal Concerns on Data Science for Large-Scale Human Mobility
- Applying ethics and ethics principles
- What is applied ethics?
- Ethics in data science
- Ethics principles
- Ethical issues in data science for migration and mobility
- Consent
- De-identification, anonymisation, and re-identification
- Black box, transparency, and explainability
- Algorithmic bias
- Dual use of technologies
- Complexity and risk assessment
- Data ethics tools
- Legal risks for data science in migration and mobility
- Data collection, processing, and sharing
- Algorithm development
- Deployment and policy
- Data ethics and the Global Compact for Safe, Orderly, and Regular Migration
- Conclusion
- Document repositories and further reading
- Part II: Data Sources.
- 3 CESSDA Data Catalogue: Opportunities and Challenges to Explore Mobility and Migration
- Consortium of European Social Science Data Archives
- Catalogues as a vehicle of collaborative research: Migration studies use cases
- The Data Catalogue explained
- Catalogue development
- Catalogues and multilinguality
- Searching the catalogue: The case of migration datasets
- FAIR in practice
- Use cases on migration
- The HumMingBird Project
- International Survey Data Network
- Challenges and prospects for collaborative research
- 4 Leveraging Mobile Phone Data for Migration Flows
- Mobile phone data: Call detail records
- Application of mobile phone data for migration
- Refugees
- Climate-based migration
- Labour migration
- Other uses of mobile phone data
- Mobile phone data for migration: Challenges and limitations
- Technical challenges
- Geographic and temporal resolution
- Representativeness of the data
- Data integration
- Timeliness of the data
- Ethical, regulatory, and financial challenges
- Ethical challenges
- Privacy and regulatory challenges
- Difficulty of access and lack of financially sustainable models
- 5 Analysing Refugees' Secondary Mobility Using Mobile Phone Call Detail Records
- Mobile phone data and mobility analysis
- Background: Syrian refugees in Turkey, and the Data for Refugees (D4R) Challenge
- Research and data ethics: Protection of research participants
- Data preparation and analysis, and ethical reflections
- A short 'portrait' of the data, and data preparation
- Data preparation
- Ethics: Bias and representation
- CDR analysis: Some key insights
- Ethics: Voluntariness and informed consent
- CDR analysis: Spatial patterns
- Ethics: Do no harm
- CDR analysis: Temporal patterns.
- Conclusion
- Further reading
- 6 Remote Sensing Data for Migration Research
- An overview of remote sensing
- Remote sensing sensors
- Remote sensing satellite systems
- Commercial remote sensing satellites
- LANDSAT programme
- Defence Meteorological Satellite Programme
- Moderate-Resolution Imaging Spectroradiometer
- Suomi National Polar-orbiting Partnership
- Copernicus programme
- The remote sensing process and data products
- Land cover and land use
- Night-time lights
- Satellite data utilisation for studying climate-induced migration
- Case study 1: Impact of ethnic migration on a protected area landscape in western Uganda
- Case study 2: Non-linearities and thresholds for the relationship between climate shocks and rural-urban migration in Mexico
- Case study 3: Dynamics of armed conflict, forced migration, and urbanisation in Colombia
- Satellite data applications for migration and humanitarian aid
- Discussion: Opportunities and limitations
- 7 Using Facebook and LinkedIn Data to Study International Mobility
- Introduction to advertising data
- Facebook examples
- LinkedIn examples
- General limitations/challenges
- Self-selection bias
- Self-reporting bias
- Brittleness of the APIs
- Privacy concerns
- Ethical and legal concerns
- Summary
- The next frontier: Combining data and fully leveraging the infrastructure of the digital age
- 8 Twitter Data for Migration Studies
- Migration case studies
- Acquiring data
- Downloading: API and libraries
- Data format
- Tweet object
- Terms of service
- User terms of service
- Developer terms of service and links to privacy and ethics in migration research
- Changes in the terms of service
- Processing Twitter data.
- Natural language processing
- Geolocation pipeline
- Gaps and biases
- Discussion and conclusions
- 9 Indicators and Survey Data to Understand Migration and Integration Policy Frameworks and Trends in the EU
- Output: Migration policy indicators
- Data accessibility
- Solano
- Topics and groups covered
- Topics
- Groups
- Geographical and temporal coverage
- An example index: The Migrant Integration Policy Index
- Outcomes: Migration and integration trends
- Data quality, gaps, and limitations
- The link between policies and migration trends and integration outcomes
- Recommended reading
- 10 Financial Datasets: Leveraging Transactional Big Data in Mobility and Migration Studies
- Literature review
- Datasets
- Case study
- Discussion and conclusion
- Part III: Visualisation
- 11 Visual Exploration of Large Multidimensional Trajectory Data
- Background
- Problem domain
- Data model
- Preliminaries
- Paths vs trails
- Modelling time
- Visualisation techniques
- Aggregating methods
- Density maps
- Bundling methods
- Techniques for time-dependent data
- Other visual encodings
- Creating visualisations
- Data collection and curing
- Attributes
- Normalisation
- Values
- Data simplification and filtering
- Size reduction
- Attribute reduction
- Designing the visual exploration
- Overview, zoom and filter, then details on demand
- Visual analytics loop
- Putting it all together
- Scalability
- Interpretability
- Quality
- Replicability
- References.
- 12 Voyage Viewer: A Multivariate Visualisation Tool for Migration Analysis
- Related work
- Visualisations for migration
- Design rationale
- Inputs and resources
- Traditional data
- Alternative data sources
- Infrastructure and system description
- Activities and outputs
- Interactive visualisations
- People Like Me
- Outcomes
- Use cases
- Citizen use case
- Policy actors
- Limitations
- Conclusions and future work
- Part IV: Case Studies and Applications
- 13 Combining Mobile Call Data and Satellite Imaging for Human Mobility
- Data and measurement
- Measuring mobility
- Measuring the characteristics of origin and destination locations
- Empirical strategy
- Results
- 14 Using Machine Learning and Synthetic Populations to Predict Support for Refugees and Asylum Seekers in European Regions
- Relocation and resettlement of refugees and asylum seekers in Europe
- Data-driven migration planning
- Method
- Predicting individual acceptance
- Predicting social acceptance
- 15 Issues about Analysing Multilingual Communication in Immigrant Contexts
- Clarification of terminology about language use in immigrant contexts
- Analysing language use in immigrant contexts through spoken data
- Analysing language use in online environments for immigrant communities
- Qualitative methods of analysis
- Computational methods of analysis
- 16 Applying Computational Linguistic and Text Analysis to Media Content about Migration
- Orienting: Approaches and limitations
- Collecting: Media data and the politics of their origins.
- Analysing: Linking textual patterns and mental schemas.
- Notes:
- This edition previously issued in print: 2022.
- Includes bibliographical references and index.
- Description based on online resource; title from PDF title page (viewed on May 23, 2023).
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 1-80596-087-3
- 1-80596-054-7
- 0-19-199173-2
- OCLC:
- 1389528287
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