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Edited by Jill A.E. Blakley, Daniel M. Franks and

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Renée Pelletier

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Thomas R. Berger

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Edited by Jill A.E. Blakley, Daniel M. Franks and

This important Handbook is an essential guide to the state-of-the-art concepts, debates and innovative practices in the field of cumulative impact assessment. It helps to strengthen the foundations of this challenging field, identify key issues demanding solutions and summarize recent trends in forward progress, particularly through the use of illustrative case examples.
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Edited by Mark Birkin, Graham Clarke, Jonathan Corcoran and Robert Stimson

This unique book demonstrates the utility of big data approaches in human geography and planning. Offering a carefully curated selection of case studies, it reveals how researchers are accessing big data, what this data looks like and how such data can offer new and important insights and knowledge.
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Field Guide for Research in Community Settings

Tools, Methods, Challenges and Strategies

Edited by M. R. Islam, Niaz A. Khan, Siti H.A.B. Ah, Haris A. Wahab and Mashitah B. Hamidi

This insightful book offers practical advice to fieldworkers in social research, enabling robust and judicious applications of research methods and techniques in data collection. It also outlines data collection challenges that are commonly faced when working in the field.
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M. Rezaul Islam, Niaz Ahmed Khan, Siti Hajar Abu Bakar Ah, Haris Abd Wahab and Mashitah Binti Hamidi

Fieldwork/data collection is one of the most important parts in the research process, and it is particularly important for social sciences research. A number of aspects that need to be considered by a researcher before starting data collection include: ethical permission from the concerned ethical body/committee, informed consent, contract with different stakeholders, field settings, time allocation and time management, field leading, data collection, contextual and cultural diversities, community settings, socioeconomic and psychological patterns of the community, political pattern, rapport building between data collectors and respondents, permission to access community, language and mode of data collection, power relations, role of gatekeepers, privacy and confidentiality issues, layers of expectations among researchers/respondents/ funding organization, data recording (written, memorization, voice recording and video recording), and so on. Many aspects are very difficult to understand before going into the field. Sometimes, a researcher’s previous experience about a particular community may help to gain field access, but it may be difficult to assess the field in advance due to rapid changes within people’s livelihoods and other shifts in the community. The change of a political paradigm sometimes seems also to be a challenge at the field level. We believe that although technological innovation has benefited some aspects of the data collection of fieldwork in social research, many other dimensions (mentioned above) of fieldwork endure unchanged.

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Monica Alexander

Understanding migration patterns and how they change over time has important implications for understanding broader population trends, effectively designing policy and allocating resources. However, data on migration movements are often lacking, and those that do exist are not produced in a timely manner. Social media data offer new opportunities to provide more up-to-date demographic estimates and to complement more-traditional data sources. Facebook, for example, can be thought of as a large digital census that is regularly updated. However, its users are not representative of the underlying population, thus using the data without appropriate adjustments would lead to biased results. This chapter discusses the use of social media advertising data to estimate migration over time. A statistical framework for combining traditional data sources and the social media data is presented, which emphasizes the importance of three main components: adjusting for non-representativeness in the social media data; incorporating historical information from reliable demographic data; and accounting for different errors in each data source. The framework is illustrated through an example that uses data from Facebook’s advertising platform to estimate migrant stocks in North America.