Georeferenced digital trace data offer unprecedented flexibility in migration estimation. Because of their high temporal granularity, many migration estimates can be generated from the same data set by changing the definition parameters. Yet despite the growing application of digital trace data to migration research, strategies for taking advantage of their temporal granularity remain largely underdeveloped. In this paper, we provide a general framework for converting digital trace data into estimates of migration transitions and for systematically analyzing their variation along a quasi-continuous time scale, analogous to a survival function. From migration theory, we develop two simple hypotheses regarding how we expect our estimated migration transition functions to behave. We then test our hypotheses on simulated data and empirical data from three platforms in two internal migration contexts: geotagged Tweets and Gowalla check-ins in the United States, and cell-phone call detail records in Senegal. Our results demonstrate the need for evaluating the internal consistency of migration estimates derived from digital trace data before using them in substantive research. At the same time, however, common patterns across our three empirical data sets point to an emergent research agenda using digital trace data to study the specific functional relationship between estimates of migration and time and how this relationship varies by geography and population characteristics.
This article contributes to improving our understanding of biases in estimates of demographic indicators, in the developing world, based on Call Detail Records (CDRs). CDRs represent an important and largely untapped source of data for the developing world. However, they are not representative of the underlying population. We combine CDRs and census data for Senegal in 2013 to evaluate biases related to estimates of population density. We show that: (i) there are systematic relationships between cell-phone use and socio-economic and geographic characteristics that can be leveraged to improve estimates of population density; (ii) when no ‘ground truth’ data is available, a difference-in-difference approach can be used to reduce bias and infer relative changes over time in population size at the subnational level; (iii) indicators of development, including urbanization and internal, circular, and temporary migration, can be monitored by integrating census data and CDRs. The paper is intended to offer a methodological contribution and examples of applications related to combining new and traditional data sources to improve our ability to monitor development indicators over time and space.
Geo-referenced digital trace data offer unprecedented flexibility in migration estimation. Due to their high temporal granularity, many different migration estimates can be generated from the same dataset by changing the definition parameters. Yet despite the growing application of digital trace data to migration research, strategies for taking advantage of their temporal granularity remain largely underdeveloped. In this paper, we provide a general framework for converting digital trace data into estimates of migration transitions and for systematically analyzing their variation along a quasi-continuous time-scale, analogous to a survival function. From migration theory, we develop two simple hypotheses regarding how we expect our estimated migration transition functions to behave. We then test our hypotheses on simulated data and empirical data from three different platforms in two internal migration contexts: geotagged Tweets and Gowalla check-ins in the U.S., and cell-phone call detail records in Senegal. Our results demonstrate the need for evaluating the internal consistency of migration estimates derived from digital trace data before using them in substantive research. At the same time, however, common patterns across our three empirical datasets point to an emergent research agenda using digital trace data to study the specific functional relationship between estimates of migration and time and how this relationship varies by geography and population characteristics.
The chapter provides an overview of the use of ICT tools to enhance education for persons with disabilities in developing countries. The chapter per the authors begins by presenting the international legislation on education and ICT access for persons with disabilities, and then provides both a typology for ICT usage in education for persons with disabilities as well as case studies. The authors then discuss how digital information derived from usage of ICT tools can be used to inform inclusive educational policies and programs. Finally, the chapter describes current challenges and opportunities, and provides recommendations for scaling up ICT in developing countries as a mean to improve education for persons with disabilities.
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