Measuring internal migration

Kirchberger, Martina

doi:10.1016/j.regsciurbeco.2021.103714

Cited by 10 publications

(7 citation statements)

References 27 publications

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“…In these studies, the migration determinants have primarily been investigated and models have been constructed to track, map, and predict human mobility (Taylor and Meissner 2020;Sîrbu et al 2021;Napierała et al 2022;Cai 2022). One of the main advantages of measuring migration with big data is that migration flow can be continuously observed in real time, while migration data conveyed in censuses and household surveys rely on self-reporting by respondents about migration and mobility (Kirchberger 2021). Pajević and Shearmur (2017) investigated how big data can be used for analyzing the geographical trajectories of workers' mobility in urban areas.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…According to Kirchberger (2021), digital trace data from mobile phones can be classified as those that are compiled from the usage of smartphone applications and those that are inferred from call detail record (CDR) data from a mobile phone operator. The latter is widely used in migration studies and allows migration to be defined, in terms of whether it is permanent, temporary, or circular (Batran et al 2018;Yang et al 2020;Kirchberger 2021). However, these data are not widely available for low-income countries and depend greatly on the willingness of mobile phone operators to give access to call detail records, while the data compiled from smartphone applications are mostly available and provide the opportunity to carry out investigations across countries.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Digital data drawn from smartphones and their applications, or Internet-based devices, are constrained by those who use digital equipment. For instance, CDR data cannot measure the movements of migrants that do not hold mobile phones, and may exclude from observation migrants in the lowest-income quintile (Kirchberger 2021). Also, countries where Internet access is expensive or where full coverage is not available may yield a biased representation of the population.…”

Section: Migration Data Issues In Kyrgyz Republicmentioning

confidence: 99%

See 3 more Smart Citations

ICT, Online Search Behavior, and Remittances: Evidence from the Kyrgyz Republic

Roland‐Holst¹,

Karymshakov²,

Sulaimanova³

et al. 2022

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Infrastructure has always been a fundamental driver of long-term economic growth, but in recent decades information and communication technology (ICT) has supported and accelerated the growth of the global economy in ways beyond the imagining of our ancestors. We examine the role of ICT infrastructure in facilitating labor markets' access and remittance flows for workers from the Kyrgyz Republic. Using a combination of traditional high frequency macroeconomic data and real time internet search information from Google Trends, we take a novel approach to explaining the inflow of remittances to a developing country. In the first attempt to model remittance behavior with GTI data in this context, we use a gravity model. We also attempt to account for both origin and destination labor market conditions, using Kyrgyz language search words to identify both push and pull factors affecting migrant decisions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Migration Data Issues In Kyrgyz Republicmentioning

confidence: 99%

See 2 more Smart Citations

ICT, Online Search Behavior, and Remittances: Evidence from the Kyrgyz Republic

Roland‐Holst¹,

Karymshakov²,

Sulaimanova³

et al. 2022

View full text Add to dashboard Cite

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“…This presents AMANDA, a versatile middleware for customized movement among social and non-social informational indexes considering a client-portrayed design a plan that offers support for different sources and targets instructive lists. We review the presence of AMANDA by evaluating the improvement in speed, question execution, demand execution, and movement rightness, from two Social Data databases [17] [18]. different sorts of data are used to evaluate development and versatility, and how corresponding data sorts out which assessment questions they can address.…”

Section: Introductionmentioning

confidence: 99%

Data Migration from Visual Basic Interfaces to Excel Tables Prevent Conflict Using Proposed Models

Hashim

2022

International Journal of Computational and Applied Mathematics &Amp; Computer Science

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In relational and complex spreadsheets, relational and non-relational database models, high-speed data migration with scalable structure using visual Basic Excel programming language implementations with proposed migration model. One of the primary purposes behind making a point of interaction through the Visual Basic Application (VBA) is that most PC clients with logical preparation will currently know about Succeed and its significant capabilities (like arranging furthermore, plotting datasets). Even though Excel itself is in many cases utilized as an information storehouse by clients. The time factor, reliability, and credibility of migrating this data from one table to another through programming interfaces were measured using the link codes between the tables. In this paper, the migration and migration of homogeneous and heterogeneous data are investigated by using two types of different migration models of data to measure and match these data and the extent of their integration after migration. Specifies the target data for migration from the input tables in the Excel program to the target tables in larger databases. Furthermore, the two models (A, B) middleware provide an architecture that can be extended to support Relational database management systems (RDBMS) and other graphing databases. Experiments were performed using excel tables, both of which are related as source information bases, and as the data set for the source and target datasets, the migration time between these tables for the two models was calculated While retaining the same characteristics.

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The migrant perspective: Measuring migrants' movements and interests using geolocated tweets

Mast,

Sapena,

Mühlbauer

et al. 2023

Population Space and Place

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Geolocated social media data hold a hitherto untapped potential for exploring the relationship between user mobility and their interests at a large scale. Using geolocated Twitter data from Nigeria, we provide a feasibility study that demonstrates how the linkage of (1) a trajectory analysis of Twitter users' geolocation and (2) natural language processing of Twitter users' text content can reveal information about the interests of migrants. After identifying migrants via a trajectory analysis, we train a language model to automatically detect the topics of the migrants' tweets. Biases of manual labelling are circumvented by learning community‐defined topics from a Nigerian web forum. Results suggest that differences in users' mobility correlate with varying interests in several topics, most notably religion. We find that Twitter data can be a flexible source for exploring the link between users' mobility and interests in large‐scale analyses of urban populations. The joint use of spatial techniques and text analysis enables migration researchers to (a) study migrant perspectives in greater detail than is possible with census data and (b) at a larger scale than is feasible with interviews. Thereby, it provides a valuable complement to interviews, surveys and censuses, and holds a large potential for further research.

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Measuring internal migration

Cited by 10 publications

References 27 publications

ICT, Online Search Behavior, and Remittances: Evidence from the Kyrgyz Republic

ICT, Online Search Behavior, and Remittances: Evidence from the Kyrgyz Republic

Data Migration from Visual Basic Interfaces to Excel Tables Prevent Conflict Using Proposed Models

The migrant perspective: Measuring migrants' movements and interests using geolocated tweets

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