Concept and Experiment of an International Demographical Information System
Notice bibliographique
Résumé
In recent years,international migration has become more and more common,while its patterns are getting increasingly complex.After several years living in a certain destination,immigrants often make repeat migrations,either to return to their home country or onward to another host country.Researchers have developed new methodologies to study the flows and patterns of transnational movements,and new theories to explain them.One of the new developments in this respect is the triangular model of human capital transfer among nations by Devoretz and Ma(2002),which emphasizes the dynamic nature of international migration in the context of regional development.It argues that immigrants often enter into an entrepot country to accumulate transnational human capital and other capital,such as citizenship.After acquiring such capital,they can choose to stay in the country,return to their home countries,or move to another host country,in order to maximize the return to their acquired transnational capital.New challenges are posed to the study of transnational flows of human capital,requiring the use of multiple censuses of the sending country,entrepot country and major hosting country.Due to structural differences,censuses from different countries are normally in different format and cover populations within different national boundaries.As a result,the most current research on international migration is often limited to either the sending or receiving countries.Efforts are needed to integrate these data sets into a standardized one,so that variables can be unified for direct comparison and further analysis.We adopt a new approach to the integration of census micro-data of different countries into one unified framework.The framework includes two major parts: the statistical part and the mapping part.We start with the statistical part by using open source software packages,such as PHP and MySQL,to implement an integrated micro database system.The micro database includes censuses from multiple countries/regions,including the US,Canada and Hong Kong.In order to enable automated analysis,we first select common variables from different censuses and then standardize each of them to the same unit or category.These standardized variables are either called identifiers or indicators.Identifiers are variables used to identify similar population groups from different censuses and indicators are variables used to compare among groups.In the demo system,we used a total of 10 identifiers and 3 indicators.With the integrated database,we designed a search module and a statistics module.The search module uses key identifiers to search specific population groups from different censuses.The result is listed as tabulations to support further studies.The statistics module takes previous tabulations as input and output results of statistical analysis,including cross country/region comparison and uni-variable analysis(maximum/minimum/mean/std) as tables,graphs,and pre-map files.The second part of the framework is to integrate the micro database with a GIS database,the result of which can be used for mapping purposes.The statistical outputs from Part 1 are processed by the mapping module of Part2,and the system creates online map visualization.This paper mainly introduces the first part of the framework.We are still working on the second part.When both parts are finished and integrated,this system will integrate spatial information with census micro-data of different countries/regions,and provide a unified web-based demographic information system to facilitate flexible and advanced international immigration analysis.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».