Rasim M.Alguliyev, Farhad F. Yusifov

The article is devoted to the formation of an e-demographic system based on public registries. Currently, the formation of an e-demographic system that allows monitoring of demographic characteristics on the e-government platform is very important in terms of building an effective management system. E-demography allows the study of the impact of digital technologies on demographic behavior and the use of new data sources for in-depth research of demographic processes. This paper article analyzes the international experience in the field of e-demography, examines the approaches to the formation of e-demography on the basis of the population registry. Recent studies has reviewed the use of data collected in population registries as a new source of information for demographic surveys and statistics. The studies shows that in countries, where the population registry is applied, personal identification numbers are used to integrate data. Currently, unique identification numbers have to be used in all countries practise population registry, and the main goal is to eliminate duplication during monitoring and improve coordination between different public registries. Conceptually, it is proposed to build an e-demography system on the basis of a single public registry. In this case, all public registries, databases and portals must be transferred to the e-demography platform. All data collected in public registries play acts as an important source for demographic research. The article provides individual characteristics of registries and databases transferred to the e-demographic system. The formation of an e-demographic system will provide ample opportunities for socio-demographic research and the study of demographic behavior (pp.3-17).

Keywords:electronic government, electronic demography, demographic characteristics, population registry, electronic registries.
DOI : 10.25045/jpis.v12.i1.01
  • Борисов В.А. Демография, М., Издательский дом “NOTABENE”, 2001, 272 с.
  • Mehr H. Artificial Intelligence for Citizen Services and Government. Cambridge, Harvard Kennedy School, 2017, intelligence for citizen services.pdf
  • Zagheni E. Data Science, Demography and Social Media: Challenges and Opportunities, 2017,
  • Zagheni E., Weber I. Demographic research with non-representative internet data // International Journal of Manpower, 2015, vol. 36, no. 1, pp. 13–25.
  • Алгулиев Р.М., Алыгулиев Р.М., Юсифов Ф.Ф., Алекперова И.Я. Формирование электронной демографии как эффективного инструмента социальных исследований и мониторинга данных о населении // Вопросы государственного и муниципального управления. Public administration issues, «Высшая школа экономики» (НИУ ВШЭ), 2019, № 4, c. 61–86.
  • Fire M., Elovici Y. Data Mining of Online Genealogy Datasets for Revealing Lifespan Patterns in Human Population // ACM Transactions on Intelligent Systems and Technology, 2015, vol. 6, issue 2, pp.1–24.
  • Billari F., D’Amuri F., Marcucci J. Forecasting births using google, Annual Meeting of the Population Association of America, 2013, New Orleans, LA.17.
  • Poulain M., Herm A. Central populatıon registers as a source of demographic statistics sin Europe // Population, 2013, vol. 68. no. 2, pp. 183–212.
  • Yildiz D., Munson J., Vitali A. and et al. Using Twitter data for demographic research // Demographic Research, 2017, vol. 37. Article 46, pp. 1477–1514.
  • Cesare N., Lee H., McCormick T. et al. Promises and Pitfalls of Using Digital Traces for Demographic Research // Demography, 2018, vol. 55, no 5, pp. 1979–1999.
  • Gil-Clavel S., Zagheni E. Demographic Differentials in Facebook Usage around the World / Proceedings of the Thirteenth International AAAI Conference on Web and Social Media (ICWSM 2019), 2019, pp. 647–650.
  • Emilio Zagheni is new MPIDR director, 2018, news_press/news/news/emilio_zagheni_is_new_mpidr_director_5556.htm
  • Gangi R.R., Rajesh N.B., Sudhakar N.P., Raviteja B., Rammohanarao K. Tracking objects, using RFID and wireless sensor networks // International Journal of Engineering Science & Advanced Technology, 2012, vol. 2, issue 3, pp. 513–517.
  • Redondi A., Chirico M., Borsani L., Cesana M., Tagliasacchi M. An integrated system based on wireless sensor networks for patient monitoring, localization and tracking // Ad Hoc Networks, 2013, vol. 11, issue 1, pp. 39–53.
  • Качагина О.В. Основы демографии: основы теории и практические задания: Учебное пособие. Ульяновск: УлГУ, 2016,129 с.
  • Alburez-Gutierrez D., Aref S., Gil-Clavel S. and et al. Demography in the Digital Era: New Data Sources for Population Research. In: Arbia G., Peluso S., Pini A., Rivellini G. (eds.), Book of short Papers SIS2019. 2019,
  • Ginsberg J., Mohebbi M.H., Patel R.S., Brammer L., Smolinski M.S., Brilliant L. Detectinginfluenza epidemics using search engine query data // Nature, 2008, vol. 457, no. 7232, pp. 1012–1014.
  • Lazer D.M., Kennedy R., King G., Vespignani A. The parable of google flu: traps inbig data analysis // Science, 2014, vol. 343, no. 6176, pp. 1203–1205.
  • De Choudhury M., Feldman M., Amer-Yahia S., Golbandi N., Lempel R., Yu C. Automatic construction of travel itineraries using social breadcrumbs / Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, ACM, 2010, pp. 35–44.
  • Ferrari L., Mamei M. Discovering daily routines from google latitude with topic models / Proceedings of theInternational Conference on Pervasive Computing and Communications Workshops, IEEE, 2011, pp. 432–437.
  • Ferrari L., Rosi A., Mamei M., Zambonelli F. Extracting urban patterns from location-based social networks / Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, ACM, 2011, pp. 9–16.
  • Noulas A., Scellato S., Mascolo C., Pontil M. An empirical study of geographic useractivity patterns in foursquare / Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 2011, vol. 11, pp. 570–573.
  • Billari F., Zagheni E. Big data and population processes: a revolution? In: Petrucci A., Verde R. (eds.) Statistics and Data Science: new challenges, new generations / Proceedings of the Conference of the Italian Statistical Society. Firenze University Press. 28–30 June, Florence (Italy), 2017, pp. 167–178.
  • Blumenstock J.E. Inferring patterns of internal migration from mobile phone call records: evidence from Rwanda // Information Technology for Development, 2012, vol. 18 no. 2, pp. 107–125.
  • Deville P., Linard C., Martin S., Gilbert M., Stevens F.R., Gaughan A.E., Blondel V.D., Tatem A.J. Dynamic population mapping using mobile phone data / Proceedings of the National Academy of Sciences, 2014, vol. 111, no. 45, pp. 15888–15893.
  • United Nations Global Pulse’s projects?,
  • Андриченко Л.В., Мещерякова М.А. Информационные регистры как эффективное средство сбора и мониторинга данных о населении // Журнал российского права, 2012, №8, с. 16–40.
  • How Credit Works,
  • Чудиновских О. Большие данные и статистика миграции // Вопросы статистики, 2018, Т.25. №2, с. 48–56.
  • В России будет создан реестр населения. Российская газета – Федеральный выпуск № 122(7585),
  • Lyngstad T.H., Skardhamar T. Nordic register data and their untapped potential for criminological knowledge // Crime and Justice, 2011, vol. 40(1), pp. 613-645.
  • Статистика на основе регистров. Статистика на основе регистров в североевропейских странах. Обзор передовых методик с уделением основного внимания статистике населения и социальной статистике / ООН, Европ. экон. комис. – Нью-Йорк; Женева: ООН, 2008.
  • Prins K. Population register data, basis for the Netherlands Population Statistics. Statistics Netherlands, Hague. 2017,
  • Careja R. & Bevelander P. Using Population Registers for Migration and Integration Research: Examples from Denmark and Sweden // Comparative Migration Studies, 2018, vol. 6, no 1, pp. 6-19.
  • Population registers in different countries: Design and developments in relation to The Netherlands, 2019,
  • Guidelines on Population Registration, OSCE’s Office for Democratic Institutions and Human Rights (ODIHR), 2009,
  • UNSD, Population registers as source of vital statistics, 2015,
  • Rivera A., Milena A., Vassil Estonia: A Successfully Integrated Population-Registration and Identity Management System, The World Bank Group,  2015,
  • “Azərbaycan Respublikası Əhalisinin Dövlət Reyestrinin aparılması qaydası haqqında Əsasnamə”nin təsdiq edilməsi barədə Azərbaycan Respublikasi Prezidentinin Fərmanı, Bakı şəhəri, 14 oktyabr 2004-cü il,­bcdf872146e26.pdf
  • CRVS and the population register,
  • Courgeau В., Bijak J., Franck R.,  Silverman E. Model-Based Demography: Towards a Research Agenda // Agent-Based Modelling in Population Studies, 2016, vol. 48, pp. 29–51.
  • Bijak J., Courgeau D., Franck R., Silverman E. Modelling in Demography: From Statistics to Simulations. In: Methodological Investigations in Agent-Based Modelling. Methodos Series, (Methodological Prospects in the Social Sciences), 2018, vol 13. Springer, Cham
  • Weber I., State B. Digital Demography / Prosessing of the International World Wide Web Conference Committee (IW3C2), April 3–7, 2017, Perth, Australia, pp. 935–939.