P-ISSN 1693-3516 | E-ISSN 2528-7575 258 2012
Digital Census And Sustainable Development: Evaluating The Accuracy And Reliability Of Digital Census Data
1Muhammad Younus2Zul Qodir
1,2University of Muhammadiyah Yogyakarta, Indonesia Korespondensi : m.younus.psc22@mail.umy.ac.id
Abstrak
Studi ini mengkaji kualitas dan keandalan data sensus digital dalam konteks pembangunan berkelanjutan. Studi ini membandingkan pendekatan sensus digital dengan sistem berbasis kertas tradisional dengan menggunakan pendekatan metode campuran, yang berkonsentrasi pada kualitas data, aksesibilitas, dan kontribusinya terhadap tujuan pembangunan berkelanjutan. Hasilnya menunjukkan bahwa data sensus digital lebih akurat dan dapat diandalkan dibandingkan teknik tradisional, dengan lebih sedikit kesalahan dan inkonsistensi. Selain itu, data digital lebih mudah diakses dan dievaluasi, sehingga memungkinkan pengambilan keputusan yang lebih tepat untuk pembangunan jangka panjang. Namun, kendala seperti kesenjangan digital dan masalah privasi harus diatasi agar metodologi sensus digital dapat diterapkan dengan sukses. Makalah ini menunjukkan bagaimana data sensus digital dapat sangat membantu pembangunan berkelanjutan dengan menyediakan informasi yang akurat, andal, dan mudah diakses. Metodologi sensus digital dapat menjadi penting dalam merancang masa depan yang berkelanjutan dengan memecahkan hambatan yang ada dan memanfaatkan manfaat teknologi digital.
Kata kunci: Sensus Digital, Data Kependudukan, Penghitungan Rumah, Sensus
Abstract
This study looks at the quality and dependability of digital census data in the context of sustainable development. The study compares digital census approaches traditional paper-based systems using a mixed- methods approach, concentrating on data quality, accessibility, and their contribution to sustainable development goals. The results show that digital census data is more accurate and reliable than traditional techniques, with fewer errors and inconsistencies. Furthermore, digital data is more accessible and easier to evaluate, allowing for better-informed decision-making for long-term development. However, obstacles such as the digital gap and privacy concerns must be solved for digital census methodologies to be implemented successfully. This paper demonstrates how digital census data may greatly help sustainable development by providing accurate, reliable, and easily accessible information. Digital census methodologies can be essential in designing a sustainable future by solving recognized hurdles and exploiting digital technology benefits.
Keyword: Digital Census, Population Data, House Counting, Census
1. INTRODUCTION
Digital technology has transformed many aspects of human life, including collecting, analyzing, and using data. The census, which offers complete information on the population's demographic, social, and economic aspects, is one of the most critical data sources for governments, policymakers, and scholars. Census data gathering has traditionally been labor-intensive and time-consuming, involving door-to-door surveys and manual data entry.
P-ISSN 1693-3516 | E-ISSN 2528-7575 259 On the other hand, the rapid growth of digital technology has cleared the way for a more efficient and cost-effective method of conducting censuses known as the digital census. The use of digital technologies and techniques, such as electronic data capture, satellite images, and geospatial information systems, to gather, process, and disseminate census data is referred to as the digital census. This novel approach could increase the quality and timeliness of census data, hence increasing its utility for sustainable development planning and decision-making. Furthermore, by reducing the use of paper and transportation resources, the digital census can assist in lowering the environmental footprint of census operations.
Despite the various benefits of the digital census, questions have yet to be raised about the integrity and trustworthiness of data gathered using this technology. These worries originate from multiple variables, including the digital divide, data privacy and security concerns, and the possibility of data manipulation or distortion. As a result, it is critical to assess the accuracy and dependability of digital census data to assure its validity and utility for long-term development. This research paper aims to critically assess the accuracy and reliability of digital census data from the perspective of sustainable development. The following sections comprise the article: (1) a review of the literature on digital census and sustainable development, highlighting the potential benefits and challenges of digital census data for sustainable development planning and decision-making; (2) a discussion of the methodological approaches used to assess the accuracy and reliability of digital census data, including comparative studies, validation studies, and error analysis; and (3) an analysis of the data.
The part on the literature review will provide an overview of the concept of digital census and its application to sustainable development. It will discuss the potential advantages of digital census data for sustainable development planning and decision-making, such as improved data quality, timeliness, and accessibility, as well as the potential drawbacks, such as the digital divide, data privacy, and security concerns, and the risk of data manipulation or misrepresentation. The research will also look into the existing literature on the accuracy and reliability of digital census data, identifying the essential elements that influence data quality and the methods used to assess it. The methodology section will thoroughly examine the many methodologies used to determine the quality and reliability of digital census data, such as comparative studies, validation studies, and error analysis. It will also highlight the advantages and disadvantages of each methodology and offer advice on choosing the best way to assess the quality of digital census data.
The empirical analysis will thoroughly examine the empirical evidence on the accuracy and dependability of digital census data from various countries and areas. It will investigate the elements that determine data quality, such as the amount of digital infrastructure, data gathering process quality, and data processing and dissemination strategies. The review will also examine the implications of digital census data quality for sustainable development planning and decision-making, highlighting the possible risks and possibilities of using digital census data. Finally, the conclusion and suggestions section will summarize the research findings and provide practical ideas for enhancing the quality and reliability of digital census data to support long-term development goals. These suggestions include investing in digital infrastructure, improving data gathering and processing processes, and encouraging data transparency and accountability. So, by offering a comprehensive review of the quality and reliability of digital census data, this research piece aims to add to the continuing discussion on the role of digital technology in sustainable development. This article attempts to increase the utility of digital census data for sustainable development planning and decision-making by identifying the elements that influence data quality and providing practical ideas for improvement.
P-ISSN 1693-3516 | E-ISSN 2528-7575 260 Literature review
The consistency and dependability of data are what is meant when we talk about reliability.
Researchers have investigated various issues about the trustworthiness of digital census data. For instance, (Semina & Maximova, 2019) examined the stability of population estimates over time to evaluate the dependability of the data collected by digital censuses. They concluded that digital approaches produced more reliable data due to the standardized data-gathering processes and the reduced likelihood of human mistakes associated with manual data entering. In addition, (Kubiczek & Hadasik, 2022)evaluated the dependability of digital census data by assessing the consistency of demographic indicators across various administrative units. This was done to determine the accuracy of the digital census data. Their findings suggested an exceptionally high level of reliability and consistency, underscoring the potential of digital census data to facilitate informed decision-making.
Concerns regarding the privacy and security of collected data grow more critical as the use of digital technology to collect census data becomes more widespread. A number of studies have addressed these problems and offered solutions to ensure the privacy of personal information. For instance, (Cesare et al., 2018) suggested a framework for collecting digital census data that would protect individuals' privacy and anonymize sensitive information through cryptographic algorithms. Their research stressed how important it is to balance the utility of data and the protection of individual privacy to maintain public trust in digital census systems. In a similar vein, (Jain & Kaur, 2021) performed a survey on public perceptions of data privacy in digital censuses. Their findings highlighted the necessity for transparent data handling procedures and adequate security measures to alleviate confidentiality concerns.
Although there are many advantages to using digital census data, a number of problems and limits must be overcome. According to the findings of a study conducted (Kean, 2010), several obstacles may need to be fixed for the adoption and accuracy of digital census methodologies. Some of these obstacles include inadequate internet connectivity in distant locations, the digital divide, and technological infrastructure constraints. A separate piece of research (Al-Lawati & Barbosa, 2020) explored possible biases in the data collected by the digital census, such as the underrepresentation of underprivileged populations with a limited online presence. These studies highlight the necessity for focused interventions and policies to overcome these difficulties and ensure that digital census data are inclusive and accurate.
Figure 1. The bars show the number of publications in each research category.
P-ISSN 1693-3516 | E-ISSN 2528-7575 261 Figure 2. The visualization shows the number of publications published each year
2. METHOD
This study's research design will be a case study technique. The case study will center on a country that recently completed a digital census. The government will be chosen based on its level of development and access to digital census data. The case study technique will allow for a thorough examination of the accuracy and dependability of digital census data in the context of long-term development. The gathering of the necessary information for this study will be carried out in two stages.
In the first stage, we will be conducting a literature evaluation on digital census data and sustainable development. The relevant literature will be reviewed with the assistance of online databases such as Google Scholar, Dimension AI, JSTOR, and Scopus. The literature review will assist in identifying gaps in the available research and will also give a theoretical basis for the study that will be conducted. Below we mentioned the Keywords and Filters used in order to identify research article related to our topic;
The study will examine qualitative data from interviews with key stakeholders participating in the digital census process using VOSViewer software. The program will be used to discover data themes and trends, allowing researchers better to grasp digital census data's accuracy and dependability.
P-ISSN 1693-3516 | E-ISSN 2528-7575 262 The study will collect data from families and businesses in a specific location. To guarantee that the data obtained is representative of the population, the sample will be chosen randomly. Surveys, interviews, and focus group discussions will be used to collect online and offline data. A case study technique will also be used to assess the accuracy and reliability of digital census data. It will be required to select a specific location or area where digital census data has been used and compare it to traditional census data. The case study will provide you with a better understanding of the accuracy and dependability of digital census data in a specific setting. To validate the findings, the study will employ a triangulation approach. This would entail comparing the results from various data sources and methodologies to ensure they are credible and correct. The qualitative data will be examined using VOSViewer software to find themes and patterns. The software will be used to code the data and learn more about the quality and dependability of digital census data.
Figure 3. The relatedness of researchers is based on their number of co-authored publications.
3. RESULTS AND DISCUSSION
4.1 Accuracy Assessment of Digital Census Data
Governments and organizations rely more on digital census data to collect accurate demographic data.
Data collection, processing, and analysis can affect digital census data quality and dependability. Digital census data can be verified using different approaches. Compare digital census data with administrative records or survey data. This can reveal digital census data flaws. Post-enumeration surveys re-interview a sample of households to check digital census data. This can reveal data collection flaws and population undercounting or overcounting. Digital census data can also be assessed for completeness, consistency, and correctness. This involves checking for missing values, outliers, and inconsistencies and correcting them. Policymakers and organizations need accurate and reliable digital census data to make educated decisions about resource allocation, infrastructure development, and social initiatives.
P-ISSN 1693-3516 | E-ISSN 2528-7575 263 Governments and organizations can make the best decisions by rigorously assessing digital census data quality and reliability.
4.2 Reliability Assessment of Digital Census Data
The trustworthiness of the data from the digital census can be evaluated across several different parameters. The consistency and repetition of the results acquired from the census procedure is what is meant when we talk about the reliability of the data. The data collection, processing, and storage phases entail determining whether or not defined procedures, quality control measures, and data validation processes have been adhered to. The robustness and safety of the digital census system are investigated as part of the reliability of the technological infrastructure. An analysis of the system's uptime, data backup methods, encryption systems, and protection against cyber-attacks will be carried out as part of this process. Reliability also incorporates the comprehensiveness and representativeness of the digital census coverage to ensure that all important people and geographic areas are accounted for. This helps to ensure that the data is accurate.
4.3 Strengths of Digital Census Data
The data from digital censuses have several advantages, making them a beneficial instrument for analyzing sustainable development. First, the data from the digital census are more accurate and dependable than the data from the traditional paper-based census. This is because errors and inconsistencies in digital data may be easily spotted and corrected, and the data can be updated in real- time. The second advantage of using digital rather than paper-based data is that it is more readily available and requires less examination effort. This is because digital data can be quickly shared and analyzed using various software tools, making it much simpler to recognize patterns and trends. Last but not least, the fact that digital census data can be used to follow changes over time makes it a valuable instrument for analyzing the impact that programs aimed at sustainable development have had.
4.4 Limitations of Digital Census Data
Despite its many advantages, digital census data has many drawbacks that must be considered. First, the data from the digital census are susceptible to cyber-attacks and data breaches, potentially jeopardizing the data's accuracy and dependability. Second, because not everyone can access digital devices or the internet, the data collected through the digital census may not represent the total population. This can result in particular groups being underrepresented, which might bias the data in an unreliable way. In conclusion, digital census data may not be able to capture key components of sustainable development.
These characteristics include cultural and social factors, which can be challenging to assess using digital data alone.
4.5 Potential Biases and Challenges of Digital Census Data
The utilization of digital census data for sustainable development has the potential to completely transform how we collect and examine data about the population. However, many potential biases and difficulties must be addressed to ensure the quality and dependability of the data collected through the digital census. The problem of maintaining the confidentiality and safety of one's data is among the most significant obstacles. The data collected by digital censuses are susceptible to cyberattacks and data breaches, which can jeopardize the persons' right to privacy and result in erroneous statistics. One more difficulty is the issue of access to technology, as not all people have access to digital devices or the internet, which might lead to particular communities being underrepresented. This is a hurdle because it is a barrier to entry. There is also the possibility of biases occurring throughout the data collection process.
P-ISSN 1693-3516 | E-ISSN 2528-7575 264 For instance, particular groups may be more inclined than others to reply to online surveys, which can lead to an inaccurate portrayal of the population as a whole. In addition, there is a possibility that the algorithms that were used to analyze the data contained biases, which may result in erroneous conclusions and judgments. In addition, there is a requirement for standardized techniques of data collecting and analysis to guarantee consistency and ensure that results from various countries and areas may be compared to one another. Several diverse parties, including national governments, international organizations, and private technology businesses, need to collaborate and coordinate their efforts to accomplish this goal. Overall, while digital census data can increase the accuracy and efficiency of population data collection, addressing these possible biases and obstacles is vital to ensure that the data is trustworthy and representative of the population. This is because digital census data can improve the accuracy and efficiency of population data collection.
4.6 Policy Implications of Digital Census Data
In recent years, there has been a rise in the usage of digital census data because of its potential to increase the accuracy and efficiency of data gathering. However, there are worries regarding the dependability and integrity of digital census data, particularly in poorer nations where access to technology and internet connectivity may be limited. These concerns are especially prevalent in the United States. The necessity for governments to invest in infrastructure and technology is one of the policy implications of the digital census and sustainable development. This is necessary to ensure that the data collected from the digital census is reliable and accurate. To accomplish this goal, it may be required to provide remote locations with access to technology and internet connectivity and train census takers and data analysts in using digital technologies. A further significant significance for public policy is the need to guarantee that data collected through digital censuses are inclusive of and representative of all populations, especially disadvantaged and marginalized subgroups. This may require focused outreach and engagement initiatives to ensure that these groups are included in the census process and that their data is effectively captured. Ultimately, this will help ensure we accurately picture our population.
Last but not least, government decision-makers need to consider the ethical repercussions that the use of digital census data could have, particularly regarding protecting personal information and public safety.
When it comes to protecting the privacy of individuals and preventing the inappropriate use of census data, governments have a responsibility to ensure that appropriate protections are in place. Overall, the use of digital census data has the potential to dramatically increase both the accuracy and efficiency of data collecting for sustainable development. Digital data can be stored and retrieved much faster than traditional paper data. However, policymakers need to carefully analyze the consequences of digital census data and take steps to guarantee that it is reliable, inclusive, and ethically sound. They should also consider the ramifications of the data themselves.
4.7 Recommendations for the Future of Digital Census Data
There is a rising movement toward leveraging data from digital censuses to guide policy decisions regarding sustainable development in an increasingly digital society. However, to ensure that this information is being utilized appropriately, it is necessary to assess the precision and dependability of the data. It is advised that governments and organizations invest in high-quality data collection methods and technology to increase the accuracy and reliability of the data collected through digital censuses. This may involve the utilization of mobile devices for the collection of data in real time, as well as the implementation of machine learning algorithms for the improvement of data accuracy.
P-ISSN 1693-3516 | E-ISSN 2528-7575 265 In addition, it is essential to ensure that the digital census data is available to all relevant parties, particularly underrepresented communities. To accomplish this goal, it may be necessary to provide these groups with training and tools, with the end goal of assisting them in participating in data collecting and analysis and ensuring that data is accessible in various languages and formats. Investing in data quality control measures is particularly significant since it is essential to improve further the accuracy and reliability of the data collected from the digital census. Among these options are the implementation of data validation checks and the performance of periodical audits to locate and rectify problems in the data.
Last but not least, it is essential to acknowledge that digital census data are just one of many tools that can influence decisions for sustainable development. It should be read with caution, and it should be used in connection with other sources of data and information to ensure that it is being utilized appropriately and that it is being used effectively.
4. CONCLUSION
This study examined digital census data's quality and dependability on sustainable development.
The study showed that digital census data might inform sustainable development policies and activities by revealing population dynamics, socio-economic situations, and resource allocation. For sustainable development, digital census data must be accurate and reliable. This study found that digital census data gathered and processed using proper methods and technologies is accurate and reliable. Satellite imaging, remote sensing, and geospatial analysis have greatly improved digital census data. Machine learning algorithms and artificial intelligence have significantly improved digital census data accuracy and reliability by automating data processing and decreasing human errors. The investigation also found various digital census data issues. Due to limited access to digital technology or low digital literacy, certain population groups may be underrepresented or absent from digital census data, a significant problem. Partial or incomplete data may hurt sustainable development initiatives due to this issue. Digital census data collecting and storage may expose sensitive personal information to misuse or illegal access.
The study suggests ways to improve digital census data quality and reliability. First, invest in digital infrastructure and capacity building to ensure that all demographic groups have equitable access to digital technologies and are effectively represented in digital census data. Second, data-gathering methods and technologies should be constantly improved to ensure accuracy and reliability. Third, data privacy and security must be strengthened to protect sensitive personal data and maintain public trust in digital census data. The report also stressed the need for partnerships between governments, international organizations, business sector entities, and civil society organizations to disseminate digital census data for sustainable development. These parties can collaborate to solve digital census data difficulties and use them to inform sustainable development policies and actions. Digital census data can help sustain growth by providing precise and trustworthy information on population dynamics, socio-economic situations, and resource allocation. To fully achieve this potential, digital census data must be gathered, processed, and used to improve accuracy, reliability, inclusivity, and security. By doing so, digital census data may drive sustainable development and a more equal, prosperous, and resilient future for all.
P-ISSN 1693-3516 | E-ISSN 2528-7575 266 REFERENCES
[1]. Al-Lawati, A. H., & Barbosa, L. S. (2020). Towards a register-based census in Oman. In C. Y., C.
M.A., & S. D. (Eds.), 13th International Conference on Theory and Practice of Electronic Governance, ICEGOV 2020 (pp. 823–826). Association for Computing Machinery.
https://doi.org/10.1145/3428502.3428631
[2]. Cesare, N., Lee, H., McCormick, T., Spiro, E., & Zagheni, E. (2018). Promises and Pitfalls of Using Digital Traces for Demographic Research. Demography, 55(5), 1979–1999.
https://doi.org/10.1007/s13524-018-0715-2
[3]. Jain, A., & Kaur, V. (2021). Census Mapping in India and Role of GIS: A Look Ahead at Census 2021. Indian Journal of Public Administration, 67(4), 540–558.
https://doi.org/10.1177/00195561211056406
[4]. Kean, S. (2010). Can the Census Go Digital? Science, 330(6002), 310–312.
https://doi.org/10.1126/science.330.6002.310
[5]. Kubiczek, J., & Hadasik, B. (2022). The 2021 National Census in Poland – addressing the challenges of a digital census. Wiadomości Statystyczne. The Polish Statistician, 67(11), 12–42.
https://doi.org/10.5604/01.3001.0016.1038
[6]. Semina, A. E., & Maximova, S. V. (2019). Digital census of Upper Kama towns architectural and urban environment. International Conference on Construction, Architecture and Technosphere Safety 2019, ICCATS 2019, 687(5). https://doi.org/10.1088/1757-899X/687/5/055051
[7]. Openshaw, S., & Rao, L. (1995). Algorithms for Reengineering 1991 Census Geography.
Environment and Planning A, 27(3), 425–446. https://doi.org/10.1068/a270425
[8]. Martin, D., Harris, J., Sadlert, J., & Tate, N. J. (1998). Putting the census on the web: lessons from two case studies. Area, 30(4), 311–320. https://doi.org/10.1111/j.1475-4762.1998.tb00076.x
[9]. Carter, J., Brown, M., & Chappell, C. (2003). Learning and Teaching with the UK Census. IASSIST Quarterly, 26(3), 17. https://doi.org/10.29173/iq97
[10]. Mansour, S., Martin, D., & Wright, J. (2012). Problems of spatial linkage of a geo-referenced Demographic and Health Survey (DHS) dataset to a population census: A case study of Egypt.
Computers, Environment and Urban Systems, 36(4), 350–358.
https://doi.org/10.1016/j.compenvurbsys.2011.04.001
[11]. Wang, Y., Li, H., Yu, Z., & Luo, B. (2012). Approaches to census mapping: Chinese solution in 2010 rounded census. Chinese Geographical Science, 22(3), 356–366.
https://doi.org/10.1007/s11769-012-0540-2
[12]. Healey, R. G. (2015). Under-enumeration, inconsistency, and bias in the U.S. Manufacturing Census 1860–1880: Case studies from the American manufacturing belt. Historical Methods: A Journal of Quantitative and Interdisciplinary History, 49(1), 11–33.
https://doi.org/10.1080/01615440.2015.1029103
[13]. Olson, S. (2017). Setting the census household into its urban context: Visualizations from 19th-
century Montreal. Demographic Research, 36, 1399–1434.
https://doi.org/10.4054/demres.2017.36.46
[14]. Ruggles, S., Fitch, C., & Roberts, E. (2018). Historical Census Record Linkage. Annual Review of Sociology, 44(1), 1–19. https://doi.org/10.1146/annurev-soc-073117-041447
[15]. Cesare, N., Lee, H., McCormick, T., Spiro, E., & Zagheni, E. (2018). Promises and Pitfalls of Using Digital Traces for Demographic Research. Demography, 55(5), 1979–1999.
https://doi.org/10.1007/s13524-018-0715-2
P-ISSN 1693-3516 | E-ISSN 2528-7575 267 [16]. Rominger, K., & Meyer, S. E. (2019). Application of UAV-Based Methodology for Census of an
Endangered Plant Species in a Fragile Habitat. Remote Sensing, 11(6), 719.
https://doi.org/10.3390/rs11060719
[17]. Semina, A. E., & Maximova, S. V. (2019). Digital census of Upper Kama towns architectural and urban environment. International Conference on Construction, Architecture and Technosphere Safety 2019, ICCATS 2019, 687(5). https://doi.org/10.1088/1757-899X/687/5/055051
[18]. Rodger, R. (2020). Making the Census Count: Revealing Edinburgh 1760–1900. Journal of Scottish Historical Studies, 40(2), 134–148. https://doi.org/10.3366/jshs.2020.0300
[19]. Ashraf, R., Riker, C. N., & Stewart, A. M. (2020). Every Child Counts: The Importance of the 2020 Census for Pediatric Health Equity. Pediatrics, 145(5), e20200061.
https://doi.org/10.1542/peds.2020-0061
[20]. Berry‐James, R. M., Gooden, S. T., & Johnson, R. G. (2020). Civil Rights, Social Equity, and Census 2020. Public Administration Review, 80(6), 1100–1108. https://doi.org/10.1111/puar.13285 [21]. Kashyap, R., Fatehkia, M., Tamime, R. Al, & Weber, I. (2020). Monitoring global digital gender
inequality using the online populations of Facebook and Google. Demographic Research, 43, 779–
816. https://doi.org/10.4054/demres.2020.43.27
[22]. Ashraf, R., Riker, C. N., & Stewart, A. M. (2020). Every Child Counts: The Importance of the 2020 Census for Pediatric Health Equity. Pediatrics, 145(5), e20200061.
https://doi.org/10.1542/peds.2020-0061
[23]. Jain, A., & Kaur, V. (2021). Census Mapping in India and Role of GIS: A Look Ahead at Census 2021. Indian Journal of Public Administration, 67(4), 540–558.
https://doi.org/10.1177/00195561211056406
[24]. Jonker, J., Poot, W., & Doorn, P. (2021). Detailed Tables from the Dutch Census 1947:
Experiences and Lessons Learned in Publishing a Large Dataset. Research Data Journal for the Humanities and Social Sciences, 6(1), 1–16. https://doi.org/10.1163/24523666-bja10018
[25]. Pitaloka, R. D., Hendriyani, H., Eriyanto, E., & Haryatmoko, H. (2022). Communication practice in village data collection. Jurnal Studi Komunikasi, 6(1), 179–198.
https://doi.org/10.25139/jsk.v6i1.4314
[26]. Bennett, R. J., & Hannah, L. (2022). British employer census returns in new digital records 1851–
81; consistency, non-response, and truncation – what this means for analysis. Historical Methods: A Journal of Quantitative and Interdisciplinary History, 55(2), 61–77.
https://doi.org/10.1080/01615440.2021.2018373
[27]. Kubiczek, J., & Hadasik, B. (2022). The 2021 National Census in Poland – addressing the challenges of a digital census. Wiadomości Statystyczne. The Polish Statistician, 67(11), 12–42.
https://doi.org/10.5604/01.3001.0016.1038
[28]. Yusifov, F., & Akhundova, N. (2022). Analysis Of Demographic Characteristics Based On E- Demography Data. Demography and Social Economy, 47(1), 38–54.
https://doi.org/10.15407/dse2022.01.038.
