The use of Big Data in Official Statistics

Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.

International migration statistics suffer from extensive gaps and shortcomings. Recently, national statistical institutions (NSIs) have started using big data to complement traditional statistics, including on migration. Although these are promising developments, we still lack answers on the extent to which NSIs are currently using big data for migration and to what extent it complements the gaps in traditional data. We gathered data by interviewing experts from 29 NSIs to investigate how big data is used for official migration statistics. We show that 15 out of 29 NSIs either used big data for migration, had a pilot project or have been involved in joint initiatives. We reveal the specific implications of big data in human migration (e.g. internal mobility, stocks, flows and mobility patterns, among others and the most common sources used to extract official statistics). Moreover, we discuss the challenges and barriers preventing NSIs from using such data. Factors deterring countries from utilising big data include limited data accessibility, an absence of legal frameworks for big data usage, ethical concerns, the possession of already high-quality data, a deficit in expertise and methodologies and a lack of perceived necessity for supplementary data or approaches. Moreover, many countries did not know which data to use and were concerned about the quality and accuracy of such data. Legal barriers were more of an issue than the ethical aspects, and overall, participating countries believe that there is a high potential for big data in the future.

The use of "Big Data" to explain fluctuations in the broader economy or guide the business decisions of a firm is now so commonplace that in some instances it has even begun to rival more traditional government statistics and business analytics. Big data sources can very often provide advantages when compared to these more traditional data sources, but with these advantages also comes the potential for pitfalls. We lay out a framework called SMALL that we have developed in order to help interested parties as they navigate the big data minefield. Based on a set of five questions, the SMALL framework should help users of big data spot concerns in their own work and that of others who rely on such data to draw conclusions with actionable public policy or business implications. To demonstrate, we provide several case studies that show a healthy dose of skepticism can be warranted when working with and interpreting these new big data sources.

Assessing the methodological quality of big data: an introduction to the Total Error framework The availability and use of big data sources is increasing exponentially. The variety of new and emerging data sources offers opportunities to complement, replace, improve or add to conventional data sources. Survey data are one kind of conventional data sources. In survey research, a framework to assess the accuracy of survey data already existed for quite some time. This framework is known as the Total Survey Error (TSE) framework. The philosophy behind this framework has only recently been universalized to (big) data in general in the form of the Total Error (TE) framework. This generic framework, which allows for assessing the accuracy of (big) data, is outlined in this article. Additionally, the TE framework is applied to big data sources that could be relevant for policing: police-registered crime data, Twitter data and mobile phone data.

Taking a byte out of big data

Research Highlights: One of the priorities of the European Commission is to build up an effective circular economy based on recycling and multiple use of materials. Wood biomass is a renewable raw material and can be used several times in a cascading sequence. Each country has a unique situation regarding the availability and utilization of wood sources. Background and Objectives: The objective of this study was to analyze wood flow in the Czech Republic using the cascading principle of biomass use. The specific situation in the Czech Republic lies in a lack of valid and reliable input data from official statistics. Therefore, the reverse input method was applied. Materials and Methods: Input data analyses of roundwood sources and foreign trade were based on official statistical data. The calculation of raw wood volume consumption in primary processing was performed based on the data after our own correction and recalculation. It was then possible to build up a basic model of multi-stage cascade wood use. The input volume of roundwood was divided among all types of primary processing production using conversion factors. Results: Cascading use of wood (CUW) showed the level of efficiency of the resource. Official statistical input data and the reversed input data regarding raw wood volume entering wood processing revealed differences at a level of 27%. The overall CUW in the Czech Republic indicates a high rate of wood use in primary processing with low added value and in generating energy. Conclusions: The reverse input method reveals the real situation of wood consumption irrespective of the level of official statistical data. It is suitable to apply in an environment of incomplete or incorrect input data. CUW in Czechia showed an opportunity for increasing the efficiency of wood utilization. The structure of wood use needs to be optimized towards creating greater added value.

A well-written, sound and highly relevant paper, with good language. Impressively economical and straight forward presentation with well-chosen figures. This must be viewed in light of the complex issues and rich data at hand. Both the two empirical studies that the paper is based on seem robust, and have been quality-checked by external reviewers due to the usual process for reports from the national council for crime prevention.

Attention on “big data” spans nursing and the health sciences, and extends as well to engineering/computer sciences through to the liberal arts in professional literature. A current Google search (3 Nov 2016) of “big data” yields 288 million entries. A focused search of “big data and nursing” yields more than 3.9 million entries. Thus we ask, “Why big data? Why nursing?” The focus of this chapter is to provide an overview of why big data has emerged now and to make the case for how big data has the capacity to change health, healthcare systems, and nursing. This chapter lays a foundation for the chapters and case studies to follow that explore what data, knowledge, and transformation processes are needed to put information and knowledge into the hands of nursing wherever nurses are working. In this chapter we examine the big data sources within and beyond nursing and healthcare that can be collected and analyzed to improve nursing and patient, family and community health. This chapter entices the reader to examine “Why big data now?” and “Why big data in the future?” This chapter is meant to stir curiosity for “Why should I be knowledgeable?” Whether the reader’s role is in clinical practice, education, research, industry, or policy, the applied uses of big data analytics are empowering change at an exponential speed across all domains. Big data has the capacity to illuminate nursing’s discovery of new knowledge and best practices that are safe, effective and lead to improved outcomes including well-being of providers; it also can expand nursing’s vision and future possibilities through increasing awareness of what nursing doesn’t know. The importance of nursing’s lens on the new discoveries obtained through big data and data science is critical to the transformation of health and healthcare systems. This transformation completes the challenge of placing the person at the center of all care initiatives and actions.

<p>Agenda 2030 goal 11 commits towards making disaster risk reduction an integral part of sustainable social and economic development. Flooding poses some of the most serious challenges in front of developing nations by hitting hardest to the most vulnerable. Focussing on the urban poor, frequently at highest risk are characterised by inadequate housing, lack of services and infrastructure with high population growth and spatial expansion in dense, lower quality urban structures. Use of big data from within these low-quality urban settlement areas can be a useful step forward in generating information to have a better understanding of their vulnerabilities. Big data for resilience is a recent field of research which offers tremendous potential for increasing disaster resilience especially in the context of social resilience. This research focusses to unleash the unrealised opportunities of big data through the differential social and economic frames that can contribute towards better-targeted information generation in disaster management. The scoping study aims to contribute to the understanding of the potential of big data in developing particularly in low-income countries to empower the vulnerable population against natural hazards such as floods. Recognising the potential of providing real-time and long-term information for emergency management in flood-affected large urban settlements this research concentrates on flood hazard and use of remotely sensed data (NASA, TRMM, LANDSAT) as the big data source for quick disaster response (and recovery) in targeted areas. The research question for the scoping study is: Can big data source provide real-time and long- term information to improve emergency disaster management in urban settlements against floods in developing countries?  Previous research has identified several potentials that big data has on faster response to the affected population but few attempts have been made to integrate the factors to develop an aggregated conceptual output . An international review of multi-discipline research, grey literature, grass-root projects, and emerging online social discourse will appraise the concepts and scope of big data to highlight the four objectives of the research and answer the specific questions around existing and future potentials of big data, operationalising and capacity building by agencies, risk associated and prospects of maximising impact. The research proposes a concept design for undertaking a thematic review of existing secondary data sources which will  be used to provide a holistic picture of how big data can support in resilience through technological change within the specific scope of social and environmental contexts of developing countries. The implications of the study lie in the system integration and understanding of the socio-economics, political, legal and ethical contexts essential for investment decision making for strategic impact and resilience-building in developing nations.</p>

More and more data are being produced by an increasing number of electronic devices physically surrounding us and on the internet. The large amount of data and the high frequency at which they are produced have resulted in the introduction of the term ‘Big Data’. Because these data reflect many different aspects of our daily lives and because of their abundance and availability, Big Data sources are very interesting from an official statistics point of view. This article discusses the exploration of both opportunities and challenges for official statistics associated with the application of Big Data. Experiences gained with analyses of large amounts of Dutch traffic loop detection records and Dutch social media messages are described to illustrate the topics characteristic of the statistical analysis and use of Big Data.

Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyze it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes. With regard to the definition of big data, IBM Company uses volume, velocity, variety, value and veracity as 5Vs to summarize the concept of big data. There are different types of big data, for example, structured, semi-structured and un-structured data. The contents of big data can be text data, audio data, video data and still image and it indicates that the big data may have diverse data types as well as data qualities. Big data has variety of sources such as healthcare center, commercial system, industries, social media, telecommunication, transportation, sensor machines and others. In this paper, I reviewed three the most security challenging perspectives and I studied lack of concentrations in these areas by most research works. To confirm security in the big data platforms, it is critical to ascertain the data rendering points and their security techniques to safeguard the data in this pacing digital world. Then I envisage directions for the future research. In this paper, I have reviewed the big data sources and its security issues in the three directions such as data at rest, data at communication and data in process/use. Keywords: Big Data, Big Data Security, Big Data source, Attribute based encryption, storage path, Transport layer security, secure shell, Cloud service Provider DOI: 10.7176/CEIS/12-3-01 Publication date: November 30 th 2021

Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial–temporal patterns of crime, and ambient population measures have a significant impact on crime rates.

The statistical sources with information on the labour market do not yield a coherent picture. In the Dutch labour accounts, the various inconsistent data are reconciled and complemented in four stages, which together form the process of statistical integration. The adjustments made during this process are explicitly described in publications, enabling data users to link source data and labour accounts data. Both the systematic approach of fragmented data described in this paper and the explanation of the adjustments to the public are far from common practice in official statistics. The authors recommend to incorporate this approach wherever reconciliation of data takes place.

Вопросы дефиниции термина «незаконные рубки» имеют большое значение для единой трактовки участниками лесных отношений требований к законности рубок. По результатам анализа отраслевой статистики и экспертных оценок объемов незаконных рубок по репрезентативным субъектам Российской Федерации сделаны выводы о необходимости адаптации термина «незаконные рубки» и корректного определения их объема. The paper highlights problems related to illegal logging. Under review are issues linked to definition of illegal logging, data in illegal logging level according to official statistics and expert estimates in the Russian Federation. Problem of illegal logging term definition has a key role both for its identification, law enforcement and countermeasure system development. Wide range of insufficient offenses during logging of forests and trees and extensive interpretation of illegal logging is unsuitable in particular for forest product exporters to international markets. With regard to its value legal liability taking into account lately established international practice of forest activity definition such logging should not be referred to illegal ones. In the Russian Federation there are 2 independent groups of assessments: official statistical data and expert assessments that vary substantially. Conducted analysis of agency level statistical data proves that both in regions and the whole RF the official statistics does not reflect and cover sufficient offenses during logging. It was found that available sufficient foreign assessments of illegal logging in Russia are not based on own studies but rather on information of various environmental including foreign organizations. Such expert assessments of illegal logging in Russia are based mainly on data that lack sufficient accuracy. Special research studies of this problem is practically unavailable. It was found the Russian federation regions lack research based procedures to calculate timber needs for local communities including apartment heating. Official agency level statistical reporting data does not give a real pattern of forest timber resources utilization for local community needs. The paper gives detailed conclusions on conducted analysis that may enable improvement of illegal logging counteraction.

Big Data on Vessel Traffic: Nowcasting Trade Flows in Real Time