Data Blog

25 May 2021 By Kyoung-Soo Eom, Chief of Geospatial Information Section, Technology Operations Service, Operations Support Division, OICT Since the establishment of the United Nations, cartography and geospatial information have been an important component of the Organization. In 1948, the Committee of Experts on Cartography sought to answer two questions: What should the United Nations do to stimulate and assist Member States in the development of the mapping sciences? What sort of cartographic service did the United Nations need to carry out this program, for its own operations, and in its relations with the specialized agencies? To facilitate the development of the mapping sciences, over the past seven decades, the Organization has been using geospatial information to meet the UN’s diverse mandates and operational requirements. Today, the United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) is the apex intergovernmental mechanism for making joint decisions and setting directions regarding the production, availability and application of geospatial information within national, regional and global policy frameworks. Under the UN-GGIM framework, the United Nations Geospatial Network, a coalition of entities within the United Nations system that engages in geospatial information management activities, has come together to strengthen the coordination and coherence of geospatial information management within the United Nations system, including its overarching trends, technology, practices, data, needs, capacity building, and partnerships. "The Geospatial Strategy for the United Nations aims to contribute to the data ecosystem by enabling location information for the Organization. The vision is to build synergy on activities and investments on geospatial information management in the United Nations, "Delivering as One" by bringing geospatial data to innovate and to act for a better world." — Mr. Patrick Carey, Acting Assistant Secretary-General, OICT In parallel, with the latest organizational reform of the United Nations, the Secretary-General launched the Secretary-General’s Data Strategy, aiming to build a United Nations data ecosystem for “better decisions and stronger support to people and planet—in the moments that matter most”. As an integral part of the data ecosystem, geospatial information is critical to gaining insight to better understand events and phenomenon patterns, and to act upon them. In this regard, the United Nations Secretariat used this as an impetus to prepare a strategy focused on geospatial information to reflect the latest developments and needs within the Organization. To draft this strategy, there was a collaboration and consultation process amongst the geospatial experts and the user community within the Secretariat. What is the Geospatial Strategy? The Geospatial Strategy aims to set direction to support the implementation of the mandates of the United Nations Secretariat. The intent of the strategy is to design, foster and build synergies for activities and investments in geospatial information management in the United Nations Secretariat, in coordination with the wider geospatial and data community of the United Nations system “Delivering as One”. The Geospatial Strategy envisions the effective, efficient and universal use of geospatial information in support of all mandates and operations of the United Nations for a better world as contained in its main pillars: Peace and Security, Human Rights, International Law, Sustainable Development, and Humanitarian Aid. The mission of the Geospatial Strategy aims to mainstream the use of geospatial information across the UN system for unified, integrated and accessible information; analysis and visualization for evidence-based decision-making; and data action in support of peace and security, human rights, international law, development, and humanitarian aid. The Geospatial Strategy is comprised of 4 goals: Goal 1: Strengthen geospatial mandates, activities and authoritative services to underpin all aspects of the work of the Organization. Goal 2: Enhance innovative geospatial services and analytics for users’ problem-solving and decision-making that support the mandates of the Organization. Goal 3: Foster a global integrated, federated and multi-disciplinary geospatial services delivery across the United Nations system " Delivering as One." Goal 4: Engage in partnerships with the global community for enhanced geospatial capacity and delivery for societal, environmental and economic benefits (including with Member States, non-governmental organizations, the private sector, innovators, academia, geospatial societies, and citizens). Way Forward To realize the Secretary-General’s Data Strategy, the geospatial community of the United Nations Secretariat will establish a “Geospatial Action Group”. This Group will include geospatial information experts and users from the Organization in order to achieve the following goals: Participate in the implementation of the wider Data Strategy and its governance mechanism, the Data Governance Council, to ensure interlinkages between the relevant data stakeholders in the Organization. Coordinate its action with the United Nations Geospatial Network and its strategic orientations and activities as described in the Blueprint. Elaborate a governance model on geospatial activities in the United Nations Secretariat Elaborate a Geospatial Action Plan to guide and build synergies between the activities in the United Nations Secretariat. Advise and coordinate programmes and projects involving geospatial data and information including in close coordination with the United Nations Data Action Group, geospatial information users and experts. Coordinate the implementation of the Geospatial Action Plan. Leveraging geospatial data is critical to the development of a UN-wide data ecosystem designed to help our Organization better deliver on its mandates and serve communities around the world. As such, the Geospatial Strategy will be an important enabler of the UN Data Strategy. Note: The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations.

12 April 2021 By Stefan Lemm, former Head, Military Operations Unit, OICT Data Privacy and Protection is one of the key aspects of the Secretary-General’s Data Strategy. It is clear that protecting people from indiscriminate data collection is important because it might result in data misuse, for instance, by businesses. But a potentially more dangerous scenario would be that data collected in good faith ends up in the hands of ill-meaning actors who intend to intimidate or even threaten the victim's personal safety. When we speak about Data Privacy and Protection, the challenge is to understand how to assess the potential risks and harms associated with data collection and, subsequently, how to actively implement protection measures so that data can still be collected for the right cause while mitigating the risks. My intention is to describe an approach taken by OICT and UN Global Pulse in a specific project for the United Nations Multidimensional Integrated Stabilization Mission in Mali (MINUSMA). Mining Radio Data in MINUSMA The MINUSMA Big Data Radio Mining and Analysis project is based on a technology developed by UN Global Pulse. This technology collects and records public radio broadcasts, applies filters to remove music and commercials, and analyzes the content for specific keywords. If a specific keyword is found, the audio clip is provided to MINUSMA for further analysis. So why should we care about radio data? Public talk radio is estimated to be the main source of information for approximately 80 percent of the Malian population – a similar percentage is found in many other countries in which peace operations are conducted. For this reason, it can be argued that radio data can be a very good source of information for understanding public sentiment and concerns. For example, imagine a discussion taking place on a local radio station about the lack of clean water in a village. If the development unit of MINUSMA was aware of this conversation, it could investigate further and potentially help the population find a solution. So how did we approach the challenge of collecting, storing, and cross-checking data, and making that data available for analysis in way that respects data privacy? My partner in this project, LtCol Stefan Sander, and I are both German military staff officers who have temporarily worked for OICT in the UN Secretariat. Neither of us had previous experience in Data Privacy and Protection assessments. Fortunately, we were able to work with UN Global Pulse and used their Risks, Harms and Benefits Assessment ToolOpens a new window to ensure that the big data radio mining project took data privacy and protection into consideration. UN Global Pulse's assessment tool (or checklist) outlines a set of minimum points to consider when embarking on a data innovation project. The tool is intended to help minimize risks and harms of a data project, and to maximize its positive impacts. The assessment includes six core sections: Type of Data Data Access and Data Use Communication About the Project Data Transfers Risks and Harms Final Assessment and Rationale for Decision. In the section Type of Data we need to determine if there is intention to use (examples: collect, store, transmit, analyze, etc.) data that directly identifies individuals. Personal data directly relating to an identified or identifiable individual may include, for example, name, date of birth, gender, age, location, user name, phone number, email address, ID/social security number, IP address, device identifiers, and account numbers. After defining the type of data at hand, the section Data Access and Data Use focuses on questions of legitimacy and fairness of data access and use, including the proportionality and necessity of data use, data retention, and data accuracy requirements. The section Communication about the Project highlights transparency as a key factor in helping to ensure accountability and is generally encouraged. The use of personal data should be carried out with transparency to the data subjects, as appropriate and whenever possible. Often, data related initiatives require collaboration with third-parties: data providers (to obtain data); data analytics companies (to assist with data analysis); and cloud or hosting companies (for computing and storage). In cases where collaboration is required, personal data should only be transferred to a third party that will afford appropriate protection for that data. It is therefore important that such potential collaborators are carefully chosen, through a proper due diligence vetting process that also includes minimum check points for data protection compliance, the presence of privacy policies, and fair and transparent data-related activities. The section Data Transfers asks whether the partners, if any, are compliant with at least as strict standards and basic principles regarding data privacy and data protection as outlined in this checklist. In the section Risks and Harms, key questions asked are: does your use of data pose any risks of harm to individuals or groups of individuals, whether they can be directly identified, visible or known? Are there any steps that can be taken to mitigate these risks? Is the project likely to cause harm to individuals or groups of individuals, whether the individuals can be identified or known? Based on answers provided for the first five sections, the section Final Assessment and Rationale for Decision looks at whether the risks and resulting harms are disproportionately high compared to the expected positive impacts of this project. Based on the assessment conducted, the project will implement technical and procedural measures to ensure data protection. These will include restricting access to the database to authorized users only, anonymization of names when they do not need to be identified, deletion of data as quickly as possible, and securing the data in every step of the process. In conclusion, when collecting information from public sources, such as radio, it is crucial to determine potential risks and harms through a guided assessment. UN Global Pulse has developed a tool that supports this task and our team found it very helpful in collecting, storing, and analyzing data in a peacekeeping setting. Note: The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations.

24 February 2021 By Lambert Hogenhout, Chief of Data, Analytics and Innovation, OICT Collecting data, organizing it, and sharing it is useful. Ultimately, however, the aim is to create business value. We want to turn data into insights that result in productivity gains, better decisions and/or faster response. There are multiple ways to do that — from simple dashboards to interactive data visualizations, deeper analysis with statistical methods, or natural language processing — and new technologies keep leading to more sophisticated techniques. One area that has been at the center of academic and commercial attention recently is Machine Learning (ML). What is Machine Learning? Traditional computer applications consist of code specifying exactly what the computer should do in any possible situation, for each type of input. In contrast, ML is an approach in which the computer is not explicitly told what to do but learns from examples or from feedback. An early use of ML was recognizing numbers written on postal mail, when envelopes with hand-written addresses was common. Everyone writes numbers slightly differently, but after seeing thousands of examples of handwritten numbers, computers using ML were able to learn to recognize the numbers with almost perfect accuracy. ML has progressed greatly since then. In fact, there are many tasks at which it is far better than humans – from analyzing x-rays for potential signs of cancer, to catching fraudulent credit card transactions as they happen. Furthermore, several companies are working on self-driving cars that have learned from millions of previously driven miles. This too is ML. Imitating the Brain One ML technique is called Neural Networks. It takes inspiration from neurons in the human brain in that it consists of digital neurons that trigger responses in one another and create bonds. For example, after seeing examples of the number 6, the computer will discover that there seems to be a circular shape at the bottom of the 6, so in the neural net, the connection between the node that represents 6 and the node that represents a circular shape will be strengthened. Neural networks work in many layers, for instance moving from light to dark areas, to detecting the edges of a shape, to detecting shapes like circles or diagonal lines, to identifying numbers. These intermediate layers are discovered and automatically created by the computer itself. This also means that, in large neural networks, humans do not necessarily have an idea about the meaning of concepts in these hidden layers. This also means that, in large neural networks, humans do not necessarily have an idea about the meaning of concepts in these hidden layers, and this can raise ethical issues if we are not careful. Applications for the UN ML is particularly useful in cases where systems are so complex that we don't have a precise model to describe their dynamics because there are too many variables at play, such as ecosystems in oceans. ML can also be well suited for environments that are subject to constant change, like social dynamics in cities and countries, and, therefore, need adaptive models that learn as they go. At the United Nations, as we deal with a wide variety of complex challenges in a world that is constantly in flux, we stand to benefit significantly from ML. Indeed, ML has been used in practical applications for social good: the Mila Project in Canada uses it to analyze (and visualize) climate change the NGO Rainforest Connection uses is in its efforts to protect forests, and the Peace Parks foundation uses it to tackle rhino poaching. Other examples include early detection of crop damage from aerial photography, combating money-laundering, and human trafficking. Automatic translation (of human language) is another area that ML has helped progress very rapidly, and that should be of great interest to the UN. Data Plays a Central Role Let us remember that data is the essential fuel for ML. To achieve success in ML, we need data that ideally has been classified or tagged in some way. In the past, ML typically needed very large amounts of data ("Big Data") from which to learn. Today, new techniques exist that allow learning from much smaller data sets. For instance, a technique called Transfer Learning allows us to transfer skills obtained in one context to another context, so less training is needed in the new ML model. You might compare it to benefiting from your snowboarding skills when learning windsurfing. But the need for quality data remains, because if we train our ML models with faulty data, it will lead to incorrect or possibly biased results (“garbage in - garbage out”). Looking Ahead To achieve the vision of the Secretary-General's Data Strategy Opens a new window we need to collect and organize a good base of quality data. With such data, ML can provide us with a wide range of applications to gain new insights rapidly, as well as to automate time-consuming tasks. For example, our team in OICT recently built an ML-based solution for the Office for Disaster Risk Reduction (UNDRR) to automatically classify documents, saving hundreds of hours of staff time. We have also used ML to build the UN Secretariat’s chat-bot, Alba Opens a new window. This is just the beginning — ML will significantly change the field of data and analytics in the coming years and OICT is excited to be leading in this space. Note: The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations.

29 January 2021 By Martijn Lampert, Research Director, Glocalities In this Q&A, Martijn Lampert, Research Director, Glocalities, shares a methodology he and his team used to measure international trust in the UN. He argues that research focusing on the links between values, issues and cultures is key to quantifying qualitative data. What is Glocalities? Glocalities is an international research program specialized in understanding and quantifying the drivers of human behavior: cultural values, belief systems, issues, aspirations, and concerns. Based on interviews held with more than 250,000 people from 40 countries, we have mapped the cultural values and behaviors of population segments within and across cultures. As our name – Glocalities – suggests, our work consists of zooming ‘’in’’ and ‘’out’’ on citizens’ values and behavior to reveal the interdependencies between global and local social issues. Since 2014, we have helped policymakers and communications professionals become more effective in dealing with international challenges such as sustainability, audience analysis and intercultural communications. Our work enables them to step into the shoes of people from around the world and rethink communications from the values perspective of citizens. What inspired Glocalities to produce its 2020 quantitative study on UN Trust? The UN was celebrating its 75th anniversary, as the world contended with concurrent global crises. Therefore, we felt the need to share the unique research insights we have gathered about trust and the UN. Most well-known international studies into ‘trust’ merely report percentages of trust and present figures at a country level. However, we believe that it is critical to understand what lies beneath these figures. A deeper understanding of such figures required an understanding of people’s perceptions and values, within and across countries. Such insights are often missing in other studies done on trust. What were the main outcomes of the survey? The survey Opens a new window, undertaken in 25 countries, showed that half of the respondents (47%) trust the UN. This is significantly higher than trust in other (inter)governmental institutions such as the EU (38%), NATO (35%), government (31%) or parliament (26%). Trust in the UN is highest among young people (18-24) at 50%. Whilst looking more closely at the results, using the Glocalities methodology (shown below), we discovered that people who trust the UN have a positive, energetic, and determined mindset. And it seems that the reason people place trust in the UN is much more layered than is often thought. While the UN is sometimes attacked for being a ''globalization elite'' project, this attitude is not reflected in its international support base. Trust in the UN is not explained by a globalist and nationalist polarity, but mostly by citizens being proud of their own country while simultaneously recognizing the interdependence of nations that need to cooperate in an international environment. The high level of trust in the United Nations mirrors the energetic and cooperative mindset of its supporters. These survey results show that there are many opportunities for countries and world leaders to build upon the trust citizens have entrust in them, and to work together in solving global challenges such as the COVID-19 pandemic, rising poverty levels and climate change. The Glocalities survey reveals that people from around the globe recognize that the world needs to live up to the ideals of the UN. Can you elaborate on the methodology your team used to measure Trust in the UN? Our methodology focuses on understanding values, lifestyles and trends among citizens. As illustrated in the Glocalities compass (see figure 1), we use 12 tools in our research – these vary from socio-cultural trends, values segments, archetypes, politics, sustainability, persuasion tactics, countries, demographics and lifestyle. Based on years of research, we have learned that many of these topics are interrelated. In particular, trust in institutions such as the UN, parliaments and education systems is an important part of our studies. We measure whether or not people show trust in a multitude of institutions and directly relate these findings to their social values, emotions, lifestyles and sustainability topics from our compass. Glocalities insights are based on system thinking and pattern analysis. In order to unlock our database to a wider audience, we’ve decided to develop the “ World of Glocalities Insights Solution Opens a new window” with a multidisciplinary team. This is the first tool of its kind, as it speaks to both the analytical/rational and the visual/creative part of the brain, based on survey data. The online application allows you to envision any international or national group of citizens and lets you view the world through their eyes. Shortly after I had the vision of creating such a tool for visually unlocking quantitative international data, we brought together a multidisciplinary team including a front- and back-end IT developer, a methodologist, an app designer, a data processor, and an online research specialist. I was overseeing the development based on my experience in conducting and presenting cross-cultural values-based research. Since the beta version in 2014, we have implemented many iterations and learnings. We keep on developing and improving the application and each year we add new countries and datasets to its environment. It is interesting to see a study quantify a qualitative term like “trust” the way you have in this report. What lessons learned or best practices could you share with people seeking to “better measure” qualitative concepts for improved decisions making? After decades of primarily focusing on economic growth, policy makers are increasingly recognizing the limits to that model. The climate crisis and the COVID-19 pandemic once more make clear that a a shift in research is needed: policy researchers need to look at cultural diversity and have a much better understanding of qualitative concepts. Social researchers increasingly need to be able to relate to other disciplines, cultures, audiences, and paradigms and be able to quantify and visualize qualitative topics. Think for example about qualitative terms such as trust, sharing, cooperation, freedom, justice, peace, et cetera – we need to be able to measure these terms more effectively for improved decision making. Indeed, a new level of thinking and research is needed for combining perspectives and conducting researching ‘’out of the box’’. That is why we take the citizens’ perspective and systems thinking as a starting point and, from there, look at issues by extracting quantitative data from these broad concepts. For example, when researching narratives of meaning, we often look at the world of mythology and the role that archetypes such as the Hero and the Caregiver have played in stories since ancient times. We believe that this kind of analysis can help identify sentiments of people today. For example, the data of our UN trust study revealed that the archetype of the Everyman (valuing friendship and togetherness) appeals to people who trust the United Nations, while the archetype of the Rebel (independent and recalcitrant) is more typical of people who distrust the UN. This suggests that the UN may want to tap into the universal value of friendship in its policy and communications strategies. Indeed, friendship is one of the highest values of humanity, which we also discovered in a study Opens a new window with religious world leaders, who called for friendship across religions. In conclusion, I would advise other researchers to consider the benefits of the research methods outlined in this blog piece. I strongly believe in the importance of looking beyond our own disciplines and learning from combining various datasets, and types of data. Most importantly, it is crucial for researchers to seek to quantify qualitative data, even if at face value it would seem challenging to do so. Note: The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations.

14 December 2020 By Olabisi Shoaga, Political Affairs Officer, Division of Policy, Evaluation and Training, Department of Peace Operations Over the past few years, there has been a rise in the use of data, particularly quantitative data, to showcase progress and performance during reporting and briefings on peacekeeping. A good deal of the work done in this regard has been in operational support, military operations, conduct and discipline, gender, rule of law and human rights related areas of work. Descriptive metrics such as numbers of patrols conducted, numbers of investigations conducted, percentage of women peacekeepers, numbers of demobilized ex-combatants reintegrated, etc., have been employed as yardsticks for measuring performance. These metrics are considered to form the basis of more rigorous, objective and structured analysis than qualitative data alone. Not all aspects of UN peacekeeping work, however, can measure progress and performance through quantitative metrics. Notably, progress on political solutions, which fundamentally are at the center of UN peacekeeping are notoriously difficult to capture in figures. It is, of course, possible to measure aspects such as the numbers of meetings held and of participants; categories of stakeholder groups represented; numbers of working days; demographic breakdown of participants; and the numbers of peace agreements signed by parties. These metrics, however, often fail to portray fully the political significance of these processes or capture the quality, the extent of engagement or even the good or bad faith of the parties. Exploiting data in political performance Indeed, many underlying dynamics are difficult, if not impossible to capture in figures obtained through statistical methods. If, for instance, a mission succeeded in organizing only one meeting among all parties in conflict, numerically this is unimpressive when all is taken into account. What this measurement fails to consider is the significance of the event. Was it the first of its kind? Could the meeting have a positive or negative multiplier effect? Should the meeting be considered a failure if the political situation deteriorates afterwards? To fill these data gaps, qualitative indicators have been used instead or alongside quantitative data. Qualitative indicators have the added advantage of being adaptable to specific circumstances and, in addition to tracking progress, could assess impact and effectiveness. For all their purported objectivity, statistics do not necessarily make for more objective, rigorous and better analysis. The quality of the statistics, the methodology employed, data gathering and analytical procedures, types of issues, contextual factors such as the economy, legal frameworks, cultural norms, available resources and stakeholder perceptions might influence results in an unexpected manner. Even though useful, statistics might be misused, especially when they are misunderstood, incorrectly interpreted and are used to uphold an opinion rather than to elucidate and clarify issues. Towards better evidence-informed analysis Notwithstanding the challenges raised above, the request for the use of data in analysis and reporting on political aspects of UN peacekeeping related work is not likely to go away anytime soon. Obtaining worthwhile and useable results from data-based analysis is contingent on a number of factors, chief of which is closing the divide between subject matter experts and statistics experts. The two parties need to bring together their expertise and resources to design indicators, which can reliably and systematically measure performance and impact on the political aspects of peacekeeping. This kind of collaboration is in line with the Secretary-General’s Data Strategy, which underscores the importance of establishing an enabling culture that values cross-sharing of knowledge, and to staff empowerment for strengthened analytical capabilities. Crucial to achieving the right composition of personnel are continuous training and exchange of experience on different analytical approaches. This is particularly crucial given the evolving nature of conflicts and their changing contexts. In addition, such blending would further contribute towards fostering better understanding of differing points of views as well as of the strengths and limitations of using different approaches. Perhaps more importantly is the need to come to terms with the fact that statistics, be they quantitative, qualitative or mixed, do not tell the whole story, especially when human beings are involved. There is need to accompany data by other evidence drawn from several sources, including empirical case studies, analogical reasoning, expert opinions and verified documentation to provide a better understanding of the phenomena under study. Note: The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations.