Data Artefact Study by Aditi Ramesh, Stefaan Verhulst, Andrew Young and Andrew Zahuranec: “In this paper, we explore new and traditional approaches to measuring population density, and ways in which density information has frequently been used by humanitarian, private-sector and government actors to advance a range of private and public goals. We explain how new innovations are leading to fresh ways of collecting data—and fresh forms of data—and how this may open up new avenues for using density information in a variety of contexts. Section III examines one particular example: Facebook’s High-Resolution Population Density Maps (also referred to as HRSL, or high resolution settlement layer). This recent initiative, created in collaboration with a number of external organizations, shows not only the potential of mapping innovations but also the potential benefits of inter-sectoral partnerships and sharing. We examine three particular use cases of HRSL, and we follow with an assessment and some lessons learned. These lessons are applicable to HRSL in particular, but also more broadly. We conclude with some thoughts on avenues for future research….(More)”.
EU Health data centre and a common data strategy for public health
Report by the European Parliament Think Tank: “Regarding health data, its availability and comparability, the Covid-19 pandemic revealed that the EU has no clear health data architecture. The lack of harmonisation in these practices and the absence of an EU-level centre for data analysis and use to support a better response to public health crises is the focus of this study. Through extensive desk review, interviews with key actors, and enquiry into experiences from outside the EU/EEA area, this study highlights that the EU must have the capacity to use data very effectively in order to make data-supported public health policy proposals and inform political decisions. The possible functions and characteristics of an EU health data centre are outlined. The centre can only fulfil its mandate if it has the power and competency to influence Member State public-health-relevant data ecosystems and institutionally link with their national level actors. The institutional structure, its possible activities and in particular its usage of advanced technologies such as AI are examined in detail….(More)”.
New York City to Require Food Delivery Services to Share Customer Data with Restaurants
Hunton Privacy Blog: “On August 29, 2021, a New York City Council bill amending the New York City Administrative Code to address customer data collected by food delivery services from online orders became law after the 30-day period for the mayor to sign or veto lapsed. Effective December 27, 2021, the law will permit restaurants to request customer data from third-party food delivery services and require delivery services to provide, on at least a monthly basis, such customer data until the restaurant “requests to no longer receive such customer data.” Customer data includes name, phone number, email address, delivery address and contents of the order.
Although customers are permitted to request that their customer data not be shared, the presumption under the law is that “customers have consented to the sharing of such customer data applicable to all online orders, unless the customer has made such a request in relation to a specific online order.” The food delivery services are required to provide on its website a way for customers to request that their data not be shared “in relation to such online order.” To “assist its customers with deciding whether their data should be shared,” delivery services must disclose to the customer (1) the data that may be shared with the restaurant and (2) the restaurant fulfilling the order as the recipient of the data.
The law will permit restaurants to use the customer data for marketing and other purposes, and prohibit delivery apps from restricting such activities by restaurants. Restaurants that receive the customer data, however, must allow customers to request and delete their customer data. In addition, restaurants are not permitted to sell, rent or disclose customer data to any other party in exchange for financial benefit, except with the express consent of the customer….(More)”.
Exploring a new governance agenda: What are the questions that matter?
Article by Nicola Nixon, Stefaan Verhulst, Imran Matin & Philips J. Vermonte: “…Late last year, we – the Governance Lab at NYU, the CSIS Indonesia, the BRAC Institute of Governance and Development, Bangladesh and The Asia Foundation – joined forces across New York, Jakarta, Dhaka, Hanoi, and San Francisco to launch the 100 Governance Questions Initiative. This is the latest iteration of the GovLab’s broader initiative to map questions across several domains.
We live in an era marked by an unprecedented amount of data. Anyone who uses a mobile phone or accesses the internet is generating vast streams of information. Covid-19 has only intensified this phenomenon.
Although this data contains tremendous potential for positive social transformation, much of that potential goes unfulfilled. In the development context, one chief problem is that data initiatives are often driven by supply (i.e., what data or data solutions are available?) rather than demand (what problems actually need solutions?). Too many projects begin with the database, the app, the dashboard–beholden to the seduction of technology– and now, many parts of the developing world are graveyards of tech pilots. As is well established in development theory but not yet fully in practice, solution-driven governance interventions are destined to fail.
The 100 Questions Initiative, pioneered by the GovLab, seeks to overcome the chasm between supply and demand. It begins not by searching for what data is available, but by asking important questions about the biggest challenges societies and countries face, and then seeking more targeted and relevant data solutions. In doing this, it narrows the gap between policy makers and constituents, providing opportunities for improved evidence-based policy and community engagement in developing countries. As part of this initiative, we seek to define the ten most important questions across several domains, including Migration, Gender, Employment, the Future of Work, and—now–Governance.
On this occasion, we invited over 100 experts and practitioners in governance and data science –whom we call “bilinguals”– from various organizations, companies, and government agencies to identify what they see as the most pressing governance questions in their respective domains. Over 100 bilinguals were encouraged to prioritize potential impact, novelty, and feasibility in their questioning — moving toward a roadmap for data-driven action and collaboration that is both actionable and ambitious.
By June, the bilinguals had articulated 170 governance-related questions. Over the next couple of months, these were sorted, discussed and refined during two rounds of collaboration with the bilinguals; first to narrow down to the top 40 and then to the top 10. Bilinguals were asked what, to them, are the most significant governance questions we must answer with data today? The result is the following 10 questions:…(More)” ( Public Voting Platform)”.
Climate change versus children: How a UNICEF data collaborative gave birth to a risk index
Jess Middleton at DataIQ: “Almost a billion children face climate-related disasters in their lifetime, according to UNICEF’s new Children’s Climate Risk Index (CCRI).
The CCRI is the first analysis of climate risk specifically from a child’s perspective. It reveals that children in Central African Republic, Chad and Nigeria are at the highest risk from climate and environmental shocks based on their access to essential services….
Young climate activists including Greta Thunberg contributed a foreword to the report that introduced the index; and the project has added another layer of pressure on governments failing to act on climate change in the run-up to the 2021 United Nations Climate Change Conference – set to be held in Glasgow in November.
While these statistics make for grim reading, the collective effort undertaken to create the Index is evidence of the power of data as a tool for advocacy and the role that data collaboratives can play in shaping positive change.
The CCRI is underpinned by data that was sourced, collated and analysed by the Data for Children Collaborative with UNICEF, a partnership between UNICEF, the Scottish Government and University of Edinburgh hosted by The Data Lab.
The collaboration brings together practitioners from diverse backgrounds to provide data-driven solutions to issues faced by children around the world.
For work on the CCRI, the collaborative sought data, skills and expertise from academia (Universities of Southampton, Edinburgh, Stirling, Highlands and Islands) as well as the public and private sectors (ONS-FCDO Data Science Hub, Scottish Alliance for Geoscience, Environment & Society).
This variety of expertise provided the knowledge required to build the two main pillars of input for the CCRI: socioeconomic and climate science data.
Socioeconomic experts sourced data and provided analytical expertise in the context of child vulnerability, social statistics, biophysical processes and statistics, child welfare and child poverty.
Climate experts focused on factors such as water scarcity, flood exposure, coastal flood risk, pollution and exposure to vector borne disease.
The success of the project hinged on the effective collaboration between distinct areas of expertise to deliver on UNICEF’s problem statement.
The director of the Data for Children Collaborative with UNICEF, Alex Hutchison, spoke with DataIQ about the success of the project, the challenges the team faced, and the benefits of working as part of a diverse collective….(More). (Report)”
Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access
Paper by Nathan Ratledge et al: “In many regions of the world, sparse data on key economic outcomes inhibits the development, targeting, and evaluation of public policy. We demonstrate how advancements in satellite imagery and machine learning can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show how a combination of satellite imagery and computer vision can be used to develop local-level livelihood measurements appropriate for inferring the causal impact of electricity access on livelihoods. We then show how ML-based inference techniques deliver more reliable estimates of the causal impact of electrification than traditional alternatives when applied to these data. We estimate that grid access improves village-level asset wealth in rural Uganda by 0.17 standard deviations, more than doubling the growth rate over our study period relative to untreated areas. Our results provide country-scale evidence on the impact of a key infrastructure investment, and provide a low-cost, generalizable approach to future policy evaluation in data sparse environments….(More)”.
Can national statistical offices shape the data revolution?
Article by Juan Daniel Oviedo, Katharina Fenz, François Fonteneau, and Simon Riedl: “In recent years, breakthrough technologies in artificial intelligence (AI) and the use of satellite imagery made it possible to disrupt the way we collect, process, and analyze data. Facilitated by the intersection of new statistical techniques and the availability of (big) data, it is now possible to create hypergranular estimates.
National statistical offices (NSOs) could be at the forefront of this change. Conventional tasks of statistical offices, such as the coordination of household surveys and censuses, will remain at the core of their work. However, just like AI can enhance the capabilities of doctors, it also has the potential to make statistical offices better, faster, and eventually cheaper.
Still, many countries struggle to make this happen. In a COVID-19 world marked by constrained financial and statistical capacities, making innovation work for statistical offices is of prime importance to create better lives for all…
In the case of Colombia, this novel method facilitated a scale-up from existing poverty estimates that contained 1,123 data points to 78,000 data points, which represents a 70-fold increase. This results in much more granular estimates highlighting Colombia’s heterogeneity between and within municipalities (see Figure 1).
Figure 1. Poverty shares (%) Colombia, in 2018
Traditional methods don´t allow for cost-efficient hypergranular estimations but serve as a reference point, due to their ground-truthing capacity. Hence, we have combined existing data with novel AI techniques, to go down to granular estimates of up to 4×4 kilometers. In particular, we have trained an algorithm to connect daytime and nighttime satellite images….(More)”.
The Innovation Project: Can advanced data science methods be a game-change for data sharing?
Report by JIPS (Joint Internal Displacement Profiling Service): “Much has changed in the humanitarian data landscape in the last decade and not primarily with the arrival of big data and artificial intelligence. Mostly, the changes are due to increased capacity and resources to collect more data quicker, leading to the professionalisation of information management as a domain of work. Larger amounts of data are becoming available in a more predictable way. We believe that as the field has progressed in filling critical data gaps, the problem is not the availability of data, but the curation and sharing of that data between actors as well as the use of that data to its full potential.
In 2018, JIPS embarked on an innovation journey to explore the potential of state-of-the-art technologies to incentivise data sharing and collaboration. This report covers the first phase of the innovation project and launches a series of articles in which we will share more about the innovation journey itself, discuss safe data sharing and collaboration, and look at the prototype we developed – made possible by the UNHCR Innovation Fund.
We argue that by making data and insights safe and secure to share between stakeholders, it will allow for a more efficient use of available data, reduce the resources needed to collect new data, strengthen collaboration and foster a culture of trust in the evidence-informed protection of people in displacement and crises.
The paper first defines the problem and outlines the processes through which data is currently shared among the humanitarian community. It explores questions such as: what are the existing data sharing methods and technologies? Which ones constitute a feasible option for humanitarian and development organisations? How can different actors share and collaborate on datasets without impairing confidentiality and exposing them to disclosure threats?…(More)”.
Building a Responsible Open Data Ecosystem: Mobility Data & COVID-19
Blog by Anna Livaccari: “Over the last year and a half, COVID-19 has changed the way people move, work, shop, and live. The pandemic has necessitated new data-sharing initiatives to understand new patterns of movement, analyze the spread of COVID-19, and inform research and decision-making. Earlier this year, Cuebiq collaborated with the Open Data Institute (ODI) and NYU’s The GovLab to explore the efficacy of these new initiatives.
The ODI is a non-profit organization that brings together commercial and non-commercial organizations and governments to address global issues as well as advise on how data can be used for positive social good. As part of a larger project titled “COVID-19: Building an open and trustworthy data ecosystem,” the ODI published a new report with Cuebiq and The GovLab, an action research center at NYU’s Tandon School of Engineering that has pioneered the concept of data collaboratives and runs the data stewards network among other initiatives to advance data-driven decision making in the public interest. This report, “The Use of Mobility Data for Responding to the COVID-19 Pandemic,” specifically addresses key enablers and obstacles to the successful sharing of mobility data between public and private organizations during the pandemic….
Since early 2020, researchers and policy makers have been eager to understand the impact of COVID-19. With the help of mobility data, organizations from different sectors were able to answer some of the most pressing questions regarding the pandemic: questions about policy decisions, mass-communication strategies, and overall socioeconomic impact. Mobility data can be applied to specific use cases and can help answer complex questions, a fact that The GovLab discusses in its short-form mobility data brief. Understanding exactly how organizations employ mobility data can also improve how institutions operate post-pandemic and make data collaboration as a whole more responsible, sustainable, and systemic.
Cuebiq and the GovLab identified 51 projects where mobility data was used for pandemic response, and then selected five case studies to analyze further. The report defines mobility data, the ethics surrounding it, and the lessons learned for the future….(More)”.
The Mobility Data Sharing Assessment
New Tool from the Mobility Data Collaborative (MDC): “…released a set of resources to support transparent and accountable decision making about how and when to share mobility data between organizations. …The Mobility Data Sharing Assessment (MDSA) is a practical and customizable assessment that provides operational guidance to support an organization’s existing processes when sharing or receiving mobility data. It consists of a collection of resources:
- 1. A Tool that provides a practical, customizable and open-source assessment for organizations to conduct a self-assessment.
- 2. An Operator’s Manual that provides detailed instructions, guidance and additional resources to assist organizations as they complete the tool.
- 3. An Infographic that provides a visual overview of the MDSA process.
“We were excited to work with the MDC to create a practical set of resources to support mobility data sharing between organizations,” said Chelsey Colbert, policy counsel at FPF. “Through collaboration, we designed version one of a technology-neutral tool, which is consistent and interoperable with leading industry frameworks. The MDSA was designed to be a flexible and scalable approach that enables mobility data sharing initiatives by encouraging organizations of all sizes to assess the legal, privacy, and ethical considerations.”
New mobility options, such as shared cars and e-scooters, have rapidly emerged in cities over the past decade. Data generated by these mobility services offers an exciting opportunity to provide valuable and timely insight to effectively develop transportation policy and infrastructure. As the world becomes more data-driven, tools like the MDSA help remove barriers to safe data sharing without compromising consumer trust….(More)”.