The Use of Mobility Data for Responding to the COVID-19 Pandemic

As the COVID-19 pandemic continues to upend the way people move, work, and gather, governments, businesses, and public health researchers have looked increasingly at mobility data to support pandemic response. This data, assets that describe human location and movement, generally has been collected for purposes directly related to a company's business model, including optimizing the delivery of consumer services, supply chain management or targeting advertisements. However, these call detail records, smartphone-mobility data, vehicle-derived GPS, and other mobility data assets can also be used to study patterns of movement. These patterns of movement have, in turn, been used by organizations to forecast disease spread and inform decisions on how to best manage activity in certain locations.Researchers at The GovLab and Cuebiq, supported by the Open Data Institute, identified 51 notable projects from around the globe launched by public sector and research organizations with companies that use mobility data for these purposes. It curated five projects among this listing that highlight the specific opportunities (and risks) presented by using this asset. Though few of these highlighted projects have provided public outputs that make assessing project success difficult, organizations interviewed considered mobility data to be a useful asset that enabled better public health surveillance, supported existing decision-making processes, or otherwise allowed groups to achieve their research goals.The report below summarizes some of the major points identified in those case studies. While acknowledging that location data can be a highly sensitive data type that can facilitate surveillance or expose data subjects if used carelessly, it finds mobility data can support research and inform decisions when applied toward narrowly defined research questions through frameworks that acknowledge and proactively mitigate risk. These frameworks can vary based on the individual circumstances facing data users, suppliers, and subjects. However, there are a few conditions that can enable users and suppliers to promote publicly beneficial and responsible data use and overcome the serious obstacles facing them.For data users (governments and research institutions), functional access to real-time and contextually relevant data can support research goals, even though a lack of data science competencies and both short and long-term funding sources represent major obstacles for this goal. Data suppliers (largely companies), meanwhile, need governance structures and mechanisms that facilitate responsible re-use, including data re-use agreements that define who, what, where, and when, and under what conditions data can be shared. A lack of regulatory clarity and the absence of universal governance and privacy standards have impeded effective and responsible dissemination of mobility for research and humanitarian purposes. Finally, for both data users and suppliers, we note that collaborative research networks that allow organizations to seek out and provide data can serve as enablers of project success by facilitating exchange of methods and resources, and closing the gap between research and practice.Based on these findings, we recommend the development of clear governance and privacy frameworks, increased capacity building around data use within the public sector, and more regular convenings of ecosystem stakeholders (including the public and data subjects) to broaden collaborative networks. We also propose solutions towards making the responsible use of mobility data more sustainable for longterm impact beyond the current pandemic. A failure to develop regulatory and governance frameworks that can responsibly manage mobility data could lead to a regression to the ad hoc and uncoordinated approaches that previously defined mobility data applications. It could also lead to disparate standards about organizations' responsibilities to the public

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Seventh Framework Programme (European Commission) (grant agreement No. 284709

AAPOR Report on Big Data

In recent years we have seen an increase in the amount of statistics in society describing different phenomena based on so called Big Data. The term Big Data is used for a variety of data as explained in the report, many of them characterized not just by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, the way in which they are collected, and disseminated are fundamental. The change constitutes a paradigm shift for survey research.There is a great potential in Big Data but there are some fundamental challenges that have to be resolved before its full potential can be realized. In this report we give examples of different types of Big Data and their potential for survey research. We also describe the Big Data process and discuss its main challenges

(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality