Using location data responsibly in cities and local government

Curated on Posted on by Stefaan Verhulst

Article by Ben Hawes: “City and local governments increasingly recognise the power of location data to help them deliver more and better services, exactly where and when they are needed. The use of this data is going to grow, with more pressure to manage resources and emerging challenges including responding to extreme weather events and other climate impacts.

But using location data to target and manage local services comes with risks to the equitable delivery of services, privacy and accountability. To make the best use of these growing data resources, city leaders and their teams need to understand those risks and address them, and to be able to explain their uses of data to citizens.

The Locus Charter, launched earlier this year, is a set of common principles to promote responsible practice when using location data. The Charter could be very valuable to local governments, to help them navigate the challenges and realise the rewards offered by data about the places they manage….

Compared to private companies, local public bodies already have special responsibilities to ensure transparency and fairness. New sources of data can help, but can also generate new questions. Local governments have generally been able to improve services as they learned more about the people they served. Now they must manage the risks of knowing too much about people, and acting intrusively. They can also risk distorting service provision because their data about people in places is uneven or unrepresentative.

Many city and local governments fully recognise that data-driven delivery comes with risks, and are developing specific local data ethics frameworks to guide their work. Some of these, like Kansas City’s, are specifically aimed at managing data privacy. Others cover broader uses of data, like Greater Manchester’s Declaration for Intelligent and Responsible Data Practice (DTPR). DTPR is an open source communication standard that helps people understand how data is being used in public places.

London is engaging citizens on an Emerging Technology Charter, to explore new and ethically charged questions around data. Govlab supports an AI Localism repository of actions taken by local decision-makers to address the use of AI within a city or community. The EU Sherpa programme (Shaping the Ethical Dimensions of Smart Information Systems) includes a smart cities strand, and has published a case-study on the Ethics of Using Smart City AI and Big Data.

Smart city applications make it potentially possible to collect data in many ways, for many purposes, but the technologies cannot answer questions about what is appropriate. In The Smart Enough City: Putting Technology in its Place to Reclaim Our Urban Future (2019), author Ben Green describes examples when some cities have failed and others succeeded in judging what smart applications should be used.

Attention to what constitutes ethical practice with location data can give additional help to leaders making that kind of judgement….(More)”

Licensure as Data Governance

Curated on Posted on by Stefaan Verhulst

Essay by Frank Pasquale: “…A licensure regime for data and the AI it powers would enable citizens to democratically shape data’s scope and proper use, rather than resigning ourselves to being increasingly influenced and shaped by forces beyond our control.To ground the case for more ex ante regulation, Part I describes the expanding scope of data collection, analysis, and use, and the threats that that scope poses to data subjects. Part II critiques consent-based models of data protection, while Part III examines the substantive foundation of licensure models. Part IV addresses a key challenge to my approach: the free expression concerns raised by the licensure of large-scale personal data collection, analysis, and use. Part V concludes with reflections on the opportunities created by data licensure frameworks and potential limitations upon them….(More)”.

Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city

Curated on Posted on by Stefaan Verhulst

Paper by Caio Pieroni, Mariana Giannotti, Bianca B.Alves, and Renato Arbex: “Smart card data (SCD) allow analyzing mobility at a fine level of detail, despite the remaining challenges such as identifying trip purpose. The use of the SCD may improve the understanding of transit users’ travel patterns from precarious settlements areas, where the residents have historically limited access to opportunities and are usually underrepresented in surveys. In this paper, we explore smart card data mining to analyze the temporal and spatial patterns of the urban transit movements from residents of precarious settlements areas in São Paulo, Brazil, and compare the similarities and differences in travel behavior with middle/high-income-class residents. One of our concerns is to identify low-paid employment travel patterns from the low-income-class residents, that are also underrepresented in transportation planning modeling due to the lack of data. We employ the k-means clustering algorithm for the analysis, and the DBSCAN algorithm is used to infer passengers’ residence locations. The results reveal that most of the low-income residents of precarious settlements begin their first trip before, between 5 and 7 AM, while the better-off group begins from 7 to 9 AM. At least two clusters formed by commuters from precarious settlement areas suggest an association of these residents with low-paid employment, with their activities placed in medium / high-income residential areas. So, the empirical evidence revealed in this paper highlights smart card data potential to unfold low-paid employment spatial and temporal patterns….(More)”.

Statistics and Data Science for Good

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Introduction to Special Issue of Chance by Caitlin Augustin, Matt Brems, and Davina P. Durgana: “One lesson that our team has taken from the past 18 months is that no individual, no team, and no organization can be successful on their own. We’ve been grateful and humbled to witness incredible collaboration—taking on forms of resource sharing, knowledge exchange, and reimagined outcomes. Some advances, like breakthrough medicine, have been widely publicized. Other advances have received less fanfare. All of these advances are in the public interest and demonstrate how collaborations can be done “for good.”

In reading this issue, we hope that you realize the power of diverse multidisciplinary collaboration; you recognize the positive social impact that statisticians, data scientists, and technologists can have; and you learn that this isn’t limited to companies with billions of dollars or teams of dozens of people. You, our reader, can get involved in similar positive social change.

This special edition of CHANCE focuses on using data and statistics for the public good and on highlighting collaborations and innovations that have been sparked by partnerships between pro bono institutions and social impact partners. We recognize that the “pro bono” or “for good” field is vast, and we welcome all actors working in the public interest into the big tent.

Through the focus of this edition, we hope to demonstrate how new or novel collaborations might spark meaningful and lasting positive change in communities, sectors, and industries. Anchored by work led through Statistics Without Borders and DataKind, this edition features reporting on projects that touch on many of the United Nations Sustainable Development Goals (SDGs).

Pro bono volunteerism is one way of democratizing access to high-skill, high-expense services that are often unattainable for social impact organizations. Statistics Without Borders (founded in 2008), DataKind (founded in 2012), and numerous other volunteer organizations began with this model in mind: If there was an organizing or galvanizing body that could coordinate the myriad requests for statistical, data science, machine learning, or data engineering help, there would be a ready supply of talented individuals who would want to volunteer to see those projects through. Or, put another way, “If you build it, they will come.”

Doing pro bono work requires more than positive intent. Plenty of well-meaning organizations and individuals charitably donate their time, their energy, their expertise, only to have an unintended adverse impact. To do work for good, ethics is an important part of the projects. In this issue, you’ll notice the writers’ attention to independent review boards (IRBs), respecting client and data privacy, discussing ethical considerations of methods used, and so on.

While no single publication can fully capture the great work of pro bono organizations working in “data for good,” we hope readers will be inspired to contribute to open source projects, solve problems in a new way, or even volunteer themselves for a future cohort of projects. We’re thrilled that this special edition represents programs, partners, and volunteers from around the world. You will learn about work that is truly representative of the SDGs, such as international health organizations’ work in Uganda, political justice organizations in Kenya, and conservationists in Madagascar, to name a few.

Several articles describe projects that are contextualized with the SDGs. While achieving many goals is interconnected, such as the intertwining of economic attainment and reducing poverty, we hope that calling out key themes here will whet your appetite for exploration.

  • • Multiple articles focused on tackling aspects of SDG 3: Ensuring healthy lives and promoting well-being for people at all ages.
  • • An article tackling SDG 8: Promote sustained, inclusive, and sustainable economic growth; full and productive employment; and decent work for all.
  • • Several articles touching on SDG 9: Build resilient infrastructure; promote inclusive and sustainable industrialization, and foster innovation; one is a reflection on building and sustaining free and open source software as a public good.
  • • A handful of articles highlighting the needs for capacity-building and systems-strengthening aligned to SDG 16: Promote peaceful and inclusive societies for sustainable development; provide access to justice for all; and build effective, accountable, and inclusive institutions at all levels.
  • • An article about migration along the southern borders of the United States addressing multiple issues related to poverty (SDG 1), opportunity (SDG 10), and peace and justice (SDG 16)….(More)”

Putting data at the heart of policymaking will accelerate London’s recovery

Curated on Posted on by Stefaan Verhulst

Mel Hobson at Computer Weekly: “…London’s mayor, Sadiq Khan, knows how important this is. His re-election manifesto committed to rebuilding the London Datastore, currently home to over 700 freely available datasets, as the central register linking data across our city. That in turn will help analysts, researchers and policymakers understand our city and develop new ideas and solutions.

To help take the next step and create a data ecosystem that can improve millions of Londoners lives, businesses across our capital are committing their expertise and insights.

At London First, we have launched the London Data Charter, expertly put together by Pinsent Masons, and setting out the guiding principles for private and public sector data collaborations, which are key to creating this ecosystem. These include a focus on protecting privacy and security of data, promoting trust and sharing learnings with others – creating scalable solutions to meet the capital’s challenges….(More)”.

UNCTAD calls on countries to make digital data flow for the benefit of all

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Press Release: “The world needs a new global governance approach to enable digital data to flow across borders as freely as necessary and possible, says UNCTAD’s Digital Economy Report 2021 released on 29 September.

The UN trade and development body says the new approach should help maximize development gains, ensure those gains are equitably distributed and minimize risks and harms.

It should also enable worldwide data sharing, develop global digital public goods, increase trust and reduce uncertainty in the digital economy.

The report says the new global system should also help avoid further fragmentation of the internet, address policy challenges emerging from the dominant positions of digital platforms and narrow existing inequalities.

“It is more important than ever to embark on a new path for digital and data governance,” says UN Secretary-General António Guterres in his preface to the report.

“The current fragmented data landscape risks us failing to capture value that could accrue from digital technologies and it may create more space for substantial harms related to privacy breaches, cyberattacks and other risks.”

UNCTAD Secretary-General Rebeca Grynspan said: “We urgently need a renewed focus on achieving global digital and data governance, developing global digital public goods, increasing trust and reducing uncertainty in the digital economy. The pandemic has shown the critical importance of sharing health data globally – the issue of digital governance can no longer be postponed.”

Pandemic underscores need for new governance

Digital data play an increasingly important role as an economic and strategic resource, a trend reinforced by the COVID-19 pandemic.

The pandemic has shown the importance of sharing health data globally to help countries cope with its consequences, and for research purposes in finding vaccines.

“The increased interconnection and interdependence challenges in the global data economy call for moving away from the silo approach towards a more holistic, coordinated global approach,” UNCTAD Deputy Secretary-General Isabelle Durant said.

“Moreover, new and innovative ways of global governance are urgently needed, as the old ways may not be well suited to respond to the new context,” she added.

New UN data-related body proposed

UNCTAD proposes the formation of a new United Nations coordinating body, with a focus on, and with the skills for, assessing and developing comprehensive global digital and data governance. Its work should be multilateral, multi-stakeholder and multidisciplinary.

It should also seek to remedy the current underrepresentation of developing countries in global and regional data governance initiatives.

The body should also function as a complement to and in coherence with national policies and provide sufficient policy space to ensure countries with different levels of digital readiness and capacities can benefit from the data-driven digital economy…(More)”.

Greece used AI to curb COVID: what other nations can learn

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Editorial at Nature: “A few months into the COVID-19 pandemic, operations researcher Kimon Drakopoulos e-mailed both the Greek prime minister and the head of the country’s COVID-19 scientific task force to ask if they needed any extra advice.

Drakopoulos works in data science at the University of Southern California in Los Angeles, and is originally from Greece. To his surprise, he received a reply from Prime Minister Kyriakos Mitsotakis within hours. The European Union was asking member states, many of which had implemented widespread lockdowns in March, to allow non-essential travel to recommence from July 2020, and the Greek government needed help in deciding when and how to reopen borders.

Greece, like many other countries, lacked the capacity to test all travellers, particularly those not displaying symptoms. One option was to test a sample of visitors, but Greece opted to trial an approach rooted in artificial intelligence (AI).

Between August and November 2020 — with input from Drakopoulos and his colleagues — the authorities launched a system that uses a machine-learning algorithm to determine which travellers entering the country should be tested for COVID-19. The authors found machine learning to be more effective at identifying asymptomatic people than was random testing or testing based on a traveller’s country of origin. According to the researchers’ analysis, during the peak tourist season, the system detected two to four times more infected travellers than did random testing.

The machine-learning system, which is among the first of its kind, is called Eva and is described in Nature this week (H. Bastani et al. Nature https://doi.org/10.1038/s41586-021-04014-z; 2021). It’s an example of how data analysis can contribute to effective COVID-19 policies. But it also presents challenges, from ensuring that individuals’ privacy is protected to the need to independently verify its accuracy. Moreover, Eva is a reminder of why proposals for a pandemic treaty (see Nature 594, 8; 2021) must consider rules and protocols on the proper use of AI and big data. These need to be drawn up in advance so that such analyses can be used quickly and safely in an emergency.

In many countries, travellers are chosen for COVID-19 testing at random or according to risk categories. For example, a person coming from a region with a high rate of infections might be prioritized for testing over someone travelling from a region with a lower rate.

By contrast, Eva collected not only travel history, but also demographic data such as age and sex from the passenger information forms required for entry to Greece. It then matched those characteristics with data from previously tested passengers and used the results to estimate an individual’s risk of infection. COVID-19 tests were targeted to travellers calculated to be at highest risk. The algorithm also issued tests to allow it to fill data gaps, ensuring that it remained up to date as the situation unfolded.

During the pandemic, there has been no shortage of ideas on how to deploy big data and AI to improve public health or assess the pandemic’s economic impact. However, relatively few of these ideas have made it into practice. This is partly because companies and governments that hold relevant data — such as mobile-phone records or details of financial transactions — need agreed systems to be in place before they can share the data with researchers. It’s also not clear how consent can be obtained to use such personal data, or how to ensure that these data are stored safely and securely…(More)”.

Where Is Everyone? The Importance of Population Density Data

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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

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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

Curated on Posted on by Stefaan Verhulst

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)”.