Governing Privacy in the Datafied City


Paper by Ira Rubinstein and Bilyana Petkova: “Privacy — understood in terms of freedom from identification, surveillance and profiling — is a precondition of the diversity and tolerance that define the urban experience, But with “smart” technologies eroding the anonymity of city sidewalks and streets, and turning them into surveilled spaces, are cities the first to get caught in the line of fire? Alternatively, are cities the final bastions of privacy? Will the interaction of tech companies and city governments lead cities worldwide to converge around the privatization of public spaces and monetization of data with little to no privacy protections? Or will we see different city identities take root based on local resistance and legal action?

This Article delves into these questions from a federalist and localist angle. In contrast to other fields in which American cities lack the formal authority to govern, we show that cities still enjoy ample powers when it comes to privacy regulation. Fiscal concerns, rather than state or federal preemption, play a role in privacy regulation, and the question becomes one of how cities make use of existing powers. Populous cosmopolitan cities, with a sizeable market share and significant political and cultural clout, are in particularly noteworthy positions to take advantage of agglomeration effects and drive hard deals when interacting with private firms. Nevertheless, there are currently no privacy front runners or privacy laggards; instead, cities engage in “privacy activism” and “data stewardship.”

First, as privacy activists, U.S. cities use public interest litigation to defend their citizens’ personal information in high profile political participation and consumer protection cases. Examples include legal challenges to the citizenship question in the 2020 Census, and to instances of data breach including Facebook third-party data sharing practices and the Equifax data breach. We link the Census 2020 data wars to sanctuary cities’ battles with the federal administration to demonstrate that political dissent and cities’ social capital — diversity — are intrinsically linked to privacy. Regarding the string of data breach cases, cities expand their experimentation zone by litigating privacy interests against private parties.

Second, cities as data stewards use data to regulate their urban environment. As providers of municipal services, they collect, analyze and act on a broad range of data about local citizens or cut deals with tech companies to enhance transit, housing, utility, telecom, and environmental services by making them smart while requiring firms like Uber and Airbnb to share data with city officials. This has proven contentious at times but in both North American and European cities, open data and more cooperative forms of data sharing between the city, commercial actors, and the public have emerged, spearheaded by a transportation data trust in Seattle. This Article contrasts the Seattle approach with the governance and privacy deficiencies accompanying the privately-led Quayside smart city project in Toronto. Finally, this Article finds the data trust model of data sharing to hold promise, not least since the European rhetoric of exclusively city-owned data presented by Barcelona might prove difficult to realize in practice….(More)”.

Strategies for Urban Network Learning: International Practices and Theoretical Reflections


Book edited by Leon van den Dool: This book presents international experiences in urban network learning. It is vital for cities to learn as it is necessary to constantly adapt and improve public performance and address complex challenges in a constantly changing environment. It is therefore highly relevant to gain more insight into how cities can learn. Cities address problems and challenges in networks of co-operation between existing and new actors, such as state actors, market players and civil society. This book presents various learning environments and methods for urban network learning, and aims to learn from experiences across the globe. How does learning take place in these urban networks? What factors and situations help or hinder these learning practices? Can we move from intuition to a strategy to improve urban network learning?…(More)”.

Exploring the role of data in post-Covid recovery


Blog by Eddie Copeland: “…how might we think about exploring the Amplify box in the diagram above? I’d suggest three approaches are likely to emerge:

Image outlines three headings: Specific fixes, new opportunities, generic capabilities

Let’s discuss these in the context of data.

Specific Fixes — A number of urgent data requests have arisen during Covid where it’s been apparent that councils simply don’t have the data they need. One example is how local authorities have needed to distribute business support grants. Many have discovered that while they have good records of local companies on their business rates database, they lack email or bank details for the majority. That makes it incredibly difficult to get payments out promptly. We can and should fix specific issues like this and ensure councils have those details in future.

New Opportunities — A crisis also prompts us to think about how things could be done differently and better. Perhaps the single greatest new opportunity we could aim to realise on a data front would be shifting from static to dynamic (if not real-time) data on a greater range of issues. As public sector staff, from CEOs to front line workers, have sought to respond to the crisis, the limitations of relying on static weekly, monthly or annual figures have been laid bare. As factors such as transport usage, high street activity and use of public spaces become deeply important in understanding the nature of recovery, more dynamic data could make a real difference.

Generic Capabilities — While the first two categories of activity are worth pursuing, I’d argue the single most positive legacy that could come out of a crisis is that we put in place generic capabilities — core foundation stones — that make us better able to respond to whatever comes next. Some of those capabilities will be about what individual councils need to have in place to use data well. However, given that few crises respect local authority boundaries, arguably the most important set of capabilities concern how different organisations can collaborate with data.

Putting in place the foundation stones for data collaboration

For years there has been discussion about the factors that make data collaboration between different public sector bodies hard.

Five stand out.

  1. Technology — some technologies make it hard to get the data out (e.g. lack of APIs); worse, some suppliers charge councils to access their own data.
  2. Data standards — the use of different standards, formats and conventions for recording data, and the lack of common identifiers like Unique Property Reference Numbers (UPRNs) makes it hard to compare, link or match records.
  3. Information Governance (IG) — Ensuring that London’s public sector organisations can use data in a way that’s legal, ethical and secure — in short, worthy of citizens’ trust and confidence — is key. Yet councils’ different approaches to IG can make the process take a long time — sometimes months.
  4. Ways of working — councils’ different processes require and produce different data.
  5. Lack of skills — when data skills are at a premium, councils understandably need staff with data competencies to work predominantly on internal projects, with little time available for collaboration.

There’s a host of reasons why progress to resolve these barriers has been slow. But perhaps the greatest is the perception that the effort required to address them is greater than the reward of doing so…(More)” –

See also Call For Action here

The tricky math of lifting coronavirus lockdowns


James Temple at MIT Technology Review: “…A crucial point of the work—which Steinhardt and MIT’s Andrew Ilyas​ wrote up in a draft paper that hasn’t yet been published or peer-reviewed—is that communities need to get much better at tracking infections. “With the data we currently have, we actually just don’t know what the level of safe mobility is,” Steinhardt says. “We need much better mechanisms for tracking prevalence in order to do any of this safely.”

The analysis relies on other noisy and less-than-optimal measurements as well, including using hospitalization admissions and deaths to estimate disease prevalence before the lockdowns. They also had to make informed assumptions, which others might disagree with, about how much shelter-in-place rules have altered the spread of the disease. Much of the overall uncertainty is due to the spottiness of testing to date. If case counts are rising, but so is testing, it’s difficult to decipher whether infections are still increasing or a greater proportion of infected people are being evaluated.

This produces some confusing results in the study for any policymaker looking for clear direction. Notably, in Los Angeles, the estimated growth rate of the disease since the shelter-in-place order went into effect ranges from negative to positive. This suggests either that the city could start loosening restrictions or that it needs to tighten them further.

Ultimately, the researchers stress that communities need to build up disease surveillance measures to reduce this uncertainty, and strike an appropriate balance between reopening the economy and minimizing public health risks.

They propose several ways to do so, including conducting virological testing on a random sample of some 20,000 people per day in a given area; setting up wide-scale online surveys that ask people to report potential symptoms, similar to what Carnegie Mellon researchers are doing through efforts with both Facebook and Google; and potentially testing for the prevalence of viral material in wastewater, a technique that has “sounded the alarm” on polio outbreaks in the past.

A team of researchers affiliated with MIT, Harvard, and startup Biobot Analytics recently analyzed water samples from a Massachusetts treatment facility, and detected levels of the coronavirus that were “significantly higher” than expected on the basis of confirmed cases in the state, according to a non-peer-reviewed paper released earlier this month….(More)”.

The Routledge Companion to Smart Cities


Book edited by Katharine S. Willis, and Alessandro Aurigi: “The Routledge Companion to Smart Cities explores the question of what it means for a city to be ‘smart’, raises some of the tensions emerging in smart city developments and considers the implications for future ways of inhabiting and understanding the urban condition. The volume draws together a critical and cross-disciplinary overview of the emerging topic of smart cities and explores it from a range of theoretical and empirical viewpoints.

This timely book brings together key thinkers and projects from a wide range of fields and perspectives into one volume to provide a valuable resource that would enable the reader to take their own critical position within the topic. To situate the topic of the smart city for the reader and establish key concepts, the volume sets out the various interpretations and aspects of what constitutes and defines smart cities. It investigates and considers the range of factors that shape the characteristics of smart cities and draws together different disciplinary perspectives. The consideration of what shapes the smart city is explored through discussing three broad ‘parts’ – issues of governance, the nature of urban development and how visions are realised – and includes chapters that draw on empirical studies to frame the discussion with an understanding not just of the nature of the smart city but also how it is studied, understood and reflected upon.

The Companion will appeal to academics and advanced undergraduates and postgraduates from across many disciplines including Urban Studies, Geography, Urban Planning, Sociology and Architecture, by providing state of the art reviews of key themes by leading scholars in the field, arranged under clearly themed sections….(More)”.

Developing better Civic Services through Crowdsourcing: The Twitter Case Study


Paper by Srushti Wadekar, Kunal Thapar, Komal Barge, Rahul Singh, Devanshu Mishra and Sabah Mohammed: “Civic technology is a fast-developing segment that holds huge potential for a new generation of startups. A recent survey report on civic technology noted that the sector saw $430 million in investment in just the last two years. It’s not just a new market ripe with opportunity it’s crucial to our democracy. Crowdsourcing has proven to be an effective supplementary mechanism for public engagement in city government in order to use mutual knowledge in online communities to address such issues as a means of engaging people in urban design. Government needs new alternatives — alternatives of modern, superior tools and services that are offered at reasonable rates.

An effective and easy-to-use civic technology platform enables wide participation. Response to, and a ‘conversation’ with, the users is very crucial for engagement, as is a feeling of being part of a society. These findings can contribute to the future design of civic technology platforms. In this research, we are trying to introduce a crowdsourcing platform, which will be helpful to people who are facing problems in their everyday practice because of the government services. This platform will gather the information from the trending twitter tweets for last month or so and try to identify which challenges public is confronting. Twitter for crowdsourcing as it is a simple social platform for questions and for the people who see the tweet to get an instant answer. These problems will be analyzed based on their significance which then will be made open to public for its solutions. The findings demonstrate how crowdsourcing tends to boost community engagement, enhances citizens ‘ views of their town and thus tends us find ways to enhance the city’s competitiveness, which faces some serious problems. Using of topic modeling with Latent Dirichlet Allocation (LDA) algorithm helped get categorized civic technology topics which was then validated by simple classification algorithm. While working on this research, we encountered some issues regarding to the tools that were available which we have discussed in the ‘Counter arguments’ section….(More)”.

Cividend: A Democratic Urban Planning Mechanism


Jordan Ostapchuk at RadicalXChange: “Urban planning as a professional discipline is implicitly flawed towards its approach to the design of cities. The term “urban planning” is a category error—it is a mistake to view urban environments as something that can be planned.

This stems from our modern desire to make messy systems ‘legible’ through maps, plans, strategies, and grids. It temporarily suppresses the underlying messiness without ever solving it.

The dominant urban planning philosophy of today assumes two contradictory stances.

On one hand, it assumes people know what is best for their life and can faithfully express it via the virtues of the free market. “If people want single family homes with yards, far from the activity of the city centre, then by rights the market has provided!” (Ignoring the five-decade legacy of race-driven zoning policies, loss-making municipal infrastructure subsidies, and hidden costs to health and wellbeing.)

On the other hand, contemporary urban planning assumes that people have no idea what is best for their life and must be saved from their follies by the maternal hand of strict zoning policies, design guidelines, and municipal bylaws. “If we do not intervene, neighbourhoods will devolve into chaos; trust the experts to masterplan your streets and buildings!” (Ignoring the irony of assuming a central bureaucrat can decide what is best for a neighbourhood that they do not live in, work in, or worship in. And the repeated failures of historically master-planned cities and the prevalence of bylaw exemptions.)

There is a better way to think about cities, how they evolve and our role in the process.

It helps to start with two fundamental truths:

  1. Incredibly complex systems arise from a set of very simple rules
  2. We cannot predict the future, but we can invent it.

By thinking about the city differently, we can reframe “the kind of problem a city is” as Jane Jacobs said, one that is better suited to our 21st century challenges and opportunities.

We need to redefine our thinking about cities as collections of interactions, rather than just physical spaces. We should think about cities as market-based, and socially-driven systems.

Michael Batty defines cities as “…aggregates of multiple decision-making processes that generate designs and decisions pertaining to the way we organize our social and economic activities in space and time,” and this is the way they will be approached here. To invent future cities, we must create a system of “radically innovative political economies and social technologies that are truer to the richness of our diversely shared lives” per RadicalxChange’s mission…(More)”.

Is Your Data Being Collected? These Signs Will Tell You Where


Flavie Halais at Wired: “Alphabet’s Sidewalk Labs is testing icons that provide “digital transparency” when information is collected in public spaces….

As cities incorporate digital technologies into their landscapes, they face the challenge of informing people of the many sensors, cameras, and other smart technologies that surround them. Few people have the patience to read through the lengthy privacy notice on a website or smartphone app. So how can a city let them know how they’re being monitored?

Sidewalk Labs, the Google sister company that applies technology to urban problems, is taking a shot. Through a project called Digital Transparency in the Public Realm, or DTPR, the company is demonstrating a set of icons, to be displayed in public spaces, that shows where and what kinds of data are being collected. The icons are being tested as part Sidewalk Labs’ flagship project in Toronto, where it plans to redevelop a 12-acre stretch of the city’s waterfront. The signs would be displayed at each location where data would be collected—streets, parks, businesses, and courtyards.

Data collection is a core feature of the project, called Sidewalk Toronto, and the source of much of the controversy surrounding it. In 2017, Waterfront Toronto, the organization in charge of administering the redevelopment of the city’s eastern waterfront, awarded Sidewalk Labs the contract to develop the waterfront site. The project has ambitious goals: It says it could create 44,000 direct jobs by 2040 and has the potential to be the largest “climate-positive” community—removing more CO2 from the atmosphere than it produces—in North America. It will make use of new urban technology like modular street pavers and underground freight delivery. Sensors, cameras, and Wi-Fi hotspots will monitor and control traffic flows, building temperature, and crosswalk signals.

All that monitoring raises inevitable concerns about privacy, which Sidewalk aims to address—at least partly—by posting signs in the places where data is being collected.

The signs display a set of icons in the form of stackable hexagons, derived in part from a set of design rules developed by Google in 2014. Some describe the purpose for collecting the data (mobility, energy efficiency, or waste management, for example). Others refer to the type of data that’s collected, such as photos, air quality, or sound. When the data is identifiable, meaning it can be associated with a person, the hexagon is yellow. When the information is stripped of personal identifiers, the hexagon is blue…(More)”.

The Economics of Maps


Abhishek Nagaraj and Scott Stern in the Journal of Economic Perspectives: “For centuries, maps have codified the extent of human geographic knowledge and shaped discovery and economic decision-making. Economists across many fields, including urban economics, public finance, political economy, and economic geography, have long employed maps, yet have largely abstracted away from exploring the economic determinants and consequences of maps as a subject of independent study. In this essay, we first review and unify recent literature in a variety of different fields that highlights the economic and social consequences of maps, along with an overview of the modern geospatial industry. We then outline our economic framework in which a given map is the result of economic choices around map data and designs, resulting in variations in private and social returns to mapmaking. We highlight five important economic and institutional factors shaping mapmakers’ data and design choices. Our essay ends by proposing that economists pay more attention to the endogeneity of mapmaking and the resulting consequences for economic and social welfare…(More)”.

Identifying Urban Areas by Combining Human Judgment and Machine Learning: An Application to India


Paper by Virgilio Galdo, Yue Li and Martin Rama: “This paper proposes a methodology for identifying urban areas that combines subjective assessments with machine learning, and applies it to India, a country where several studies see the official urbanization rate as an under-estimate. For a representative sample of cities, towns and villages, as administratively defined, human judgment of Google images is used to determine whether they are urban or rural in practice. Judgments are collected across four groups of assessors, differing in their familiarity with India and with urban issues, following two different protocols. The judgment-based classification is then combined with data from the population census and from satellite imagery to predict the urban status of the sample.

The Logit model, and LASSO and random forests methods, are applied. These approaches are then used to decide whether each of the out-of-sample administrative units in India is urban or rural in practice. The analysis does not find that India is substantially more urban than officially claimed. However, there are important differences at more disaggregated levels, with ?other towns? and ?census towns? being more rural, and some southern states more urban, than is officially claimed. The consistency of human judgment across assessors and protocols, the easy availability of crowd-sourcing, and the stability of predictions across approaches, suggest that the proposed methodology is a promising avenue for studying urban issues….(More)”.