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)”.

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)”.

Smart Urban Development


Open Access Book edited by Vito Bobek: “Debates about the future of urban development in many countries have been increasingly influenced by discussions of smart cities. Despite numerous examples of this “urban labelling” phenomenon, we know surprisingly little about so-called smart cities. This book provides a preliminary critical discussion of some of the more important aspects of smart cities. Its primary focus is on the experience of some designated smart cities, with a view to problematizing a range of elements that supposedly characterize this new urban form. It also questions some of the underlying assumptions and contradictions hidden within the concept….(More)”.

Urban Systems Design Creating Sustainable Smart Cities in the Internet of Things Era


Book edited by Yoshiki Yamagata and Perry P.J. Yang: “…shows how to design, model and monitor smart communities using a distinctive IoT-based urban systems approach. Focusing on the essential dimensions that constitute smart communities energy, transport, urban form, and human comfort, this helpful guide explores how IoT-based sharing platforms can achieve greater community health and well-being based on relationship building, trust, and resilience. Uncovering the achievements of the most recent research on the potential of IoT and big data, this book shows how to identify, structure, measure and monitor multi-dimensional urban sustainability standards and progress.

This thorough book demonstrates how to select a project, which technologies are most cost-effective, and their cost-benefit considerations. The book also illustrates the financial, institutional, policy and technological needs for the successful transition to smart cities, and concludes by discussing both the conventional and innovative regulatory instruments needed for a fast and smooth transition to smart, sustainable communities….(More)”.

Realizing the Potential of AI Localism


Stefaan G. Verhulst and Mona Sloane at Project Syndicate: “Every new technology rides a wave from hype to dismay. But even by the usual standards, artificial intelligence has had a turbulent run. Is AI a society-renewing hero or a jobs-destroying villain? As always, the truth is not so categorical.

As a general-purpose technology, AI will be what we make of it, with its ultimate impact determined by the governance frameworks we build. As calls for new AI policies grow louder, there is an opportunity to shape the legal and regulatory infrastructure in ways that maximize AI’s benefits and limit its potential harms.

Until recently, AI governance has been discussed primarily at the national level. But most national AI strategies – particularly China’s – are focused on gaining or maintaining a competitive advantage globally. They are essentially business plans designed to attract investment and boost corporate competitiveness, usually with an added emphasis on enhancing national security.

This singular focus on competition has meant that framing rules and regulations for AI has been ignored. But cities are increasingly stepping into the void, with New York, Toronto, Dubai, Yokohama, and others serving as “laboratories” for governance innovation. Cities are experimenting with a range of policies, from bans on facial-recognition technology and certain other AI applications to the creation of data collaboratives. They are also making major investments in responsible AI research, localized high-potential tech ecosystems, and citizen-led initiatives.

This “AI localism” is in keeping with the broader trend in “New Localism,” as described by public-policy scholars Bruce Katz and the late Jeremy Nowak. Municipal and other local jurisdictions are increasingly taking it upon themselves to address a broad range of environmental, economic, and social challenges, and the domain of technology is no exception.

For example, New York, Seattle, and other cities have embraced what Ira Rubinstein of New York University calls “privacy localism,” by filling significant gaps in federal and state legislation, particularly when it comes to surveillance. Similarly, in the absence of a national or global broadband strategy, many cities have pursued “broadband localism,” by taking steps to bridge the service gap left by private-sector operators.

As a general approach to problem solving, localism offers both immediacy and proximity. Because it is managed within tightly defined geographic regions, it affords policymakers a better understanding of the tradeoffs involved. By calibrating algorithms and AI policies for local conditions, policymakers have a better chance of creating positive feedback loops that will result in greater effectiveness and accountability….(More)”.

Smarter government or data-driven disaster: the algorithms helping control local communities


Release by MuckRock: “What is the chance you, or your neighbor, will commit a crime? Should the government change a child’s bus route? Add more police to a neighborhood or take some away?

Every day government decisions from bus routes to policing used to be based on limited information and human judgment. Governments now use the ability to collect and analyze hundreds of data points everyday to automate many of their decisions.

Does handing government decisions over to algorithms save time and money? Can algorithms be fairer or less biased than human decision making? Do they make us safer? Automation and artificial intelligence could improve the notorious inefficiencies of government, and it could exacerbate existing errors in the data being used to power it.

MuckRock and the Rutgers Institute for Information Policy & Law (RIIPL) have compiled a collection of algorithms used in communities across the country to automate government decision-making.

Go right to the database.

We have also compiled policies and other guiding documents local governments use to make room for the future use of algorithms. You can find those as a project on DocumentCloud.

View policies on smart cities and technologies

These collections are a living resource and attempt to communally collect records and known instances of automated decision making in government….(More)”.

Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?


Paper by Geoff Boeing et al: “Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities.

Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases….(More)”.