Global Traffic Scorecard


Press Release: “…the 2019 INRIX Global Traffic Scorecard… identified, analyzed and ranked congestion and mobility trends in more than 900 cities, across 43 countries. To reflect an increasingly diverse mobility landscape, the 2019 Global Traffic Scorecard includes both public transport and biking metrics for the first time….

At the global level, Bogota topped the list of the cities most impacted by traffic congestion with drivers losing 191 hours a year to congestion, followed by Rio de Janeiro (190 hours), Mexico City (158 hours) and Istanbul (150 hours). Latin American and European cities again dominated the Top 10, highlighting the rapid urbanisation occurring in Latin America and historic European cities that took shape long before the age of automobile….

INRIX fuses anonymous data from diverse datasets – such as phones, cars, trucks and cities – that leads to robust and accurate insights. The data used in the 2019 Global Traffic Scorecard is the congested or uncongested status of every segment of road for every minute of the day, as used by millions of drivers around the world that rely on INRIX-based traffic services….(More)”

Milwaukee’s Amani Neighborhood Uses Data to Target Traffic Safety and Build Trust


Article by Kassie Scott: “People in Milwaukee’s Amani neighborhood are using data to identify safety issues and build relationships with the police. It’s a story of community-engaged research at its best.

In 2017, the Milwaukee Police Department received a grant under the federal Byrne Criminal Justice Innovation program, now called the Community Based Crime Reduction Program, whose purpose is to bridge the gap between practitioners and researchers and advance the use of data in making communities safer. Because of its close ties in the Amani neighborhood, the Dominican Center was selected to lead this initiative, known as the Amani Safety Initiative, and they partnered with local churches, the district attorney’s office, LISC-Milwaukee, and others. To support the effort with data and coaching, the police department contracted with Data You Can Use.

Together with Data You Can Use, the Amani Safety Initiative team first implemented a survey to gauge perceptions of public safety and police legitimacy. Neighborhood ambassadors were trained (and paid) to conduct the survey themselves, going door to door to gather the information from nearly 300 of their neighbors. The ambassadors shared these results with their neighborhood during what they called “data chats.” They also printed summary survey results on door hangers, which they distributed throughout the neighborhood.

Neighbors and community organizations were surprised by the survey results. Though violent crime and mistrust in the police were commonly thought to be the biggest issues, the data showed that residents were most concerned about traffic safety. Ultimately, residents decided to post slow-down signs in intersections.

This project stands out for letting the people in the neighborhood lead the way. Neighbors collected data, shared results, and took action. The partnership between neighbors, police, and local organizations shows how people can drive decision-making for their neighborhood.

The larger story is one of social cohesion and mutual trust. Through participating in the initiative and learning more about their neighborhood, Amani neighbors built stronger relationships with the police. The police began coming to neighborhood community meetings, which helped them build relationships with people in the community and understand the challenges they face….(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)”.

Imagining Regulation Differently: Co-creating for Engagement


Book edited by Morag McDermont, Tim Cole, Janet Newman and Angela Piccini: “There is an urgent need to rethink relationships between systems of government and those who are ‘governed’. This book explores ways of rethinking those relationships by bringing communities normally excluded from decision-making to centre stage to experiment with new methods of regulating for engagement.

Using original, co-produced research, it innovatively shows how we can better use a ‘bottom-up’ approach to design regulatory regimes that recognise the capabilities of communities at the margins and powerfully support the knowledge, passions and creativity of citizens. The authors provide essential guidance for all those working on co-produced research to make impactful change…(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)”.

Smart Village Technology


Book by Srikanta Patnaik, Siddhartha Sen and Magdi S. Mahmoud: “This book offers a transdisciplinary perspective on the concept of “smart villages” Written by an authoritative group of scholars, it discusses various aspects that are essential to fostering the development of successful smart villages. Presenting cutting-edge technologies, such as big data and the Internet-of-Things, and showing how they have been successfully applied to promote rural development, it also addresses important policy and sustainability issues. As such, this book offers a timely snapshot of the state-of-the-art in smart village research and practice….(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)”.