Crowdsourced social media data for disaster management: Lessons from the PetaJakarta.org project


R.I.Ogie, R.J.Clarke, H.Forehead and P.Perez in Computers, Environment and Urban Systems: “The application of crowdsourced social media data in flood mapping and other disaster management initiatives is a burgeoning field of research, but not one that is without challenges. In identifying these challenges and in making appropriate recommendations for future direction, it is vital that we learn from the past by taking a constructively critical appraisal of highly-praised projects in this field, which through real-world implementations have pioneered the use of crowdsourced geospatial data in modern disaster management. These real-world applications represent natural experiments, each with myriads of lessons that cannot be easily gained from computer-confined simulations.

This paper reports on lessons learnt from a 3-year implementation of a highly-praised project- the PetaJakarta.org project. The lessons presented derive from the key success factors and the challenges associated with the PetaJakarta.org project. To contribute in addressing some of the identified challenges, desirable characteristics of future social media-based disaster mapping systems are discussed. It is envisaged that the lessons and insights shared in this study will prove invaluable within the broader context of designing socio-technical systems for crowdsourcing and harnessing disaster-related information….(More)”.

Urban Science: Putting the “Smart” in Smart Cities


Introduction to Special Issue on Urban Modeling and Simulation by Shade T. Shutters: “Increased use of sensors and social data collection methods have provided cites with unprecedented amounts of data. Yet, data alone is no guarantee that cities will make smarter decisions and many of what we call smart cities would be more accurately described as data-driven cities.

Parallel advances in theory are needed to make sense of those novel data streams and computationally intensive decision support models are needed to guide decision makers through the avalanche of new data. Fortunately, extraordinary increases in computational ability and data availability in the last two decades have led to revolutionary advances in the simulation and modeling of complex systems.

Techniques, such as agent-based modeling and systems dynamic modeling, have taken advantage of these advances to make major contributions to diverse disciplines such as personalized medicine, computational chemistry, social dynamics, or behavioral economics. Urban systems, with dynamic webs of interacting human, institutional, environmental, and physical systems, are particularly suited to the application of these advanced modeling and simulation techniques. Contributions to this special issue highlight the use of such techniques and are particularly timely as an emerging science of cities begins to crystallize….(More)”.

Creative Placemaking and Community Safety: Synthesizing Cross-Cutting Themes


Mark Treskon, Sino Esthappan, Cameron Okeke and Carla Vasquez-Noriega at the Urban Institute: “This report synthesizes findings from four cases where stakeholders are using creative placemaking to improve community safety. It presents cross-cutting themes from these case studies to show how creative placemaking techniques can be used from the conception and design stage through construction and programming, and how they can build community safety by promoting empathy and understanding, influencing law and policy, providing career opportunities, supporting well-being, and advancing the quality of place. It also discusses implementation challenges, and presents evaluative techniques of particular relevance for stakeholders working to understand the effects of these programs….(More)”.

How Smart Should a City Be? Toronto Is Finding Out


Laura Bliss at CityLab: “A data-driven “neighborhood of the future” masterminded by a Google corporate sibling, the Quayside project could be a milestone in digital-age city-building. But after a year of scandal in Silicon Valley, questions about privacy and security remain…

Quayside was billed as “the world’s first neighborhood built from the internet up,” according to Sidewalk Labs’ vision plan, which won the RFP to develop this waterfront parcel. The startup’s pitch married “digital infrastructure” with an utopian promise: to make life easier, cheaper, and happier for Torontonians.

Everything from pedestrian traffic and energy use to the fill-height of a public trash bin and the occupancy of an apartment building could be counted, geo-tagged, and put to use by a wifi-connected “digital layer” undergirding the neighborhood’s physical elements. It would sense movement, gather data, and send information back to a centralized map of the neighborhood. “With heightened ability to measure the neighborhood comes better ways to manage it,” stated the winning document. “Sidewalk expects Quayside to become the most measurable community in the world.”

“Smart cities are largely an invention of the private sector—an effort to create a market within government,” Wylie wrote in Canada’s Globe and Mail newspaper in December 2017. “The business opportunities are clear. The risks inherent to residents, less so.” A month later, at a Toronto City Council meeting, Wylie gave a deputation asking officials to “ensure that the data and data infrastructure of this project are the property of the city of Toronto and its residents.”

In this case, the unwary Trojans would be Waterfront Toronto, the nonprofit corporation appointed by three levels of Canadian government to own, manage, and build on the Port Lands, 800 largely undeveloped acres between downtown and Lake Ontario. When Waterfront Toronto gave Sidewalk Labs a green light for Quayside in October, the startup committed $50 million to a one-year consultation, which was recently extended by several months. The plan is to submit a final “Master Innovation and Development Plan” by the end of this year.

That somewhat Orwellian vision of city management had privacy advocates and academics concerned from the the start. Bianca Wylie, the co-founder of the technology advocacy group Tech Reset Canada, has been perhaps the most outspoken of the project’s local critics. For the last year, she’s spoken up at public fora, written pointed op-edsand Medium posts, and warned city officials of what she sees as the “Trojan horse” of smart city marketing: private companies that stride into town promising better urban governance, but are really there to sell software and monetize citizen data.

But there has been no guarantee about who would own the data at the core of its proposal—much of which would ostensibly be gathered in public space. Also unresolved is the question of whether this data could be sold. With little transparency about what that means from the company or its partner, some Torontonians are wondering what Waterfront Toronto—and by extension, the public—is giving away….(More)”.

Searching for the Smart City’s Democratic Future


Article by Bianca Wylie at the Center for International Governance Innovation: “There is a striking blue building on Toronto’s eastern waterfront. Wrapped top to bottom in bright, beautiful artwork by Montreal illustrator Cecile Gariepy, the building — a former fish-processing plant — stands out alongside the neighbouring parking lots and a congested highway. It’s been given a second life as an office for Sidewalk Labs — a sister company to Google that is proposing a smart city development in Toronto. Perhaps ironically, the office is like the smart city itself: something old repackaged to be light, fresh and novel.

“Our mission is really to use technology to redefine urban life in the twenty-first century.”

Dan Doctoroff, CEO of Sidewalk Labs, shared this mission in an interview with Freakonomics Radio. The phrase is a variant of the marketing language used by the smart city industry at large. Put more simply, the term “smart city” is usually used to describe the use of technology and data in cities.

No matter the words chosen to describe it, the smart city model has a flaw at its core: corporations are seeking to exert influence on urban spaces and democratic governance. And because most governments don’t have the policy in place to regulate smart city development — in particular, projects driven by the fast-paced technology sector — this presents a growing global governance concern.

This is where the story usually descends into warnings of smart city dystopia or failure. Loads of recent articles have detailed the science fiction-style city-of-the-future and speculated about the perils of mass data collection, and for good reason — these are important concepts that warrant discussion. It’s time, however, to push past dystopian narratives and explore solutions for the challenges that smart cities present in Toronto and globally…(More)”.

Data Publics: Urban Protest, Analytics and the Courts


Article by Anthony McCosker and Timothy Graham in MC Journal: “There are many examples globally of the use of social media to engage publics in battles over urban development or similar issues (e.g. Fredericks and Foth). Some have asked how social media might be better used by neighborhood organisations to mobilise protest and save historic buildings, cultural landmarks or urban sites (Johnson and Halegoua). And we can only note here the wealth of research literature on social movements, protest and social media. To emphasise Gerbaudo’s point, drawing on Mattoni, we “need to account for how exactly the use of these media reshapes the ‘repertoire of communication’ of contemporary movements and affects the experience of participants” (2). For us, this also means better understanding the role that social data plays in both aiding and reshaping urban protest or arming third sector groups with evidence useful in social institutions such as the courts.

New modes of digital engagement enable forms of distributed digital citizenship, which Meikle sees as the creative political relationships that form through exercising rights and responsibilities. Associated with these practices is the transition from sanctioned, simple discursive forms of social protest in petitions, to new indicators of social engagement in more nuanced social media data and the more interactive forms of online petition platforms like change.org or GetUp (Halpin et al.). These technical forms code publics in specific ways that have implications for contemporary protest action. That is, they provide the operational systems and instructions that shape social actions and relationships for protest purposes (McCosker and Milne).

All protest and social movements are underwritten by explicit or implicit concepts of participatory publics as these are shaped, enhanced, or threatened by communication technologies. But participatory protest publics are uneven, and as Kelty asks: “What about all the people who are neither protesters nor Twitter users? In the broadest possible sense this ‘General Public’ cannot be said to exist as an actual entity, but only as a kind of virtual entity” (27). Kelty is pointing to the porous boundary between a general public and an organised public, or formal enterprise, as a reminder that we cannot take for granted representations of a public, or the public as a given, in relation to Like or follower data for instance.

If carefully gauged, the concept of data publics can be useful. To start with, the notions of publics and publicness are notoriously slippery. Baym and boyd explore the differences between these two terms, and the way social media reconfigures what “public” is. Does a Comment or a Like on a Facebook Page connect an individual sufficiently to an issues-public? As far back as the 1930s, John Dewey was seeking a pragmatic approach to similar questions regarding human association and the pluralistic space of “the public”. For Dewey, “the machine age has so enormously expanded, multiplied, intensified and complicated the scope of the indirect consequences [of human association] that the resultant public cannot identify itself” (157). To what extent, then, can we use data to constitute a public in relation to social protest in the age of data analytics?

There are numerous well formulated approaches to studying publics in relation to social media and social networks. Social network analysis (SNA) determines publics, or communities, through links, ties and clustering, by measuring and mapping those connections and to an extent assuming that they constitute some form of sociality. Networked publics (Ito, 6) are understood as an outcome of social media platforms and practices in the use of new digital media authoring and distribution tools or platforms and the particular actions, relationships or modes of communication they afford, to use James Gibson’s sense of that term. “Publics can be reactors, (re)makers and (re)distributors, engaging in shared culture and knowledge through discourse and social exchange as well as through acts of media reception” (Ito 6). Hashtags, for example, facilitate connectivity and visibility and aid in the formation and “coordination of ad hoc issue publics” (Bruns and Burgess 3). Gray et al., following Ruppert, argue that “data publics are constituted by dynamic, heterogeneous arrangements of actors mobilised around data infrastructures, sometimes figuring as part of them, sometimes emerging as their effect”. The individuals of data publics are neither subjugated by the logics and metrics of digital platforms and data structures, nor simply sovereign agents empowered by the expressive potential of aggregated data (Gray et al.).

Data publics are more than just aggregates of individual data points or connections. They are inherently unstable, dynamic (despite static analysis and visualisations), or vibrant, and ephemeral. We emphasise three key elements of active data publics. First, to be more than an aggregate of individual items, a data public needs to be consequential (in Dewey’s sense of issues or problem-oriented). Second, sufficient connection is visible over time. Third, affective or emotional activity is apparent in relation to events that lend coherence to the public and its prevailing sentiment. To these, we add critical attention to the affordising processes – or the deliberate and incidental effects of datafication and analysis, in the capacities for data collection and processing in order to produce particular analytical outcomes, and the data literacies these require. We return to the latter after elaborating on the Save the Palace case….(More)”.

Knowledge, Policymaking and Learning for European Cities and Regions: From Research to Practice


Knowledge, Policymaking and Learning for European Cities and Regions

Book edited by Nicola Francesco Dotti: “This book provides theories, experiences, reflections and future directions for social scientists who wish to engage with policy-oriented research in, and for, cities and regions. The ‘policy learning’ perspective is comprehensively discussed, focusing on actors promoting ‘policy knowledge’ and interaction among different stakeholders.

Theoretical frameworks and practical experiences of policy-orientated research for European regions and cities are comprehensively explored in this timely book. The authors review current theories and present novel case studies of policy-orientated research. By combining policy analysis with urban and regional studies, the book highlights how researchers can be agents of policy learning, helping policymakers to learn how to learn.

This book offers unique, real world insights for researchers, practitioners and stakeholders interested in research-based approaches to cities and regions….(More)”

To the smart city and beyond? Developing a typology of smart urban innovation


Maja Nilssen in Technological Forecasting and Social Change: “The smart city is an increasingly popular topic in urban development, arousing both excitement and skepticism. However, despite increasing enthusiasm regarding the smartness of cities, the concept is still regarded as somewhat evasive. Encouraged by the multifaceted character of the concept, this article examines how we can categorize the different dimensions often included in the smart city concept, and how these dimensions are coupled to innovation. Furthermore, the article examines the implications of the different understandings of the smart city concept for cities’ abilities to be innovative.

Building on existing scholarly contributions on the smartness of cities and innovation literature, the article develops a typology of smart city initiatives based on the extent and types of innovations they involve. The typology is structured as a smart city continuum, comprising four dimensions of innovation: (1) technological, (2) organizational, (3) collaborative, (4) experimental.

The smart city continuum is then utilized to analyze empirical data from a Norwegian urban development project triggered by a critical juncture. The empirical data shows that the case holds elements of different dimensions of the continuum, supporting the need for a typology of smart cities as multifaceted urban innovation. The continuum can be used as an analytical model for different types of smart city initiatives, and thus shed light on what types of innovation are central in the smart city. Consequently, the article offers useful insights for both practitioners and scholars interested in smart city initiatives….(More)”

Technology, Activism, and Social Justice in a Digital Age


Book edited by John G. McNutt: “…offers a close look at both the present nature and future prospects for social change. In particular, the text explores the cutting edge of technology and social change, while discussing developments in social media, civic technology, and leaderless organizations — as well as more traditional approaches to social change.

It effectively assembles a rich variety of perspectives to the issue of technology and social change; the featured authors are academics and practitioners (representing both new voices and experienced researchers) who share a common devotion to a future that is just, fair, and supportive of human potential.

They come from the fields of social work, public administration, journalism, law, philanthropy, urban affairs, planning, and education, and their work builds upon 30-plus years of research. The authors’ efforts to examine changing nature of social change organizations and the issues they face will help readers reflect upon modern advocacy, social change, and the potential to utilize technology in making a difference….(More)”

To Better Predict Traffic, Look to the Electric Grid


Linda Poon at CityLab: “The way we consume power after midnight can reveal how we bad the morning rush hour will be….

Commuters check Google Maps for traffic updates the same way they check the weather app for rain predictions. And for good reasons: By pooling information from millions of drivers already on the road, Google can paint an impressively accurate real-time portrait of congestion. Meanwhile, historical numbers can roughly predict when your morning commutes may be particularly bad.

But “the information we extract from traffic data has been exhausted,” said Zhen (Sean) Qian, who directs the Mobility Data Analytics Center at Carnegie Mellon University. He thinks that to more accurately predict how gridlock varies from day to day, there’s a whole other set of data that cities haven’t mined yet: electricity use.

“Essentially we all use the urban system—the electricity, water, the sewage system and gas—and when people use them and how heavily they do is correlated to the way they use the transportation system,” he said. How we use electricity at night, it turns out, can reveal when we leave for work the next day. “So we might be able to get new information that helps explain travel time one or two hours in advance by having a better understanding of human activity.”

 In a recent study in the journal Transportation Research Part C, Qian and his student Pinchao Zhang used 2014 data to demonstrate how electricity usage patterns can predict when peak congestion begins on various segments of a major highway in Austin, Texas—the 14th most congested city in the U.S. They crunched 79 days worth of electricity usage data for 322 households (stripped of all private information, including location), feeding it into a machine learning algorithm that then categorized the households into 10 groups according to the time and amount of electricity use between midnight and 6 a.m. By extrapolating the most critical traffic-related information about each group for each day, the model then predicted what the commute may look like that morning.
When compared with 2014 traffic data, they found that 8 out of the 10 patterns had an impact on highway traffic. Households that show a spike of electricity use from midnight to 2 a.m., for example, may be night owls who sleep in, leave late, and likely won’t contribute to the early morning congestion. In contrast, households that report low electricity use from midnight to 5 a.m., followed by a rise after 5:30 a.m., could be early risers who will be on the road during rush hour. If the researchers’ model detects more households falling into the former group, it might predict that peak congestion will start closer to, say, 7:45 a.m. rather than the usual 7:30….(More)”.