Eigenmorality

Curated on Posted on by Stefaan Verhulst

Blog from Scott Aaronson: “This post is about an idea I had around 1997, when I was 16 years old and a freshman computer-science major at Cornell.  Back then, I was extremely impressed by a research project called CLEVER, which one of my professors, Jon Kleinberg, had led while working at IBM Almaden.  The idea was to use the link structure of the web itself to rank which web pages were most important, and therefore which ones should be returned first in a search query.  Specifically, Kleinberg defined “hubs” as pages that linked to lots of “authorities,” and “authorities” as pages that were linked to by lots of “hubs.”  At first glance, this definition seems hopelessly circular, but Kleinberg observed that one can break the circularity by just treating the World Wide Web as a giant directed graph, and doing some linear algebra on its adjacency matrix.  Equivalently, you can imagine an iterative process where each web page starts out with the same hub/authority “starting credits,” but then in each round, the pages distribute their credits among their neighbors, so that the most popular pages get more credits, which they can then, in turn, distribute to their neighbors by linking to them.
I was also impressed by a similar research project called PageRank, which was proposed later by two guys at Stanford named Sergey Brin and Larry Page.  Brin and Page dispensed with Kleinberg’s bipartite hubs-and-authorities structure in favor of a more uniform structure, and made some other changes, but otherwise their idea was very similar.  At the time, of course, I didn’t know that CLEVER was going to languish at IBM, while PageRank (renamed Google) was going to expand to roughly the size of the entire world’s economy.
In any case, the question I asked myself about CLEVER/PageRank was not the one that, maybe in retrospect, I should have asked: namely, “how can I leverage the fact that I know the importance of this idea before most people do, in order to make millions of dollars?”
Instead I asked myself: “what other ‘vicious circles’ in science and philosophy could one unravel using the same linear-algebra trick that CLEVER and PageRank exploit?”  After all, CLEVER and PageRank were both founded on what looked like a hopelessly circular intuition: “a web page is important if other important web pages link to it.”  Yet they both managed to use math to defeat the circularity.  All you had to do was find an “importance equilibrium,” in which your assignment of “importance” to each web page was stable under a certain linear map.  And such an equilibrium could be shown to exist—indeed, to exist uniquely.
Searching for other circular notions to elucidate using linear algebra, I hit on morality.  Philosophers from Socrates on, I was vaguely aware, had struggled to define what makes a person “moral” or “virtuous,” without tacitly presupposing the answer.  Well, it seemed to me that, as a first attempt, one could do a lot worse than the following:

A moral person is someone who cooperates with other moral people, and who refuses to cooperate with immoral people.

Obviously one can quibble with this definition on numerous grounds: for example, what exactly does it mean to “cooperate,” and which other people are relevant here?  If you don’t donate money to starving children in Africa, have you implicitly “refused to cooperate” with them?  What’s the relative importance of cooperating with good people and withholding cooperation with bad people, of kindness and justice?  Is there a duty not to cooperate with bad people, or merely the lack of a duty to cooperate with them?  Should we consider intent, or only outcomes?  Surely we shouldn’t hold someone accountable for sheltering a burglar, if they didn’t know about the burgling?  Also, should we compute your “total morality” by simply summing over your interactions with everyone else in your community?  If so, then can a career’s worth of lifesaving surgeries numerically overwhelm the badness of murdering a single child?
For now, I want you to set all of these important questions aside, and just focus on the fact that the definition doesn’t even seem to work on its own terms, because of circularity.  How can we possibly know which people are moral (and hence worthy of our cooperation), and which ones immoral (and hence unworthy), without presupposing the very thing that we seek to define?
Ah, I thought—this is precisely where linear algebra can come to the rescue!  Just like in CLEVER or PageRank, we can begin by giving everyone in the community an equal number of “morality starting credits.”  Then we can apply an iterative update rule, where each person A can gain morality credits by cooperating with each other person B, and A gains more credits the more credits B has already.  We apply the rule over and over, until the number of morality credits per person converges to an equilibrium.  (Or, of course, we can shortcut the process by simply finding the principal eigenvector of the “cooperation matrix,” using whatever algorithm we like.)  We then have our objective measure of morality for each individual, solving a 2400-year-old open problem in philosophy….”

Want to Brainstorm New Ideas? Then Limit Your Online Connections

Curated on Posted on by Stefaan Verhulst

Steve Lohr in the New York Times: “The digitally connected life is both invaluable and inevitable.

Anyone who has the slightest doubt need only walk down the sidewalk of any city street filled with people checking their smartphones for text messages, tweets, news alerts or weather reports or any number of things. So glued to their screens, they run into people or create pedestrian traffic jams.

Just when all the connectedness is useful and when it’s not is often difficult to say. But a recent research paper, published on the Social Science Research Network, titled “Facts and Figuring,” sheds some light on that question.

The research involved customizing a Pentagon lab program for measuring collaboration and information-sharing — a whodunit game, in which the subjects sitting at computers search for clues and solutions to figure out the who, what, when and where of a hypothetical terrorist attack.

The 417 subjects, played more than 1,100 rounds of the 25-minute web-based game, and they were mostly students from the Boston area, selected from the pool of volunteers in the Harvard Decision Science Laboratory and Harvard Business School’s Computer Lab for Experimental Research.

They could share clues and solutions. But the study was designed to measure the results from different network structures — densely clustered networks and unclustered networks of communication. Problem solving, the researchers write, involves “both search for information and search for solutions.” They found that “clustering promotes exploration in information space, but decreases exploration in solution space.”

In looking for unique facts or clues, clustering helped since members of the dense communications networks effectively split up the work and redundant facts were quickly weeded out, making them five percent more efficient. But the number of unique theories or solutions was 17.5 percent higher among subjects who were not densely connected. Clustering reduced the diversity of ideas.

The research paper, said Jesse Shore, a co-author and assistant professor at the Boston University School of Management, contributes to “the growing awareness that being connected all the time has costs. And we put a number to it, in an experimental setting.”

The research, of course, also showed where the connection paid off — finding information, the vital first step in decision making. “There are huge, huge benefits to information sharing,” said Ethan Bernstein, a co-author and assistant professor at the Harvard Business School. “But the costs are harder to measure.”…

Facebook tinkered with users’ feeds for a massive psychology experiment

Curated on Posted on by Stefaan Verhulst

William Hughes in AVClub: “Scientists at Facebook have published a paper showing that they manipulated the content seen by more than 600,000 users in an attempt to determine whether this would affect their emotional state. The paper, “Experimental evidence of massive-scale emotional contagion through social networks,” was published in The Proceedings Of The National Academy Of Sciences. It shows how Facebook data scientists tweaked the algorithm that determines which posts appear on users’ news feeds—specifically, researchers skewed the number of positive or negative terms seen by randomly selected users. Facebook then analyzed the future postings of those users over the course of a week to see if people responded with increased positivity or negativity of their own, thus answering the question of whether emotional states can be transmitted across a social network. Result: They can! Which is great news for Facebook data scientists hoping to prove a point about modern psychology. It’s less great for the people having their emotions secretly manipulated.

In order to sign up for Facebook, users must click a box saying they agree to the Facebook Data Use Policy, giving the company the right to access and use the information posted on the site. The policy lists a variety of potential uses for your data, most of them related to advertising, but there’s also a bit about “internal operations, including troubleshooting, data analysis, testing, research and service improvement.” In the study, the authors point out that they stayed within the data policy’s liberal constraints by using machine analysis to pick out positive and negative posts, meaning no user data containing personal information was actually viewed by human researchers. And there was no need to ask study “participants” for consent, as they’d already given it by agreeing to Facebook’s terms of service in the first place.

Facebook data scientist Adam Kramer is listed as the study’s lead author. In an interview the company released a few years ago, Kramer is quoted as saying he joined Facebook because “Facebook data constitutes the largest field study in the history of the world.”

See also:
Facebook Experiments Had Few Limits, Data Science Lab Conducted Tests on Users With Little Oversight, Wall Street Journal.
Stop complaining about the Facebook study. It’s a golden age for research, Duncan Watts

Urban Analytics (Updated and Expanded)

Curated on Posted on by Andrew Young

As part of an ongoing effort to build a knowledge base for the field of opening governance by organizing and disseminating its learnings, the GovLab Selected Readings series provides an annotated and curated collection of recommended works on key opening governance topics. In this edition, we explore the literature on Urban Analytics. To suggest additional readings on this or any other topic, please email biblio@thegovlab.org.

Data and its uses for Governance

Urban Analytics places better information in the hands of citizens as well as government officials to empower people to make more informed choices. Today, we are able to gather real-time information about traffic, pollution, noise, and environmental and safety conditions by culling data from a range of tools: from the low-cost sensors in mobile phones to more robust monitoring tools installed in our environment. With data collected and combined from the built, natural and human environments, we can develop more robust predictive models and use those models to make policy smarter.

With the computing power to transmit and store the data from these sensors, and the tools to translate raw data into meaningful visualizations, we can identify problems as they happen, design new strategies for city management, and target the application of scarce resources where they are most needed.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)
Amini, L., E. Bouillet, F. Calabrese, L. Gasparini, and O. Verscheure. “Challenges and Results in City-scale Sensing.” In IEEE Sensors, 59–61, 2011. http://bit.ly/1doodZm.

  • This paper examines “how city requirements map to research challenges in machine learning, optimization, control, visualization, and semantic analysis.”
  • The authors raises several research challenges including how to extract accurate information when the data is noisy and sparse; how to represent findings from digital pervasive technologies; and how people interact with one another and their environment.

Batty, M., K. W. Axhausen, F. Giannotti, A. Pozdnoukhov, A. Bazzani, M. Wachowicz, G. Ouzounis, and Y. Portugali. “Smart Cities of the Future.” The European Physical Journal Special Topics 214, no. 1 (November 1, 2012): 481–518. http://bit.ly/HefbjZ.

  • This paper explores the goals and research challenges involved in the development of smart cities that merge ICT with traditional infrastructures through digital technologies.
  • The authors put forth several research objectives, including: 1) to explore the notion of the city as a laboratory for innovation; 2) to develop technologies that ensure equity, fairness and realize a better quality of city life; and 3) to develop technologies that ensure informed participation and create shared knowledge for democratic city governance.
  • The paper also examines several contemporary smart city initiatives, expected paradigm shifts in the field, benefits, risks and impacts.

Budde, Paul. “Smart Cities of Tomorrow.” In Cities for Smart Environmental and Energy Futures, edited by Stamatina Th Rassia and Panos M. Pardalos, 9–20. Energy Systems. Springer Berlin Heidelberg, 2014. http://bit.ly/17MqPZW.

  • This paper examines the components and strategies involved in the creation of smart cities featuring “cohesive and open telecommunication and software architecture.”
  • In their study of smart cities, the authors examine smart and renewable energy; next-generation networks; smart buildings; smart transport; and smart government.
  • They conclude that for the development of smart cities, information and communication technology (ICT) is needed to build more horizontal collaborative structures, useful data must be analyzed in real time and people and/or machines must be able to make instant decisions related to social and urban life.

Cardone, G., L. Foschini, P. Bellavista, A. Corradi, C. Borcea, M. Talasila, and R. Curtmola. “Fostering Participaction in Smart Cities: a Geo-social Crowdsensing Platform.” IEEE Communications
Magazine 51, no. 6 (2013): 112–119. http://bit.ly/17iJ0vZ.

  • This article examines “how and to what extent the power of collective although imprecise intelligence can be employed in smart cities.”
  • To tackle problems of managing the crowdsensing process, this article proposes a “crowdsensing platform with three main original technical aspects: an innovative geo-social model to profile users along different variables, such as time, location, social interaction, service usage, and human activities; a matching algorithm to autonomously choose people to involve in participActions and to quantify the performance of their sensing; and a new Android-based platform to collect sensing data from smart phones, automatically or with user help, and to deliver sensing/actuation tasks to users.”

Chen, Chien-Chu. “The Trend towards ‘Smart Cities.’” International Journal of Automation and Smart Technology. June 1, 2014. http://bit.ly/1jOOaAg.

  • In this study, Chen explores the ambitions, prevalence and outcomes of a variety of smart cities, organized into five categories:
    • Transportation-focused smart cities
    • Energy-focused smart cities
    • Building-focused smart cities
    • Water-resources-focused smart cities
    • Governance-focused smart cities
  • The study finds that the “Asia Pacific region accounts for the largest share of all smart city development plans worldwide, with 51% of the global total. Smart city development plans in the Asia Pacific region tend to be energy-focused smart city initiatives, aimed at easing the pressure on energy resources that will be caused by continuing rapid urbanization in the future.”
  • North America, on the other hand is generally more geared toward energy-focused smart city development plans. “In North America, there has been a major drive to introduce smart meters and smart electric power grids, integrating the electric power sector with information and communications technology (ICT) and replacing obsolete electric power infrastructure, so as to make cities’ electric power systems more reliable (which in turn can help to boost private-sector investment, stimulate the growth of the ‘green energy’ industry, and create more job opportunities).”
  • Looking to Taiwan as an example, Chen argues that, “Cities in different parts of the world face different problems and challenges when it comes to urban development, making it necessary to utilize technology applications from different fields to solve the unique problems that each individual city has to overcome; the emphasis here is on the development of customized solutions for smart city development.”

Domingo, A., B. Bellalta, M. Palacin, M. Oliver and E. Almirall. “Public Open Sensor Data: Revolutionizing Smart Cities.” Technology and Society Magazine, IEEE 32, No. 4. Winter 2013. http://bit.ly/1iH6ekU.

  • In this article, the authors explore the “enormous amount of information collected by sensor devices” that allows for “the automation of several real-time services to improve city management by using intelligent traffic-light patterns during rush hour, reducing water consumption in parks, or efficiently routing garbage collection trucks throughout the city.”
  • They argue that, “To achieve the goal of sharing and open data to the public, some technical expertise on the part of citizens will be required. A real environment – or platform – will be needed to achieve this goal.” They go on to introduce a variety of “technical challenges and considerations involved in building an Open Sensor Data platform,” including:
    • Scalability
    • Reliability
    • Low latency
    • Standardized formats
    • Standardized connectivity
  • The authors conclude that, despite incredible advancements in urban analytics and open sensing in recent years, “Today, we can only imagine the revolution in Open Data as an introduction to a real-time world mashup with temperature, humidity, CO2 emission, transport, tourism attractions, events, water and gas consumption, politics decisions, emergencies, etc., and all of this interacting with us to help improve the future decisions we make in our public and private lives.”

Harrison, C., B. Eckman, R. Hamilton, P. Hartswick, J. Kalagnanam, J. Paraszczak, and P. Williams. “Foundations for Smarter Cities.” IBM Journal of Research and Development 54, no. 4 (2010): 1–16. http://bit.ly/1iha6CR.

  • This paper describes the information technology (IT) foundation and principles for Smarter Cities.
  • The authors introduce three foundational concepts of smarter cities: instrumented, interconnected and intelligent.
  • They also describe some of the major needs of contemporary cities, and concludes that Creating the Smarter City implies capturing and accelerating flows of information both vertically and horizontally.

Hernández-Muñoz, José M., Jesús Bernat Vercher, Luis Muñoz, José A. Galache, Mirko Presser, Luis A. Hernández Gómez, and Jan Pettersson. “Smart Cities at the Forefront of the Future Internet.” In The Future Internet, edited by John Domingue, Alex Galis, Anastasius Gavras, Theodore Zahariadis, Dave Lambert, Frances Cleary, Petros Daras, et al., 447–462. Lecture Notes in Computer Science 6656. Springer Berlin Heidelberg, 2011. http://bit.ly/HhNbMX.

  • This paper explores how the “Internet of Things (IoT) and Internet of Services (IoS), can become building blocks to progress towards a unified urban-scale ICT platform transforming a Smart City into an open innovation platform.”
  • The authors examine the SmartSantander project to argue that, “the different stakeholders involved in the smart city business is so big that many non-technical constraints must be considered (users, public administrations, vendors, etc.).”
  • The authors also discuss the need for infrastructures at the, for instance, European level for realistic large-scale experimentally-driven research.

Hoon-Lee, Jung, Marguerite Gong Hancock, Mei-Chih Hu. “Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco.” Technological Forecasting and Social Change. Ocotober 3, 2013. http://bit.ly/1rzID5v.

  • In this study, the authors aim to “shed light on the process of building an effective smart city by integrating various practical perspectives with a consideration of smart city characteristics taken from the literature.”
  • They propose a conceptual framework based on case studies from Seoul and San Francisco built around the following dimensions:
    • Urban openness
    • Service innovation
    • Partnerships formation
    • Urban proactiveness
    • Smart city infrastructure integration
    • Smart city governance
  • The authors conclude with a summary of research findings featuring “8 stylized facts”:
    • Movement towards more interactive services engaging citizens;
    • Open data movement facilitates open innovation;
    • Diversifying service development: exploit or explore?
    • How to accelerate adoption: top-down public driven vs. bottom-up market driven partnerships;
    • Advanced intelligent technology supports new value-added smart city services;
    • Smart city services combined with robust incentive systems empower engagement;
    • Multiple device & network accessibility can create network effects for smart city services;
    • Centralized leadership implementing a comprehensive strategy boosts smart initiatives.

Kamel Boulos, Maged N. and Najeeb M. Al-Shorbaji. “On the Internet of Things, smart cities and the WHO Healthy Cities.” International Journal of Health Geographics 13, No. 10. 2014. http://bit.ly/Tkt9GA.

  • In this article, the authors give a “brief overview of the Internet of Things (IoT) for cities, offering examples of IoT-powered 21st century smart cities, including the experience of the Spanish city of Barcelona in implementing its own IoT-driven services to improve the quality of life of its people through measures that promote an eco-friendly, sustainable environment.”
  • The authors argue that one of the central needs for harnessing the power of the IoT and urban analytics is for cities to “involve and engage its stakeholders from a very early stage (city officials at all levels, as well as citizens), and to secure their support by raising awareness and educating them about smart city technologies, the associated benefits, and the likely challenges that will need to be overcome (such as privacy issues).”
  • They conclude that, “The Internet of Things is rapidly gaining a central place as key enabler of the smarter cities of today and the future. Such cities also stand better chances of becoming healthier cities.”

Keller, Sallie Ann, Steven E. Koonin, and Stephanie Shipp. “Big Data and City Living – What Can It Do for Us?” Significance 9, no. 4 (2012): 4–7. http://bit.ly/166W3NP.

  • This article provides a short introduction to Big Data, its importance, and the ways in which it is transforming cities. After an overview of the social benefits of big data in an urban context, the article examines its challenges, such as privacy concerns and institutional barriers.
  • The authors recommend that new approaches to making data available for research are needed that do not violate the privacy of entities included in the datasets. They believe that balancing privacy and accessibility issues will require new government regulations and incentives.

Kitchin, Rob. “The Real-Time City? Big Data and Smart Urbanism.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, July 3, 2013. http://bit.ly/1aamZj2.

  • This paper focuses on “how cities are being instrumented with digital devices and infrastructure that produce ‘big data’ which enable real-time analysis of city life, new modes of technocratic urban governance, and a re-imagining of cities.”
  • The authors provide “a number of projects that seek to produce a real-time analysis of the city and provides a critical reflection on the implications of big data and smart urbanism.”

Mostashari, A., F. Arnold, M. Maurer, and J. Wade. “Citizens as Sensors: The Cognitive City Paradigm.” In 2011 8th International Conference Expo on Emerging Technologies for a Smarter World (CEWIT), 1–5, 2011. http://bit.ly/1fYe9an.

  • This paper argues that. “implementing sensor networks are a necessary but not sufficient approach to improving urban living.”
  • The authors introduce the concept of the “Cognitive City” – a city that can not only operate more efficiently due to networked architecture, but can also learn to improve its service conditions, by planning, deciding and acting on perceived conditions.
  • Based on this conceptualization of a smart city as a cognitive city, the authors propose “an architectural process approach that allows city decision-makers and service providers to integrate cognition into urban processes.”

Oliver, M., M. Palacin, A. Domingo, and V. Valls. “Sensor Information Fueling Open Data.” In Computer Software and Applications Conference Workshops (COMPSACW), 2012 IEEE 36th Annual, 116–121, 2012. http://bit.ly/HjV4jS.

  • This paper introduces the concept of sensor networks as a key component in the smart cities framework, and shows how real-time data provided by different city network sensors enrich Open Data portals and require a new architecture to deal with massive amounts of continuously flowing information.
  • The authors’ main conclusion is that by providing a framework to build new applications and services using public static and dynamic data that promote innovation, a real-time open sensor network data platform can have several positive effects for citizens.

Perera, Charith, Arkady Zaslavsky, Peter Christen and Dimitrios Georgakopoulos. “Sensing as a service model for smart cities supported by Internet of Things.” Transactions on Emerging Telecommunications Technologies 25, Issue 1. January 2014. http://bit.ly/1qJLDP9.

  • This paper looks into the “enormous pressure towards efficient city management” that has “triggered various Smart City initiatives by both government and private sector businesses to invest in information and communication technologies to find sustainable solutions to the growing issues.”
  • The authors explore the parallel advancement of the Internet of Things (IoT), which “envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities.”
  • The paper proposes the sensing as a service model “as a solution based on IoT infrastructure.” The sensing as a service model consists of four conceptual layers: “(i) sensors and sensor owners; (ii) sensor publishers (SPs); (iii) extended service providers (ESPs); and (iv) sensor data consumers. They go on to describe how this model would work in the areas of waste management, smart agriculture and environmental management.

Privacy, Big Data, and the Public Good: Frameworks for Engagement. Edited by Julia Lane, Victoria Stodden, Stefan Bender, and Helen Nissenbaum; Cambridge University Press, 2014. http://bit.ly/UoGRca.

  • This book focuses on the legal, practical, and statistical approaches for maximizing the use of massive datasets while minimizing information risk.
  • “Big data” is more than a straightforward change in technology.  It poses deep challenges to our traditions of notice and consent as tools for managing privacy.  Because our new tools of data science can make it all but impossible to guarantee anonymity in the future, the authors question whether it possible to truly give informed consent, when we cannot, by definition, know what the risks are from revealing personal data either for individuals or for society as a whole.
  • Based on their experience building large data collections, authors discuss some of the best practical ways to provide access while protecting confidentiality.  What have we learned about effective engineered controls?  About effective access policies?  About designing data systems that reinforce – rather than counter – access policies?  They also explore the business, legal, and technical standards necessary for a new deal on data.
  • Since the data generating process or the data collection process is not necessarily well understood for big data streams, authors discuss what statistics can tell us about how to make greatest scientific use of this data. They also explore the shortcomings of current disclosure limitation approaches and whether we can quantify the extent of privacy loss.

Schaffers, Hans, Nicos Komninos, Marc Pallot, Brigitte Trousse, Michael Nilsson, and Alvaro Oliveira. “Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation.” In The Future Internet, edited by John Domingue, Alex Galis, Anastasius Gavras, Theodore Zahariadis, Dave Lambert, Frances Cleary, Petros Daras, et al., 431–446. Lecture Notes in Computer Science 6656. Springer Berlin Heidelberg, 2011. http://bit.ly/16ytKoT.

  • This paper “explores ‘smart cities’ as environments of open and user-driven innovation for experimenting and validating Future Internet-enabled services.”
  • The authors examine several smart city projects to illustrate the central role of users in defining smart services and the importance of participation. They argue that, “Two different layers of collaboration can be distinguished. The first layer is collaboration within the innovation process. The second layer concerns collaboration at the territorial level, driven by urban and regional development policies aiming at strengthening the urban innovation systems through creating effective conditions for sustainable innovation.”

Suciu, G., A. Vulpe, S. Halunga, O. Fratu, G. Todoran, and V. Suciu. “Smart Cities Built on Resilient Cloud Computing and Secure Internet of Things.” In 2013 19th International Conference on Control Systems and Computer Science (CSCS), 513–518, 2013. http://bit.ly/16wfNgv.

  • This paper proposes “a new platform for using cloud computing capacities for provision and support of ubiquitous connectivity and real-time applications and services for smart cities’ needs.”
  • The authors present a “framework for data procured from highly distributed, heterogeneous, decentralized, real and virtual devices (sensors, actuators, smart devices) that can be automatically managed, analyzed and controlled by distributed cloud-based services.”

Townsend, Anthony. Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. W. W. Norton & Company, 2013.

  • In this book, Townsend illustrates how “cities worldwide are deploying technology to address both the timeless challenges of government and the mounting problems posed by human settlements of previously unimaginable size and complexity.”
  • He also considers “the motivations, aspirations, and shortcomings” of the many stakeholders involved in the development of smart cities, and poses a new civics to guide these efforts.
  • He argues that smart cities are not made smart by various, soon-to-be-obsolete technologies built into its infrastructure, but how citizens use these ever-changing technologies to be “human-centered, inclusive and resilient.”

To stay current on recent writings and developments on Urban Analytics, please subscribe to the GovLab Digest.
Did we miss anything? Please submit reading recommendations to biblio@thegovlab.org or in the comments below.

Predicting crime, LAPD-style

Curated on Posted on by Stefaan Verhulst

The Guardian: “The Los Angeles Police Department, like many urban police forces today, is both heavily armed and thoroughly computerised. The Real-Time Analysis and Critical Response Division in downtown LA is its central processor. Rows of crime analysts and technologists sit before a wall covered in video screens stretching more than 10 metres wide. Multiple news broadcasts are playing simultaneously, and a real-time earthquake map is tracking the region’s seismic activity. Half-a-dozen security cameras are focused on the Hollywood sign, the city’s icon. In the centre of this video menagerie is an oversized satellite map showing some of the most recent arrests made across the city – a couple of burglaries, a few assaults, a shooting.

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On a slightly smaller screen the division’s top official, Captain John Romero, mans the keyboard and zooms in on a comparably micro-scale section of LA. It represents just 500 feet by 500 feet. Over the past six months, this sub-block section of the city has seen three vehicle burglaries and two property burglaries – an atypical concentration. And, according to a new algorithm crunching crime numbers in LA and dozens of other cities worldwide, it’s a sign that yet more crime is likely to occur right here in this tiny pocket of the city.
The algorithm at play is performing what’s commonly referred to as predictive policing. Using years – and sometimes decades – worth of crime reports, the algorithm analyses the data to identify areas with high probabilities for certain types of crime, placing little red boxes on maps of the city that are streamed into patrol cars. “Burglars tend to be territorial, so once they find a neighbourhood where they get good stuff, they come back again and again,” Romero says. “And that assists the algorithm in placing the boxes.”
Romero likens the process to an amateur fisherman using a fish finder device to help identify where fish are in a lake. An experienced fisherman would probably know where to look simply by the fish species, time of day, and so on. “Similarly, a really good officer would be able to go out and find these boxes. This kind of makes the average guys’ ability to find the crime a little bit better.”
Predictive policing is just one tool in this new, tech-enhanced and data-fortified era of fighting and preventing crime. As the ability to collect, store and analyse data becomes cheaper and easier, law enforcement agencies all over the world are adopting techniques that harness the potential of technology to provide more and better information. But while these new tools have been welcomed by law enforcement agencies, they’re raising concerns about privacy, surveillance and how much power should be given over to computer algorithms.
P Jeffrey Brantingham is a professor of anthropology at UCLA who helped develop the predictive policing system that is now licensed to dozens of police departments under the brand name PredPol. “This is not Minority Report,” he’s quick to say, referring to the science-fiction story often associated with PredPol’s technique and proprietary algorithm. “Minority Report is about predicting who will commit a crime before they commit it. This is about predicting where and when crime is most likely to occur, not who will commit it.”…”

The Strength of the Strongest Ties in Collaborative Problem Solving

Curated on Posted on by Stefaan Verhulst

Yves-Alexandre de Montjoye, Arkadiusz Stopczynski, Erez Shmueli, Alex Pentland & Sune Lehmann in Nature (Scientific Reports) : “Complex problem solving in science, engineering, and business has become a highly collaborative endeavor. Teams of scientists or engineers collaborate on projects using their social networks to gather new ideas and feedback. Here we bridge the literature on team performance and information networks by studying teams’ problem solving abilities as a function of both their within-team networks and their members’ extended networks. We show that, while an assigned team’s performance is strongly correlated with its networks of expressive and instrumental ties, only the strongest ties in both networks have an effect on performance. Both networks of strong ties explain more of the variance than other factors, such as measured or self-evaluated technical competencies, or the personalities of the team members. In fact, the inclusion of the network of strong ties renders these factors non-significant in the statistical analysis. Our results have consequences for the organization of teams of scientists, engineers, and other knowledge workers tackling today’s most complex problems.”

We Need a Citizen Maker Movement

Curated on Posted on by Stefaan Verhulst

Lorelei Kelly at the Huffington Post: “It was hard to miss the giant mechanical giraffe grazing on the White House lawn last week. For the first time ever, the President organized a Maker Faire–inviting entrepreneurs and inventors from across the USA to celebrate American ingenuity in the service of economic progress.
The maker movement is a California original. Think R2D2 serving margaritas to a jester with an LED news scroll. The #nationofmakers Twitter feed has dozens of examples of collaborative production, of making, sharing and learning.
But since this was the White House, I still had to ask myself, what would the maker movement be if the economy was not the starting point? What if it was about civics? What if makers decided to create a modern, hands-on democracy?
What is democracy anyway but a never ending remix of new prototypes? Last week’s White House Maker Faire heralded a new economic bonanza. This revolution’s poster child is 3-D printing– decentralized fabrication that is customized to meet local needs. On the government front, new design rules for democracy are already happening in communities, where civics and technology have generated a front line of maker cities.
But the distance between California’s tech capacity and DC does seem 3000 miles wide. The NSA’s over collection/surveillance problem and Healthcare.gov’s doomed rollout are part of the same system-wide capacity deficit. How do we close the gap between California’s revolution and our institutions?

  • In California, disruption is a business plan. In DC, it’s a national security threat.
  • In California, hackers are artists. In DC, they are often viewed as criminals.
  • In California, “cyber” is a dystopian science fiction word. In DC, cyber security is in a dozen oversight plans for Congress.
  • in California, individuals are encouraged to “fail forward.” In DC, risk-aversion is bipartisan.

Scaling big problems with local solutions is a maker specialty. Government policymaking needs this kind of help.
Here’s the issue our nation is facing: The inability of the non-military side of our public institutions to process complex problems. Today, this competence and especially the capacity to solve technical challenges often exist only in the private sector. If something is urgent and can’t be monetized, it becomes a national security problem. Which increasingly means that critical decision making that should be in the civilian remit instead migrates to the military. Look at our foreign policy. Good government is a counter terrorism strategy in Afghanistan. Decades of civilian inaction on climate change means that now Miami is referred to as a battle space in policy conversations.
This rhetoric reflects an understandable but unacceptable disconnect for any democracy.
To make matters more confusing, much of the technology in civics (like list building petitions) is suited for elections, not for governing. It is often antagonistic. The result? policy making looks like campaigning. We need some civic tinkering to generate governing technology that comes with relationships. Specifically, this means technology that includes many voices, but has identifiable channels for expertise that can sort complexity and that is not compromised by financial self-interest.
Today, sorting and filtering information is a huge challenge for participation systems around the world. Information now ranks up there with money and people as a lever of power. On the people front, the loud and often destructive individuals are showing up effectively. On the money front, our public institutions are at risk of becoming purely pay to play (wonks call this “transactional”).
Makers, ask yourselves, how can we turn big data into a political constituency for using real evidence–one that can compete with all the negative noise and money in the system? For starters, technologists out West must stop treating government like it’s a bad signal that can be automated out of existence. We are at a moment where our society requires an engineering mindset to develop modern, tech-savvy rules for democracy. We need civic makers….”

How Crowdsourced Astrophotographs on the Web Are Revolutionizing Astronomy

Curated on Posted on by Stefaan Verhulst

Emerging Technology From the arXiv: “Astrophotography is currently undergoing a revolution thanks to the increased availability of high quality digital cameras and the software available to process the pictures after they have been taken.
Since photographs of the night sky are almost always better with long exposures that capture more light, this processing usually involves combining several images of the same part of the sky to produce one with a much longer effective exposure.
That’s all straightforward if you’ve taken the pictures yourself with the same gear under the same circumstances. But astronomers want to do better.
“The astrophotography group on Flickr alone has over 68,000 images,” say Dustin Lang at Carnegie Mellon University in Pittsburgh and a couple of pals. These and other images represent a vast source of untapped data for astronomers.
The problem is that it’s hard to combine images accurately when little is known about how they were taken. Astronomers take great care to use imaging equipment in which the pixels produce a signal that is proportional to the number of photons that hit.
But the same cannot be said of the digital cameras widely used by amateurs. All kinds of processes can end up influencing the final image.
So any algorithm that combines them has to cope with these variations. “We want to do this without having to infer the (possibly highly nonlinear) processing that has been applied to each individual image, each of which has been wrecked in its own loving way by its creator,” say Lang and co.
Now, these guys say they’ve cracked it. They’ve developed a system that automatically combines images from the same part of the sky to increase the effective exposure time of the resulting picture. And they say the combined images can rival those from much professional telescopes.
They’ve tested this approach by downloading images of two well-known astrophysical objects: the NGC 5907 Galaxy and the colliding pair of galaxies—Messier 51a and 51b.
For NGC 5907, they ended up with 4,000 images from Flickr, 1,000 from Bing and 100 from Google. They used an online system called astrometry.net that automatically aligns and registers images of the night sky and then combined the images using their new algorithm, which they call Enhance.
The results are impressive. They say that the combined images of NGC5907 (bottom three images) show some of the same faint features that revealed a single image taken over 11 hours of exposure using a 50 cm telescope (the top left image). All the images reveal the same kind of fine detail such as a faint stellar stream around the galaxy.
The combined image for the M51 galaxies is just as impressive, taking only 40 minutes to produce on a single processor. It reveals extended structures around both galaxies, which astronomers know to be debris from their gravitational interaction as they collide.
Lang and co say these faint features are hugely important because they allow astronomers to measure the age, mass ratios, and orbital configurations of the galaxies involved. Interestingly, many of these faint features are not visible in any of the input images taken from the Web. They emerge only once images have been combined.
One potential problem with algorithms like this is that they need to perform well as the number of images they combine increases. It’s no good if they grind to a halt as soon as a substantial amount of data becomes available.
On this score, Lang and co say astronomers can rest easy. The performance of their new Enhance algorithm scales linearly with the number of images it has to combine. That means it should perform well on large datasets.
The bottom line is that this kind of crowd-sourced astronomy has the potential to make a big impact, given that the resulting images rival those from large telescopes.
And it could also be used for historical images, say Lang and co. The Harvard Plate Archives, for example, contain half a million images dating back to the 1880s. These were all taken using different emulsions, with different exposures and developed using different processes. So the plates all have different responses to light, making them hard to compare.
That’s exactly the problem that Lang and co have solved for digital images on the Web. So it’s not hard to imagine how they could easily combine the data from the Harvard archives as well….”
Ref: arxiv.org/abs/1406.1528 : Towards building a Crowd-Sourced Sky Map

Towards a comparative science of cities: using mobile traffic records in New York, London and Hong Kong

Curated on Posted on by Stefaan Verhulst

Book chapter by S. Grauwin, S. Sobolevsky, S. Moritz, I. Gódor, C. Ratti, to be published in “Computational Approaches for Urban Environments” (Springer Ed.), October 2014: “This chapter examines the possibility to analyze and compare human activities in an urban environment based on the detection of mobile phone usage patterns. Thanks to an unprecedented collection of counter data recording the number of calls, SMS, and data transfers resolved both in time and space, we confirm the connection between temporal activity profile and land usage in three global cities: New York, London and Hong Kong. By comparing whole cities typical patterns, we provide insights on how cultural, technological and economical factors shape human dynamics. At a more local scale, we use clustering analysis to identify locations with similar patterns within a city. Our research reveals a universal structure of cities, with core financial centers all sharing similar activity patterns and commercial or residential areas with more city-specific patterns. These findings hint that as the economy becomes more global, common patterns emerge in business areas of different cities across the globe, while the impact of local conditions still remains recognizable on the level of routine people activity.”

Every citizen a scientist? An EU project tries to change the face of research

Curated on Posted on by Stefaan Verhulst
Project News from the European Commission:  “SOCIENTIZE builds on the concept of ‘Citizen Science’, which sees thousands of volunteers, teachers, researchers and developers put together their skills, time and resources to advance scientific research. Thanks to open source tools developed under the project, participants can help scientists collect data – which will then be analysed by professional researchers – or even perform tasks that require human cognition or intelligence like image classification or analysis.

Every citizen can be a scientist
The project helps usher in new advances in everything from astronomy to social science.
‘One breakthrough is our increased capacity to reproduce, analyse and understand complex issues thanks to the engagement of large groups of volunteers,’ says Mr Fermin Serrano Sanz, researcher at the University of Zaragoza and Project Coordinator of SOCIENTIZE. ‘And everyone can be a neuron in our digitally-enabled brain.’
But how can ordinary citizens help with such extraordinary science? The key, says Mr Serrano Sanz, is in harnessing the efforts of thousands of volunteers to collect and classify data. ‘We are already gathering huge amounts of user-generated data from the participants using their mobile phones and surrounding knowledge,’ he says.
For example, the experiment ‘SavingEnergy@Home’ asks users to submit data about the temperatures in their homes and neighbourhoods in order to build up a clearer picture of temperatures in cities across the EU, while in Spain, GripeNet.es asks citizens to report when they catch the flu in order to monitor outbreaks and predict possible epidemics.
Many Hands Make Light Work
But citizens can also help analyse data. Even the most advanced computers are not very good at recognising things like sun spots or cells, whereas people can tell the difference between living and dying cells very easily, given only a short training.
The SOCIENTIZE projects ‘Sun4All’ and ‘Cell Spotting’ ask volunteers to label images of solar activity and cancer cells from an application on their phone or computer. With Cell Spotting, for instance, participants can observe cell cultures being studied with a microscope in order to determine their state and the effectiveness of medicines. Analysing this data would take years and cost hundreds of thousands of euros if left to a small team of scientists – but with thousands of volunteers helping the effort, researchers can make important breakthroughs quickly and more cheaply than ever before.
But in addition to bringing citizens closer to science, SOCIENTIZE also brings science closer to citizens. On 12-14 June, the project participated in the SONAR festival with ‘A Collective Music Experiment’ (CME). ‘Two hundred people joined professional DJs and created musical patterns using a web tool; participants shared their creations and re-used other parts in real time. The activity in the festival also included a live show of RdeRumba and Mercadal playing amateurs rhythms’ Mr. Serrano Sanz explains.
The experiment – which will be presented in a mini-documentary to raise awareness about citizen science – is expected to help understand other innovation processes observed in emergent social, technological, economic or political transformations. ‘This kind of event brings together a really diverse set of participants. The diversity does not only enrich the data; it improves the dialogue between professionals and volunteers. As a result, we see some new and innovative approaches to research.’
The EUR 0.7 million project brings together 6 partners from 4 countries: Spain (University of Zaragoza and TECNARA), Portugal (Museu da Ciência-Coimbra, MUSC ; Universidade de Coimbra),  Austria (Zentrum für Soziale Innovation) and Brazil (Universidade Federal de Campina Grande, UFCG).
SOCIENTIZE will end in October 2104 after bringing together 12000 citizens in different phases of research activities for 24 months.”