Sharing Data Is a Form of Corporate Philanthropy

Curated on Posted on by Stefaan Verhulst

Matt Stempeck in HBR Blog:  “Ever since the International Charter on Space and Major Disasters was signed in 1999, satellite companies like DMC International Imaging have had a clear protocol with which to provide valuable imagery to public actors in times of crisis. In a single week this February, DMCii tasked its fleet of satellites on flooding in the United Kingdom, fires in India, floods in Zimbabwe, and snow in South Korea. Official crisis response departments and relevant UN departments can request on-demand access to the visuals captured by these “eyes in the sky” to better assess damage and coordinate relief efforts.

DMCii is a private company, yet it provides enormous value to the public and social sectors simply by periodically sharing its data.
Back on Earth, companies create, collect, and mine data in their day-to-day business. This data has quickly emerged as one of this century’s most vital assets. Public sector and social good organizations may not have access to the same amount, quality, or frequency of data. This imbalance has inspired a new category of corporate giving foreshadowed by the 1999 Space Charter: data philanthropy.
The satellite imagery example is an area of obvious societal value, but data philanthropy holds even stronger potential closer to home, where a wide range of private companies could give back in meaningful ways by contributing data to public actors. Consider two promising contexts for data philanthropy: responsive cities and academic research.
The centralized institutions of the 20th century allowed for the most sophisticated economic and urban planning to date. But in recent decades, the information revolution has helped the private sector speed ahead in data aggregation, analysis, and applications. It’s well known that there’s enormous value in real-time usage of data in the private sector, but there are similarly huge gains to be won in the application of real-time data to mitigate common challenges.
What if sharing economy companies shared their real-time housing, transit, and economic data with city governments or public interest groups? For example, Uber maintains a “God’s Eye view” of every driver on the road in a city:
stempeck2
Imagine combining this single data feed with an entire portfolio of real-time information. An early leader in this space is the City of Chicago’s urban data dashboard, WindyGrid. The dashboard aggregates an ever-growing variety of public datasets to allow for more intelligent urban management.
stempeck3
Over time, we could design responsive cities that react to this data. A responsive city is one where services, infrastructure, and even policies can flexibly respond to the rhythms of its denizens in real-time. Private sector data contributions could greatly accelerate these nascent efforts.
Data philanthropy could similarly benefit academia. Access to data remains an unfortunate barrier to entry for many researchers. The result is that only researchers with access to certain data, such as full-volume social media streams, can analyze and produce knowledge from this compelling information. Twitter, for example, sells access to a range of real-time APIs to marketing platforms, but the price point often exceeds researchers’ budgets. To accelerate the pursuit of knowledge, Twitter has piloted a program called Data Grants offering access to segments of their real-time global trove to select groups of researchers. With this program, academics and other researchers can apply to receive access to relevant bulk data downloads, such as an period of time before and after an election, or a certain geographic area.
Humanitarian response, urban planning, and academia are just three sectors within which private data can be donated to improve the public condition. There are many more possible applications possible, but few examples to date. For companies looking to expand their corporate social responsibility initiatives, sharing data should be part of the conversation…
Companies considering data philanthropy can take the following steps:

  • Inventory the information your company produces, collects, and analyzes. Consider which data would be easy to share and which data will require long-term effort.
  • Think who could benefit from this information. Who in your community doesn’t have access to this information?
  • Who could be harmed by the release of this data? If the datasets are about people, have they consented to its release? (i.e. don’t pull a Facebook emotional manipulation experiment).
  • Begin conversations with relevant public agencies and nonprofit partners to get a sense of the sort of information they might find valuable and their capacity to work with the formats you might eventually make available.
  • If you expect an onslaught of interest, an application process can help qualify partnership opportunities to maximize positive impact relative to time invested in the program.
  • Consider how you’ll handle distribution of the data to partners. Even if you don’t have the resources to set up an API, regular releases of bulk data could still provide enormous value to organizations used to relying on less-frequently updated government indices.
  • Consider your needs regarding privacy and anonymization. Strip the data of anything remotely resembling personally identifiable information (here are some guidelines).
  • If you’re making data available to researchers, plan to allow researchers to publish their results without obstruction. You might also require them to share the findings with the world under Open Access terms….”

Selected Readings on Sentiment Analysis

Curated on Posted on by sophie

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of sentiment analysis was originally published in 2014.

Sentiment Analysis is a field of Computer Science that uses techniques from natural language processing, computational linguistics, and machine learning to predict subjective meaning from text. The term opinion mining is often used interchangeably with Sentiment Analysis, although it is technically a subfield focusing on the extraction of opinions (the umbrella under which sentiment, evaluation, appraisal, attitude, and emotion all lie).

The rise of Web 2.0 and increased information flow has led to an increase in interest towards Sentiment Analysis — especially as applied to social networks and media. Events causing large spikes in media — such as the 2012 Presidential Election Debates — are especially ripe for analysis. Such analyses raise a variety of implications for the future of crowd participation, elections, and governance.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Choi, Eunsol et al. “Hedge detection as a lens on framing in the GMO debates: a position paper.” Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics 13 Jul. 2012: 70-79. http://bit.ly/1wweftP

  • Understanding the ways in which participants in public discussions frame their arguments is important for understanding how public opinion is formed. This paper adopts the position that it is time for more computationally-oriented research on problems involving framing. In the interests of furthering that goal, the authors propose the following question: In the controversy regarding the use of genetically-modified organisms (GMOs) in agriculture, do pro- and anti-GMO articles differ in whether they choose to adopt a more “scientific” tone?
  • Prior work on the rhetoric and sociology of science suggests that hedging may distinguish popular-science text from text written by professional scientists for their colleagues. The paper proposes a detailed approach to studying whether hedge detection can be used to understand scientific framing in the GMO debates, and provides corpora to facilitate this study. Some of the preliminary analyses suggest that hedges occur less frequently in scientific discourse than in popular text, a finding that contradicts prior assertions in the literature.

Michael, Christina, Francesca Toni, and Krysia Broda. “Sentiment analysis for debates.” (Unpublished MSc thesis). Department of Computing, Imperial College London (2013). http://bit.ly/Wi86Xv

  • This project aims to expand on existing solutions used for automatic sentiment analysis on text in order to capture support/opposition and agreement/disagreement in debates. In addition, it looks at visualizing the classification results for enhancing the ease of understanding the debates and for showing underlying trends. Finally, it evaluates proposed techniques on an existing debate system for social networking.

Murakami, Akiko, and Rudy Raymond. “Support or oppose?: classifying positions in online debates from reply activities and opinion expressions.” Proceedings of the 23rd International Conference on Computational Linguistics: Posters 23 Aug. 2010: 869-875. https://bit.ly/2Eicfnm

  • In this paper, the authors propose a method for the task of identifying the general positions of users in online debates, i.e., support or oppose the main topic of an online debate, by exploiting local information in their remarks within the debate. An online debate is a forum where each user posts an opinion on a particular topic while other users state their positions by posting their remarks within the debate. The supporting or opposing remarks are made by directly replying to the opinion, or indirectly to other remarks (to express local agreement or disagreement), which makes the task of identifying users’ general positions difficult.
  • A prior study has shown that a link-based method, which completely ignores the content of the remarks, can achieve higher accuracy for the identification task than methods based solely on the contents of the remarks. In this paper, it is shown that utilizing the textual content of the remarks into the link-based method can yield higher accuracy in the identification task.

Pang, Bo, and Lillian Lee. “Opinion mining and sentiment analysis.” Foundations and trends in information retrieval 2.1-2 (2008): 1-135. http://bit.ly/UaCBwD

  • This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Its focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. It includes material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

Ranade, Sarvesh et al. “Online debate summarization using topic directed sentiment analysis.” Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining 11 Aug. 2013: 7. http://bit.ly/1nbKtLn

  • Social networking sites provide users a virtual community interaction platform to share their thoughts, life experiences and opinions. Online debate forum is one such platform where people can take a stance and argue in support or opposition of debate topics. An important feature of such forums is that they are dynamic and grow rapidly. In such situations, effective opinion summarization approaches are needed so that readers need not go through the entire debate.
  • This paper aims to summarize online debates by extracting highly topic relevant and sentiment rich sentences. The proposed approach takes into account topic relevant, document relevant and sentiment based features to capture topic opinionated sentences. ROUGE (Recall-Oriented Understudy for Gisting Evaluation, which employ a set of metrics and a software package to compare automatically produced summary or translation against human-produced onces) scores are used to evaluate the system. This system significantly outperforms several baseline systems and show improvement over the state-of-the-art opinion summarization system. The results verify that topic directed sentiment features are most important to generate effective debate summaries.

Schneider, Jodi. “Automated argumentation mining to the rescue? Envisioning argumentation and decision-making support for debates in open online collaboration communities.” http://bit.ly/1mi7ztx

  • Argumentation mining, a relatively new area of discourse analysis, involves automatically identifying and structuring arguments. Following a basic introduction to argumentation, the authors describe a new possible domain for argumentation mining: debates in open online collaboration communities.
  • Based on our experience with manual annotation of arguments in debates, the authors propose argumentation mining as the basis for three kinds of support tools, for authoring more persuasive arguments, finding weaknesses in others’ arguments, and summarizing a debate’s overall conclusions.

When Technologies Combine, Amazing Innovation Happens

Curated on Posted on by Stefaan Verhulst

FastCoexist: “Innovation occurs both within fields, and in combinations of fields. It’s perhaps the latter that ends up being most groundbreaking. When people of disparate expertise, mindset and ideas work together, new possibilities pop up.
In a new report, the Institute for the Future argues that “technological change is increasingly driven by the combination and recombination of foundational elements.” So, when we think about the future, we need to consider not just fundamental advances (say, in computing, materials, bioscience) but also at the intersection of these technologies.
The report uses combination-analysis in the form of a map. IFTF selects 13 “territories”–what it calls “frontiers of innovation”–and then examines the linkages and overlaps. The result is 20 “combinational forecasts.” “These are the big stories, hot spots that will shape the landscape of technology in the coming decade,” the report explains. “Each combinatorial forecast emerges from the intersection of multiple territories.”…

Quantified Experiences

Advances in brain-imaging techniques will make bring new transparency to our thoughts and feelings. “Assigning precise measurements to feelings like pain through neurofeedback and other techniques could allow for comparison, modulation, and manipulation of these feelings,” the report says. “Direct measurement of our once-private thoughts and feelings can help us understand other people’s experience but will also present challenges regarding privacy and definition of norms.”…

Code Is The Law

The law enforcement of the future may increasingly rely on sensors and programmable devices. “Governance is shifting from reliance on individual responsibility and human policing toward a system of embedded protocols and automatic rule enforcement,” the report says. That in turn means greater power for programmers who are effectively laying down the parameters of the new relationship between government and governed….”

Privacy-Invading Technologies and Privacy by Design

Curated on Posted on by Stefaan Verhulst

New book by Demetrius Klitou: “Challenged by rapidly developing privacy-invading technologies (PITs), this book provides a convincing set of potential policy recommendations and practical solutions for safeguarding both privacy and security. It shows that benefits such as public security do not necessarily come at the expense of privacy and liberty overall.
Backed up by comprehensive study of four specific PITs – Body scanners; Public space CCTV microphones; Public space CCTV loudspeakers; and Human-implantable microchips (RFID implants/GPS implants) – the author shows how laws that regulate the design and development of PITs may more effectively protect privacy than laws that only regulate data controllers and the use of such technologies. New rules and regulations should therefore incorporate fundamental privacy principles through what is known as ‘Privacy by Design’.
The numerous sources explored by the author provide a workable overview of the positions of academia, industry, government and relevant international organizations and NGOs.

  • Explores a relatively novel approach of protecting privacy
  • Offers a convincing set of potential policy recommendations and practical solutions
  • Provides a workable overview of the positions of academia, industry, government and relevant international organizations and NGOs”

No silver bullet: De-identification still doesn’t work

Curated on Posted on by Stefaan Verhulst

Arvind Narayanan and Edward W. Felten: “Paul Ohm’s 2009 article Broken Promises of Privacy spurred a debate in legal and policy circles on the appropriate response to computer science research on re-identification techniques. In this debate, the empirical research has often been misunderstood or misrepresented. A new report by Ann Cavoukian and Daniel Castro is full of such inaccuracies, despite its claims of “setting the record straight.” In a response to this piece, Ed Felten and I point out eight of our most serious points of disagreement with Cavoukian and Castro. The thrust of our arguments is that (i) there is no evidence that de-identification works either in theory or in practice and (ii) attempts to quantify its efficacy are unscientific and promote a false sense of security by assuming unrealistic, artificially constrained models of what an adversary might do. Specifically, we argue that:

  1. There is no known effective method to anonymize location data, and no evidence that it’s meaningfully achievable.
  2. Computing re-identification probabilities based on proof-of-concept demonstrations is silly.
  3. Cavoukian and Castro ignore many realistic threats by focusing narrowly on a particular model of re-identification.
  4. Cavoukian and Castro concede that de-identification is inadequate for high-dimensional data. But nowadays most interesting datasets are high-dimensional.
  5. Penetrate-and-patch is not an option.
  6. Computer science knowledge is relevant and highly available.
  7. Cavoukian and Castro apply different standards to big data and re-identification techniques.
  8. Quantification of re-identification probabilities, which permeates Cavoukian and Castro’s arguments, is a fundamentally meaningless exercise.

Data privacy is a hard problem. Data custodians face a choice between roughly three alternatives: sticking with the old habit of de-identification and hoping for the best; turning to emerging technologies like differential privacy that involve some trade-offs in utility and convenience; and using legal agreements to limit the flow and use of sensitive data. These solutions aren’t fully satisfactory, either individually or in combination, nor is any one approach the best in all circumstances. Change is difficult. When faced with the challenge of fostering data science while preventing privacy risks, the urge to preserve the status quo is understandable. However, this is incompatible with the reality of re-identification science. If a “best of both worlds” solution exists, de-identification is certainly not that solution. Instead of looking for a silver bullet, policy makers must confront hard choices.”

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 [email protected].

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.”

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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.”…”

Privacy and Open Government

Curated on Posted on by Stefaan Verhulst

Paper by Teresa Scassa in Future Internet: “The public-oriented goals of the open government movement promise increased transparency and accountability of governments, enhanced citizen engagement and participation, improved service delivery, economic development and the stimulation of innovation. In part, these goals are to be achieved by making more and more government information public in reusable formats and under open licences. This paper identifies three broad privacy challenges raised by open government. The first is how to balance privacy with transparency and accountability in the context of “public” personal information. The second challenge flows from the disruption of traditional approaches to privacy based on a collapse of the distinctions between public and private sector actors. The third challenge is that of the potential for open government data—even if anonymized—to contribute to the big data environment in which citizens and their activities are increasingly monitored and profiled.”

Open for Business: How Open Data Can Help Achieve the G20 Growth Target

Curated on Posted on by Stefaan Verhulst

New Report commissioned by Omydiar Network on the Business Case for Open Data: “Economic analysis has confirmed the significant contribution to economic growth and productivity achievable through an open data agenda. Governments, the private sector, individuals and communities all stand to benefit from the innovation and information that will inform investment, drive the creation of new industries, and inform decision making and research. To mark a step change in the way valuable information is created and reused, the G20 should release information as open data.
In May 2014, Omidyar Network commissioned Lateral Economics to undertake economic analysis on the potential of open data to support the G20’s 2% growth target and illustrate how an open data agenda can make a significant contribution to economic growth and productivity. Combining all G20 economies, output could increase by USD 13 trillion cumulatively over the next five years. Implementation of open data policies would thus boost cumulative G20 GDP by around 1.1 percentage points (almost 55%) of the G20’s 2% growth target over five years.
Recommendations
Importantly, open data cuts across a number of this year’s G20 priorities: attracting private infrastructure investment, creating jobs and lifting participation, strengthening tax systems and fighting corruption. This memo suggests an open data thread that runs across all G20 priorities. The more data is opened, the more it can be used, reused, repurposed and built on—in combination with other data—for everyone’s benefit.
We call on G20 economies to sign up to the Open Data Charter.
The G20 should ensure that data released by G20 working groups and themes is in line with agreed open data standards. This will lead to more accountable, efficient, effective governments who are going further to expose inadequacy, fight corruption and spur innovation.
Data is a national resource and open data is a ‘win-win’ policy. It is about making more of existing resources. We know that the cost of opening data is smaller than the economic returns, which could be significant. Methods to respect privacy concerns must be taken into account. If this is done, as the public and private sector share of information grows, there will be increasing positive returns.
The G20 opportunity
This November, leaders of the G20 Member States will meet in Australia to drive forward commitments made in the St Petersburg G20 Leaders Declaration last September and to make firm progress on stimulating growth. Actions across the G20 will include increasing investment, lifting employment and participation, enhancing trade and promoting competition.
The resulting ‘Brisbane Action Plan’ will encapsulate all of these commitments with the aim of raising the level of G20 output by at least 2% above the currently projected level over the next five years. There are major opportunities for cooperative and collective action by G20 governments.
Governments should intensify the release of existing public sector data – both government and publicly funded research data. But much more can be done to promote open data than simply releasing more government data. In appropriate circumstances, governments can mandate public disclosure of private sector data (e.g. in corporate financial reporting).
Recommendations for action

  • G20 governments should adopt the principles of the Open Data Charter to encourage the building of stronger, more interconnected societies that better meet the needs of our citizens and allow innovation and prosperity to flourish.
  • G20 governments should adopt specific open data targets under each G20 theme, as illustrated below, such as releasing open data related to beneficial owners of companies, as well revenues from extractive industries
  • G20 governments should consider harmonizing licensing regimes across the G20
  • G20 governments should adopt metrics for measuring the quantity and quality of open data publication, e.g. using the Open Data Institute’s Open Data Certificates as a bottom-up mechanism for driving the adoption of common standards.

Illustrative G20 examples
Fiscal and monetary policy
Governments possess rich real time data that is not open or accessed by government macro-economic managers. G20 governments should:

  • Open up models that lie behind economic forecasts and help assess alternative policy settings;
  • Publish spending and contractual data to enable comparative shopping by government between government suppliers.

Anti corruption
Open data may directly contribute to reduced corruption by increasing the likelihood corruption will be detected. G20 governments should:

  • Release open data related to beneficial owners of companies as well as revenues from extractive industries,
  • Collaborate on harmonised technical standards that permit the tracing of international money flows – including the tracing of beneficial owners of commercial entities, and the comparison and reconciliation of transactions across borders.

Trade
Obtaining and using trade data from multiple jurisdictions is difficult. Access fees, specific licenses, and non-machine readable formats all involve large transaction costs. G20 governments should:

  • Harmonise open data policies related to trade data.
  • Use standard trade schema and formats.

Employment
Higher quality information on employment conditions would facilitate better matching of employees to organizations, producing greater job-satisfaction and improved productivity. G20 governments should:

  • Open up centralised job vacancy registers to provide new mechanisms for people to find jobs.
  • Provide open statistical information about the demand for skills in particular areas to help those supporting training and education to hone their offerings.

Energy
Open data will help reduce the cost of energy supply and improve energy efficiency. G20 governments should:

  • Provide incentives for energy companies to publish open data from consumers and suppliers to enable cost savings through optimizing energy plans.
  • Release energy performance certifications for buildings
  • Publish real-time energy consumption for government buildings.

Infrastructure
Current infrastructure asset information is fragmented and inefficient. Exposing current asset data would be a significant first step in understanding gaps and providing new insights. G20 governments should:

  • Publish open data on governments’ infrastructure assets and plans to better understand infrastructure gaps, enable greater efficiency and insights in infrastructure development and use and analyse cost/benefits.
  • Publish open infrastructure data, including contracts via Open Contracting Partnership, in a consistent and harmonised way across G20 countries…”

Big Data, My Data

Curated on Posted on by Stefaan Verhulst

Jane Sarasohn-Kahn  at iHealthBeat: “The routine operation of modern health care systems produces an abundance of electronically stored data on an ongoing basis,” Sebastian Schneeweis writes in a recent New England Journal of Medicine Perspective.
Is this abundance of data a treasure trove for improving patient care and growing knowledge about effective treatments? Is that data trove a Pandora’s black box that can be mined by obscure third parties to benefit for-profit companies without rewarding those whose data are said to be the new currency of the economy? That is, patients themselves?
In this emerging world of data analytics in health care, there’s Big Data and there’s My Data (“small data”). Who most benefits from the use of My Data may not actually be the consumer.
Big focus on Big Data. Several reports published in the first half of 2014 talk about the promise and perils of Big Data in health care. The Federal Trade Commission’s study, titled “Data Brokers: A Call for Transparency and Accountability,” analyzed the business practices of nine “data brokers,” companies that buy and sell consumers’ personal information from a broad array of sources. Data brokers sell consumers’ information to buyers looking to use those data for marketing, managing financial risk or identifying people. There are health implications in all of these activities, and the use of such data generally is not covered by HIPAA. The report discusses the example of a data segment called “Smoker in Household,” which a company selling a new air filter for the home could use to target-market to an individual who might seek such a product. On the downside, without the consumers’ knowledge, the information could be used by a financial services company to identify the consumer as a bad health insurance risk.
“Big Data and Privacy: A Technological Perspective,” a report from the President’s Office of Science and Technology Policy, considers the growth of Big Data’s role in helping inform new ways to treat diseases and presents two scenarios of the “near future” of health care. The first, on personalized medicine, recognizes that not all patients are alike or respond identically to treatments. Data collected from a large number of similar patients (such as digital images, genomic information and granular responses to clinical trials) can be mined to develop a treatment with an optimal outcome for the patients. In this case, patients may have provided their data based on the promise of anonymity but would like to be informed if a useful treatment has been found. In the second scenario, detecting symptoms via mobile devices, people wishing to detect early signs of Alzheimer’s Disease in themselves use a mobile device connecting to a personal couch in the Internet cloud that supports and records activities of daily living: say, gait when walking, notes on conversations and physical navigation instructions. For both of these scenarios, the authors ask, “Can the information about individuals’ health be sold, without additional consent, to third parties? What if this is a stated condition of use of the app? Should information go to the individual’s personal physicians with their initial consent but not a subsequent confirmation?”
The World Privacy Foundation’s report, titled “The Scoring of America: How Secret Consumer Scores Threaten Your Privacy and Your Future,” describes the growing market for developing indices on consumer behavior, identifying over a dozen health-related scores. Health scores include the Affordable Care Act Individual Health Risk Score, the FICO Medication Adherence Score, various frailty scores, personal health scores (from WebMD and OneHealth, whose default sharing setting is based on the user’s sharing setting with the RunKeeper mobile health app), Medicaid Resource Utilization Group Scores, the SF-36 survey on physical and mental health and complexity scores (such as the Aristotle score for congenital heart surgery). WPF presents a history of consumer scoring beginning with the FICO score for personal creditworthiness and recommends regulatory scrutiny on the new consumer scores for fairness, transparency and accessibility to consumers.
At the same time these three reports went to press, scores of news stories emerged discussing the Big Opportunities Big Data present. The June issue of CFO Magazine published a piece called “Big Data: Where the Money Is.” InformationWeek published “Health Care Dives Into Big Data,” Motley Fool wrote about “Big Data’s Big Future in Health Care” and WIRED called “Cloud Computing, Big Data and Health Care” the “trifecta.”
Well-timed on June 5, the Office of the National Coordinator for Health IT’s Roadmap for Interoperability was detailed in a white paper, titled “Connecting Health and Care for the Nation: A 10-Year Vision to Achieve an Interoperable Health IT Infrastructure.” The document envisions the long view for the U.S. health IT ecosystem enabling people to share and access health information, ensuring quality and safety in care delivery, managing population health, and leveraging Big Data and analytics. Notably, “Building Block #3” in this vision is ensuring privacy and security protections for health information. ONC will “support developers creating health tools for consumers to encourage responsible privacy and security practices and greater transparency about how they use personal health information.” Looking forward, ONC notes the need for “scaling trust across communities.”
Consumer trust: going, going, gone? In the stakeholder community of U.S. consumers, there is declining trust between people and the companies and government agencies with whom people deal. Only 47% of U.S. adults trust companies with whom they regularly do business to keep their personal information secure, according to a June 6 Gallup poll. Furthermore, 37% of people say this trust has decreased in the past year. Who’s most trusted to keep information secure? Banks and credit card companies come in first place, trusted by 39% of people, and health insurance companies come in second, trusted by 26% of people.
Trust is a basic requirement for health engagement. Health researchers need patients to share personal data to drive insights, knowledge and treatments back to the people who need them. PatientsLikeMe, the online social network, launched the Data for Good project to inspire people to share personal health information imploring people to “Donate your data for You. For Others. For Good.” For 10 years, patients have been sharing personal health information on the PatientsLikeMe site, which has developed trusted relationships with more than 250,000 community members…”