Stephen Goldsmith and Susan Crawford at the Boston Globe: “…Federal laws prevent sharing of information among state workers helping the same family. In one state’s public health agency, workers fighting obesity cannot receive information from another official inside the same agency assigned to a program aimed at fighting diabetes. In areas where citizens are worried about environmental justice, sensors collecting air quality information are feared — because they could monitor the movements of people. Cameras that might provide a crucial clue to the identity of a terrorist are similarly feared because they might capture images of innocent bystanders.
In order for the public to develop confidence that data tools work for its betterment, not against it, we have work to do. Leaders need to establish policies covering data access, retention, security, and transparency. Forensic capacity — to look back and see who had access to what for what reason — should be a top priority in the development of any data system. So too should clear consequences for data misuse by government employees.
If we get this right, the payoffs for democracy will be enormous. Data can provide powerful insights into the equity of public services and dramatically increase the effectiveness of social programs. Existing 311 digital systems can become platforms for citizen engagement rather than just channels for complaints. Government services can be graded by citizens and improved in response to a continuous loop of interaction. Cities can search through anonymized data in a huge variety of databases for correlations between particular facts and desired outcomes and then apply that knowledge to drive toward results — what can a city do to reduce rates of obesity and asthma? What bridges are in need of preventative maintenance? And repurposing dollars from ineffective programs and vendors to interventions that work will help cities be safer, cleaner, and more effective.
The digital revolution has finally reached inside the walls of city hall, making this the best time within living memory to be involved in local government. We believe that doing many small things right using data will build trust, making it more likely that citizens will support their city’s need to do big things — including addressing economic dislocation.
Data rules should genuinely protect individuals, not limit our ability to serve them better. When it comes to data, unreasoning fear is our greatest enemy…”
Measuring the Impact of Public Innovation in the Wild
Beth Noveck at Governing: “With complex, seemingly intractable problems such as inequality, climate change and affordable access to health care plaguing contemporary society, traditional institutions such as government agencies and nonprofit organizations often lack strategies for tackling them effectively and legitimately. For this reason, this year the MacArthur Foundation launched its Research Network on Opening Governance.
The Network, which I chair and which also is supported by Google.org, is what MacArthur calls a “research institution without walls.” It brings together a dozen researchers across universities and disciplines, with an advisory network of academics, technologists, and current and former government officials, to study new ways of addressing public problems using advances in science and technology.
Through regular meetings and collaborative projects, the Network is exploring, for example, the latest techniques for more open and transparent decision-making, the uses of data to transform how we govern, and the identification of an individual’s skills and experiences to improve collaborative problem-solving between government and citizen.
One of the central questions we are grappling with is how to accelerate the pace of research so we can learn better and faster when an innovation in governance works — for whom, in which contexts and under which conditions. With better methods for doing fast-cycle research in collaboration with government — in the wild, not in the lab — our hope is to be able to predict with accuracy, not just know after the fact, whether innovations such as opening up an agency’s data or consulting with citizens using a crowdsourcing platform are likely to result in real improvements in people’s lives.
An example of such an experiment is the work that members of the Network are undertaking with the Food and Drug Administration. As one of its duties, the FDA manages the process of pre-market approval of medical devices to ensure that patients and providers have timely access to safe, effective and high-quality technology, as well as the post-market review of medical devices to ensure that unsafe ones are identified and recalled from the market. In both of these contexts, the FDA seeks to provide the medical-device industry with productive, consistent, transparent and efficient regulatory pathways.
With thousands of devices, many of them employing cutting-edge technology, to examine each year, the FDA is faced with the challenge of finding the right internal and external expertise to help it quickly study a device’s safety and efficacy. Done right, lives can be saved and companies can prosper from bringing innovations quickly to market. Done wrong, bad devices can kill…”
The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance
New Paper by Ming-Hsiang Tsou et al in the Journal of Medical Internet Research: “Existing influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza.
Objective: There were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego.
Methods: Tweets containing the keyword “flu” were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was “valid”, or from a user who was likely ill with the flu.
Results: Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier.
Conclusions: Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data…”
Off the map
The Economist: “Rich countries are deluged with data; developing ones are suffering from drought…
AFRICA is the continent of missing data. Fewer than half of births are recorded; some countries have not taken a census in several decades. On maps only big cities and main streets are identified; the rest looks as empty as the Sahara. Lack of data afflicts other developing regions, too. The self-built slums that ring many Latin American cities are poorly mapped, and even estimates of their population are vague. Afghanistan is still using census figures from 1979—and that count was cut short after census-takers were killed by mujahideen.
As rich countries collect and analyse data from as many objects and activities as possible—including thermostats, fitness trackers and location-based services such as Foursquare—a data divide has opened up. The lack of reliable data in poor countries thwarts both development and disaster-relief. When Médecins Sans Frontières (MSF), a charity, moved into Liberia to combat Ebola earlier this year, maps of the capital, Monrovia, fell far short of what was needed to provide aid or track the disease’s spread. Major roads were marked, but not minor ones or individual buildings.
Poor data afflict even the highest-profile international development effort: the Millennium Development Goals (MDGs). The targets, which include ending extreme poverty, cutting infant mortality and getting all children into primary school, were set by UN members in 2000, to be achieved by 2015. But, according to a report by an independent UN advisory group published on November 6th, as the deadline approaches, the figures used to track progress are shaky. The availability of data on 55 core indicators for 157 countries has never exceeded 70%, it found (see chart)….
Some of the data gaps are now starting to be filled from non-government sources. A volunteer effort called Humanitarian OpenStreetMap Team (HOT) improves maps with information from locals and hosts “mapathons” to identify objects shown in satellite images. Spurred by pleas from those fighting Ebola, the group has intensified its efforts in Monrovia since August; most of the city’s roads and many buildings have now been filled in (see maps). Identifying individual buildings is essential, since in dense slums without formal roads they are the landmarks by which outbreaks can be tracked and assistance targeted.
On November 7th a group of charities including MSF, Red Cross and HOT unveiled MissingMaps.org, a joint initiative to produce free, detailed maps of cities across the developing world—before humanitarian crises erupt, not during them. The co-ordinated effort is needed, says Ivan Gayton of MSF: aid workers will not use a map with too little detail, and are unlikely, without a reason, to put work into improving a map they do not use. The hope is that the backing of large charities means the locals they work with will help.
In Kenya and Namibia mobile-phone operators have made call-data records available to researchers, who have used them to combat malaria. By comparing users’ movements with data on outbreaks, epidemiologists are better able to predict where the disease might spread. mTrac, a Ugandan programme that replaces paper reports from health workers with texts sent from their mobile phones, has made data on medical cases and supplies more complete and timely. The share of facilities that have run out of malaria treatments has fallen from 80% to 15% since it was introduced.
Private-sector data are also being used to spot trends before official sources become aware of them. Premise, a startup in Silicon Valley that compiles economics data in emerging markets, has found that as the number of cases of Ebola rose in Liberia, the price of staple foods soared: a health crisis risked becoming a hunger crisis. In recent weeks, as the number of new cases fell, prices did, too. The authorities already knew that travel restrictions and closed borders would push up food prices; they now have a way to measure and track price shifts as they happen….”
A New Ebola Crisis Page Built with Open Data
HDX team: “We are introducing a new Ebola crisis page that provides an overview of the data available in HDX. The page includes an interactive map of the worst-affected countries, the top-line figures for the crisis, a graph of cumulative Ebola cases and deaths, and over 40 datasets.
We have been working closely with UNMEER and WHO to make Ebola data available for public use. We have also received important contributions from the British Red Cross, InterAction, MapAction, the Standby Task Force, the US Department of Defense, and WFP, among others.
How we built it
The process to create this page started a couple of months ago by simply linking to existing data sites, such as Open Street Map’s geospatial data or OCHA’s common operational datasets. We then created a service by extracting the data on Ebola cases and deaths from the bi-weekly WHO situation report and making the raw files available for analysts and developers.
The OCHA Regional Office in Dakar contributed a dataset that included Ebola cases by district, which they had been collecting from reports by the national Ministries of Health since March 2014. This data was picked up by The New York Times graphics team and by Gapminder which partnered with Google Crisis Response to add the data to the Google Public Data Explorer.
As more organizations shared Ebola datasets through HDX, users started to transform the data into useful graphs and maps. These visuals were then shared back with the wider community through the HDX gallery. We have incorporated many of these user-generated visual elements into the design of our new Ebola crisis page….”
See also Hacking Ebola.
A New Taxonomy of Smart City Projects
New paper by Guido Perboli et al: “City logistics proposes an integrated vision of freight transportation systems within urban area and it aims at the optimization of them as a whole in terms of efficiency, security, safety, viability and environmental sustainability. Recently, this perspective has been extended by the Smart City concept in order to include other aspects of city management: building, energy, environment, government, living, mobility, education, health and so on. At the best of our knowledge, a classification of Smart City Projects has not been created yet. This paper introduces such a classification, highlighting success factors and analyzing new trends in Smart City.”
Stories of Innovative Democracy at Local Level
Special Issue of Field Actions Science Reports published in partnership with CIVICUS, coordinated by Dorothée Guénéheux, Clara Bosco, Agnès Chamayou and Henri Rouillé d’Orfeuil: “This special issue presents many and varied field actions, such as the promotion of the rights of young people, the resolution of the conflicts of agropastoral activities, or the process of participatory decisionmaking on community budgetary allocations, among many others. It addresses projects developed all over the world, on five continents, and covering both the northern and southern hemispheres. The legitimate initial queries and doubts that assailed those who started this publication as regards its feasibility, have been swept away by the enthusiasm and the large number of papers that have been sent in….”
Politics, Policy and Privatisation in the Everyday Experience of Big Data in the NHS
Chapter by Andrew Goffey ; Lynne Pettinger and Ewen Speed in Martin Hand , Sam Hillyard (ed.) Big Data? Qualitative Approaches to Digital Research (Studies in Qualitative Methodology, Volume 13) : “This chapter explains how fundamental organisational change in the UK National Health Service (NHS) is being effected by new practices of digitised information gathering and use. It analyses the taken-for-granted IT infrastructures that lie behind digitisation and considers the relationship between digitisation and big data.
Design/methodology/approach
Qualitative research methods including discourse analysis, ethnography of software and key informant interviews were used. Actor-network theories, as developed by Science and technology Studies (STS) researchers were used to inform the research questions, data gathering and analysis. The chapter focuses on the aftermath of legislation to change the organisation of the NHS.
Findings
The chapter shows the benefits of qualitative research into specific manifestations information technology. It explains how apparently ‘objective’ and ‘neutral’ quantitative data gathering and analysis is mediated by complex software practices. It considers the political power of claims that data is neutral.
Originality/value
The chapter provides insight into a specific case of healthcare data and. It makes explicit the role of politics and the State in digitisation and shows how STS approaches can be used to understand political and technological practice.”
Logged On: Smart Government Solutions from South Asia
Book by Zubair K. Bhatti, Jody Zall Kusek and Tony Verheijen: “Logged On looks at mobile and smart phone technology through the lens of good government management. How will developing governments deliver goods and services that citizens care about? How will government in these countries leapfrog over traditional public management reforms to help reach out to and collaborate directly with the citizen? This book provides example after example where this has happened and how mobile technology has helped provide solutions to old problems. Our astounding revelation that mobile technology is helping to fight corruption in Pakistan, improve health delivery in Bangladesh, provide access to government by the ordinary citizen in India, and help monitor elections in Afghanistan. If this Is possible in some place in poor South Asian countries considered the most poor in the world, then how can these examples be spread to further in these counties or in other countries? Logged on Government provides a look back on conventional solutions that have mostly not worked and why mobile solutions are taking hold. The book offers a model called Smart Proactive Government based on a Feedback model being used in Punjab, Pakistan. The book also offers five solutions that are present in every successful mobile and smart phone example that the authors reviewed. (Book PDF (20.18 MB) | View Chapters).”
OpenUp Corporate Data while Protecting Privacy
Much of the data generated by these devices is today controlled by corporations. These companies are in effect “owners” of terabytes of data and metadata. Companies use this data to aggregate, analyze, and track individual preferences, provide more targeted consumer experiences, and add value to the corporate bottom line.
At the same time, even as we witness a rapid “datafication” of the global economy, access to data is emerging as an increasingly critical issue, essential to addressing many of our most important social, economic, and political challenges. While the rise of the Open Data movement has opened up over a million datasets around the world, much of this openness is limited to government (and, to a lesser extent, scientific) data. Access to corporate data remains extremely limited. This is a lost opportunity. If corporate data—in the form of Web clicks, tweets, online purchases, sensor data, call data records, etc.—were made available in a de-identified and aggregated manner, researchers, public interest organizations, and third parties would gain greater insights on patterns and trends that could help inform better policies and lead to greater public good (including combatting Ebola).
Corporate data sharing holds tremendous promise. But its potential—and limitations—are also poorly understood. In what follows, we share early findings of our efforts to map this emerging open data frontier, along with a set of reflections on how to safeguard privacy and other citizen and consumer rights while sharing. Understanding the practice of shared corporate data—and assessing the associated risks—is an essential step in increasing access to socially valuable data held by businesses today. This is a challenge certainly worth exploring during the forthcoming OpenUp conference!
Understanding and classifying current corporate data sharing practices
Corporate data sharing remains very much a fledgling field. There has been little rigorous analysis of different ways or impacts of sharing. Nonetheless, our initial mapping of the landscape suggests there have been six main categories of activity—i.e., ways of sharing—to date:…
Assessing risks of corporate data sharing
Although the shared corporate data offers several benefits for researchers, public interest organizations, and other companies, there do exist risks, especially regarding personally identifiable information (PII). When aggregated, PII can serve to help understand trends and broad demographic patterns. But if PII is inadequately scrubbed and aggregated data is linked to specific individuals, this can lead to identity theft, discrimination, profiling, and other violations of individual freedom. It can also lead to significant legal ramifications for corporate data providers….”