Aleem Walji at SSI Review: “When I joined the World Bank five years ago to lead a new innovation practice, the organization asked me to help expand the space for experimentation and learning with an emphasis on emergent technologies. But that mandate was intimidating and counter-intuitive in an “expert-driven” culture. Experts want detailed plans, budgets, clear success indicators, and minimal risk. But innovation is about managing risk and navigating uncertainty intelligently. You fail fast and fail forward. It has been a step-by-step process, and the journey is far from over, but the World Bank today sees innovation as essential to achieving its mission.
It’s taught me a lot about seeding innovation in a culture of expertise, including phasing change across approaches to technology, teaming, problem solving, and ultimately leadership.
Innovating technologies: As a newcomer, my goal was not to try to change the World Bank’s culture. I was content to carve out a space where my team could try new things we couldn’t do elsewhere in the institution, learn fast, and create impact. Our initial focus was leveraging technologies with approaches that, if they took root, could be very powerful.
Over the first 18 to 24 months, we served as an incubator for ideas and had a number of successes that built on senior management’s support for increased access to information. The Open Data Initiative, for example, made our trove of information on countries, people, projects, and programs widely available and searchable. To our surprise, people came in droves to access it. We also launched the Mapping for Results initiative, which mapped project results and poverty data to show the relationship between where we lend and where the poor live, and the results of our work. These programs are now mainstream at the World Bank and have penetrated other development institutions….
Innovating teams: The lab idea—phase two—would require collaboration and experimentation in an unprecedented way. For example, we worked with other parts of the World Bank and a number of outside organizations to incubate the Open Development Technology Alliance, now part of the digital engagement unit of the World Bank. It worked to enhance accountability, and improve the delivery and quality of public services through technology-enabled citizen engagement such as using mobile phones, interactive mapping, and social media to draw citizens into collective problem mapping and problem solving….
Innovating problem solving: At the same time, we recognized that we face some really complex problems that the World Bank’s traditional approach of lending to governments and supervising development projects is not solving. For this, we needed another type of lab that innovated the very way we solve problems. We needed a deliberate process for experimenting, learning, iterating, and adapting. But that’s easier said than done. At our core, we are an expert-driven organization with know-how in disciplines ranging from agricultural economics and civil engineering to maternal health and early childhood development. Our problem-solving architecture is rooted in designing technical solutions to complicated problems. Yet the hardest problems in the world defy technical fixes. We work in contexts where political environments shift, leaders change, and conditions on the ground constantly evolve. Problems like climate change, financial inclusion, food security, and youth unemployment demand new ways of solving old problems.
The innovation we most needed was innovation in the leadership architecture of how we confront complex challenges. We share knowledge and expertise on the “what” of reform, but the “how” is what we need most. We need to marry know-how with do-how. We need multiyear, multi-stakeholder, and systems approaches to solving problems. We need to get better at framing and reframing problems, integrative thinking, and testing a range of solutions. We need to iterate and course-correct as we learn what works and doesn’t work in which context. That’s where we are right now with what we call “integrated leadership learning innovation”—phase four. It’s all about shaping an innovative process to address complex problems….”
Can Government Mine Tweets to Assess Public Opinion?
What if instead of going to a city meeting, you could go on Twitter, tweet your opinion, and still be heard by those in government? New research suggests this is a possibility.
The Urban Attitudes Lab at Tufts University has conducted research on accessing “big data” on social networking sites for civic purposes, according to Justin Hollander, associate professor in the Department of Urban and Environmental Policy and Planning at Tufts.
About six months ago, Hollander began researching new ways of accessing how people think about the places they live, work and play. “We’re looking to see how tapping into social media data to understand attitudes and opinions can benefit both urban planning and public policy,” he said.
Harnessing natural comments — there are about one billion tweets per day — could help governments learn what people are saying and feeling, said Hollander. And while formal types of data can be used as proxies for how happy people are, people openly share their sentiments on social networking sites.
Twitter and other social media sites can also provide information in an unobtrusive way. “The idea is that we can capture a potentially more valid and reliable view [of people’s] opinions about the world,” he said. As an inexact science, social science relies on a wide range of data sources to inform research, including surveys, interviews and focus groups; but people respond to being the subject of study, possibly affecting outcomes, Hollander said.
Hollander is also interested in extracting data from social sites because it can be done on a 24/7 basis, which means not having to wait for government to administer surveys, like the Decennial Census. Information from Twitter can also be connected to place; Hollander has approximated that about 10 percent of all tweets are geotagged to location.
In its first study earlier this year, the lab looked at using big data to learn about people’s sentiments and civic interests in New Bedford, Mass., comparing Twitter messages with the city’s published meeting minutes.
To extract tweets over a six-week period from February to April, researchers used the lab’s own software to capture 122,186 tweets geotagged within the city that also had words pertaining to the New Bedford area. Hollander said anyone can get API information from Twitter to also mine data from an area as small as a neighborhood containing a couple hundred houses.
Researchers used IBM’s SPSS Modeler software, comparing this to custom-designed software, to leverage a sentiment dictionary of nearly 3,000 words, assigning a sentiment score to each phrase — ranging from -5 for awful feelings to +5 for feelings of elation. The lab did this for the Twitter messages, and found that about 7 percent were positive versus 5.5 percent negative, and correspondingly in the minutes, 1.7 percent were positive and .7 percent negative. In total, about 11,000 messages contained sentiments.
The lab also used NVivo qualitative software to analyze 24 key words in a one-year sample of the city’s meeting minutes. By searching for the same words in Twitter posts, the researchers found that “school,” “health,” “safety,” “parks,” “field” and “children” were used frequently across both mediums.
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Next up for the lab is a new study contrasting Twitter posts from four Massachusetts cities with the recent election results.
Knowledge Sharing in the Networked World of the Internet of Things
Pew Research Internet Project: “Many experts say the rise of embedded and wearable computing will bring the next revolution in digital technology. They say the upsides are enhanced health, convenience, productivity, safety, and more useful information for people/organizations. At KMWorld Confererence, Lee Rainie shared the latest findings from Pew Research about the internet and puts it into organizational context with the expanding Internet of Things.”
Good data make better cities
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….”