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.
….
Next up for the lab is a new study contrasting Twitter posts from four Massachusetts cities with the recent election results.
Get the Data Button
BetaNYC: “A Web Button to Link Web Projects with their Source Data
How to use
When building a website, report, map, data visualization, or any other project, use this button to link to the underlying data. That’s it! Tell your friends! Let’s make this a thing!
Born on a discussion of the NYC Open Data Working Group on 14 November 2014.
Icon made by Freepik from www.flaticon.com is licensed by CC BY 3.0“
The Next Frontier of Engagement: Civic Innovation Labs
Maayan Dembo at Planetizen: “As described by Clayton Christensen, a professor at the Harvard Business School who developed the term “disruptive innovation,” a successful office for social innovation should employ four main tactics to accomplish its mission. First, governments should invest “in innovations that are developed and identified by citizens outside of government who better understand the problems.” Second, the office should support “‘bottom-up’ initiatives, in preference to ‘trickle-down’ philanthropy—because the societal impact of the former is typically greater.” Third, Christensen argues that the office should utilize impact metrics to measure performance and, finally, that it should also invest in social innovation outside of the non-profit sector.
Los Angeles’ most recent citizen-driven social innovation initiative, the Civic Innovation Lab, is an 11-month project aimed at prototyping new solutions for issues within the city of Los Angeles. It is supported by the HubLA, Learn Do Share, the Los Angeles *City Tech Bullpen, and Innovate LA, a membership organization within the Los Angeles County Economic Development Corporation. Private and public sector support for such labs, in one of the largest cities in America, is highly unprecedented, and because this initiative in Los Angeles is a new mechanism explicitly supported by the public sector, it warrants a critical check on its motivations and accomplishments. Depending on its success, the Civic Innovation Lab could serve as a model for future municipalities.
The Los Angeles Civic Innovation Lab operates in three main phases: 1) workshops where citizens learn about the possibilities of Open Data and discuss what deep challenges face Los Angeles (called the “Discover, Define, Design” stage), 2) a call for solutions to solve the design challenges brought to light in the first phase, and 3) a six-month accelerator program to prototype selected solutions. I participated in the most recent Civic Innovation Lab session, a three-day workshop concluding the “Discover, Define, Design” phase….”
Future Crimes
New book by Marc Goodman: “Technological advances have benefited our world in immeasurable ways—but there is an ominous flip side. Criminals are often the earliest, and most innovative, adopters of technology, and modern times have led to modern crimes. Today’s criminals are stealing identities, draining online bank accounts and wiping out computer servers. It’s disturbingly easy to activate baby monitors to spy on families, pacemakers can be hacked to deliver a lethal jolt of electricity, and thieves are analyzing your social media in order to determine the best time for a home invasion. Meanwhile, 3D printers produce AK-47s, terrorists can download the recipe for the Ebola virus, and drug cartels are building drones. This is just the beginning of the tsunami of technological threats coming our way. In Future Crimes, Marc Goodman rips opens his database of hundreds of real cases to give us front-row access to these impending perils. Reading like a sci-fi thriller, but based in startling fact, Future Crimes raises tough questions about the expanding role of technology in our lives. Future Crimes is a call to action for better security measures worldwide, but most importantly, it will empower readers to protect themselves against looming technological threats—before it’s too late.”
Digital Sociology
New book by Deborah Lupton: “We now live in a digital society. New digital technologies have had a profound influence on everyday life, social relations, government, commerce, the economy and the production and dissemination of knowledge. People’s movements in space, their purchasing habits and their online communication with others are now monitored in detail by digital technologies. We are increasingly becoming digital data subjects, whether we like it or not, and whether we choose this or not.
The sub-discipline of digital sociology provides a means by which the impact, development and use of these technologies and their incorporation into social worlds, social institutions and concepts of selfhood and embodiment may be investigated, analysed and understood. This book introduces a range of interesting social, cultural and political dimensions of digital society and discusses some of the important debates occurring in research and scholarship on these aspects. It covers the new knowledge economy and big data, reconceptualising research in the digital era, the digitisation of higher education, the diversity of digital use, digital politics and citizen digital engagement, the politics of surveillance, privacy issues, the contribution of digital devices to embodiment and concepts of selfhood and many other topics.”
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…”
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.”
Code of Conduct: Cyber Crowdsourcing for Good
Patrick Meier at iRevolution: “There is currently no unified code of conduct for digital crowdsourcing efforts in the development, humanitarian or human rights space. As such, we propose the following principles (displayed below) as a way to catalyze a conversation on these issues and to improve and/or expand this Code of Conduct as appropriate.
This initial draft was put together by Kate Chapman, Brooke Simons and myself. The link above points to this open, editable Google Doc. So please feel free to contribute your thoughts by inserting comments where appropriate. Thank you.
An organization that launches a digital crowdsourcing project must:
- Provide clear volunteer guidelines on how to participate in the project so that volunteers are able to contribute meaningfully.
- Test their crowdsourcing platform prior to any project or pilot to ensure that the system will not crash due to obvious bugs.
- Disclose the purpose of the project, exactly which entities will be using and/or have access to the resulting data, to what end exactly, over what period of time and what the expected impact of the project is likely to be.
- Disclose whether volunteer contributions to the project will or may be used as training data in subsequent machine learning research
- ….
An organization that launches a digital crowdsourcing project should:
- Share as much of the resulting data with volunteers as possible without violating data privacy or the principle of Do No Harm.
- Enable volunteers to opt out of having their tasks contribute to subsequent machine learning research. Provide digital volunteers with the option of having their contributions withheld from subsequent machine learning studies
- … “