Potholes and Big Data: Crowdsourcing Our Way to Better Government

Curated on Posted on by Stefaan Verhulst

Phil Simon in Wired: “Big Data is transforming many industries and functions within organizations with relatively limited budgets.
Consider Thomas M. Menino, up until recently Boston’s longest-serving mayor. At some point in the past few years, Menino realized that it was no longer 1950. Perhaps he was hobnobbing with some techies from MIT at dinner one night. Whatever his motivation, he decided that there just had to be a better, more cost-effective way to maintain and fix the city’s roads. Maybe smartphones could help the city take a more proactive approach to road maintenance.
To that end, in July 2012, the Mayor’s Office of New Urban Mechanics launched a new project called Street Bump, an app that allows drivers to automatically report the road hazards to the city as soon as they hear that unfortunate “thud,” with their smartphones doing all the work.
The app’s developers say their work has already sparked interest from other cities in the U.S., Europe, Africa and elsewhere that are imagining other ways to harness the technology.
Before they even start their trip, drivers using Street Bump fire up the app, then set their smartphones either on the dashboard or in a cup holder. The app takes care of the rest, using the phone’s accelerometer — a motion detector — to sense when a bump is hit. GPS records the location, and the phone transmits it to an AWS remote server.
But that’s not the end of the story. It turned out that the first version of the app reported far too many false positives (i.e., phantom potholes). This finding no doubt gave ammunition to the many naysayers who believe that technology will never be able to do what people can and that things are just fine as they are, thank you. Street Bump 1.0 “collected lots of data but couldn’t differentiate between potholes and other bumps.” After all, your smartphone or cell phone isn’t inert; it moves in the car naturally because the car is moving. And what about the scores of people whose phones “move” because they check their messages at a stoplight?
To their credit, Menino and his motley crew weren’t entirely discouraged by this initial setback. In their gut, they knew that they were on to something. The idea and potential of the Street Bump app were worth pursuing and refining, even if the first version was a bit lacking. Plus, they have plenty of examples from which to learn. It’s not like the iPad, iPod, and iPhone haven’t evolved considerably over time.
Enter InnoCentive, a Massachusetts-based firm specializing in open innovation and crowdsourcing. The City of Boston contracted InnoCentive to improve Street Bump and reduce the amount of tail chasing. The company accepted the challenge and essentially turned it into a contest, a process sometimes called gamification. InnoCentive offered a network of 400,000 experts a share of $25,000 in prize money donated by Liberty Mutual.
Almost immediately, the ideas to improve Street Bump poured in from unexpected places. This crowd had wisdom. Ultimately, the best suggestions came from:

  • A group of hackers in Somerville, Massachusetts, that promotes community education and research
  • The head of the mathematics department at Grand Valley State University in Allendale, MI.
  • An anonymous software engineer

…Crowdsourcing roadside maintenance isn’t just cool. Increasingly, projects like Street Bump are resulting in substantial savings — and better government.”

Crowdsourced transit app shows what time the bus will really come

Curated on Posted on by Stefaan Verhulst

Springwise: “The problem with most transport apps is that they rely on fixed data from transport company schedules and don’t truly reflect exactly what’s going on with the city’s trains and buses at any given moment. Operating like a Waze for public transport, Israel’s Ototo app crowdsources real-time information from passengers to give users the best suggestions for their commute.
The app relies on a community of ‘Riders’, who allow anonymous location data to be sent from their smartphone whenever they’re using public transport. By collating this data together, Ototo offers more realistic information about bus and train routes. While a bus may be due in five minutes, a Rider currently on that bus might be located more than five minutes away, indicating that the bus isn’t on time. Ototo can then suggest a quicker route for users. According to Fast Company, the service currently has a 12,000-strong global Riders community that powers its travel recommendations. On top of this, the app is designed in an easy-to-use infographic format that quickly and efficiently tells users where they need to be going and how long it will take. The app is free to download from the App Store, and the video below offers a demonstration:


Ototo faces competition from similar services such as New York City’s Moovit, which also details how crowded buses are.”

Building a More Open Government

Curated on Posted on by Stefaan Verhulst

Corinna Zarek at the White House: “It’s Sunshine Week again—a chance to celebrate transparency and participation in government and freedom of information. Every year in mid-March, we take stock of our progress and where we are headed to make our government more open for the benefit of citizens.
In December, 2013, the Administration announced 23 ambitious commitments to further open up government over the next two years in U.S. Government’s  second Open Government National Action Plan. Those commitments are now all underway or in development, including:
·         Launching an improved Data.gov: The updated Data.gov debuted in January, 2014, and continues to grow with thousands of updated or new government data sets being proactively made available to the public.
·         Increasing public collaboration: Through crowdsourcing, citizen science, and other methods, Federal agencies continue to expand the ways they collaborate with the public. For example, the National Aeronautics and Space Administration, for instance, recently launched its third Asteroid Grand Challenge, a broad call to action, seeking the best and brightest ideas from non-traditional partners to enhance and accelerate the work NASA is already doing for planetary defense.
·         Improving We the People: The online petition platform We the People gives the public a direct way to participate in their government and is currently incorporating improvements to make it easier for the public to submit petitions and signatures.”

The Next Frontier in Crowdsourcing: Your Smartphone

Curated on Posted on by Stefaan Verhulst

Rachel Metz in MIT TechnologyReview: “Rather than swiping the screen or entering a passcode to unlock the smartphone in my hand, I have to tell it how energetic the people around me are feeling by tapping one of four icons. I’m the only one here, and the one that best fits my actual energy level, to be honest, is a figure lying down and emitting a trail of z’s.
I’m trying out an Android app called Twitch. Created by Stanford researchers, it asks you to complete a few simple tasks—contributing information, as with the reported energy levels, or performing simple tasks like ranking images or structuring data extracted from Wikipedia pages—each time you unlock your phone. The information collected by apps like Twitch could be useful to academics, market researchers, or local businesses. Such software could also provide a low-cost way to perform useful work that can easily be broken up into pieces and fed to millions of devices.

Twitch is one of several projects exploring crowdsourcing via the lock screen. Plenty of people already contribute freely to crowdsourcing websites like Wikipedia and Quora or paid services like Amazon’s Mechanical Turk, and the sustained popularity of traffic app Waze shows that people are willing to contribute to a common cause from their handsets if it provides a timely, helpful result.
There are certainly enough smartphones with lock screens ready to be harnessed. According to data from market researcher comScore, 160 million people in the U.S.—or 67 percent of cell phone users—have smartphones, and nearly 52 percent of these run Google’s Android OS, which allows apps like Twitch to replace the standard lock screen….”

Putting Crowdsourcing on the Map

Curated on Posted on by Stefaan Verhulst

MIT Technology Review: “Even in San Francisco, where Google’s roving Street View cars have mapped nearly every paved surface, there are still places that have remained untouched, such as the flights of stairs that serve as pathways between streets in some of the city’s hilliest neighborhoods.
It’s these places that a startup called Mapillary is focusing on. Cofounders Jan Erik Solem and Johan Gyllenspetz are attempting to build an open, crowdsourced, photographic map that lets smartphone users log all sorts of places, creating a richer view of the world than what is offered by Street View and other street-level mapping services. If contributors provide images often, that view could be more representative of how things look right now.
Google itself is no stranger to the benefits of crowdsourced map content: it paid $966 million last year for traffic and navigation app Waze, whose users contribute data. Google also lets people augment Street View content with their own images. But Solem and Gyllenspetz think there’s still plenty of room for Mapillary, which they say can be used for everything from tracking a nature hike to offering more up-to-date images to house hunters and Airbnb users.
Solem and Gyllenspetz have only been working on the project for four months; they released an iPhone app in November, and an Android app in January. So far, there are just a few hundred users who have shared about 100,000 photos on the service. While it’s free for anyone to use, the startup plans to eventually make money by licensing the data its users generate to companies.
With the app, a user can choose to collect images by walking, biking, or driving. Once you press a virtual shutter button within the app, it takes a photo every two seconds, until you press the button again. You can then upload the images to Mapillary’s service via Wi-Fi, where each photo’s location is noted through its GPS tag. Computer-vision software compares each photo with others that are within a radius of about 100 meters, searching for matching image features so it can find the geometric relationship between the photos. It then places those images properly on the map, and stitches them all together. When new images come in of an area that has already been mapped, Mapillary will add them to its database, too.
It can take less than 30 seconds for the images to show up on the Web-based map, but several minutes for the images to be fully processed. As with Google’s Street View photos, image-recognition software blurs out faces and license plate numbers.
Users can edit Mapillary’s map by moving around the icons that correspond to images—to fix a misplaced image, for instance. Eventually, users will also be able to add comments and tags.
So far, Mapillary’s map is quite sparse. But the few hundred users trying out Mapillary include some map providers in Europe, and the 100,000 or so images to the service ranging from a bike path on Venice Beach in California to a snow-covered ski slope in Sweden.
Street-level images can be viewed on the Web or through Mapillary’s smartphone apps (though the apps just pull up the Web page within the app). Blue lines and colored tags indicate where users have added photos to the map; you can zoom in to see them at the street level.

Navigating through photos is still quite rudimentary; you can tap or click to move from one image to the next with onscreen arrows, depending on the direction you want to explore.
Beyond technical and design challenges, the biggest issue Mapillary faces is convincing a large enough number of users to build up its store of images so that others will start using it and contributing as well, and then ensuring that these users keep coming back.”

The Problem With Serious Games–Solved

Curated on Posted on by Stefaan Verhulst
Emerging Technology From the arXiv:” Serious games are becoming increasingly popular but the inability to generate realistic new content has hampered their progress. Until now.

Here’s an imaginary scenario: you’re a law enforcement officer confronted with John, a 21-year-old male suspect who is accused of breaking into a private house on Sunday evening and stealing a laptop, jewellery and some cash. Your job is to find out whether John has an alibi and if so whether it is coherent and believable.
That’s exactly the kind of scenario that police officers the world over face on a regular basis. But how do you train for such a situation? How do you learn the skills necessary to gather the right kind of information?
An increasingly common way of doing this is with serious games, those designed primarily for purposes other than entertainment. In the last 10 years or so, medical, military and commercial organisations all over the world began to experiment with game-based scenarios that are designed to teach people how to perform their jobs and tasks in realistic situations.
But there is a problem with serious games which require realistic interaction is with another person. It’s relatively straightforward to design one or two scenarios that are coherent, lifelike and believable but it’s much harder to generate them continually on an ongoing basis.
Imagine in the example above, that John is a computer-generated character. What kind of activities could he describe that would serve as a believable, coherent alibi for Sunday evening? And how could he do it a thousand times, each describing a different realistic alibi. Therein lies the problem.
Today, Sigal Sina at Bar-Ilan University in Israel, and a couple pals, say they’ve solved this probelm. These guys have come up with a novel way of generating ordinary, realistic scenarios that can be cut and pasted into a serious game to serve exactly this purpose. The secret sauce in their new approach is to crowdsource the new scenarios from real people using Amazon’s Mechanical Turk service.
The approach is straightforward. Sina and co simply ask Turkers to answer a set of questions asking what they did during each one-hour period throughout various days, offering bonuses to those who provide the most varied detail.
They then analyse the answers, categorising activities by factors such as the times they are performed, the age and sex of the person doing it, the number of people involved and so on.
This then allows a computer game to cut and paste activities into the action at appropriate times. So for example, the computer can select an appropriate alibi for John on a Sunday evening by choosing an activity described by a male Turker for the same time while avoiding activitiesthat a woman might describe for a Friday morning, which might otherwise seem unbelievable. The computer also changes certain details in the narrative, such as names, locations and so on to make the narrative coherent with John’s profile….
That solves a significant problem with serious games. Until now, developers have had to spend an awful lot of time producing realistic content, a process known as procedural content generation. That’s always been straightforward for things like textures, models and terrain in game settings. Now, thanks to this new crowdsourcing technique, it can be just as easy for human interactions in serious games too.
Ref:  arxiv.org/abs/1402.5034 : Using the Crowd to Generate Content for Scenario-Based Serious-Games”

Crowdsourcing voices to study Parkinson’s disease

Curated on Posted on by Stefaan Verhulst

TedMed: “Mathematician Max Little is launching a project that aims to literally give Parkinson’s disease (PD) patients a voice in their own diagnosis and help them monitor their disease progression.
Patients Voice Analysis (PVA) is an open science project that uses phone-based voice recordings and self-reported symptoms, along with software Little designed, to track disease progression. Little, a TEDMED 2013 speaker and TED Fellow, is partnering with the online community PatientsLikeMe, co-founded by TEDMED 2009 speaker James Heywood, and Sage Bionetworks, a non-profit research organization, to conduct the research.
The new project is an extension of Little’s Parkinson’s Voice Initiative, which used speech analysis algorithms to diagnose Parkinson’s from voice records with the help of 17,000 volunteers. This time, he seeks to not only detect markers of PD, but also to add information reported by patients using PatientsLikeMe’s Parkinson’s Disease Rating Scale (PDRS), a tool that documents patients’ answers to questions that measure treatment effectiveness and disease progression….
As openly shared information, the collected data has potential to help vast numbers of individuals by tapping into collective ingenuity. Little has long argued that for science to progress, researchers need to democratize research and move past jostling for credit. Sage Bionetworks has designed a platform called Synapse to allow data sharing with collaborative version control, an effort led by open data advocate John Wilbanks.
“If you can’t share your data, how can you reproduce your science? One of the big problems we’re facing with this kind of medical research is the data is not open and getting access to it is a nightmare,” Little says.
With the PVA project, “Basically anyone can log on download the anonymized data and play around with data mining techniques. We don’t really care what people are able to come up with. We just want the most accurate prediction we can get.
“In research, you’re almost always constrained by what you think is the best way to do things. Unless you open it to the community at large, you’ll never know,” he says.”

L’intelligence d’une ville : ses citoyens

Curated on Posted on by Stefaan Verhulst

Michel Dumais: “Tic toc! disions-nous. Bientôt la centième. Et avec la cent-unième, de nouveaux défis. Ville intelligente, disiez-vous? Je subodore le traditionnel appel de pied aux trois lettres et à une logique administrative archaïque. Et si on faisait plutôt appel à l’intelligence de ceux qui connaissent le plus leur ville, ses citoyens?

Pour régler un problème (et même à l’occasion, un «pas d’problème»), les administrations regardent du côté de ces logiciels mammouth qui, sur papier, sont censés faire tout, qui engloutissent des centaines de millions de dollars, mais qui, finalement, font les manchettes des médias parce qu’il faut y injecter encore plus d’argent. Et qui permettent aux TI d’asseoir encore plus leur contrôle sur une administration.

Bref, lorsque l’on parle de ville intelligente, plusieurs y voient le pactole. Ah! Reste que ce qui était «acceptable», hier, ne l’est plus aujourd’hui. Et que la réalisation d’une ville intelligente n’est surtout pas un défi technologique, loin de là.

LA QUESTION DU SANS-FIL
Il y a des années de cela, la simple logique eut voulu que la Ville cesse de penser «big telcos» afin de conclure rapidement une alliance avec l’organisme communautaire «Île sans fil» et ainsi favoriser le déploiement rapide sur l’île de la technologie sans fil.

Une telle alliance, un modèle dans le genre, existe.

Mais pas à Montréal. Plutôt à Québec, alors que la Ville et l’organisme communautaire «Zap Québec» travaillent main dans la main pour le plus grand bénéfice des citoyens de Québec et des touristes. Et à Montréal? On jase, on jase.

Donc, une ville intelligente. C’est une ville qui sait, à l’aide des technologies, comment harnacher ses infrastructures et les mettre au service de ses citoyens tout en réalisant des économies et en favorisant le développement durable.

C’est aussi une ville qui sait écouter et mobiliser ses citoyens, ses militants et ses entrepreneurs, tout en leur donnant des outils (comme des données utilisables) afin qu’ils puissent eux aussi créer des services destinés à leur organisation et à tous les citoyens de la ville. Sans compter que tous ces outils facilitent la prise de décisions chez les maires d’arrondissement et le comité exécutif.

Bref, une ville intelligente selon le professeur Rudolf Giffinger, c’est ça: «une économie intelligente, une mobilité intelligente, un environnement intelligent, des habitants intelligents, un mode de vie intelligent et, enfin, une administration intelligente».…

J’invite le lecteur à regarder LifeApps, une extraordinaire série télé diffusée sur le site de la chaîne AlJazeera. Le sujet: des jeunes et de moins jeunes militants, bidouilleurs, qui s’impliquent et créent des services pour leur communauté.”

Are bots taking over Wikipedia?

Curated on Posted on by Stefaan Verhulst

Kurzweil News: “As crowdsourced Wikipedia has grown too large — with more than 30 million articles in 287 languages — to be entirely edited and managed by volunteers, 12 Wikipedia bots have emerged to pick up the slack.

The bots use Wikidata — a free knowledge base that can be read and edited by both humans and bots — to exchange information between entries and between the 287 languages.

Which raises an interesting question: what portion of Wikipedia edits are generated by humans versus bots?

To find out (and keep track of other bot activity), Thomas Steiner of Google Germany has created an open-source application (and API): Wikipedia and Wikidata Realtime Edit Stats, described in an arXiv paper.
The percentages of bot vs. human edits as shown in the application is constantly changing.  A KurzweilAI snapshot on Feb. 20 at 5:19 AM EST showed an astonishing 42% of Wikipedia being edited by bots. (The application lists the 12 bots.)


Anonymous vs. logged-In humans (credit: Thomas Steiner)
The percentages also vary by language. Only 5% of English edits were by bots; but for Serbian pages, in which few Wikipedians apparently participate, 96% of edits were by bots.

The application also tracks what percentage of edits are by anonymous users. Globally, it was 25 percent in our snapshot and a surprising 34 percent for English — raising interesting questions about corporate and other interests covertly manipulating Wikipedia information.

NatureNet: a model for crowdsourcing the design of citizen science systems

Curated on Posted on by Stefaan Verhulst

Paper in CSCW Companion ’14, the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing: “NatureNet is citizen science system designed for collecting bio-diversity data in nature park settings. Park visitors are encouraged to participate in the design of the system in addition to collecting bio-diversity data. Our goal is to increase the motivation to participate in citizen science via crowdsourcing: the hypothesis is that when the crowd plays a role in the design and development of the system, they become stakeholders in the project and work to ensure its success. This paper presents a model for crowdsourcing design and citizen science data collection, and the results from early trials with users that illustrate the potential of this approach.”