Glen Martin at Forbes: “If the modern city is a symbol for randomness — even chaos — the city of the near future is shaping up along opposite metaphorical lines. The urban environment is evolving rapidly, and a model is emerging that is more efficient, more functional, more — connected, in a word.
This will affect how we work, commute, and spend our leisure time. It may well influence how we relate to one another, and how we think about the world. Certainly, our lives will be augmented: better public transportation systems, quicker responses from police and fire services, more efficient energy consumption. But there could also be dystopian impacts: dwindling privacy and imperiled personal data. We could even lose some of the ferment that makes large cities such compelling places to live; chaos is stressful, but it can also be stimulating.
It will come as no surprise that converging digital technologies are driving cities toward connectedness. When conjoined, ISM band transmitters, sensors, and smart phone apps form networks that can make cities pretty darn smart — and maybe more hygienic. This latter possibility, at least, is proposed by Samrat Saha of the DCI Marketing Group in Milwaukee. Saha suggests “crowdsourcing” municipal trash pick-up via BLE modules, proximity sensors and custom mobile device apps.
“My idea is a bit tongue in cheek, but I think it shows how we can gain real efficiencies in urban settings by gathering information and relaying it via the Cloud,” Saha says. “First, you deploy sensors in garbage cans. Each can provides a rough estimate of its fill level and communicates that to a BLE 112 Module.”
As pedestrians who have downloaded custom “garbage can” apps on their BLE-capable iPhone or Android devices pass by, continues Saha, the information is collected from the module and relayed to a Cloud-hosted service for action — garbage pick-up for brimming cans, in other words. The process will also allow planners to optimize trash can placement, redeploying receptacles from areas where need is minimal to more garbage-rich environs….
Garbage can connectivity has larger implications than just, well, garbage. Brett Goldstein, the former Chief Data and Information Officer for the City of Chicago and a current lecturer at the University of Chicago, says city officials found clear patterns between damaged or missing garbage cans and rat problems.
“We found areas that showed an abnormal increase in missing or broken receptacles started getting rat outbreaks around seven days later,” Goldstein said. “That’s very valuable information. If you have sensors on enough garbage cans, you could get a temporal leading edge, allowing a response before there’s a problem. In urban planning, you want to emphasize prevention, not reaction.”
Such Cloud-based app-centric systems aren’t suited only for trash receptacles, of course. Companies such as Johnson Controls are now marketing apps for smart buildings — the base component for smart cities. (Johnson’s Metasys management system, for example, feeds data to its app-based Paoptix Platform to maximize energy efficiency in buildings.) In short, instrumented cities already are emerging. Smart nodes — including augmented buildings, utilities and public service systems — are establishing connections with one another, like axon-linked neurons.
But Goldstein, who was best known in Chicago for putting tremendous quantities of the city’s data online for public access, emphasizes instrumented cities are still in their infancy, and that their successful development will depend on how well we “parent” them.
“I hesitate to refer to ‘Big Data,’ because I think it’s a terribly overused term,” Goldstein said. “But the fact remains that we can now capture huge amounts of urban data. So, to me, the biggest challenge is transitioning the fields — merging public policy with computer science into functional networks.”…”
The future of law and legislation?
prior probability: “Mike Gatto, a legislator in California, recently set up the world’s first Wiki-bill in order to enable private citizens to act as cyber-legislators and help draft an actual law. According to Assemblyman Gatto:
Government has a responsibility to listen to the people and to enable everyone to be an active part of the legislative process. That’s why I’ve created this space for you to draft real legislation. Just like a Wikipedia entry, you can see what the current draft is, and propose minor or major edits. The marketplace of ideas will decide the final draft. We’re starting with a limited topic: probate. Almost everyone will face the prospect of working through the details of a deceased loved one’s finances and estate at some point during their life. I want to hear your ideas for how to make this process less burdensome.”
What Jelly Means
Steven Johnson: “A few months ago, I found this strange white mold growing in my garden in California. I’m a novice gardener, and to make matters worse, a novice Californian, so I had no idea what these small white cells might portend for my flowers.
This is one of those odd blank spots — I used the call them Googleholes in the early days of the service — where the usual Delphic source of all knowledge comes up relatively useless. The Google algorithm doesn’t know what those white spots are, the way it knows more computational questions, like “what is the top-ranked page for “white mold?” or “what is the capital of Illinois?” What I want, in this situation, is the distinction we usually draw between information and wisdom. I don’t just want to know what the white spots are; I want to know if I should be worried about them, or if they’re just a normal thing during late summer in Northern California gardens.
Now, I’m sure I know a dozen people who would be able to answer this question, but the problem is I don’t really know which people they are. But someone in my extended social network has likely experienced these white spots on their plants, or better yet, gotten rid of them. (Or, for all I know, ate them — I’m trying not to be judgmental.) There are tools out there that would help me run the social search required to find that person. I can just bulk email my entire address book with images of the mold and ask for help. I could go on Quora, or a gardening site.
But the thing is, it’s a type of question that I find myself wanting to ask a lot, and there’s something inefficient about trying to figure the exact right tool to use to ask it each time, particularly when we have seen the value of consolidating so many of our queries into a single, predictable search field at Google.
This is why I am so excited about the new app, Jelly, which launched today. …
Jelly, if you haven’t heard, is the brainchild of Biz Stone, one of Twitter’s co-founders. The service launches today with apps on iOS and Android. (Biz himself has a blog post and video, which you should check out.) I’ve known Biz since the early days of Twitter, and I’m excited to be an adviser and small investor in a company that shares so many of the values around networks and collective intelligence that I’ve been writing about since Emergence.
The thing that’s most surprising about Jelly is how fun it is to answer questions. There’s something strangely satisfying in flipping through the cards, reading questions, scanning the pictures, and looking for a place to be helpful. It’s the same broad gesture of reading, say, a Twitter feed, and pleasantly addictive in the same way, but the intent is so different. Scanning a twitter feed while waiting for the train has the feel of “Here we are now, entertain us.” Scanning Jelly is more like: “I’m here. How can I help?”
Open Government Strategy Continues with US Currency Production API
Eric Carter in the ProgrammableWeb: “Last year, the Executive branch of the US government made huge strides in opening up government controlled data to the developer community. Projects such as the Open Data Policy and the Machine Readable Executive Order have led the US government to develop an API strategy. Today, ProgrammableWeb takes a look at another open government API: the Annual Production Figures of United States Currency API.
The US Treasury’s Bureau of Engraving and Printing (BEP) provides the dataset available through the Production Figures API. The data available consists of the number of $1, $5, $10, $20, $50, $100 notes printed each year from 1980 to 2012. With this straightforward, seemingly basic set of data available, the question becomes: “Why is this data useful“? To answer this, one should consider the purpose of the Executive Order:
“Openness in government strengthens our democracy, promotes the delivery of efficient and effective services to the public, and contributes to economic growth. As one vital benefit of open government, making information resources easy to find, accessible, and usable can fuel entrepreneurship, innovation, and scientific discovery that improves Americans’ lives and contributes significantly to job creation.”
The API uses HTTP and can return requests in XML, JSON, or CSV data formats. As stated, the API retrieves the number of bills of a designated currency for the desired year. For more information and code samples, visit the API docs.”
E-government research in the United States
Paper by JT Snead, E Wright in Government Information Quarterly: “The purpose of this exploratory study is to review scholarly publications and assess egovernment research efforts as a field of study specific to the United States e-government environment. Study results reveal that researchers who focus on the U.S. e-government environment assess specific e-government topics at the federal, state, and local levels; however, there are gaps in the research efforts by topic areas and across different levels of government, which indicate opportunities for future areas of research. Results also find that a multitude of methodology approaches are used to assess e-government. Issues, however, exist that include lack of or weak presentations of methodologies in publications, few studies include multi-method evaluation approaches for data collection and analysis efforts, and few studies take a theory-based approach to understanding the U.S. e-government environment.”
Protecting personal data in E-government: A cross-country study
Paper by Yuehua Wu in Government Information Quarterly: “This paper presents the findings of a comparative study of laws and policies employed to protect personal data processed in the context of e-government in three countries (the United States, Germany, and China) with rather different approaches. Drawing on governance theory, the paper seeks to document the mechanisms utilized and to understand the factors that shape the governance modes adopted. The cases reveal that national government regulations have not kept pace with technological change and with the current information practices of the public sector. Nonetheless, traditional government regulation remains the major governance mode for the issue under discussion. Self-regulation and code-based regulation serve supplementary roles to traditional government regulation. National context is found to impact the form and level of data protection and the choice of governance modes.”
The GovLab Index: Open Data
Please find below the latest installment in The GovLab Index series, inspired by Harper’s Index. “The GovLab Index: Open Data — December 2013” provides an update on our previous Open Data installment, and highlights global trends in Open Data and the release of public sector information. Previous installments include Measuring Impact with Evidence, The Data Universe, Participation and Civic Engagement and Trust in Institutions.
Value and Impact
- Potential global value of open data estimated by McKinsey: $3 trillion annually
- Potential yearly value for the United States: $1.1 trillion
- Europe: $900 billion
- Rest of the world: $1.7 trillion
- How much the value of open data is estimated to grow per year in the European Union: 7% annually
- Value of releasing UK’s geospatial data as open data: 13 million pounds per year by 2016
- Estimated worth of business reuse of public sector data in Denmark in 2010: more than €80 million a year
- Estimated worth of business reuse of public sector data across the European Union in 2010: €27 billion a year
- Total direct and indirect economic gains from easier public sector information re-use across the whole European Union economy, as of May 2013: €140 billion annually
- Economic value of publishing data on adult cardiac surgery in the U.K., as of May 2013: £400 million
- Economic value of time saved for users of live data from the Transport for London apps, as of May 2013: between £15 million and £58 million
- Estimated increase in GDP in England and Wales in 2008-2009 due to the adoption of geospatial information by local public services providers: +£320m
- Average decrease in borrowing costs in sovereign bond markets for emerging market economies when implementing transparent practices (measured by accuracy and frequency according to IMF policies, across 23 countries from 1999-2002): 11%
- Open weather data supports an estimated $1.5 billion in applications in the secondary insurance market – but much greater value comes from accurate weather predictions, which save the U.S. annually more than $30 billion
- Estimated value of GPS data: $90 billion
Efforts and Involvement
- Number of U.S. based companies identified by the GovLab that use government data in innovative ways: 500
- Number of open data initiatives worldwide in 2009: 2
- Number of open data initiatives worldwide in 2013: over 300
- Number of countries with open data portals: more than 40
- Countries who share more information online than the U.S.: 14
- Number of cities globally that participated in 2013 International Open Data Hackathon Day: 102
- Number of U.S. cities with Open Data Sites in 2013: 43
- U.S. states with open data initiatives: 40
- Membership growth in the Open Government Partnership in two years: from 8 to 59 countries
- Number of time series indicators (GDP, foreign direct investment, life expectancy, internet users, etc.) in the World Bank Open Data Catalog: over 8,000
- How many of 77 countries surveyed by the Open Data Barometer have some form of Open Government Data Initiative: over 55%
- How many OGD initiatives have dedicated resources with senior level political backing: over 25%
- How many countries are in the Open Data Index: 70
- How many of the 700 key datasets in the Index are open: 84
- Number of countries in the Open Data Census: 77
- How many of the 727 key datasets in the Census are open: 95
- How many countries surveyed have formal data policies in 2013: 55%
- Those who have machine-readable data available: 25%
- Top 5 countries in Open Data rankings: United Kingdom, United States, Sweden, New Zealand, Norway
- The different levels of Open Data Certificates a data user or publisher can achieve “along the way to world-class open data”: 4 levels, Raw, Pilot, Standard and Expert
- The number of data ecosystems categories identified by the OECD: 3, data producers, infomediaries, and users
Examining Datasets…
FULL VERSION AT http://thegovlab.org/govlab-index-open-data-updated/
When Lean Startup Arrives in a Trojan Horse–Innovation in Extreme Bureaucracy
Steven Hodas @ The Lean Startup Conference 2013 –…Steven runs an procurement-innovation program in one of the world’s most notorious bureaucracies: the New York City Department of Education. In a fear-driven atmosphere, with lots of incentive to not be embarrassed, he’ll talk about the challenges he’s faced and progress he’s made testing new ideas.
When Tech Culture And Urbanism Collide
John Tolva: “…We can build upon the success of the work being done at the intersection of technology and urban design, right now.
For one, the whole realm of social enterprise — for-profit startups that seek to solve real social problems — has a huge overlap with urban issues. Impact Engine in Chicago, for instance, is an accelerator squarely focused on meaningful change and profitable businesses. One of their companies, Civic Artworks, has set as its goal rebalancing the community planning process.
The Code for America Accelerator and Tumml, both located in San Francisco, morph the concept of social innovation into civic/urban innovation. The companies nurtured by CfA and Tumml are filled with technologists and urbanists working together to create profitable businesses. Like WorkHands, a kind of LinkedIn for blue collar trades. Would something like this work outside a city? Maybe. Are its effects outsized and scale-ready in a city? Absolutely. That’s the opportunity in urban innovation.
Scale is what powers the sharing economy and it thrives because of the density and proximity of cities. In fact, shared resources at critical density is one of the only good definitions for what a city is. It’s natural that entrepreneurs have overlaid technology on this basic fact of urban life to amplify its effects. Would TaskRabbit, Hailo or LiquidSpace exist in suburbia? Probably, but their effects would be minuscule and investors would get restless. The city in this regard is the platform upon which sharing economy companies prosper. More importantly, companies like this change the way the city is used. It’s not urban planning, but it is urban (re)design and it makes a difference.
A twist that many in the tech sector who complain about cities often miss is that change in a city is not the same thing as change in city government. Obviously they are deeply intertwined; change is mighty hard when it is done at cross-purposes with government leadership. But it happens all the time. Non-government actors — foundations, non-profits, architecture and urban planning firms, real estate developers, construction companies — contribute massively to the shape and health of our cities.
Often this contribution is powered through policies of open data publication by municipal governments. Open data is the raw material of a city, the vital signs of what has happened there, what is happening right now, and the deep pool of patterns for what might happen next.
Tech entrepreneurs would do well to look at the organizations and companies capitalizing on this data as the real change agents, not government itself. Even the data in many cases is generated outside government. Citizens often do the most interesting data-gathering, with tools like LocalData. The most exciting thing happening at the intersection of technology and cities today — what really makes them “smart” — is what is happening at the periphery of city government. It’s easy to belly-ache about government and certainly there are administrations that to do not make data public (or shut it down), but tech companies who are truly interested in city change should know that there are plenty of examples of how to start up and do it.
And yet, the somewhat staid world of architecture and urban-scale design presents the most opportunity to a tech community interested in real urban change. While technology obviously plays a role in urban planning — 3D visual design tools like Revit and mapping services like ArcGIS are foundational for all modern firms — data analytics as a serious input to design matters has only been used in specialized (mostly energy efficiency) scenarios. Where are the predictive analytics, the holistic models, the software-as-a-service providers for the brave new world of urban informatics and The Internet of Things? Technologists, it’s our move.
Something’s amiss when some city governments — rarely the vanguard in technological innovation — have more sophisticated tools for data-driven decision-making than the private sector firms who design the city. But some understand the opportunity. Vannevar Technology is working on it, as is Synthicity. There’s plenty of room for the most positive aspects of tech culture to remake the profession of urban planning itself. (Look to NYU’s Center for Urban Science and Progress and the University of Chicago’s Urban Center for Computation and Data for leadership in this space.)…”
Brainlike Computers, Learning From Experience
The New York Times: “Computers have entered the age when they are able to learn from their own mistakes, a development that is about to turn the digital world on its head.
The first commercial version of the new kind of computer chip is scheduled to be released in 2014. Not only can it automate tasks that now require painstaking programming — for example, moving a robot’s arm smoothly and efficiently — but it can also sidestep and even tolerate errors, potentially making the term “computer crash” obsolete.
The new computing approach, already in use by some large technology companies, is based on the biological nervous system, specifically on how neurons react to stimuli and connect with other neurons to interpret information. It allows computers to absorb new information while carrying out a task, and adjust what they do based on the changing signals.
In coming years, the approach will make possible a new generation of artificial intelligence systems that will perform some functions that humans do with ease: see, speak, listen, navigate, manipulate and control. That can hold enormous consequences for tasks like facial and speech recognition, navigation and planning, which are still in elementary stages and rely heavily on human programming.
Designers say the computing style can clear the way for robots that can safely walk and drive in the physical world, though a thinking or conscious computer, a staple of science fiction, is still far off on the digital horizon.
“We’re moving from engineering computing systems to something that has many of the characteristics of biological computing,” said Larry Smarr, an astrophysicist who directs the California Institute for Telecommunications and Information Technology, one of many research centers devoted to developing these new kinds of computer circuits.
Conventional computers are limited by what they have been programmed to do. Computer vision systems, for example, only “recognize” objects that can be identified by the statistics-oriented algorithms programmed into them. An algorithm is like a recipe, a set of step-by-step instructions to perform a calculation.
But last year, Google researchers were able to get a machine-learning algorithm, known as a neural network, to perform an identification task without supervision. The network scanned a database of 10 million images, and in doing so trained itself to recognize cats.
In June, the company said it had used those neural network techniques to develop a new search service to help customers find specific photos more accurately.
The new approach, used in both hardware and software, is being driven by the explosion of scientific knowledge about the brain. Kwabena Boahen, a computer scientist who leads Stanford’s Brains in Silicon research program, said that is also its limitation, as scientists are far from fully understanding how brains function.”