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The #Algorithmic City | #smartcities #opendata

The #Algorithmic City | #smartcities #opendata | Homo Numericus Bis | Scoop.it
:snips - We Scale Cities

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luiy's curator insight, July 10, 6:00 AM

What else can we predict? In theory, any event that is not random, provided we have enough data to model the context. Examples include passenger load in public transports, availability of parking spots, traffic jams, waste production, energy consumption and revenues of a shop in a specific street. These all share a common underlying principle: use context rather than history to predict behavior.

 

In themselves, each of these predictions could lead to amazing new products and services. The real power though comes from integrating everything together and modeling an entire city and its interactions with people. For instance, if you can predict where people will need to go tomorrow, then you can create optimal bus routes, minimizing time to destination and walking distance, taking into account predicted traffic, weather and garbage collection schedules. In this ideal system, all services would be optimal and available to citizens at anytime. We call this new way of designing cities "Algorithmic Urbanism".

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Quantified Self Ideology: #Personal Data becomes #BigData | #sensors #predictive

A key contemporary trend emerging in big data science is the quantified self: individuals engaged in the deliberate self-tracking of any kind of biological, ...

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big data comes to the social sciences

big data comes to the social sciences | Homo Numericus Bis | Scoop.it
Bookmark on Delicious Digg this post Recommend on Facebook share via Reddit Share with Stumblers Tweet about it Subscribe to the comments on this post Tell a friend

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What Will Happen to ‘#BigData’ In Education? | #learning #analytics

What Will Happen to ‘#BigData’ In Education? | #learning #analytics | Homo Numericus Bis | Scoop.it
Privacy concerns have put the breaks on many efforts to use "big data" in education. Why are people so skittish of education data when other kinds of digital information are readily accessible?

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luiy's curator insight, April 15, 7:50 PM

InBloom’s trajectory has shined a spotlight on the public’s sensitivity around what happens to student data. When it first began as a mammoth ed-tech project in 2011 by the Council of Chief State School Officers, the Bill and Melinda Gates Foundation and the Carnegie Corporation called the Shared Learning Infrastructure, the purpose was to provide open-source software to safely organize, pool, and store student data from multiple states and multiple sources in the cloud. That included everything from demographics to attendance to discipline to grades to the detailed, moment-by-moment, data produced by learning analytics programs like Dreambox and Khan Academy. An API — application programming interface — would allow software developers to connect to that data, creating applications that could, at least in theory, be used by any school in the infrastructure.

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7 ways #BigData could revolutionize life | #health #hadoop #algorithms

7 ways #BigData could revolutionize life | #health #hadoop #algorithms | Homo Numericus Bis | Scoop.it
7 ways Big Data could revolutionize life by 2020 #infographic | See more about big data.

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Delivering on the promise of open government data #opengob #opendata

Delivering on the promise of open government data #opengob #opendata | Homo Numericus Bis | Scoop.it
The open data vision is a bold one but it will take considerable work to make a reality. What impact has it really made so far?

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luiy's curator insight, November 7, 2013 5:55 PM

Our research finds that 55% of countries surveyed have open data initiatives in place, yet less than 10% of key government datasets across the world are truly open for reuse. It also found that valuable but potentially controversial datasets – such as company registers and land registers – are among the least likely to be openly released. This could be because governments want to preserve lucrative access charges, or from a desire to keep a lid on politically sensitive information, or both. However, the net effect is to severely limit the accountability, development and economic benefits of open data. The barometer ranks the UK as the most advanced country, scoring above the US (2nd), Sweden (3rd), New Zealand (4th) and Denmark and Norway (joint 5th). The leading developing country is Kenya, joint 22nd with the Czech Republic and Switzerland .

 

The barometer also highlights that strong evidence on the impacts of OGD is almost universally lacking. Few OGD programmes have yet been formally evaluated, and the majority of discussion surrounding impacts remains anecdotal. Our research asked about six kinds of OGD impact (government efficiency, transparency and accountability, environmental sustainability, inclusion of marginalised groups, economic growth, and supporting entrepreneurs) and in countries no examples of impact could be found, and on average evidence of impact was scored at just 1.7 out of 10.