Tags: big data*

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  1. There are three main factors driving this shift from GIS technologies to location intelligence:

    New data streams. Location intelligence incorporates open data, real-time data streams, and big datasets from all of kinds of internet-connected systems, devices and sensors, many from sources external to the business. GIS has relied primarily on proprietary geographic datasets owned by the business. For example, with LI:

    Insurers are using location intelligence and open weather data to make real-time decisions on how to support their policyholders during a natural disaster.
    Real estate investors are using location intelligence and open transit data to evaluate new opportunities for growth.
    Banks are using credit card transactions and demographic data to understand city dynamics.

    New methods of analysis. Location intelligence embraces new methods of analyzing location data for business process optimization and prediction, while traditional GIS analysis methods focus on reporting historic geographic information. For example, with LI:
    https://carto.com/blog/location-intelligence-end-of-gis-as-we-know-it
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  2. The rise of big data means that probabilities are becoming a larger part of life. And our misunderstandings have real costs

    Surprisingly cogent meditation on probabilistic thinking in the NYT.

    Takeaways:

    1. People round probabilities up to 100% or down to 0%.

    2. People call probability “wrong” if <50% events happen

    3. People need a story to take <50% scenarios seriously


    I was wrong about a major aspect of probabilities.

    They are inherently hard to grasp. That’s especially true for an individual event, like a war or election. People understand that if they roll a die 100 times, they will get some 1’s. But when they see a probability for one event, they tend to think: Is this going to happen or not?

    They then effectively round to 0 or to 100 percent.

    For an individual event, people can’t resist saying that a probability was “right” if it was above 50 percent and “wrong” if it was below 50 percent. When this happens, those of us who believe in probabilities can’t just shake our heads and mutter about white Christmases. We have to communicate more effectively.

    I think part of the answer lies with Kahneman’s insight: Human beings need a story.

    It’s not enough to say an event has a 10 percent probability. People need a story that forces them to visualize the unlikely event — so they don’t round 10 to zero.

    Imagine that a forecast giving Candidate X a 10 percent chance included a prominent link, “How X wins.” It would explain how the polling could be off and include a winning map for X. It would all but shout: This really may happen.
    https://www.nytimes.com/2017/12/24/opinion/2017-wrong-numbers.html
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  3. no serious scholar of modern geopolitics disputes that we are now at war — a new kind of information-based war, but war, nevertheless — with Russia in particular, but in all honesty, with a multitude of nation states and stateless actors bent on destroying western democratic capitalism. They are using our most sophisticated and complex technology platforms to wage this war — and so far, we’re losing. Badly.

    Why? According to sources I’ve talked to both at the big tech companies and in government, each side feels the other is ignorant, arrogant, misguided, and incapable of understanding the other side’s point of view. There’s almost no data sharing, trust, or cooperation between them. We’re stuck in an old model of lobbying, soft power, and the occasional confrontational hearing.

    Not exactly the kind of public-private partnership we need to win a war, much less a peace.

    Am I arguing that the government should take over Google, Amazon, Facebook, and Apple so as to beat back Russian info-ops? No, of course not. But our current response to Russian aggression illustrates the lack of partnership and co-ordination between government and our most valuable private sector companies. And I am hoping to raise an alarm: When the private sector has markedly better information, processing power, and personnel than the public sector, one will only strengthen, while the latter will weaken. We’re seeing it play out in our current politics, and if you believe in the American idea, you should be extremely concerned.
    https://shift.newco.co/data-power-and-war-465933dcb372
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  4. Similarly, GOOG in 2014 started reorganizing itself to focus on artificial intelligence only. In January 2014, GOOG bought DeepMind, and in September they shutdown Orkut (one of their few social products which had momentary success in some countries) forever. The Alphabet Inc restructuring was announced in August 2015 but it likely took many months of meetings and bureaucracy. The restructuring was important to focus the web-oriented departments at GOOG towards a simple mission. GOOG sees no future in the simple Search market, and announces to be migrating “From Search to Suggest” (in Eric Schmidt’s own words) and being an “AI first company” (in Sundar Pichai’s own words). GOOG is currently slightly behind FB in terms of how fast it is growing its dominance of the web, but due to their technical expertise, vast budget, influence and vision, in the long run its AI assets will play a massive role on the internet. They know what they are doing.

    These are no longer the same companies as 4 years ago. GOOG is not anymore an internet company, it’s the knowledge internet company. FB is not an internet company, it’s the social internet company. They used to attempt to compete, and this competition kept the internet market diverse. Today, however, they seem mostly satisfied with their orthogonal dominance of parts of the Web, and we are losing diversity of choices. Which leads us to another part of the internet: e-commerce and AMZN.

    AMZN does not focus on making profit.
    https://staltz.com/the-web-began-dying-in-2014-heres-how.html
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  5. A snap decision by Google has begun to reshape the drug treatment industry, tilting the playing field toward large conglomerates — the precise opposite outcome Google had hoped to achieve.

    The fateful decision was made September 14. Google faced pressure from an exposé in The Verge released a week earlier, documenting how shady lead generators game its AdWords system. High-cost ads based on rehab keywords referred users to phone hotlines that gave the impression of being independent information services, but were actually owned by treatment center conglomerates. Representatives, who reap large fees based on how many patients they sign up, employ high-pressure sales tactics to push people into their favored facilities, whether or not that facility is the right one for the patient.

    This deceptive marketing can lead to substandard treatment and massive overbilling. It also made lots of money for Google, which was shown in the story actively courting addiction treatment advertisers.

    And so Google made a quick call: It effectively stopped running ads from treatment facilities. At first blush, that may look like a happy alignment of the public good and a company’s need for good public relations, with Google taking a hit to make the world a better place in the midst of an epidemic.

    But the problem of economic concentration is so deep in the United States today that peeling back one layer merely reveals another. Without ads, addicts or their parents are left only with the organic search results.
    https://theintercept.com/2017/10/17/google-search-drug-use-opioid-epidemic
    Tags: , , , by M. Fioretti (2017-10-30)
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  6. Imagine a world where many of your daily activities were constantly monitored and evaluated: what you buy at the shops and online; where you are at any given time; who your friends are and how you interact with them; how many hours you spend watching content or playing video games; and what bills and taxes you pay (or not). It's not hard to picture, because most of that already happens, thanks to all those data-collecting behemoths like Google, Facebook and Instagram or health-tracking apps such as Fitbit. But now imagine a system where all these behaviours are rated as either positive or negative and distilled into a single number, according to rules set by the government. That would create your Citizen Score and it would tell everyone whether or not you were trustworthy. Plus, your rating would be publicly ranked against that of the entire population and used to determine your eligibility for a mortgage or a job, where your children can go to school - or even just your chances of getting a date.

    A futuristic vision of Big Brother out of control? No, it's already getting underway in China, where the government is developing the Social Credit System (SCS) to rate the trustworthiness of its 1.3 billion citizens. The Chinese government is pitching the system as a desirable way to measure and enhance "trust" nationwide and to build a culture of "sincerity". As the policy states, "It will forge a public opinion environment where keeping trust is glorious. It will strengthen sincerity in government affairs, commercial sincerity, social sincerity and the construction of judicial credibility."
    http://www.wired.co.uk/article/chines...-social-credit-score-privacy-invasion
    Tags: , , , by M. Fioretti (2017-10-26)
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  7. Since Islam instructs followers to pray 5x daily at specific times, I wondered if one could identify devout Muslim hacks solely from their trip data. For drivers that do pray regularly, there are surely difficulties finding a place to park, wash up and pray at the exact time, but in many cases banding near prayer times is quite clear. I plotted a few examples.
    Each image shows fares for one cabbie in 2013. Yellow=active fare (carrying passengers). A minute is 1 pixel wide; a day is 2 pixels tall. Blue stripes indicate the 5 daily prayer start times which vary with the sun’s position throughout the year.
    http://www.theiii.org/index.php/997/u...-data-to-identify-muslim-taxi-drivers
    Tags: , , , , by M. Fioretti (2017-10-17)
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  8. Speaking as a statistician, it is quite easy to identify people in anonymous datasets. There are only so many 5'4" jews living in San Francisco with chronic back pain. Every bit of information we reveal about ourselves will be one more disease that we can track, and another life saved.

    If I want to know whether I will suffer a heart attack, I will have to release my data for public research. In the end, privacy will be an early death sentence.

    Already, health insurers are beginning to offer discounts for people who wear health trackers and let others analyze their personal movements. Many, if not most, consumers in the next generation will choose cash and a longer life in exchange for publicizing their most intimate details.

    What can we tell with basic health information, such as calories burned throughout the day? Pretty much everything.

    With a rudimentary step and calorie counter, I was able to distinguish whether I was having sex or at the gym, since the minute-by-minute calorie burn profile of sex is quite distinct (the image below from my health tracker shows lots of energy expended at the beginning and end, with few steps taken. Few activities besides sex have this distinct shape)
    https://medium.com/the-ferenstein-wir...rs-of-history-in-50-images-614c26059e
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  9. Shortly after The Guardian published its 2015 article, Facebook contacted Global Science Research and requested that it delete the data it had taken from Facebook users. Facebook’s policies give Facebook the right to delete data gathered by any app deemed to be “negatively impacting the Platform.” The company believes that Kogan and SCL complied with the request, which was made during the Republican primary, before Cambridge Analytica switched over from Ted Cruz’s campaign to Donald Trump’s. It remains unclear what was ultimately done with the Facebook data, or whether any models or algorithms derived from it wound up being used by the Trump campaign.

    In public, Facebook continues to maintain that whatever happened during the run-up to the election was business as usual. “Our investigation to date has not uncovered anything that suggests wrongdoing,” a Facebook spokesperson told The Intercept.

    Facebook appears not to have considered Global Science Research’s data collection to have been a serious ethical lapse. Joseph Chancellor, Kogan’s main collaborator on the SCL project and a former co-owner of Global Science Research, is now employed by Facebook Research. “The work that he did previously has no bearing on the work that he does at Facebook,” a Facebook spokesperson told The Intercept.

    Chancellor declined to comment.

    Cambridge Analytica has marketed itself as classifying voters using five personality traits known as OCEAN — Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism — the same model used by University of Cambridge researchers for in-house, non-commercial research. The question of whether OCEAN made a difference in the presidential election remains unanswered. Some have argued that big data analytics is a magic bullet for drilling into the psychology of individual voters; others are more skeptical. The predictive power of Facebook likes is not in dispute. A 2013 study by three of Kogan’s former colleagues at the University of Cambridge showed that likes alone could predict race with 95 percent accuracy and political party with 85 percent accuracy. Less clear is their power as a tool for targeted persuasion; Cambridge Analytica has claimed that OCEAN scores can be used to drive voter and consumer behavior through “microtargeting,” meaning narrowly tailored messages. Nix has said that neurotic voters tend to be moved by “rational and fear-based” arguments, while introverted, agreeable voters are more susceptible to “tradition and habits and family and community.”

    Dan Gillmor, director of the Knight Center at Arizona State University, said he was skeptical of the idea that the Trump campaign got a decisive edge from data analytics. But, he added, such techniques will likely become more effective in the future. “It’s reasonable to believe that sooner or later, we’re going to see widespread manipulation of people’s decision-making, including in elections, in ways that are more widespread and granular, but even less detectable than today,” he wrote in an email.
    https://theintercept.com/2017/03/30/f...harvested-by-trump-campaign-affiliate
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  10. Mallach’s non-profit focused on revitalizing distressed neighborhoods, particularly in “legacy cities.” These are towns like St. Louis, Flint, Dayton, and Baltimore, that have experienced population loss and economic contraction in recent years, and suffer from property vacancies, blight, and unemployment. Mallach is interested in understanding which neighborhoods are likely to continue down that path, and which ones will do a 180-degree turn. Right now, he can intuitively make those predictions, based on his observations on neighborhood characteristics like housing stock, median income, and race. But an objective assessment can help confirm or deny his hypotheses.

    That’s where Steif comes in. Having consulted with cities and non-profits on place-based data analytics, Steif has developed a number of algorithms that predict the movement of housing markets using expensive private data from entities like Zillow. Mallach suggested he try his algorithms on Census data, which is free and standardized.

    The phenomenon he tested was ‘endogenous gentrification’—this idea that an increase in home prices moves from wealthy neighborhoods to less expensive ones in its vicinity, like a wave. In his blog post, Steif explains:
    https://www.citylab.com/cityfixer/201...ms-that-predict-gentrification/516945
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