nowcasting

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Data Action: Using Data for Public Good by Sarah Williams

affirmative action, Amazon Mechanical Turk, Andrei Shleifer, augmented reality, autonomous vehicles, Brexit referendum, Cambridge Analytica, Charles Babbage, City Beautiful movement, commoditize, coronavirus, COVID-19, crowdsourcing, data acquisition, data is the new oil, data philanthropy, data science, digital divide, digital twin, Donald Trump, driverless car, Edward Glaeser, fake news, four colour theorem, global village, Google Earth, informal economy, Internet of things, Jane Jacobs, John Snow's cholera map, Kibera, Lewis Mumford, Marshall McLuhan, mass immigration, mass incarceration, megacity, military-industrial complex, Minecraft, neoliberal agenda, New Urbanism, Norbert Wiener, nowcasting, oil shale / tar sands, openstreetmap, place-making, precautionary principle, RAND corporation, ride hailing / ride sharing, selection bias, self-driving car, sentiment analysis, Sidewalk Labs, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Steven Levy, the built environment, The Chicago School, The Death and Life of Great American Cities, transatlantic slave trade, Uber for X, upwardly mobile, urban planning, urban renewal, W. E. B. Du Bois, Works Progress Administration

Social media data and tools like Humanitarian Tracker have been used for monitoring everything from smoking cessation patterns to infectious disease. This method is often referred to as “nowcasting,” a term used to describe the ability to predict a range of phenomena happening in the present, near future, or recent past such as the spread of diseases, economic analyses or forecasting,55 and weather.56 Aside from its typical use of social media data, nowcasting can also draw from the data exhaust, such as search history or logs of purchases on Amazon, generated by interacting with digital technologies. The use of data exhaust has become commonplace in public health research because of its potential for predicting seasonal epidemics such as influenza, which could have impacts on the public.57 In 2009, for instance, Google announced it used its vast database of search terms to predict with very high accuracy the number of cases of the flu as well as their locations, claiming that these statistics were comparable to CDC reports for the same period.58 The data analytics model, called Google Flu Trends (GFT), worked by comparing the pattern of the search term with the known CDC reported physician visits for influenza-like illness (ILI) for the five-year period from 2003 to 2008, and Google made sure to remove seasonally related terms such as high-school basketball.59 The research was heralded as the next revolution in public health research using big data.60 But even though Google's initial results proved successful, the algorithms began to fail over time because the model relied too heavily on seasonal search terms and search terms associated with illnesses such as bronchitis and pneumonia.

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Kass-Hout and Hend Alhinnawi, “Social Media in Public Health,” British Medical Bulletin 108, no. 1 (2013): 5–24. 54 “Humanitarian Tracker,” Humanitarian Tracker, October 15, 2018, https://www.humanitariantracker.org. 55 Domenico Giannone, Lucrezia Reichlin, and David Small, “Nowcasting: The Real-Time Informational Content of Macroeconomic Data,” Journal of Monetary Economics 55, no. 4 (May 2008): 665–676, https://doi.org/10.1016/j.jmoneco.2008.05.010. 56 Marta Banbura et al., “Now-Casting and the Real-Time Data Flow,” Handbook of Economic Forecasting 2, no. Part A (2013): 195–237. 57 Ali Alessa and Miad Faezipour, “A Review of Influenza Detection and Prediction through Social Networking Sites,” Theoretical Biology & Medical Modelling 15 (February 1, 2018), https://doi.org/10.1186/s12976-017-0074-5. 58 Jeremy Ginsberg et al., “Detecting Influenza Epidemics Using Search Engine Query Data,” Nature 457, no. 7232 (February 2009): 1012–1014, https://doi.org/10.1038/nature07634. 59 Ginsberg et al., “Detecting Influenza Epidemics.” 60 Miguel Helft, “Google Uses Web Searches to Track Flu's Spread,” New York Times, November 11, 2008, https://www.nytimes.com/2008/11/12/technology/internet/12flu.html; “The Next Chapter for Flu Trends,” Google AI Blog (blog), October 15, 2018, http://ai.googleblog.com/2015/08/the-next-chapter-for-flu-trends.html. 61 Samantha Cook et al., “Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic,” PLOS ONE 6, no. 8 (August 19, 2011): e23610, https://doi.org/10.1371/journal.pone.0023610. 62 Donald R.

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This emerging field is often framed as digital humanitarianism.67 Applications and analyses have been deployed to understand natural disasters such as wildfires, floods, earthquakes,68 and even terrorist attacks,69 since social media can provide real-time information from those on the ground and responding—and allowing people to know where to respond. The work is similar to the “nowcasting” described earlier in the chapter, as it helps identify the problems associated with a disaster. But rather than being predictive, disaster response tells us what is actually happening on the ground based on messages interpreted by those affected. Jakob Rogstadius, who studies and develops disaster systems for IBM, categorizes disaster response systems that use social media in three ways: First, those that address disaster management directly, sending information through social media about rescue or evacuation efforts.

The COVID-19 Catastrophe: What's Gone Wrong and How to Stop It Happening Again by Richard Horton

Anthropocene, biodiversity loss, Boris Johnson, cognitive bias, contact tracing, coronavirus, COVID-19, Deng Xiaoping, disinformation, Dominic Cummings, Donald Trump, fake news, Future Shock, global pandemic, global village, Herbert Marcuse, informal economy, lockdown, nowcasting, Panopticon Jeremy Bentham, Peace of Westphalia, Slavoj Žižek, social distancing, South China Sea, zoonotic diseases

., A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission, The Lancet, 24 January 2020. 2. Chaolin Huang et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, The Lancet, 24 January 2020. 3. Joseph T. Wu et al., Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China, The Lancet, 31 January 2020. 4. Adam Kucharski, The Rules of Contagion: Why Things Spread – and Why They Stop (London: Profile Books, 2020). 5. Adam J. Kucharski et al., Early dynamics of transmission and control of COVID-19, Lancet Infectious Diseases, 11 March 2020. 6.

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., A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission, The Lancet, 24 January 2020. 3. Roujian Lu et al., Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding, The Lancet, 29 January 2020. 4. Joseph T. Wu et al., Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China, The Lancet, 31 January 2020. 5. Huijun Chen et al., Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women, The Lancet, 12 February 2020. 6.

Oil Panic and the Global Crisis: Predictions and Myths by Steven M. Gorelick

California gold rush, carbon footprint, energy security, energy transition, flex fuel, Ford Model T, income per capita, invention of the telephone, Jevons paradox, meta-analysis, North Sea oil, nowcasting, oil shale / tar sands, oil shock, peak oil, price elasticity of demand, price stability, profit motive, purchasing power parity, RAND corporation, statistical model, stock buybacks, Thomas Malthus

With this retrospective fit, Hubbert’s approach is easily able to match much of the historical production data, including the peak. Yet even with the advantage of post-peak hindsight, the “nowcast” does not match actual production data for the past 10 years and substantially underestimates production, as displayed in Figure 4.4 (for 2008, applying Hubbert’s method gives a predicted value of 0.92 billion barrels per year versus the actual 1.55 billion barrels per year). This figure presents an enlargement of the nowcast prediction for the years 1994 through 2008, showing the descending limb of 92 Counter-Arguments to Imminent Global Oil Depletion the predicted curve versus actual production data.

Calling Bullshit: The Art of Scepticism in a Data-Driven World by Jevin D. West, Carl T. Bergstrom

airport security, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Andrew Wiles, Anthropocene, autism spectrum disorder, bitcoin, Charles Babbage, cloud computing, computer vision, content marketing, correlation coefficient, correlation does not imply causation, crowdsourcing, cryptocurrency, data science, deep learning, deepfake, delayed gratification, disinformation, Dmitri Mendeleev, Donald Trump, Elon Musk, epigenetics, Estimating the Reproducibility of Psychological Science, experimental economics, fake news, Ford Model T, Goodhart's law, Helicobacter pylori, Higgs boson, invention of the printing press, John Markoff, Large Hadron Collider, longitudinal study, Lyft, machine translation, meta-analysis, new economy, nowcasting, opioid epidemic / opioid crisis, p-value, Pluto: dwarf planet, publication bias, RAND corporation, randomized controlled trial, replication crisis, ride hailing / ride sharing, Ronald Reagan, selection bias, self-driving car, Silicon Valley, Silicon Valley startup, social graph, Socratic dialogue, Stanford marshmallow experiment, statistical model, stem cell, superintelligent machines, systematic bias, tech bro, TED Talk, the long tail, the scientific method, theory of mind, Tim Cook: Apple, twin studies, Uber and Lyft, Uber for X, uber lyft, When a measure becomes a target

When you have Google-scale data, argued Wired editor Chris Anderson, “the numbers speak for themselves.” The scientific method was no longer necessary, he argued; the huge volumes of data would tell us everything we need to know. Data scientists didn’t need years of epidemiological training or clinicians to diagnose flu symptoms. They just need enough data to “nowcast”*12 the flu and inform the CDC where to deliver Tamiflu. Or so we were told. In all the excitement, we forget that if it sounds too good to be true, it probably is. And it was. By 2014, the headlines had turned from celebratory to monitory: “Google and the Flu: How Big Data Will Help Us Make Gigantic Mistakes,” “Why Google Flu Is a Failure,” “What We Can Learn from the Epic Failure of Google Flu Trends.”

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*11 For example, the new General Data Protection Regulation (GDPR) in the European Union includes a “right to explanation” in Recital 71. This companion document will be influential going forward, but it is the most debated of all subjects in the GDPR, partly because of the difficulty in defining what is meant by “explain.” *12 “Nowcasting” is a flashy word made up to describe the practice of “predicting” aspects of the present or recent past using computer models, before anyone has time to measure them directly. *13 The study included Web queries between 2003 and 2008. The suggest (autocomplete) functionality became broadly available on Google’s website in 2008.

Data and the City by Rob Kitchin,Tracey P. Lauriault,Gavin McArdle

A Declaration of the Independence of Cyberspace, algorithmic management, bike sharing, bitcoin, blockchain, Bretton Woods, Chelsea Manning, citizen journalism, Claude Shannon: information theory, clean water, cloud computing, complexity theory, conceptual framework, corporate governance, correlation does not imply causation, create, read, update, delete, crowdsourcing, cryptocurrency, data science, dematerialisation, digital divide, digital map, digital rights, distributed ledger, Evgeny Morozov, fault tolerance, fiat currency, Filter Bubble, floating exchange rates, folksonomy, functional programming, global value chain, Google Earth, Hacker News, hive mind, information security, Internet of things, Kickstarter, knowledge economy, Lewis Mumford, lifelogging, linked data, loose coupling, machine readable, new economy, New Urbanism, Nicholas Carr, nowcasting, open economy, openstreetmap, OSI model, packet switching, pattern recognition, performance metric, place-making, power law, quantum entanglement, RAND corporation, RFID, Richard Florida, ride hailing / ride sharing, semantic web, sentiment analysis, sharing economy, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, smart grid, smart meter, social graph, software studies, statistical model, tacit knowledge, TaskRabbit, technological determinism, technological solutionism, text mining, The Chicago School, The Death and Life of Great American Cities, the long tail, the market place, the medium is the message, the scientific method, Toyota Production System, urban planning, urban sprawl, web application

This is an important requirement for cities and citizens. With the rise of citizen data scientist and the corporate use of urban data, sharing data about cities using various bindings is advantageous. The architecture also can efficiently communicate with big data technologies. Current city dashboards are useful tool for now-casting. With big data technologies and data science, cities need to have other systems for sharing data using polyglot bindings and providing indicators and metrics about city for future (forecasting) using predictive analytics. References Amirian, P., Alesheikh, A. and Bassiri, A. (2010a) ‘Standards-based, interoperable services for accessing urban services data for the city of Tehran’, Computers, Environment and Urban Systems 34(4): 309–321.

Virtual Competition by Ariel Ezrachi, Maurice E. Stucke

"World Economic Forum" Davos, Airbnb, Alan Greenspan, Albert Einstein, algorithmic management, algorithmic trading, Arthur D. Levinson, barriers to entry, behavioural economics, cloud computing, collaborative economy, commoditize, confounding variable, corporate governance, crony capitalism, crowdsourcing, Daniel Kahneman / Amos Tversky, David Graeber, deep learning, demand response, Didi Chuxing, digital capitalism, disintermediation, disruptive innovation, double helix, Downton Abbey, driverless car, electricity market, Erik Brynjolfsson, Evgeny Morozov, experimental economics, Firefox, framing effect, Google Chrome, independent contractor, index arbitrage, information asymmetry, interest rate derivative, Internet of things, invisible hand, Jean Tirole, John Markoff, Joseph Schumpeter, Kenneth Arrow, light touch regulation, linked data, loss aversion, Lyft, Mark Zuckerberg, market clearing, market friction, Milgram experiment, multi-sided market, natural language processing, Network effects, new economy, nowcasting, offshore financial centre, pattern recognition, power law, prediction markets, price discrimination, price elasticity of demand, price stability, profit maximization, profit motive, race to the bottom, rent-seeking, Richard Thaler, ride hailing / ride sharing, road to serfdom, Robert Bork, Ronald Reagan, search costs, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, smart meter, Snapchat, social graph, Steve Jobs, sunk-cost fallacy, supply-chain management, telemarketer, The Chicago School, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, Travis Kalanick, turn-by-turn navigation, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, Watson beat the top human players on Jeopardy!, women in the workforce, yield management

According to the study, “differential pricing based on demographics (whereby Netflix would adjust prices based on a customer’s race, age, income, geographic location, and family size) could increase profit by 0.8 percent, while using 5,000 Web browsing variables (such as the amount of time a user typically spends online or whether she has recently visited Wikipedia or IMDB) could increase profits by as much as 12.2 percent.” Ibid., citing Benjamin Shiller, “First-Degree Price Discrimination Using Big Data” (2014), http://benjaminshiller.com /images/First_Degree_PD_Using _Big _Data_Apr_8,_2014.pdf. For a discussion of the “nowcasting radar,” see Stucke and Grunes, Big Data and Competition Policy. Yoko Kubota, “Toyota Aims to Make Self-Driving Cars by 2020,” Wall Street Journal, October 6, 2015, http://www.wsj.com/articles/toyota-aims-to-make -self-driving-cars-by-2020 -1444136396; Yoko Kubota, “Behind Toyota’s Late Shift into Self-Driving Cars,” Wall Street Journal, January 12, 2016, 338 25. 26. 27. 28. 29. 30. 31. 32.

The Technology Trap: Capital, Labor, and Power in the Age of Automation by Carl Benedikt Frey

3D printing, AlphaGo, Alvin Toffler, autonomous vehicles, basic income, Bernie Sanders, Branko Milanovic, British Empire, business cycle, business process, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Charles Babbage, Clayton Christensen, collective bargaining, computer age, computer vision, Corn Laws, Cornelius Vanderbilt, creative destruction, data science, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, demographic transition, desegregation, deskilling, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Glaeser, Elon Musk, Erik Brynjolfsson, everywhere but in the productivity statistics, factory automation, Fairchild Semiconductor, falling living standards, first square of the chessboard / second half of the chessboard, Ford Model T, Ford paid five dollars a day, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, game design, general purpose technology, Gini coefficient, Great Leap Forward, Hans Moravec, high-speed rail, Hyperloop, income inequality, income per capita, independent contractor, industrial cluster, industrial robot, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of movable type, invention of the steam engine, invention of the wheel, Isaac Newton, James Hargreaves, James Watt: steam engine, Jeremy Corbyn, job automation, job satisfaction, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kickstarter, Kiva Systems, knowledge economy, knowledge worker, labor-force participation, labour mobility, Lewis Mumford, Loebner Prize, low skilled workers, machine translation, Malcom McLean invented shipping containers, manufacturing employment, mass immigration, means of production, Menlo Park, minimum wage unemployment, natural language processing, new economy, New Urbanism, Nick Bostrom, Norbert Wiener, nowcasting, oil shock, On the Economy of Machinery and Manufactures, OpenAI, opioid epidemic / opioid crisis, Pareto efficiency, pattern recognition, pink-collar, Productivity paradox, profit maximization, Renaissance Technologies, rent-seeking, rising living standards, Robert Gordon, Robert Solow, robot derives from the Czech word robota Czech, meaning slave, safety bicycle, Second Machine Age, secular stagnation, self-driving car, seminal paper, Silicon Valley, Simon Kuznets, social intelligence, sparse data, speech recognition, spinning jenny, Stephen Hawking, tacit knowledge, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Malthus, total factor productivity, trade route, Triangle Shirtwaist Factory, Turing test, union organizing, universal basic income, warehouse automation, washing machines reduced drudgery, wealth creators, women in the workforce, working poor, zero-sum game

Today, futurologists are just as ill equipped to predict the jobs that AI will create. Official employment statistics are always behind the curve when it comes to capturing new occupations, which are not included in the data until they have reached a critical mass in terms of the number of people in them. But other sources, like LinkedIn data, allow us at least to nowcast some emerging jobs. Among them are the jobs of machine learning engineers, big data architects, data scientists, digital marketing specialists, and Android developers.14 But we also find jobs like Zumba instructors and Beachbody coaches.15 In a world that is becoming increasingly technologically sophisticated, rising returns on skills are unlikely to disappear and likely to intensify.

Alpha Trader by Brent Donnelly

Abraham Wald, algorithmic trading, Asian financial crisis, Atul Gawande, autonomous vehicles, backtesting, barriers to entry, beat the dealer, behavioural economics, bitcoin, Boeing 747, buy low sell high, Checklist Manifesto, commodity trading advisor, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, currency risk, deep learning, diversification, Edward Thorp, Elliott wave, Elon Musk, endowment effect, eurozone crisis, fail fast, financial engineering, fixed income, Flash crash, full employment, global macro, global pandemic, Gordon Gekko, hedonic treadmill, helicopter parent, high net worth, hindsight bias, implied volatility, impulse control, Inbox Zero, index fund, inflation targeting, information asymmetry, invisible hand, iterative process, junk bonds, Kaizen: continuous improvement, law of one price, loss aversion, low interest rates, margin call, market bubble, market microstructure, Market Wizards by Jack D. Schwager, McMansion, Monty Hall problem, Network effects, nowcasting, PalmPilot, paper trading, pattern recognition, Peter Thiel, prediction markets, price anchoring, price discovery process, price stability, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, reserve currency, risk tolerance, Robert Shiller, secular stagnation, Sharpe ratio, short selling, side project, Stanford marshmallow experiment, Stanford prison experiment, survivorship bias, tail risk, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, time dilation, too big to fail, transaction costs, value at risk, very high income, yield curve, you are the product, zero-sum game

As of December 2020, it is the least attractive it has ever been, with yields close to zero. And they upgraded it. Note that yield moves opposite to the price of the bond so the ratings agency downgraded the bond at the lows and upgraded it at the highs. My point is not to troll the ratings agencies, and I know technically they are not forecasting, they are just nowcasting. The point is just that when an asset is in trouble, everyone hates it. When an asset is ripping, everybody loves it. Now let’s do oil (see Figure 7.5). Again, note how the price moves first and the forecast follows and has no predictive value. Extrapolation bias does not just impact economists and forecasters, it impacts traders (big time).