An award-winning guess at London’s fate in 50 years

Here is part of the winning entry of a UK contest to predict how social science research will affect life in 50 years:

Amidst decentralisation, London continued to grow, steadily gaining devolved powers. As 2043 arrived, the city into which I had moved 28 years previously was unrecognisable. From the 900m high tower in which I now lived, I surveyed a transforming cityscape, embracing recent technological developments. In 2022, Saudi Arabia completed Kingdom Tower, the world’s first kilometre high building. Besides technological innovation, it also had profound cultural implications; a range of social science consultants having pioneered community creation models. Under their guidance, its 5.5million ft2 of floor space offered offices, malls, accommodation and even artificial forests, stimulating a self-contained society with a culture of independence. Twelve years and four towers later, Kingdom City was a thriving metropolis of 2.1 million people. It represented a triumph for private finance and social science collaboration, setting a precedent for socially conscious corporation rule with minimal state involvement. Kingdom City prompted numerous equivalent developments throughout the Middle East and Asia in the late 2030s; social theory informed, self-contained, and privately administered. These express-cities dealt with population problems and boosted economies with ease, vindicating social planning.

Meanwhile, London had developed an immense housing crisis; its ballooning population shackled by construction regulation. London was desperate to emulate aforementioned eastern successes. It turned to its collection of world leading institutions, representing internationally renowned social psychologists, human geographers and many more, to plan ground-breaking reinvention. Throughout the 2040s, backed by multinational finance, London set about implementing their vision. Whilst primarily based around sociological community-seeding-housing ideas, this also facilitated a transport revolution. London already scorned cars, championing cycling and enjoying an unrivalled underground system following four Crossrail projects. Driverless electric vehicles had been increasingly present since the mid-2020s as battery technology improved. By the mid-30s, London proposed banning all human-driven petrol-fuelled vehicles, but the UK government was opposed; concerned that decreased fuel imports might jeopardise Gulf State relations. By the early-2040s, London was powerful enough to press ahead. Again the social sciences, bolstered by increasingly successful corporate ventures into city design, were instrumental in infrastructure planning, embedding the belief that public and corporate desires for liveability and efficiency were compatible. Resultantly, in 2053, the last human drove through the city. Simultaneously, influential internet scholars drove complete 5G rollout, providing unparalleled internet access. Contrastingly, large parts of the rest of the UK lacked 4G, creating a national digital divide. The scene was now set for divorce. In 2056 the government accepted a federalisation referendum. On May 4th 2058, London voted to become the UK’s fifth state.

Today, whilst technically federalised, London is essentially sovereign. Since the early-50s, state involvement has been nominal, particularly following parliament’s relocation to Manchester. London, like many 20th century capitals, now more closely resembles the Martian colonies than the nation surrounding it. These old nation states, largely unaltered from 2015, are increasingly inferior, especially as Space X’s mines and hydroponic innovations further improve city living standards. Social science’s guidance of private capital has enabled Jakarta, Doha and many more to smoothly transcend state structures, each now existing as a well-organised corporate amalgamation. This change is evident in my current work. Whilst trickledown economics and stringent immigration controls have all but ended real-term deprivation, inequality remains entrenched. Employed by London Inc., who are concerned by talent prevention, I am currently developing proposals to stimulate social mobility. This is just one example of how corporate-social science synergy is cultivating prosperous city societies in 2065.

These predictions appear to hinge on social science and private industry working together for London’s good, or at least for technological advancement. How many social scientists today would be interested in such collaboration, particularly if it meant that corporations could profit immensely or that the rich continue to get richer? As the essay hints, such improvements could come at the expense of many other UK residents who are left behind as London continues to grow and the rest of the country falls behind.

Maybe we should just file this away for five decades from now to see if any of this comes true…

Competing population projections for Chicago

I highlighted one recent prediction that Chicago would soon trail Houston in population. Yet, another projection has Chicago gaining people and holding off Houston for longer. Which is right?

Data released by the Illinois Department of Health in February show that the population for Chicago, about 2.7 million in 2010, could decrease by 3 percent to 2.5 million by 2025. Meanwhile, Houston’s population could reach 2.54 million to 2.7 million in 2025, according to the Reuters report. But a recent population estimate by the Census Bureau shows an increase in population, rather than a decrease.

Census estimates released in June show that the population of Chicago increased by 1 percent from 2010 to 2014. So why is one projection showing a decrease, but another an increase?

Both data sets are based on estimates and assumptions, says Rob Paral, a Chicago-based demographer. Unlike the 2000 or 2010 census, where all residents answer a questionnaire, any interim projections or estimates must use sampling or a formula based on past population statistics to calculate population…

“Trend data do not support any increase in the projections for Chicago in the next 10 years,” said Bill Dart, the deputy director of policy, planning and statistics at the health department. Dart explained that the estimates from the census use a different formula than the health department. And factors such as births, deaths, migration, economic boons or natural disasters can disrupt projections.

Two groups dealing with population data that come to opposite conclusions. Two ways we might approach this:

  1. The differences are due to slightly different data, whether in the variables used or the projection models. We could have a debate about which model or variables are better for predicting population. Have these same kind of variables and models proven themselves in other cities? (Alternately, are there factors that both models leave out?)
  2. Perhaps the two predictions aren’t that different: one is suggesting a slight decline and one predicts a slight increase. Could both predictions be within the margin of error? We might be really worried if one saw a huge drop-off coming and the other disagreed but both projections here are not too different from no change at all. Sure, the media might be able to say the predictions disagree but statistically there is not much difference.

The answer will come in time. Still, projections like these still carry weight as they provide grist for the media, things for politicians to grab onto, and may just influence the actions of some (is Chicago or Houston a city on the rise?).

Gang “homicides spread like infectious disease”; other homicides do not

A new study adds to the social network analysis of gang activity by comparing clusters of gang homicides to other kinds of homicides:

Using police data from Newark, New Jersey, Zeoli and fellow MSU researchers Sue Grady, Jesenia Pizarro and Chris Melde were the first to show, in 2012, that homicide spreads like infectious disease. Similar to the flu, homicide needs a susceptible population, an infectious agent and a vector to spread. (The infectious agent could be the code of the street – i.e., guarding one’s respect at all cost, including by resorting to violence – while the vector could be word of mouth or other publicity, Zeoli said.)With the new study, the interdisciplinary team of researchers analyzed the Newark data to gauge whether specific types of homicide cluster and spread differently. In addition to gang-related murders, the researchers looked at homicide motives such as robbery, revenge, domestic violence and drugs. These other motive types were not directly connected to gang participation.

The study found that the various homicide types do, in fact, show different patterns. Homicides stemming from domestic violence and robberies, for example, show no signs of clustering or spreading out.

Gang-related killings were the only type of homicide that spread in a systematic pattern. Specifically, there were four contiguous clusters of gang-related homicides that started in central Newark and moved roughly clockwise from July 2002 through December 2005.

Such findings, adding to previous research showing a relatively small cluster of gang members in a big city can be responsible for a large number of homicides, should help lead to better prevention and policing efforts. All homicide is not alike as the root causes and people involved can differ.

Two other things are interesting about this coverage:

1. The medical analogy – an infectious disease that needs to be cured – is likely to be appealing to a broad number of people. This might work better than the rhetoric of needing to find the killers and lock them up.

2. The headline of the story is “Can sociology predict gang killings?” and one quote in the story might provide evidence for this: “Taken together, this provides one piece of the puzzle that may allow us to start forecasting where homicide is going to be the worst – and that may be preceded in large part by changes in gang networks.” However, forecasting where homicides are more likely to happen is not exactly the same as predicting gang killings.

Predicting riots using social media

In addition to the identified factors from research coming out of the 1960s and 1970s, one sociologist suggests social media activity can show how riots and protests spread:

The most promising method of “predicting” unrest might be through social media. Dan Braha, a professor at the University of Massachusetts and affiliate of the New England Complex Systems Institute, has studied unrest in hundreds of countries and the phenomenon of “contagion,” or how it spreads. In the past, printed newspapers, televisions, and other media played an important role, he said. “Today, the use of Twitter, Facebook, and other social media platforms is fundamental to the rapid self-organization and spreading of unrest activities—much like the spread of fire in a forest.” And the data from these media can be tracked. Riots, he claims, are certainly foreseeable, but “prediction regarding ‘when’ and ‘where’ becomes more precise on short time scales.”

It sounds like social media is just part of the puzzle here. There are certain underlying conditions mentioned in this article – such as hot weather or precipitating incidents (such as police violence) – but these do not always lead to riots. (In fact, given the inequalities present in many American cities, riots and protests could be considered relatively rare.)  Just as with the analysis of the Arab Spring activity, social media does not cause protests or riots but it can help facilitate it. This was reported in Egypt as protestors shared information through social media and even peer-to-peer options. This was also reported in Baltimore as protestors selected places to show up. This is not a new phenomena; riots in the 1960s spread in a contagion like manner and the dispersion could be tracked through news coverage in the New York Times. But, the availability of social media now makes it theoretically possible to watch things develop in real time, an advantage for both protestors and authorities.

Thinking to the future, what happens when protestors make use of non-public social media or peer-to-peer options that cannot be viewed by authorities?

“Using a Real Life SimCity to Design a Massive Development”

As a massive SimCity fan, I find this use of predictive urban models intriguing:

596 acres, 50,000 residents, $4 billion dollars and even a 1,500-boat marina: Everything about the proposed Chicago Lakeside Development, developer Dan McCaffery’s massive micro-city being built at the former site of the U.S. Steel Southworks Plant, is on a different scale. It follows that the design process for this mixed-use project requires a different set of tools, in this case, LakeSim, an advanced computer modeling program. Developed as part of a collaboration between the University of Chicago, Argonne National Laboratory, Skidmore, Owings & Merrill and McCaffery Interests, this program functions like a customized SimCity, analyzing and simulating weather, traffic patterns and energy usage to help architects and designers plan for a site that may eventually contain more than 500 buildings.

“A lot of the Big Data approaches tend to be statistical in nature, looking at past data,” says Argonne scientist Jonathan Ozik. “We’re modeling a complex system of interactive components, running the data forward, so what we end up having is your SimCity analogy, energy systems interacting, vehicles and people moving. What we’re doing here is using a complex systems approach to tackle the problem.”…

The challenge for planners is predicting how so many different systems and variables will interact. LakeSim gives them a framework to analyze these systems over long timelines and run millions of scenarios much quicker than past models — hours as opposed to days — asking “hundreds of questions at once,” according to Ozik. The program is a step forward from similar modeling software, especially valuable at a site that in most respects is being built from scratch.

This seems quite useful at this point but it will be necessary to look at this down the road once the site is developed. How much time did the model save? How accurate was the model? Did relying on such a model lead to negative outcomes? If this is a predictive model, it may be only as good as the outcome.

Interesting to note that the commenters at the bottom are wondering where all the people to live in this development are going to come from. I assume that demand is appropriately accounted for in the model?

LA to have the worst traffic on Thanksgiving getaway day

Add in more drivers this Thanksgiving and one firm says Los Angeles will have the worst getaway traffic on Wednesday:

Trips on Wednesday—deceptively nicknamed “getaway day”—are expected to take 36 percent longer on average, with peak times from 3 to 5 in the afternoon. Perhaps it would be more efficient for everyone to just Skype their family members while eating meals at home.

A rep for Inrix tells the LA Times that leaving either before or after the late afternoon peak times might spare drivers a little road pain, but that leaving the morning of Thanksgiving would be even better. The unfortunate folks picking up at LAX on Wednesday and traveling on the 110 south can expect the worst delays on their trip between 7 and 8 a.m., with delays of up to 15 minutes, which actually just sounds like regular holiday traffic at the airport.

Some interesting info – the data we have these days is pretty amazing – but this is such a limited issue. The two hour window in Los Angeles between 3 and 5 PM is expected to be worse and some of the other cities on the list have only one hour windows. If this is the case, then people should take note and try to leave at other times. Many people may not be able to help it given plane or work schedules but the traffic doesn’t have be as bad as suggested if people use the information well.

Predicting more Thanksgiving traffic due to a closed car-to-plane gap?

One way to predict traffic on the roads at Thanksgiving is to look at the car-to-plane travel gap:

Drivers will make up about 89.5 percent of holiday travelers this year, a gain of 0.1 percentage point from 2013, while air passengers will drop by the same amount to 7.5, forecasts prepared by Englewood, Colorado-based IHS Inc. show. A 0.1 point increase may not seem like a lot, but based on last year’s estimate that 39.6 million people traveled by car for Thanksgiving, that would roughly equate to at least another 40,000 people piling onto America’s highways.

The car-over-plane travel choice is made easier by the fact that airfares aren’t coming down like gasoline pump prices are. While the plunge in oil has driven down wholesale jet fuel prices 17 percent since August, almost matching the 18 percent drop in retail gasoline, airfares have risen 3.4 percent over that time, data compiled by industry groups show…

“Right now the airlines aren’t in the sharing mood,” Rick Seaney, chief executive officer of the Dallas-based travel website FareCompare.com, said. “They just went through six years of multi-megamergers and dividing the country up by city with little or no competition, so they’ll pocket whatever difference they may get for a while.”

Gasoline’s drop will save the average U.S. driver about $500 annually, helping boost consumer spending, according to IHS. U.S. auto sales have risen 5.5 percent to 13.7 million in the first 10 months of 2014, on pace to be the strongest in eight years, Woodcliff Lake, New Jersey-based data provider Autodata Corp. said.

A few thoughts:

1. Having 40,000 more people on the roads at Thanksgiving is going to complicate traffic all across the United States? Spread these people cross hundreds of metropolitan areas and assume they aren’t all leaving at the same time (Wednesday after work) and adding that kind of volume may not matter much at all.

2. The prediction of future traffic is interesting to me. This reminds me of Carmageddon fears, first in Los Angeles (twice) and then in Chicago earlier this year. This seems like the creation of news: get prepared for more Thanksgiving traffic now! It is the kind of fear-based reporting done by many local news outlets about things like weather or traffic, fairly mundane events that occasionally turn out to be horrible.

3. The Carmageddon cases hint at another piece of this prediction: making such claims could change future behavior. If Americans hear that there will be more drivers at Thanksgiving, even just a few of them changing their plans (not driving or changing their departure times) might go a long ways toward relieving the predicted traffic. Perhaps this forecast is all part of some plan to actually reduce Thanksgiving traffic?

4. Just from personal observation: plane tickets appear to be really high during Thanksgiving, Christmas, and New Year’s this year. As the article notes, airlines are looking to make money and haven’t budged much in their prices even with the recent gas price drops.