According to physicist Geoffrey West, the world's cities have what one might call a growing problem. As they grow bigger, their problems grow worse at a super-linear pace, which means it takes an ever-faster pace of innovation to keep things in check. We can either figure out a way to innovate faster, watch our cities crash and burn, or - perhaps worst of all for capitalists - figure out a way to live without constant economic growth. West says the scientist in him doesn't see us being able to innovate fast enough, but I think big data might be the key to making that happen.
I heard West espouse his theory at The Economist's Ideas Economy: Information event in early June, but you can read about it in this in-depth interview he did with Edge last year. Here's a very simple explanation for a very complex theory that involved analyzing lots and lots of data.
To some degree, cities are just like biological beings in that the networks that support them grow both systematically and sublinearly. Whereas, for example, mammals' metabolic systems get more efficient as you move up the chain from rats to monkeys to elephants, so too do the infrastructural systems of cities as you move from Branson to Santa Fe to New York.
But there's a catch when it comes to cities. While biological entities are designed to keep existing, cities are really designed to maximize wealth, innovation and invention. That means cities also produce a host of socioeconomic effects - both good and bad - that have no analogs in nature. These things, it turns out, grow at a super-linear pace, leading to what West calls "socio-economic entropy."
Here's West's CliffsNotes version of the big picture from Edge:
It's that latter group of effects that causes all the problems. The kind of open-ended growth that cities rely upon in order to keep up with the demands of capitalist societies is only possible if we innovate our way around collapse. Disease, lack of resources, crime run amok - something is destined to push a city to its breaking point save for innovation that solves the problem at hand.
West's research, however, suggests the world's cities are growing faster than innovation can keep up. As a scientist, he said at the Economist event, he's pessimistic about our chances to pick up the pace of innovation to the necessary rate, but as a human he assumes we'll muddle our way through and figure out a way to stave off collapse. Perhaps, he said, that means adapting to the idea of far slower growth than we've become accustomed to.
If we take West's conclusions as truth, the natural question becomes how we can pick up the pace of innovation to avoid or at least stave off cities' inevitable collapses. I think the answer might lie in turning data to fight the socioeconomic calamities that population growth produces.
One could point to the rest of The Economist's event as evidence of how that might play out. For example, preceding West on stage were representatives from Siemens and Cisco talking about how data-driven infrastructure in cities (even down to the level of mobile apps for finding open parking spaces), powered by mechanical sensors and even human observation, can help make cities run smarter and more efficiently. All of this means more capital and human resources for cities to spend on solving even greater societal problems.
The technology for capturing and analyzing data just keeps getting better, and so to the applications of it to real-world problems. IBM, of course, has a whole a vision around Smarter Cities that includes everything from systems to reducing gridlock to predicting crime to having its Watson system acting as mayor (of sorts) of a city. At our Structure: Data conference this year, we looked at big data applications ranging from monitoring your own emissions to solving cancer via genome analysis to providing loans to individuals with low credit scores. And there are plenty more examples where those came from.
The best part about the spate of innovation around big data is that it's part of a greater shift toward a web- and mobile-centric world than many ever imagined. Earlier this year, for example, I covered a mobile app called Skin Scan that lets users take a picture of a mole, have that processed against a cloud-based algorithm to determine if it might be cancerous, and then connect users with doctors in their area. Because of the prevalence of mobile phones across the world, Skin Scan hopes to create a database that tracks the severity of skin cancer in different regions over the course of years.
Anyone would be a fool to dismiss West's concern out of hand, but it's also foolish to not look at the current pace of innovation and think perhaps there's a chance for salvation. Capturing and analyzing massive amounts of data has never been easier, and if accurate analytics are as world-changing as they're made out to be, our innovato
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