Mobile data traces the contours of urban experience
Cities are temporal rhythms of patterned activity. Just as each community has a distinctive spatial footprint when viewed from space, each has an ambient footprint.
You can sense a city's ambient footprint at a coarse or a fine grain. At coarse granularity, you can sense it at night when the artificial illumination (houses, offices, stores, cars and so on) switches on and glows so brightly that populated areas are clearly visible from Earth orbit. At a finer grain, you can also sense urban ambient patterns at or near surface level—with your naked eye, of course, but also by tracking the noise, heat, smells, radio signals and other traces of human activity.
Urban experience is the blend of these spatial and ambient patterns playing out over regular intervals of time. To the extent that we can track these patterns and view them from the ground-level perspectives of individuals and groups, we can tune that experience. And to the extent that we can use mobile-phone calling records, metadata and message data to analyze urban behavioral patterns, we can drill deeply into the tangled temporal, spatial and ambient experience of the average person on the street.
Densities define the urban ambience. They are big part of the allure of urban life but also contribute directly to what's most maddening, dysfunctional and unlivable about many cities. As this recent article illustrates, a new science of cities is relying on mobile-phone data analysis to assess how densities intensify, ebb, flow and evolve in the daily lived experience of cities. Some refer to this user-generated mobile-device data as digital exhaust.
The article reports on a recently published study in which European social scientists use mobile phone data to plot the daily density cycles of various urban areas. The researchers use that data to classify cities by their various and shifting density structures. As illustrated in the study, mobile-phone usage densities clearly show how urban area density structures evolve over the course of a day as people commute, shop, entertain themselves and so forth. As the article states, "Every city undergoes a kind of respiration in which people converge into the center and then withdraw on a daily basis, almost like breathing."
Though the researchers say their findings “suggest the existence of a single ‘urban rhythm’ common to all cities,” clear variations exist across diverse communities. Urban structures also vary from city to city in terms of the persistent density distributions. The research illustrates how cities vary: some are more "monocentric" (such as a distinct downtown that densely hubs most business, residential and other activities), while others are "polycentric" (which means to have two or more such "hotspots" that have varying densities and that may have distinct economic, social and other uses, called out by mobile-phone behavioral patterns).
According to one of the researchers, "These results point towards the possibility of a new, quantitative classification of cities using high resolution spatio-temporal data." Though they don't directly tie their findings to urban quality of life, it's clear that such data could easily be geo-mapped to various data sets that have a clear connection in that regard. Anyone who has ever set foot in a city can see how variables such as health, crime, congestion, pollution and so forth correlate with population densities. By overlaying mobile data patterns with these variables, as well as demographic and socio-economic data sets, it's a short step, conceptually, to generating a dynamic street view of urban livability.
Yes, of course, there are privacy and surveillance issues associated with use of mobile data, but the potential benefits are just as compelling. If aggregated and anonymized at a level that passes muster with local privacy mandates and sensitivities, mobile data analysis can serve as a powerful tool in many social sciences, ranging from sociology to political science to epidemiology, as well as in urban planning, quality-of-life assessment, congestion management, emergency response and more.
Already, as mentioned in the article, other researchers have leveraged mobile-phone usage data to reveal patterns in wealth distribution, commuting and even dating. It will be interesting to see how various ambient data sources from the Internet of Things (environmental sensors, connected vehicles, smart homes and more) flesh out these patterns.
So, in all these ways, cities of the future will thrive on their population's own nonstop feed of digital exhaust. If only the analog exhaust spewing from vehicle tailpipes were as beneficial, city living would be a paradise on Earth.