It may seem that the people who are in a city's center at any one time are random. New research using a simple mathematical rule shows that urban travel patterns around the world are remarkably predictable, regardless of where they are located. This insight could be used to improve models of disease spread and optimize city planning.
Researchers discovered an inverse square relationship between the number and distance traveled to reach a given urban area, as well the frequency of their trips, by analysing anonymized cell phone data. Although it may seem obvious that people visit close-by locations more often than distant ones, the new relation puts this concept in numerical terms. For example, it predicts that people will travel from two kilometers away five times per semaine, while those who travel from five kilometers once a week will come from the same distance. Nature published the new visitation law and flexible model that it derived from individuals' movements within cities.
It is an amazing, solid result, according to Laura Alessandretti (a computational social scientist at Technical University of Denmark), who wasn't involved in the study, but wrote a commentary. It is easy to believe that many contextual factors affect how we move. These include the transportation system, the morphology and socioeconomic aspects. While this is true to a certain extent, it shows that there are solid laws that can be applied everywhere.
Researchers analyzed data from approximately eight million people in six cities between 2006 and 2013. These were: Boston, Singapore and Lisbon in Portugal, Porto in Portugal and Porto in Portugal, Dakar and Senegal, Abidjan in Ivory Coast, and Porto in Portugal. The cell-phone data used in previous analyses was used to track individuals' travel routes. This study, however, focused on the locations and looked at how many people visited each location. All the choices people make-from dropping their kids off at school to shopping for groceries or getting to work-all of them violate this inverse square law when taken together. Geoffrey West is a senior author of the paper and urban scaling theorist at Santa Fe Institute.
This strong statistical pattern could be explained by the fact that traveling takes time and energy and people have limited resources. This is where something fundamental is at work. Markus Schlpfer, the study's lead author, from ETH Zurich’s Future Cities Laboratory, Singapore, said that you should optimize your day no matter where you live. The core of the study is the willingness to collectively invest in travel to certain places.
These patterns are important for planning new shopping centers and public transportation, but also for modeling disease transmission within cities. Kathleen Stewart, a University of Maryland geographer and mobility researcher, was not part of the study.
Gravity models are used by many researchers to estimate travel. They assume that the movement between cities is proportional with their populations. These models don't account for travel patterns within cities, which is crucial in dealing with disease transmission. Sam Scarpino, an epidemiologist at Northeastern University, believes models that are based on this finding could better track this flow. New York City residents, for example, are more likely than others to travel to their own borough (such Manhattan or the Bronx), and less to distant boroughs.
Scarpino states that these organizational patterns can have profound consequences on the spread of COVID. A small rural area where people frequent the same grocery store or church will see a sharp rise in the number of cases as the virus spreads. He explains that the spread of the virus can take longer in larger cities because each neighborhood may experience a mini epidemic.
Stewart says: Stewart also said that the authors have demonstrated that their visitation law, which takes into consideration both travel distances and visits frequently, outperforms other models in terms of predicting flows between different locations.