“Data from 350,000 Smartphones Visualize the Urban Segregation in Chile”
What is urban segregation?
As urban form cannot be seen as a neutral background to human activity, on how space affects our social life, the way we interact with others or gather in specific places is essential in explaining the reality of urban segregation. Urban segregation is considered a major social problem in several countries.
Although the segregation is an inherently spatial concept and can be found out by simple descriptions using the relation between spatial and social phenomena, in cities, physical separation between people and activities has a direct connection to spacial properties, including urban design and town planning. As one can see, how different social categories of people are distributed in our cities according to where they live and built environment, this influences accessibility, resources to other people.
Urban segregation by income has increased since the past three decades across the US and in 27 of the nation’s 30 largest metropolitan cities. This increased index resulted in a long term rise in income inequality, which led to shrinkage in the middle class and mixed class neighborhoods.
Where you live or work or who you are friends with or whom you visit, you usually tend to move around similar neighborhoods and streets of your city. One might imagine how many cities are within your own city, depending on income groups, communities, and classes.
In a recent article published by The Royal Society Open Science, researchers discovered that big data was used to analyze and visualize urban segregation and developing strategies in a city of many cities. The research carried out was to understand how we travel across the city through smartphones. Every time you connect to the Internet of call someone, your smartphone will connect to the nearest available antenna, allowing the X/CDR database to access your personal record of checked-in locations anonymously.
The researchers worked with a 350,000 user database in Santiago to allow them to track and create a movement network. These established movement networks further indicated communities. Each community arises from a series of exposing algorithms that divide the overall call network. The algorithms created social bubbles.
According to the researchers, chances of people living in these identified communities will generally interact with someone from the same community of the workplace, hence giving rise to mixing rates depending on the comparison expected through random scenarios. These common meeting or greeting spots were then deducted from their mapping system.
The research team concluded that the urban segregation investigation corroborates the most active level of segregation in Santiago. The probability of meeting a co-worker belonging to the same community reached 40%, and in random scenarios, it reaches only 27%. Hence, proving that Santiago is not statistically segregated.
By this urban segregation movement, mobility and urban infrastructure was a key factor stimulating interactions between people of the same city and thus decreasing urban segregation. People shall be more socially active and interactive to change the dynamics from static to innovative.
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