Urban mobility and neighborhood isolation in America’s 50 largest cities

Abstract

SignificanceLiving in disadvantaged neighborhoods is widely assumed to undermine life chances because residents are isolated from neighborhoods with greater resources. Yet, residential isolation may be mitigated by individuals spending much of their everyday lives outside their home neighborhoods, a possibility that has been difficult to assess on a large scale. Using new methods to analyze urban mobility in the 50 largest American cities, we find that residents of primarily black and Hispanic neighborhoods—whether poor or not—are far less exposed to either nonpoor or white middle-class neighborhoods than residents of primarily white neighborhoods. Although residents of disadvantaged neighborhoods regularly travel as far and to as many different neighborhoods as those from advantaged neighborhoods, their relative isolation and segregation persist.

Quantifying segregation in an integrated urban physical-social space

“…Third, the segregation measurements used in this study are mainly approached from the perspective of income inequality based on housing data. As suggested by previous literature [6,15,40], racial segregation is a significant factor that impacts social segregation in cities. As the datasets do not capture the racial make-up of the population, we are unable to investigate or control the impact of racial segregation on our measurements.…”

“…The proposed segregation indices can be applied or modified over data that are already available in many countries and cities. Examples include mobile operator's data [46,47], social media data [6,48], geolocated transactions [49,50] and multi-modal datasets that couple the social-spatial and economic activities (e.g. spending behaviour) of the populations [30,51].…”

“…The homophily mechanism in the formation of social relations [4], along with other political and economic forces, seems to fabricate an uneven distribution of social groups in cities [5]. These socio-economic imbalances have led to varying degrees of urban segregation [6], causing issues of crime [7], inequalities in education attainment [8][9][10], disparities in health [11] and others. Besides these social problems, a more worrying fact is that segregations in urban areas have grown rapidly in the past few decades [12,13].…”

Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?

“…They test for differences in the amount and type of information contained within listings. They first examine the number of optional information categories provided, the number of images included, and the overall number of words in each listing to capture differences in information available to searchers in census tracts with varying racial/ethnic and poverty compositions (see Wang et al 2018). They then estimate differences in each of these measures across a sociodemographic typology of tracts, then use computational text analysis techniques, including structural topic modeling, to examine differences in the kinds of information included in tracts' listings.…”

Exploring crime patterns in Mexico City

“…Similarly, even though Twitter data use the Global Positioning System (GPS) for generating geographical information with a relatively high resolution; with the worstcase accuracy of 7.8 m with 95% confidence, the precision of the data can be affected by atmospheric effects, receiver quality, sky blockage, and noise caused by weather or device factors. Therefore, two tweets sent from the same exact location could be reported as slightly different locations [32].…”