Diversity beyond density: Experienced social mixing of urban streets

Abstract

Urban density, in the form of residents’ and visitors’ concentration, is long considered to foster diverse exchanges of interpersonal knowledge and skills, which are intrinsic to sustainable human settlements. However, with current urban studies primarily devoted to city and district-level analysis, we cannot unveil the elemental connection between urban density and diversity. Here we use an anonymized and privacy-enhanced mobile data set of 0.5 million opted-in users from three metropolitan areas in the U.S. to show that at the scale of urban streets, density is not the only path to diversity. We represent the diversity of each street with the Experienced Social Mixing (ESM), which describes the chances of people meeting diverse income groups throughout their daily experience. We conduct multiple experiments and show that the concentration of visitors only explains 26% of street-level ESM. However, adjacent amenities, residential diversity, and income level account for 44% of the ESM. Moreover, using longitudinal business data, we show that streets with an increased number of food businesses have seen an increased ESM from 2016 to 2018. Lastly, although streets with more visitors are more likely to have crime, diverse streets tend to have fewer crimes. These findings suggest that cities can leverage many tools beyond density to curate a diverse and safe street experience for people.

Crime, inequality and public health: a survey of emerging trends in urban data science

“…The place where a person lives can have substantial impacts on health (Wilkinson and Pickett, 2006), economic opportunities (Chetty et al, 2014(Chetty et al, , 2016, infrastructure and services accessibility (Glaeser et al, 2001;Reid et al, 2016;Florida, 2017), and many other aspects, both at a city and national scale (Chetty et al, 2014;Shelton et al, 2015). Thus, measuring inequalities and segregation with timely and accurate data is of paramount importance, and alternative data sources and ubiquitous technologies are starting to play a central role in deeply analyzing factors and behaviors associated to inequalities such as environmental inequalities (Brazil, 2022;Dass et al, 2022), social mixing and income segregation (Shelton et al, 2015;Moro et al, 2021;Fan et al, 2023), and community resilience (Hong et al, 2021).…”

“…The authors discovered inequalities, where communities with higher infections and higher prevalence of black residents experienced greater infection exposure per visit. Fan et al ( 2023 ) employed mobile phone data of half a million people located in three different metropolitan areas in the US to study how people experienced social mixing in urban streets. The authors found that the density of people's street visits only explains the 26% of street-level diversity (e.g., social mixing), while the adjacent amenities, residential diversity, and income level explain the 44% of the designed diversity score.…”

“…The authors found that the density of people's street visits only explains the 26% of street-level diversity (e.g., social mixing), while the adjacent amenities, residential diversity, and income level explain the 44% of the designed diversity score. Also, Fan et al ( 2023 ) shows that while streets densely visited tend to have more crime, diverse streets have fewer crimes. Moro et al ( 2021 ) leveraged high-resolution mobility data of more than 4.5 million users in eleven big US cities to study income segregation.…”

Nonlinear and Synergistic Effects of Built Environment Indicators on Street Vitality: A Case Study of Humid and Hot Urban Cities

“…Construction intensity, defined as the density of population and buildings, engenders a bustling, diverse urban milieu, thereby invigorating urban streets [38]. Diversity, encompassing cultural, commercial, social, and economic facets, enriches urban streets by offering a variety of activities and choices [39]. The functional nature signifies the primary purposes and characteristics of a street.…”

Multiple gravity laws for human mobility within cities

“…Mobility as the fabric of human societies has been studied for understanding the mechanism of movements [1][2][3][4][5][6][7], diffusive processes [8,9], and its association with socioeconomic attributes [10][11][12][13][14][15] at both individual and population levels. Taking advantage of rich data, several population-level mobility models were developed to describe travel patterns as a function of geographical factors, e.g., population distribution and travel distance.…”