[Paper Review] Diversity beyond density: Experienced social mixing of urban streets.

Zhuangyuan Fan, Tianyu Su+5 more|arXiv (Cornell University)|Sep 15, 2022

Human Mobility and Location-Based Analysis1 citations

TL;DR

This study introduces Experienced Social Mixing (ESM), a street-level measure of social diversity based on anonymized mobile phone data from 0.5 million users across three U.S. metropolitan areas. It finds that while visitor density explains only 26% of ESM variation, adjacent amenities, residential diversity, and income levels collectively explain 44%, with food businesses playing a key role in enhancing social mixing throughout the day.

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 analyses, we cannot unveil the elemental connection between urban density and diversity. Here we use an anonymized and privacy-enhanced mobile dataset of 0.5 million opted-in users from three metropolitan areas in the United States 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.

Motivation & Objective

• To move beyond city- and district-level analysis and examine how social mixing emerges at the street level.
• To develop a new measure, Experienced Social Mixing (ESM), capturing the likelihood of individuals encountering diverse income groups through daily street-level experiences.
• To identify urban design and socioeconomic factors that contribute to social mixing independently of population density.
• To assess how changes in street-level business composition, particularly food venues, affect ESM over time.
• To investigate the relationship between ESM, crime rates, and perceived street safety at the street segment level.

Proposed method

• Constructed ESM using anonymized, privacy-enhanced mobile phone data from 0.5 million opted-in users across 40 counties in three U.S. metropolitan areas.
• Defined ESM as the probability of individuals encountering people from different income groups during their daily movements along street segments.
• Used multivariate OLS regression models to estimate the contribution of density, residential mixing, income levels, and venue types to ESM variation.
• Incorporated longitudinal business data (2016–2018) to analyze changes in ESM linked to new food business openings.
• Applied a computer vision model to Google Street View images to predict street safety scores, which were then used as a built environment proxy.
• Controlled for street length, visitor volume, and county fixed effects to isolate the impact of structural and socioeconomic factors.

Experimental results

Research questions

• RQ1 To what extent does street-level visitor density explain variation in Experienced Social Mixing (ESM)?
• RQ2 Which urban design and socioeconomic factors—beyond density—contribute significantly to ESM at the street level?
• RQ3 How do changes in the number of food-related businesses affect ESM over time (2016–2018)?
• RQ4 What is the relationship between ESM and crime rates at the street segment level?
• RQ5 How does perceived street safety, derived from visual analysis of street scenes, correlate with ESM and crime?

Key findings

• Visitor density alone explains only 26% of the variation in Experienced Social Mixing (ESM), indicating that density is not the primary driver of social diversity at the street level.
• Residential mixing, income levels, and the presence of adjacent venues collectively explain 44% of ESM variation, highlighting the importance of mixed-use and socioeconomically diverse neighborhoods.
• Streets with more food-related businesses show a significantly higher ESM, especially during daytime hours, with food venues contributing more to ESM than other venue types.
• An increase in the number of new food businesses from 2017 to 2018 was associated with a 0.008 increase in ESM per 100 new businesses, controlling for other factors.
• Diverse streets—those with higher ESM—tend to have lower crime rates, while higher visitor density is positively correlated with increased crime.
• Perceived street safety, derived from image analysis of Google Street View, showed a positive correlation with ESM and a negative correlation with crime, suggesting safer-looking streets support greater social mixing.