Search for: All records

Abstract Unlike refueling at gas stations, charging an electric vehicle (EV) requires significantly more time, ranging from 20 minutes to over an hour, even with the fastest chargers. This prolonged charging duration introduces unique complexities to the fundamental understanding of public charging station (PCS) accessibility, raises questions about the ongoing relevance of spatial proximity for different population groups, and highlights the need to consider amenities near PCS that users can access along their daily routines. To account for these critical distinctions, we introduce visit-based accessibility, a metric that evaluates PCS availability relative to amenities frequented during urban routine activities. Leveraging data from over 28,000 PCSs, 5.2 million points of interest (POIs), and mobility patterns from more than 35 million cell phone users across 20 major U.S. metropolitan areas, we reveal a striking mismatch between PCS locations and the spatial proximity to and the daily needs of their intended users. While existing PCSs are spatially proximate to lower-income communities, our visit-based measure reveals significant disparities, as these communities' routine activities align significantly less with PCS locations. Furthermore, when considering dwell time at nearby amenities, higher-income communities exhibit better alignment of PCSs with routine activities and longer dwell times at nearby POIs, which are also the most extensively covered by existing PCSs. Finally, our analysis reveals that experienced income and racial segregation are likely the major impedance for accessing PCSs that are spatially adjacent, which highlights a hidden social barrier where greater divergence from typical social encounters near charging infrastructure is associated with lower visit rates to PCSs. These findings underscore the necessity for targeted strategies to ensure equitable access to charging infrastructure, addressing both spatial and functional disparities to promote sustainable and inclusive urban development. 

While research on racial segregation in cities has grown rapidly over the last several decades, its foundation remains the analysis of the neighbourhoods where people reside. However, contact between racial groups depends not merely on where people live, but also on where they travel over the course of everyday activities. To capture this reality, we propose a new measure of racial segregation – the segregated mobility index (SMI) – that captures the extent to which neighbourhoods of given racial compositions are connected to other types of neighbourhoods in equal measure. Based on hundreds of millions of geotagged tweets sent by over 375,000 Twitter users in the 50 largest US cities, we show that the SMI captures a distinct element of racial segregation, one that is related to, but not solely a function of, residential segregation. A city’s racial composition also matters; minority group threat, especially in cities with large Black populations and a troubled legacy of racial conflict, appears to depress movement across neighbourhoods in ways that produce previously undocumented forms of racial segregation. Our index, which could be constructed using other data sources, expands the possibilities for studying dynamic forms of racial segregation including their effects and shifts over time. 

The social integration of a city depends on the extent to which people from different neighborhoods have the opportunity to interact with one another, but most prior work has not developed formal ways of conceptualizing and measuring this kind of connectedness. In this article, we develop original, network-based measures of what we call “structural connectedness” based on the everyday travel of people across neighborhoods. Our principal index captures the extent to which residents in each neighborhood of a city travel to all other neighborhoods in equal proportion. Our secondary index captures the extent to which travels within a city are concentrated in a handful of receiving neighborhoods. We illustrate the value of our indices for the 50 largest American cities based on hundreds of millions of geotagged tweets over 18 months. We uncover important features of major American cities, including the extent to which their connectedness depends on a few neighborhood hubs, and the fact that in several cities, contact between some neighborhoods is all but nonexistent. We also show that cities with greater population densities, more cosmopolitanism, and less racial segregation have higher levels of structural connectedness. Our indices can be applied to data at any spatial scale, and our measures pave the way for more powerful and precise analyses of structural connectedness and its effects across a broad array of social phenomena.