Shifts in job accessibility reflect, in part, the degree to which land use and transportation decisions help bring job opportunities closer to labor forces. In this paper we argue for the wider use of accessibility indicators as part of the long-range transportation planning process. As a case example, changes in job accessibility indices are traced for the San Francisco Bay Area from 1980 to 1990, computed for 100 residential areas and the region's 22 largest employment centers. Indices are refined based on occupational match indicators that weigh the consistency between residents' employment roles and labor-force occupational characteristics at workplaces. The analysis reveals that peripheral areas tend to be the least job accessible. Moreover, employment centers that are home to highly skilled professional workers are generally the most accessible when occupational matching is accounted for. This is thought to reflect the existence of housing markets that are more responsive to the preferences of upper-income workers. Our analyses also show that residents of low-income, inner-city neighborhoods generally face the greatest occupational mismatches. Through a path analysis, the variable 'race' was found to be far more strongly associated with unemployment than was job accessibility, however, even after controlling for educational levels and other factors. We conclude that an important purpose of tracking changes in accessibility is to provide feedback on the degree to which resource allocation decisions in the urban transportation field are helping to redress serious inequities in accessibility to jobs, medical facilities, and other important destinations.
Several studies have recently applied sentiment-based lexicons to Twitter to gauge local sentiment to understand health behaviors and outcomes for local areas. While this research has demonstrated the vast potential of this approach, lingering questions remain regarding the validity of Twitter mining and surveillance in local health research. First, how well does this approach predict health outcomes at very local scales, such as neighborhoods? Second, how robust are the findings garnered from sentiment signals when accounting for spatial effects? To evaluate these questions, we link 2,076,025 tweets from 66,219 distinct users in the city of San Diego over the period of 2014-12-06 to 2017-05-24 to the 500 Cities Project data and 2010–2014 American Community Survey data. We determine how well sentiment predicts self-rated mental health, sleep quality, and heart disease at a census tract level, controlling for neighborhood characteristics and spatial autocorrelation. We find that sentiment is related to some outcomes on its own, but these relationships are not present when controlling for other neighborhood factors. Evaluating our encoding strategy more closely, we discuss the limitations of existing measures of neighborhood sentiment, calling for more attention to how race/ethnicity and socio-economic status play into inferences drawn from such measures.
Gentrification, the rise of affluent socioeconomic populations in economically depressed urban neighborhoods, has been accused of disrupting community in these neighborhoods. Social media networks meanwhile have been recognized not only to create new communities in neighborhoods, but are also associated with gentrification. What relation then does gentrification and social media networks have to urban communities? To explore this question, this study uses social media networks found on Twitter to identify communities in Washington, DC. With space-time analysis of 821,095 geo-tagged tweets generated by 77,528 users captured from 15 October 2015 to 18 July 2016, we create a location-based interaction measure of tweets which overlays the social networks of the comprising users based on their followers and followees. We identify gentrifying neighborhoods with the 2000 Census and the 2010–2014 American Community Survey at the block group level. We then compare the density of location-based interactions between gentrifying and nongentrifying neighborhoods. We find that gentrification is significantly related to these location-based interactions. This suggests that gentrification indeed is associated with some communities in neighborhoods, though questions remain as to who has access. Making novel use of big data, these results demonstrate the important role built environment has on social connections forged “online.”
“Livability” has become a popular term in planning, design, and engineering circles, yet there continues to be a lack of clear consensus about what livability actually means, let alone how to measure it and how to achieve it. In response, this article draws deeply on the literature to develop a comprehensive understanding of this complex concept. The presented analysis suggests that livability is best understood as an individual's ability to access opportunities to improve his or her quality of life. However, one person's pursuit of quality of life can actually detract from the livability of another. This concept is particularly true in transportation, as one person's travel inherently touches the lives of others along the pathway. As wealth and social status often play a key role in determining whose pursuit of quality of life wins, a moral and ethical framework must be at the heart of the achievement of livability. Therefore, livability in a just society requires that all individuals be ensured equal access to such opportunities. Rather than one monolithic definition of livability, a need exists for a theoretical moral basis to measure, understand, and judge activities toward livability achievement through a set of clear, concise, and easily applicable livability ethics. Toward this goal, this paper first presents a comprehensive examination of the literature and then provides guidance to professionals on the application of livability concepts in practice by articulating (a) an overarching definition of livability and a set of supporting metaprinciples, (b) a set of ethical livability principles, and (c) a set of livability process principles.
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