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.
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Discovering Underground Maps from Fashion
The fashion sense -- meaning the clothing styles people wear -- in a geographical region can reveal information about that region. For example, it can reflect the kind of activities people do there, or the type of crowds that frequently visit the region (e.g., tourist hot spot, student neighborhood, business center). We propose a method to automatically create underground neighborhood maps of cities by analyzing how people dress. Using publicly available images from across a city, our method finds neighborhoods with a similar fashion sense and segments the map without supervision. For 37 cities worldwide, we show promising results in creating good underground maps, as evaluated using experiments with human judges and underground map benchmarks derived from non-image data. Our approach further allows detecting distinct neighborhoods (what is the most unique region of LA?) and answering analogy questions between cities (what is the "Downtown LA" of Bogota?).
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- Award ID(s):
- 1900783
- NSF-PAR ID:
- 10377839
- Date Published:
- Journal Name:
- 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
- Page Range / eLocation ID:
- 497 to 506
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Geovisualizations play a central role in communicating hurricane storm surge risks to the public by connecting information about the hazard to a place. Meanwhile, people connect to places through meaning, functions, and emotional bond, known as a sense of place. The mixed-method approach presented in this paper focuses on the intersection of sense of place, geovisualization, and risk communication. We explored place meaning, scale of place, and place attachment in the coastal communities in Georgia and South Carolina. We conducted cognitive mapping focus groups and developed a series of geovisualizations of storm surge risk with varying representations of place. We then investigated people’s ability to connect visual storm surge information to a place and understand their risk by testing these geovisualizations in a large population survey (
n = 1442). We found that a 2D regional-scale map displayed together with a 3D abstract representation of a neighborhood was the most helpful in enabling people to relate to a place, quickly make sense of the information, and understand the risk. Our results showed that while the geovisualizations of storm surge risk can be effective generally, they were less effective in several important and vulnerable groups. We found substantial impacts of race, income, map-reading ability, place attachment, and scale of place on how people connected the storm surge risk shown in the visual to a place. These findings have implications for future research and for considering the way weather forecasters and emergency managers communicate storm surge information with diverse audiences using geovisualizations.Significance Statement Weather forecasters and emergency managers often use geovisualizations to communicate hurricane storm surge risks and threats to the public. Despite the important role that geovisualizations play, few studies have empirically investigated their effectiveness in hazardous weather risk communication. With the overarching goal of understanding how geovisualizations enable coastal residents to understand and respond to risk, we use an interdisciplinary approach to create new knowledge about the effectiveness of geovisualizations in storm surge risk communication. Our results show substantial impacts of sociodemographic factors across many of the measures that enable people to connect to a place through visualizations. These findings have implications for communicating geospatially varying risk to diverse audiences.
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Lenormand, Maxime (Ed.)Neighborhoods are the building blocks of cities, and thus significantly impact urban planning from infrastructure deployment to service provisioning. However, existing definitions of neighborhoods are often ill suited for planning in both scale and pattern of aggregation. Here, we propose a generalized, scalable approach using topological data analysis to identify barrier-enclosed neighborhoods on multiple scales with implications for understanding social mixing within cities and the design of urban infrastructure. Our method requires no prior domain knowledge and uses only readily available building parcel information. Results from three American cities (Houston, New York, San Francisco) indicate that our method identifies neighborhoods consistent with historical approaches. Additionally, we uncover a consistent scale in all three cities at which physical isolation drives neighborhood emergence. However, our methods also reveal differences between these cities: Houston, although more disconnected on larger spatial scales than New York and San Francisco, is less disconnected at smaller scales.more » « less
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Hart, John P. (Ed.)Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization.more » « less
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A longstanding tradition of research linking neighborhood disadvantage to higher rates of violence is based on the characteristics of where people reside. This Essay argues that we need to look beyond residential neighborhoods to consider flows of movement throughout the wider metropolis. Our basic premise is that a neighborhood’s well-being depends not only on its own socioeconomic conditions but also on the conditions of neighborhoods that its residents visit and are visited by—connections that form through networks of everyday urban mobility. Based on the analysis of large-scale urban-mobility data, we find that while residents of both advantaged and disadvantaged neighborhoods in Chicago travel far and wide, their relative isolation by race and class persists. Among large U.S. cities, Chicago’s level of racially segregated mobility is the second highest. Consistent with our major premise, we further show that mobility-based socioeconomic disadvantage predicts rates of violence in Chicago’s neighborhoods beyond their residence-based disadvantage and other neighborhood characteristics, including during recent years that witnessed surges in violence and other broad social changes. Racial disparities in mobility-based disadvantage are pronounced—more so than residential neighborhood disadvantage. We discuss implications of these findings for theories of neighborhood effects on crime and criminal justice contact, collective efficacy, and racial inequality.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/WACV51458.2022.00057
