A method for estimating neighborhood characterization in studies of the association with availability of sit-down restaurants and supermarkets

Background Although neighborhood-level access to food differs by sociodemographic factors, a majority of research on neighborhoods and food access has used a single construct of neighborhood context, such as income or race. Therefore, the many interrelated built environment and sociodemographic characteristics of neighborhoods obscure relationships between neighborhood factors and food access. Methods The objective of this study was to account for the many interrelated characteristics of food-related neighborhood environments and examine the association between neighborhood type and relative availability of sit-down restaurants and supermarkets. Using cluster analyses with multiple measures of neighborhood characteristics (e.g., population density, mix of land use, and sociodemographic factors) we identified six neighborhood types in 1993 in the Twin Cities Region, Minnesota. We then used mixed effects regression models to estimate differences in the relative availability of sit-down restaurants and supermarkets in 1993, 2001, and 2011 across the six neighborhood types. Results We defined six types of neighborhoods that existed in 1993, namely, urban core, inner city, urban, aging suburb, high-income suburb, and suburban edge. Between 1993 and 2011, inner city neighborhoods experienced a greater increase in the percent of sit-down restaurants compared with urban core, urban, and aging suburbs. Differences in the percent of sit-down restaurants between inner city and aging suburbs, high-income suburbs and suburban edge neighborhoods increased between 1993 and 2011. Similarly, aging suburb neighborhoods had a greater percent of supermarkets compared with urban and high-income suburb neighborhoods in 2001 and 2011, but not in 1993, suggesting a more varied distribution of food stores across neighborhoods over time. Thus, the classification of neighborhood type based on sociodemographic and built environment characteristics resulted in a complex and increasingly varied distribution of restaurants and food stores. Conclusions The temporal increase in the relative availability of sit-down restaurants in inner cities after accounting for all restaurants might be partly related to a higher proportion of residents who eat-away-from-home, which is associated with higher calorie and fat intake.

Fear follows form: A study of the relationship between neighborhood type, income and fear of crime at train stations

In pursuing fear-reduction strategies in public transport, the total experience of accessing rail stations should be taken into account. This article correlates passengers’ fear of crime at train stations with neighborhood types and income data within the pedestrian catchment area. The research is based on urban form and income around 84 S-train stations in the Copenhagen metropolitan area and nine years of passenger surveys on fear of crime at these stations. The study reveals a significant positive correlation between low income and fear of crime; the lower the income in an urban area, the more unsafe passengers feel at the station. However, when controlling for the relationship between income, safety and neighborhood type, stations in neighborhoods with urban form associated with low incomes have the lowest ratings of safety. The research indicates that train passengers’ sense of security is connected to neighborhood type and the city’s planning characteristics. This is an important finding for urban designers and planners working on the integration of public transport and station design in urban areas.

A method for estimating the associations of neighborhood type with availability of sit-down restaurants and supermarkets

Background: Although neighborhood-level access to food differs by sociodemographic factors, almost all research on neighborhoods and food access has used a single construct of neighborhood context, such as income or race. Neighborhoods possess many interrelated built environment and sociodemographic characteristics, a condition that obscures relationships between neighborhood factors and food access.Methods: To account for the effects of many interrelated aspects of neighborhoods in food access, we developed a generally applicable method that used multiple measures (e.g., population density, mix of land use, and sociodemographic factors) that are easy to obtain to define six neighborhood types in 1993 in the Twin Cities Region, Minnesota. We then used mixed effects regression models to estimate differences in the relative availability of sit-down restaurants and supermarkets in 1993, 2001, and 2011 across the six neighborhood types.Results: We observed a higher relative availability of sit-down restaurants in inner city neighborhoods than in urban, aging suburbs, high-income suburbs, or suburban edge neighborhoods for 2011; we did not observe these same patterns for 1993 or 2001. Using nuanced classification of neighborhood type, we found a complex and increasingly varied distribution of restaurants and food stores.Conclusions: The temporal increase in sit-down restaurants in inner cities may be associated with a higher proportion of residents who eat-away-from-home, which is associated with higher calorie and fat intake.

DOG WALKING MEDIATES THE RELATIONSHIP BETWEEN DOG OWNERSHIP AND NEIGHBORHOOD SOCIAL INTERACTION

Social interaction may be facilitated by dog ownership. We surveyed 421 pet owners about neighborhood social interactions. Dog owners also completed a dog walking questionnaire. Among adults aged 55+ (n=99; 62.2±5.6 years; 90% female), we tested our hypotheses that (1) dog owners were more likely to meet neighbors than non-dog owners, and (2) increased dog walking frequency was associated with increased neighborhood social interaction. Inverse probability weighting was used to control for differences in age and neighborhood type (rural, suburban/urban) between groups. The probability of meeting neighbors was 2.4x higher (95%CI: 1.5-3.9) for dog than cat owners, after controlling for age and neighborhood type. Among dog owners, the odds of meeting a neighbor were 1.7x higher (95%CI: 0.9-3.1) with each unit increase in dog walking frequency (unit=5walks/week). Our findings suggest that programming to support dog ownership and dog walking among older adults may help reduce social isolation.

The Increasing Sociospatial Fragmentation of Urban America

This analysis examines the spatial fragmentation of the urban landscape with respect to neighborhoods classified according to their racial, demographic, housing and socioeconomic characteristics. The analysis is performed on the 50 largest metropolitan areas throughout the United States from 1990–2010, and looks at both global trends over time using a landscape ecology metric of edge density to quantify fragmentation over time. It then analyzes the spatial clustering of each neighborhood type over time, for each city. Results illustrate an increasingly fragmented urban landscape with respect to neighborhood type, led by Los Angeles as the most fragmented metropolitan area. Decomposed by neighborhood type, both racially concentrated high-poverty neighborhoods, as well as neighborhoods with a highly educated population, have increased in spatial concentration in large cities over time, exposing rises in spatial inequalities even as global patterns suggest a breaking up of neighborhood types. The global patterns are therefore driven by declines in more moderate-income and multiethnic neighborhoods, and a decline in the spatial concentration of newer, white, single-family housing neighborhoods.

Shape Recognition Based on Projected Edges and Global Statistical Features

A combined shape descriptor for object recognition is presented, along with an offline and online learning method. The descriptor is composed of a local edge-based part and global statistical features. We also propose a two-level, nearest neighborhood type multiclass classification method, in which classes are bounded, defining an inherent rejection region. In the first stage, global features are used to filter model instances, in contrast to the second stage, in which the projected edge-based features are compared. Our experimental results show that the combination of independent features leads to increased recognition robustness and speed. The core algorithms map easily to cellular architectures or dedicated VLSI hardware.