“Good” Neighborhoods in Portland, Oregon: Focus on Both Social and Physical Environments

ABSTRACT:

We conduct an empirical investigation of the social environment of “good” neighborhoods in physical form in a model of the “compact city,” Portland, Oregon and discuss the implications for design and evaluation of policies inspired by smart growth and new urbanist movements that focus on the urban form and transportation dimensions of neighborhoods, and of housing assistance policies designed to change the economic mix in neighborhoods. We conceptualize the physical and social dimensions of the “good” neighborhood environment and develop an approach to operationalization that uses publicly available data. Our findings indicate that for the most part, Portland has been successful in creating neighborhoods at several economic scales that feature not only the connectivity, accessibility, mixed land use, and access to public transit that characterize “good” neighborhoods from a physical perspective, but also a “good” social environment indicative of strong ties and collective efficacy. However, there are signs that in the process, Portland may be creating poverty areas that lack connectivity, accessibility, and access to public transit and a mix of destinations.

Notes

1 For more on neighborhood definition, see Cervero and Gorham (1995) and Crane and Crepeau (1998). We do not argue that census blockgroups are the best aerial unit for measuring urban form. We only claim that it is a convenient unit that illustrates the effects we seek to capture.

2 Data availability constrained the measures we could include. Had they been available, we would have liked to include other neighborhood social indicators and in particular measures of the different forms of social capital (Woolcock, 1998).

3 The measure of low level of participation in the labor market was dropped from the analysis because its variation was not well explained by any of the factors.

4 As with most other studies, data availability constrained the measures we could include. If data had been available, we would have liked to include other important information reflecting characteristics of neighborhood form such as: types of commercial stores, availability of recreational facilities, availability of sidewalks, and availability of bike lanes in the census block group.

5 Varimax is used to maximize the variance of the squared loadings. Varimax is an orthogonal rotation method that simply rotates the axes of the first factor to a variable or group of variables and then rotates the subsequent factors to be at right angles (uncorrelated) with the first. It thereby removes the effects of variables that could be highly loaded on the first factor. Compared to the unrotated factor solution, an orthogonal rotation minimizes the number of samples needed to account for the variation of distinct groups of variables.

6 K-means clustering is used here. K-means clustering begins with a grouping of observations into a predefined number of clusters. It evaluates each observation and moves it into its nearest cluster. The nearest cluster is the one that has smallest Euclidean distance between the observation and the centroid of the cluster. When a cluster changes by losing or gaining an observation, the cluster centroid is recalculates. At the end, all observations are in their nearest cluster.

7 The centroid of a cluster is the center of gravity for the respective cluster and is the average point in the multidimensional space defined by the dimensions.

8 Tables showing mean difference tests for the residential stability and proportion single-parent household factor scores were not included here because they add little new information, but are available from the authors.

9 Quartiles are defined by the three values of a particular variable (25th, 50th, and 75th percentiles) which divide the distribution into four equal parts, so that each part represents one-fourth of the sampled population. Thus in our study, the lowest quartile includes the population falling below the 25% percentile and the highest quartile includes the population falling above the 75th percentile.