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Definitions of environmental child friendliness offer broad criteria that are not easy to study or assess. We suggest that due to this broadness, these definitions have produced surprisingly few attempts to evaluate how child-friendly various types of physical environments are. The purpose of this study is to analyse how the structure of the built environment contributes to environmental child friendliness. We define child friendliness by two central criteria: children’s possibilities for independent mobility and their opportunities to actualize environmental affordances. We study how built environment qualities condition environmental child friendliness in place-based ways by asking children and youth in Turku, Finland, to tell about their meaningful places and their mobility to these. The data consists of over 12,000 affordances, localized by the respondents. This experiential and behavioural place-based knowledge is combined with objectively measured data on residential and building density, and quantity of green structures. Moderate urban density seems to have child-friendly characteristics such as an ability to promote independent access to meaningful places and the diversity of affordances. We find that affordances situated on residential areas are likely to be reached alone, whereas access to affordances situated in densely built urban cores is less independent. The proportion of green structures is not associated with independent access. The diversity of affordances is highest in areas that are densely populated and not very green. Green areas are important settings for doing things, and green structures around emotional affordances increase the likelihood of liking the place significantly. Combining children’s place-based experiences with information derived from objective measurable qualities of the physical environment provides a valuable methodological contribution to studies on environmental child friendliness, and the two proposed criteria of child friendliness are supported by this study. There is no one environment that is child-friendly, but different environments have different uses and meanings.

The collection of large-scale administrative records in electronic form by many cities provides a new opportunity for the measurement and longitudinal tracking of neighborhood characteristics, but one that will require novel methodologies that convert such data into research-relevant measures. The authors illustrate these challenges by developing measures of “broken windows” from Boston’s constituent relationship management (CRM) system (aka 311 hotline). A 16-month archive of the CRM database contains more than 300,000 address-based requests for city services, many of which reference physical incivilities (e.g., graffiti removal). The authors carry out three ecometric analyses, each building on the previous one. Analysis 1 examines the content of the measure, identifying 28 items that constitute two independent constructs, private neglect and public denigration. Analysis 2 assesses the validity of the measure by using investigator-initiated neighborhood audits to examine the “civic response rate” across neighborhoods. Indicators of civic response were then extracted from the CRM database so that measurement adjustments could be automated. These adjustments were calibrated against measures of litter from the objective audits. Analysis 3 examines the reliability of the composite measure of physical disorder at different spatiotemporal windows, finding that census tracts can be measured at two-month intervals and census block groups at six-month intervals. The final measures are highly detailed, can be tracked longitudinally, and are virtually costless. This framework thus provides an example of how new forms of large-scale administrative data can yield ecometric measurement for urban science while illustrating the methodological challenges that must be addressed.

Despite the fact that virtual environments are increasingly deployed to study the relation between urban planning, physical and social disorder, and fear of crime, their ecological validity for this type of research has not been established. This study compares the effects of similar signs of public disorder (litter, warning signs, cameras, signs of vandalism and car burglary) in an urban neighborhood and in its virtual counterpart on the subjective perception of safety and livability of the neighborhood. Participants made a walking tour through either the real or the virtual neighborhood, which was either in an orderly (baseline) state or adorned with numerous signs of public disorder. During their tour they reported the signs of disorder they noticed and the degree to which each of these affected their emotional state and feelings of personal safety. After finishing their tour they appraised the perceived safety and livability of the environment. Both in the real and in the simulated urban neighborhood, signs of disorder evoked associations with social disorder. In all conditions, neglected greenery was spontaneously reported as a sign of disorder. Disorder did not inspire concern for personal safety in reality and in the virtual environment with a realistic soundscape. However, in the absence of sound disorder compromised perceived personal safety in the virtual environment. Signs of disorder were associated with negative emotions more frequently in the virtual environment than in its real-world counterpart, particularly in the absence of sound. Also, signs of disorder degraded the perceived livability of the virtual, but not of the real neighborhood. Hence, it appears that people focus more on details in a virtual environment than in reality. We conclude that both a correction for this focusing effect and realistic soundscapes are required to make virtual environments an appropriate medium for both etiological (e.g. the effects of signs of disorder on fear of crime) and intervention (e.g. CPTED) research.

A number of recent studies have used surveys of neighborhood informants and direct observation of city streets to assess aspects of community life such as collective efficacy, the density of kin networks, and social disorder. Raudenbush and Sampson (1999a) have coined the term “ecometrics” to denote the study of the reliability and validity of such assessments. Random errors of measurement will attenuate the associations between these assessments and key outcomes. To address this problem, some studies have used empirical Bayes methods to reduce such biases, while assuming that neighborhood random effects are statistically independent. In this paper we show that the precision and validity of ecometric measures can be considerably improved by exploiting the spatial dependence of neighborhood social processes within the framework of empirical Bayes shrinkage. We compare three estimators of a neighborhood social process: the ordinary least squares estimator (OLS), an empirical Bayes estimator based on the independence assumption (EBE), and an empirical Bayes estimator that exploits spatial dependence (EBS). Under our model assumptions, EBS performs better than EBE and OLS in terms of expected mean squared error loss. The benefits of EBS relative to EBE and OLS depend on the magnitude of spatial dependence, the degree of neighborhood heterogeneity, as well as neighborhood's sample size. A cross-validation study using the original 1995 data from the Project on Human Development in Chicago Neighborhoods and a replication of that survey in 2002 show that the empirical benefits of EBS approximate those expected under our model assumptions; EBS is more internally consistent and temporally stable and demonstrates higher concurrent and predictive validity. A fully Bayes approach has the same properties as does the empirical Bayes approach, but it is preferable when the number of neighborhoods is small.

Ordinary kriging, a spatial interpolation technique, is commonly used in social sciences to estimate neighborhood attributes such as physical disorder. Universal kriging, developed and used in physical sciences, extends ordinary kriging by supplementing the spatial model with additional covariates. We measured physical disorder on 1,826 sampled block faces across four U.S. cities (New York, Philadelphia, Detroit, and San Jose) using Google Street View imagery. We then compared leave-one-out cross-validation accuracy between universal and ordinary kriging and used random subsamples of our observed data to explore whether universal kriging could provide equal measurement accuracy with less spatially dense samples. Universal kriging did not always improve accuracy. However, a measure of housing vacancy did improve estimation accuracy in Philadelphia and Detroit (7.9 percent and 6.8 percent lower root mean square error, respectively) and allowed for equivalent estimation accuracy with half the sampled points in Philadelphia. Universal kriging may improve neighborhood measurement.