Two objective measures of street layout (intersection density and street integration) were calculated.
Evolution in layout design is discussed in terms of the 4Ds of urban form factors: density, diversity, design (street connectivity and intersection density) and destination accessibility (proximity).
They also found positive associations of walking for transport with land use mix, residential density, intersection density, and the number of parks.
Using data from the National Longitudinal Study of Adolescent Health (Wave I; 1994 95), we compared associations between moderate-vigorous PA (MVPA) and PA facility counts and street connectivity measures (intersection density and link:node ratio) within 1, 3, 5, and 8.05 km of each respondent's residence (Euclidean neighborhood buffers).
The fiber architecture was computer generated utilizing network features, namely fiber orientation distribution, intersection density and areal concentration, obtained from image analysis of multiphoton microscopy images taken from human aneurysmal ascending thoracic aortic media specimens with bicuspid aortic valve (BAV) phenotype.
We examined whether two street network measures (intersection density and street integration from space syntax) were independently associated with walking for transport (WT); and, to what extent the relationship of street integration with WT may be explained by the presence of destinations.
In California and Massachusetts, population density, intersection density, and diversity and density of facilities in two higher physical activity clusters were significantly greater than in neighborhoods outside of clusters.
Among Equation 1 Z-scores, traffic is least correlated with intersection density and most correlated with residential density (automobile traffic) and land-use mixing (truck traffic).
We calculated three built environment variables to represent neighborhood type: a) population density, b) intersection density, and c) land use mix.
Children living in neighborhoods with fewer recreational open spaces, and less residential density, less traffic density, less sidewalk completeness, less intersection density, and less land use mix had a higher BMI z-score.
