BACKGROUND: Despite substantial evidence for neighborhood characteristics correlating with walking, so far there has been limited attention to possible practical implications for neighborhood design. This study investigates to what extent design guidelines are likely to stimulate walking. METHODS: Four of the New Urbanism Smart Scorecard criteria and two other measures were tested for their influence on walking. Data were obtained from the 1995 National Personal Transportation Survey, U.S. Census 2000, and InfoUSA. Propensity-score methodology was used to control for potential confounders. RESULTS: Higher levels of business diversity and higher percentages of four-way intersections were associated with more walking. For example, the odds ratio (OR) for walking in a neighborhood with four business types present compared to three business types was 1.24 (confidence interval [CI] 1.07-1.44) and neighborhoods with 50%-74% four-way intersections had an OR for walking of 1.4 (CI 1.09-1.78) relative to those with 25%-49% four-way intersections. The effects of housing density on walking are mixed. Higher parking pressure and older median housing age did not significantly affect walking after covariate adjustment. Block length did not appear to be associated with walking. CONCLUSIONS: When considering the New Urbanism Smart Scorecard from the perspective walking, some, but not all, of its criteria that appear to have a correlation with walking are likely to be useful for designing walkable communities.
BACKGROUND: Despite substantial evidence for neighborhood characteristics correlating with walking, so far there has been limited attention to possible practical implications for neighborhood design. This study investigates to what extent design guidelines are likely to stimulate walking. METHODS: Four of the New Urbanism Smart Scorecard criteria and two other measures were tested for their influence on walking. Data were obtained from the 1995 National Personal Transportation Survey, U.S. Census 2000, and InfoUSA. Propensity-score methodology was used to control for potential confounders. RESULTS: Higher levels of business diversity and higher percentages of four-way intersections were associated with more walking. For example, the odds ratio (OR) for walking in a neighborhood with four business types present compared to three business types was 1.24 (confidence interval [CI] 1.07-1.44) and neighborhoods with 50%-74% four-way intersections had an OR for walking of 1.4 (CI 1.09-1.78) relative to those with 25%-49% four-way intersections. The effects of housing density on walking are mixed. Higher parking pressure and older median housing age did not significantly affect walking after covariate adjustment. Block length did not appear to be associated with walking. CONCLUSIONS: When considering the New Urbanism Smart Scorecard from the perspective walking, some, but not all, of its criteria that appear to have a correlation with walking are likely to be useful for designing walkable communities.
Authors: A A Hakim; H Petrovitch; C M Burchfiel; G W Ross; B L Rodriguez; L R White; K Yano; J D Curb; R D Abbott Journal: N Engl J Med Date: 1998-01-08 Impact factor: 91.245
Authors: Shannon N Zenk; Joellen Wilbur; Edward Wang; Judith McDevitt; April Oh; Richard Block; Sue McNeil; Nina Savar Journal: Health Educ Behav Date: 2008-07-31
Authors: Philippa Clarke; Jennifer A Ailshire; Michael Bader; Jeffrey D Morenoff; James S House Journal: Am J Epidemiol Date: 2008-07-30 Impact factor: 4.897