Crime prevention through environmental design (CPTED) has become a popular urban planning approach to preventing crime and mitigating fear of crime through the improvement of physical neighborhood environments. CPTED is widely used to improve deteriorated neighborhoods that suffer from crime. However, few studies have empirically examined the complex relationships among CPTED, fear of crime, and active living. Our study, therefore, investigated the effects of CPTED measures on walking frequency and fear of crime, analyzing behavioral data of residents living in participatory neighborhood regeneration areas and matched neighborhoods. We analyzed survey data from 12 neighborhoods that implemented CPTED approaches and 12 matched neighborhoods in Seoul, Korea, using structural equation modeling, which could consistently estimate complex direct and indirect relationships between a latent variable (fear of crime) and observable variables (CPTED measures and walking frequency). We designed the survey instrument as a smartphone app. Participants were recruited from 102 locations within the 24 selected neighborhoods; in total, 623 individuals returned surveys. The results revealed that sufficient closed-circuit television, street lighting, and maintenance played a significant role in mitigating fear of crime. This study has implications for planning and policy issues related to CPTED, mental health, and active living.

To assess the relative accuracy and usefulness of web tools in evaluating and measuring street-scale built environment characteristics. A well-known audit tool was used to evaluate 84 street segments at the urban edge of metropolitan Boston, Massachusetts, using on-site visits and three web-based tools. The assessments were compared to evaluate their relative accuracy and usefulness. Web-based audits, based-on Google Maps, Google Street View, and MS Visual Oblique, tend to strongly agree with on-site audits on land-use and transportation characteristics (e.g., types of buildings, commercial destinations, and streets). However, the two approaches to conducting audits (web versus on-site) tend to agree only weakly on fine-grain, temporal, and qualitative environmental elements. Among the web tools used, auditors rated MS Visual Oblique as the most valuable. Yet Street View tends to be rated as the most useful in measuring fine-grain features, such as levelness and condition of sidewalks. While web-based tools do not offer a perfect substitute for on-site audits, they allow for preliminary audits to be performed accurately from remote locations, potentially saving time and cost and increasing the effectiveness of subsequent on-site visits.

This article focuses on micro-level walkability, which is measured at the route level and thus named “path walkability,” and tests whether path walkability influences transit users' mode choices to the station. To test its impact on access mode choices, a case study is conducted in downtown Mountain View, California. A station user survey is administered to collect access mode choice, socioeconomic status, and trip origins and walking routes of 249 transit users with previous experience of walking to the station. Using a path walkability measurement instrument developed for this research, 38 path walkability indicators are extracted from each of the 249 walking routes. The 38 walkability indicators are grouped by using factor analysis yielding four path walkability factors: “sidewalk amenities,” “traffic impacts,” “street scale and enclosure,” and “landscaping elements.” The four factors are utilized as new walkability variables for modeling access mode choices. With 150 walkers and 99 habitual auto users/occasional walkers, two access mode choice models are estimated. The basic model is first estimated without walkability variables and then the four path walkability variables are introduced for the expanded model. All four path walkability variables enter the expanded model and significantly improve the predictability of the mode choice model. The model result suggests that micro-level walkability influences access mode choices in a statistically significant way and having more walking-conducive walkability available for access trips increases the chance of choosing walking over driving. This research shows that improving micro-level walkability could be a cost-beneficial incentive for more walking to the station.

This pilot study had two major research goals. The first goal was to develop a perception-based composite walkability index, with which researchers could evaluate the microlevel quality of the walking environment in an objective and systematic way. The second goal was to test the impact of microlevel walkability on mode choices, including walking as a travel option. For the development of a perception-based composite walkability index, a case study was conducted in the station area near the downtown of Mountain View, California, in the San Francisco Bay Area. Three surveys were conducted: a walker perception survey, a station user survey, and a street survey measuring microlevel walkability. A series of regression models was performed with the perception scores from surveyed transit users on the basis of their access trips to the station and with the objective microlevel walkability measured from their routes. The final outcome was a new formula for a composite walkability index. On the basis of the results of the station user survey, a pair of mode choice models was estimated to find the probability of transit users choosing walking over automobiles for their access trips to the station. The study introduced the new walkability index as a new walkability variable, which successfully entered the extended model and showed that it could improve the model in a statistically significant way.