This paper investigates the impact of street network connectivity on pedestrian volume. Street
network connectivity measured in most current studies captures only the metric characteristics
of streets or physical connectivity. A whole different type of connectivity, visual connectivity, is
largely ignored. Described in basic terms, higher physical connectivity means shorter travel time
to reach the same number of destinations while higher visual connectivity means fewer turns to
see the same number of destinations. Despite the correlation of these two connectivity constructs, studying both physical and visual connectivity is essential to better understand the role of
street network on pedestrian activity. Using pedestrian counts of 302 street segments in Buffalo,
New York, structural equation modelling highlights the multiple relationships between street network connectivity, built environment characteristics, and pedestrian volumes. Our findings
suggest that both the conventional metric-based measure of physical connectivity and geometric based measure of visual connectivity have significant positive impacts on pedestrian volumes,
together with job density and land use mix. This outcome can encourage practitioners to pay
attention to both the geometry of street network and its metric characteristics in order to create
a pedestrian-friendly environment.

New sources of data such as ‘big data’ and computational analytics have stimulated innovative pedestrian oriented research. Current studies, however, are still limited and subjective with regard to the use of Google Street View and other online sources for environment audits or pedestrian counts because of the manual information extraction and compilation, especially for large areas. This study aims to provide future research an alternative method to conduct large scale data collection more consistently and objectively on pedestrian counts and possibly for environment audits and stimulate discussion of the use of ‘big data’ and recent computational advances for planning and design. We explore and report information needed to automatically download and assemble Google Street View images, as well as other image parameters for a wide range of analysis and visualization, and explore extracting pedestrian count data based on these images using machine vision and learning technology. The reliability tests results based on pedestrian information collected from over 200 street segments in Buffalo, NY, Washington, D.C., and Boston, MA respectively suggested that the image detection method used in this study are capable of determining the presence of pedestrian with a reasonable level of accuracy. The limitation and potential improvement of the proposed method is also discussed.

Abstract:

Due to rapid urbanization, auto-mobility, and industrialization, the increasing desire to protect environments and satisfy residents has led to an emphasis on the creation of sustainable urban environments in China. This paper is an empirical study using hedonic price models to examine a comprehensive set of environmental sustainability elements including green space, transit systems, and central business districts (CBDs) and compare their relative importance in Wuhan, China. The results show that among all housing characteristics, environmental sustainability elements had the greatest impacts on house prices. Natural water resources have the most significant positive effects on property values when they are integrated with cultural, tourism, and commercial resources to form natural recreation clusters or areas. Also, home buyers are willing to pay more for housing clusters or subdivisions with proximity to CBDs. In addition, the significant negative effects of light rail on house prices within a 1-mile radius indicate that it has not become an attractive amenity to home buyers, due to combined effects of other neighborhood amenities, little land use diversity, and the fare system. These results have implications for local and regional governments in setting priorities for sustainable development.

The proliferation of impervious surfaces results in a series of environmental issues, such as the decrease of vegetated areas and the aggravation of the urban heat island effects. The mapping of impervious surface and its spatial distributions is of significance for the ecological study of urban environment. Currently, the integration of optical and synthetic aperture radar (SAR) data has shown advantages in accurately characterizing impervious surface. However, the fusion mainly occurs at the pixel and feature levels which are subject to influences of data noises and feature selections, respectively. In this paper, an innovative and effective method was developed to extract urban impervious surface by synergistically utilizing optical and SAR images at the decision level. The objective of this paper was to obtain an accurate urban impervious surface map based on the random forest classifier and the evidence theory and to provide a detailed uncertainty analysis accompanying the fused impervious surface maps. In this study, both the GaoFen (GF-1) and Sentinel-1A imagery were first used as independent data sources for mapping urban impervious surfaces. Then additional spectral features and texture features were extracted and integrated with the original GF-1 and Sentinel-1A images in generating impervious surfaces. Finally, based on the Dempster-Shafer (D-S) theory, impervious surfaces were produced by fusing the previously estimated impervious surfaces from different datasets at the decision level. Results showed that impervious surfaces estimated from the combined use of original images and features yielded a higher accuracy than those from the original optical or SAR data. Further validations suggested that optical data was better than SAR data in separating impervious surfaces from non-impervious surfaces. The fused impervious surfaces at the decision level had a higher overall accuracy than those produced independently by optical or SAR data. It was also highlighted that the fusion of GF-1 and Sentinel-1A images reduced the amount of confusions among the low reflectance of impervious surface and water, as well as for low reflectance of bare land. An overall accuracy of 95.33% was achieved for extracting urban impervious surfaces by fused datasets. The spatial distributions of uncertainties provided by the evidence theory displayed a confidence level of at least 75% for the impervious surfaces derived from the fused datasets.

Abstract:

Despite the documented importance of the neighbourhood environment on youth physical activity, little empirical research exists regarding the geographical boundaries of neighbourhoods within which youth are physically active around their homes. Studies and public policies often arbitrarily assume the extent of these boundaries, which vary from study to study. This paper combines GPS data, diaries and accelerometry to delineate empirically the local area and distance within which youth play in Erie County, New York. The study found that youth tend to be physically active within a quarter-mile radius around their homes and to focus on one section of the often assumed circled neighbourhood.

Abstract:

Significant research has been conducted on how environmental attributes influence people’s decisions to walk. In much of this
research, however, environmental attributes are averaged for neighborhoods or census geographies for sampled populations. Moreover, the
effect of an agent’s walking choices on other actors is not adequately represented by either objective or perceived measures in the literature.
Macro-level patterns of walkability arise from interactions across actors and urban environments. The agent-based approach allows for
modeling individual uses of the environment by treating the populations as objects that can interact with the environment and other people.
This study builds on previous research on pedestrian movement and geographic information system (GIS) measures of the built environment
using the agent-based approach to explore the dynamics of the built environment and people’s decision-making processes concerning walking. The results show that models that take individual perspective into account and include social interaction can better capture characteristics
of the built and social environment that influence people’s walking choices. This method lays out a new framework for assessing macro-level
patterns of walkability across a city using micro-level data.