Many studies have been made on street quality, physical activity and public health. However, most studies so far have focused on only few features, such as street greenery or accessibility. These features fail to capture people’s holistic perceptions. The potential of fine grained, multi-sourced urban data creates new research avenues for addressing multi-feature, intangible, human-oriented issues related to the built environment. This study proposes a systematic, multi-factor quantitative approach for measuring street quality with the support of multi-sourced urban data taking Yangpu District in Shanghai as case study. This holistic approach combines typical and new urban data in order to measure street quality with a human-oriented perspective. This composite measure of street quality is based on the well-established 5Ds dimensions: Density, Diversity, Design, Destination accessibility and Distance to transit. They are combined as a collection of new urban data and research techniques, including location-based service (LBS) positioning data, points of interest (PoIs), elements and visual quality of street-view images extraction with supervised machine learning, and accessibility metrics using network science. According to these quantitative measurements from the five aspects, streets were classified into eight feature clusters and three types reflecting the value of street quality using a hierarchical clustering method. The classification was tested with experts. The analytical framework developed through this study contributes to human-oriented urban planning practices to further encourage physical activity and public health.

Accessibility determination has become one of the key issues to interpret the relationship between urban form and travel pattern in metropolitan areas. Although the use of urban planning and design and associated accessibility influence travel makes intuitive sense, researchers have found it difficult to provide clear evidence of the influence of urban form. At the same time, it is generally recognized that land use patterns and transportation patterns are closely related to each other through accessibility change. The spatial organization of human activities creates a pattern of personal travel and goods transport, thus influences the mobility behaviour of actors such as households and firms. Conversely the availability of infrastructure makes certain locations more or less accessible. This paper identifies spatially desegregated micro-macro configurations of structural flow derived from accessibility analysis and their relationships with urban block size, road and metro-line network design, metro stations and bus stop locations, commercial land use locations distribution and station usage in Shanghai, China. Using GIS and spatial Design Network Analysis (sDNA) software to perform multi-level accessibility analysis of each link in each network, this revealed that most of the metro stations, bus stops and commercial land use are co-located on parts of the road network with the highest level of micro to macro accessibility. This indicates a 'coupling multiplier effect' between metro stations, commercial land use and multi-level multi network structural flow derived from accessibility analysis. Moreover block size is revealed as an important variable. These findings suggest the possibility of strategically appraising the impact of block size and land use planning on micro-macro accessibility change due to transportation system change and thus further the potential for systematically guiding transport oriented development planning and design place making. commercial land use and multi-level multi network structural flow derived from accessibility analysis. Moreover block size is revealed as an important variable. These findings suggest the possibility of evaluating and appraising strategically the effectiveness of block size and land use planning in relation to micro-macro accessibility change due to transportation system change and thus further the potential for systematically guiding transport oriented development planning and design place making.

Transit-oriented development (TOD) has been widely adopted as a primary urban planning strategy to better integrate transit and land use; further, the pedestrian-oriented perspective has been receiving increasing attention. However, most studies so far have only focused on few features and fail to capture comprehensive perceptions in the transportation (T), pedestrian-oriented accessibility (O), and urban development (D) dimensions. New emerging urban datasets provide a more refined and systematic approach to quantify the characteristics of metro station areas. This study offers a more efficient and convenient process and comprehensive approach to measure TOD performance. With a combination of traditional data collected by an official department, high-resolution open data, and innovative technology, large-scale analyses of 347 metro stations in Shanghai were conducted. Fifteen indicators for T, O, and D were chosen to categorize TOD performance into five clusters. Radar charts, boxplots, and colored maps were used to display numerous quantitative factors for each type. Combining the results with the Shanghai Comprehensive Plan (2017–2035) showed that the majority of Cluster 4 is located at the center of the Five New Towns. The correlation analysis between ridership and TOD performance showed that the transportation dimension indicator has a strong correlation with daily ridership, followed by the O and D indicators. Moreover, ridership per capita was found to be affected by resident density, employment density, O value, and D value, whereas no significant correlation was found between ridership per capita and T value. Population plays a pivotal role in metro passenger traffic, indicating ridership per capita had a high, strong correlation with resident density, with R = 0.658 for weekdays and R = 0.654 for weekends. This study reinterpreted the node-place method and 5Ds framework, resulting in a renewal method with new datasets and analysis tools. It contributes to providing pedestrian-oriented TOD planning methodology for urban planners and policymakers by combining T, O, and D dimensions and visualizing the results with current urban planning.