How Did Built Environment Affect Urban Vitality in Urban Waterfronts? A Case Study in Nanjing Reach of Yangtze River

Demographic disparity in diurnal surface urban Heat Island exposure across local climate zones: A case study of Chongqing, China

Surface urban heat island (SUHI) exposure significantly harms human health during rapid urbanization. Identifying the areas and demographic groups under high SUHI exposure is critical for mitigating heat-related hazards. However, despite broad concern in US-European countries, rare studies discuss the diurnal SUHI exposure of demographic subgroups across Local Climate Zones (LCZs) in Chinese cities. Therefore, taking Chongqing as the case study, we measured the diurnal SUHI exposure of demographic subgroups (e.g., gender, age, and income) across different LCZs (compact, open, and sparsely-built zones) by coupling the ECOSTRESS data and mobile phone signaling data. The results indicated that Chongqing's compact high/middle-rise zones suffered a higher SUHI exposure due to high land surface temperature (LST) and a larger size of population than open zones. Despite a relatively low population density, extremely high LST in compact low-rise zones (e.g., industrial parks) contributes to considerable accumulated SUHI exposure. The SUHI exposure risk exhibited the differences between daytime and nighttime, resulting from SUHI variation and population flow. The demographic analysis showed that Chongqing's demographic subgroups are exposed disproportionately to SUHI. Elderly groups suffered relatively high exposure in compact high-rise zones. Low-incomers witnessed a high exposure in open zones. These findings call for alleviating SUHI exposure risk by targeting vulnerable groups and high-intensity exposure areas.

Spatio-temporal effects of built environment on running activity based on a random forest approach in nanjing, China

Running activity is closely related to the urban built environment in terms of mental and physical health, and this relationship can change as a result of spatio-temporal changes. Most studies, however, do not account for this and assume a linear relationship exists between the built environment and running activity. This study, therefore, collected running data spanning 2019-2022, studied spatial distribution of four-year running activity, established built environment indicators, used a random forest approach to investigate the non-linear relationship between them, and evaluated spatio-temporal changes in the relationships over time. The findings suggested that running activities are spatially clustered and the degree of clustering varies over time, and nonlinear relationships and threshold effects between the built environment and running activity can be found through the random forest algorithm and partial dependence plots. Urban park green space, greenway, and the normalized difference vegetation index had the most significant effects on running activity. The effects of population, buildings, streets, road intersections, and points of interest on running activity changed during the Coronavirus disease 2019 pandemic. In 2022, however, these effects were consistent with those during the pre-pandemic period. Our findings fill research gaps by using spatio-temporal analysis and a non-linear approach; they can also provide a reference for urban planners in building running-suitable and healthy cities.

Evaluating Urban Vitality of Street Blocks Based on Multi-Source Geographic Big Data: A Case Study of Shenzhen

Urban vitality is the comprehensive form of regional development quality, sustainability, and attractiveness. Urban vitality of various regions within the cities has difference, and the quantitative evaluation of urban vitality within the cities can help guide to future city constructions. Evaluation of urban vitality needs the combination of multi-source data. Existing studies have developed index method and estimation models mainly based on geographic big data to evaluate urban vitality. This study aims to combine remote sensing data with geographic big data to evaluate urban vitality of Shenzhen at street block scale and build the estimation model by random forest method. Indexes and random forest model were built, and some further analyses were conducted. The results were: (1) urban vitality in Shenzhen was high in the coastal areas, business areas, and new towns; (2) compared to indexes, the estimation model had advantages of more accurate results, combination of various data, and the ability to analyze feature contributions; and (3) taxi trajectory, nighttime light, and housing rental data had the strongest influence on urban vitality.

Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model

Urban vitality is a major indicator used for evaluating the sustainability and attractiveness of an urban environment. Global experience indicates that urban vitality can be stimulated through a reasonable urban design. However, it remains incompletely understood in the literature which building-related indicators can substantially affect urban vitality in Asian countries. To give an insight into this question, our study took a step forward by focusing specifically on the influence of the three-dimensional built environment on urban vitality, based on which decision makers could enhance urban vitality from the perspective of vertical building design. A machine-learning-based framework was developed in this study. First, we utilized several building-related indicators to thoroughly measure the spatial characteristics of buildings at the township level. Second, the relationship between a three-dimensional built environment and urban vitality was revealed based on a combined use of the correlation method, scatter charts, and a random forest. In the random forest, both a benchmark and a new model were constructed to evaluate the importance of those building-related indicators. The results suggested that urban vitality was closely related to the three-dimensional built environment, which played an even more important role than common benchmark factors in stimulating urban vitality. The building coverage ratio, density of tall buildings, and floor area ratio were essential spatial drivers behind urban vitality. Therefore, urban designers and decision makers should not only take traditional factors into account but also carefully consider the potential influence of high-rise buildings and the outdoor thermal environment so that urban vitality can be enhanced. Our study's results can offer practical recommendations for improving urban vitality from the perspective of vertical building design. The proposed framework can also be used for measuring the potential influence of the three-dimensional built environment in other areas.

Tourist Landscape Preferences in a Historic Block Based on Spatiotemporal Big Data-A Case Study of Fuzhou, China

Historic blocks are valuable architectural and landscape heritage, and it is important to explore the distribution characteristics of tourists to historic blocks and their landscape preferences to realize the scientific construction and conservation of historic blocks and promote their sustainable development. At present, few studies combine the analysis of tourist distribution characteristics with landscape preferences. This study takes the historic block of Three Lanes and Seven Alleys in Fuzhou as an example, combines field research and questionnaires to construct a landscape preference evaluation indicator system for the historic block, measures the distribution characteristics of tourists in the block through the heat value of tourist flow obtained from the Tencent regional heat map, and analyses the influence of landscape preference indicators on the heat value of tourist flow in the block through stepwise multiple linear regression. The research shows that: (1) the spatial and temporal variation in the heat value of tourist flow tends to be consistent throughout the block, from 7 a.m. to 6 p.m., showing a "rising, slightly fluctuating and then stabilizing" state, both on weekdays and on weekends. (2) The factors influencing the heat value of tourist flow in the different spatial samples are various, with commercial atmosphere, plant landscape, accessibility of the road space, architecture, and the surrounding environment having a significant impact on the heat value of tourist flow. Based on the analysis of the landscape preferences of tourists in the historic block, a landscape optimization strategy is proposed to provide a reference for the management and construction of the block.

Do Vibrant Places Promote Active Living? Analyzing Local Vibrancy, Running Activity, and Real Estate Prices in Beijing

Although extensive research has investigated urban vibrancy as a critical indicator for spatial planning, urban design, and economic development, the unclear relationship between local vibrancy and active living needs to be clarified and requires more in-depth analysis. This study localizes urban vibrancy at both hyper-local and neighborhood scales by integrating high-resolution, large-scale, and heterogeneous urban datasets and analyzing interactions among variables representing vibrancy's environmental, economic, and social aspects. We utilize publicly available urban open data, Points of Interest requested from API, and leisure running trajectories acquired through data mining to investigate the spatial distribution of various vibrancy indicators and how they interact with physical activity at the local scale. Based on these variables, we then construct linear regression models and Geographically Weighted Regression (GWR) models to test and estimate how local vibrancy and physical activity relate to residential real estate characteristics. The results reveal the strong impact of urban form on local vibrancy but not physical activeness. At the neighborhood level, all vibrancy factors are statistically significant to local residential real estate prices but with different interactions based on location. Our study highlights the importance of accounting for locality and different physical, environmental, social, and economic factors when analyzing and interpreting urban vibrancy at a granular scale within a city.