Previous literature has examined the relationship between the amount of green space and perceived safety in urban areas, but little is known about the effect of street-view neighborhood greenery on perceived neighborhood safety. Using a deep learning approach, we derived greenery from a massive set of street view images in central Guangzhou. We further tested the relationships and mechanisms between street-view greenery and fear of crime in the neighborhood. Results demonstrated that a higher level of neighborhood street-view greenery was associated with a lower fear of crime, and its relationship was mediated by perceived physical incivilities. While increasing street greenery of the micro-environment may reduce fear of crime, this paper also suggests that social factors should be considered when designing ameliorative programs.

In the prioritized vehicle traffic environment, motorized transportation has been obtaining more spatial and economic resources, posing potential threats to the travel quality and life safety of non-motorized transportation participants. It is becoming urgent to improve the safety situation of non-motorized transportation participants. Most previous studies have focused on the psychological aspects of pedestrians and cyclists exposed to the actual road environment rather than quantifying the objective safety hazards, which has led to a non-rigorous evaluation of their basic safety situation. An integrated processing approach is proposed to comprehensively and objectively evaluate the overall safety level of non-motorized transportation participants on each road segment. Our main contributions include (1) the universal approach is established to automatically identify hazard scenarios related to non-motorized transportation and their direct causing factors from street view images based on multiple deep learning models; (2) a seed points spreading algorithm is designed to convert semantic images into target detection results with detail contour, which breaks the functional limitation of these two types of methods to a certain extent; (3) The safety situation of non-motorized transportation on various road sections in Gulou District, Nanjing, China has been evaluated and based on this, a series of suggestions have been put forward to guide the better adaptation among multiple transportation participants.

Since China acceded to the WTO, the industrial economy of urban areas has experienced a prosperous phase. However, disturbed by the global financial crisis and reverse globalization since 2008, the past crude development path has been unsustainable. Therefore, it is urgent and necessary to improve industrial resilience to avoid falling into a declining trap. This study integrates multi-source spatiotemporal information such as enterprise big data and panel data using the methods of GIS spatial analysis, complex network analysis, and multi-indicator comprehensive evaluation to evaluate the industrial economic resilience of Taihu Lake Basin (TLB). Resistance indicators such as resistance sensitivity, industrial land area, and regional economic connections are used to evaluate the resistance ability of the industrial economy in the TLB during the 2008 financial crisis and the 2018 Sino-US trade conflict. Resistance sensitivity and independent innovation ability are introduced to assess the recovery ability after two rounds of shocks, and comprehensive economic resilience is evaluated based on the entropy weighting method. The results show that in the face of the two economic shocks, the industrial economy in the TLB is increasingly vulnerable to external economic shocks and has a significantly stronger ability to adapt to economic shocks. Under successive shocks, the industrial economy of the TLB continues to transition to a new path of innovation, which contributes to higher value-added and more efficient use of industrial land. Shanghai and Suzhou, which not only have shown strong economic resilience of their own but are also centers of independent innovation in the TLB, badly need to further reduce their reliance on low-end manufacturing in the future. Among the other cities, Huzhou and Zhenjiang show the highest level of resilience, while Changzhou, Wuxi, and Jiaxing are at the middle level, and Hangzhou is evaluated as the city with the lowest industrial economic resilience. Changzhou and Wuxi need to further increase the technical complexity of their industrial products, while Jiaxing, Huzhou, and Zhenjiang are supposed to strengthen their economic connections with Shanghai, Suzhou, and Hangzhou to expand the industrial scale further. Although Hangzhou shows the lowest comprehensive resilience, it still has a catalytic role to play in the development of industrial land and the upgrading and transformation of manufacturing in Jiaxing and Huzhou.

Accurate monitoring of the spatiotemporal dynamics of snow and ice is essential for under-standing and predicting the impacts of climate change on Arctic ecosystems and their feedback on global climate. Traditional optical and Synthetic Aperture Radar (SAR) remote sensing still have limitations in the long-time series observation of polar regions. Although several studies have demonstrated the potential of moonlight remote sensing for mapping polar snow/ice covers, systematic evaluation on applying moonlight remote sensing to monitoring spatiotemporal dynamics of polar snow/ice covers, especially during polar night periods is highly demanded. Here we present a systematic assessment in Svalbard, Norway and using data taken from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) Day/Night Band (DNB) sensor to monitor the spatiotemporal dynamics of snow/ice covers during dark Arctic winters when no solar illumination available for months. We successfully revealed the spatiotemporal dynamics of snow/ice covers from 2012 to 2022 during polar night/winter periods, using the VIIRS/DNB time series data and the object-oriented Random Forests (RF) algorithm, achieving the average accuracy and kappa coefficient of 96.27% and 0.93, respectively. Our findings indicate that the polar snow/ice covers show seasonal and inter-seasonal dynamics, thus requiring more frequent observations. Our results confirm and realize the potential of moonlight remote sensing for continuous monitoring of snow/ice in the Arctic region and together with other types of remote sensing data, moonlight remote sensing will be a very useful tool for polar studies and climate change.

How to assess the risk of flood disasters and improve the resilience of coastal cities has become a scientific problem that must be solved urgently. This paper aims to construct a resilience assessment model for transport systems in the context of climate change based on an analysis of the spatial characteristics of regional transport networks and complex network theory, using the Pudong New Area in Shanghai, China as the empirical object. Other objectives of the developed model are to establish a system of homogeneity, efficiency, and stability indicators and to assess the impact of flood depth (up to 7 m) on the resilience of transport networks in terms of static network structure and dynamic network performance by designing flood inundation disturbance scenarios. Finally, the characteristics, change trends, and conceptual connotations of the resilience of transport networks in coastal cities are condensed. The results of this study provide a solid scientific basis for future flood disaster risk management in global coastal cities.