Correction: Assessment of network structure characteristics and factors of corporate flows in Guangdong Province.

[This corrects the article DOI: 10.1371/journal.pone.0293870.].

Assessment of network structure characteristics and factors of corporate flows in Guangdong Province.

With the rapid development of the world city network, the traditional location theory has gradually been disproven, and the advantages of the flow space over the traditional vertical organizational structure are gradually being revealed. Therefore, from corporate branch networks and corporate investment networks, 21 cities in urban agglomerations of Guangdong are taken as case studies for this paper. Furthermore, in this paper, 5 representative types of corporate contact data (catering service, financial service, life service, sports and leisure and accommodation service) are selected, the social network analysis (SNA) method is used to quantitatively analyze the network structure characteristics of urban agglomerations, and a spatial interaction model is constructed to explore the factors influencing. The results indicate that secondary networks have developed in Guangdong. The financial service network is the most complex, followed by the life services, sports and leisure and catering networks. The accommodation service network structure is the simplest. Among all kinds of networks, Guangzhou and Shenzhen have the highest status. The catering and accommodation corporations in Yangjiang in the west have a relatively major external development. Shantou in the east has many branches of various types, while most of the capital exchange in the region is concentrated in Heyuan and Qingyuan in the north. The coefficients of geographical proximity and the urban development level play a significant role in promoting the development of networks. However, administrative capacity limits the attractiveness of origin cities to a certain extent.

Spatio-temporal evolution characteristics analysis and optimization prediction of urban green infrastructure: a case study of Beijing, China.

The reasonable layout of green infrastructure is conducive to the low-carbon, livable and high-quality sustainable development of cities. The framework of spatio-temporal evolution characteristics and prediction analysis of Urban Green Infrastructure (UGI) was constructed by integrating morphological spatial pattern analysis (MSPA) and CA-Markov in the study. We analyzed the spatio-temporal evolution characteristics of UGI in Beijing from 1990 to 2019, predicted its future change trend in 2030, and put forward the optimization scheme for the ecological network of UGI. The area change of UGI presented a "V" shape from 1990 to 2019 in Beijing, and the turning point was around 2009. Its spatial distribution revealed a significant heterogeneity. The comprehensive change rate index showed a "rising and then falling" trend from 1990 to 2019. Core with an area of over 1000 km2 had inclined "C" shape, connecting the north, west and south of the study area. Among the three prediction scenarios for 2030, the area of UGI under the ecological conservation priority scenario is the largest, accounting for 86.35% of the total area. The area of UGI under the economic development priority scenario is the smallest, accounting for 76.85%. The optimization of zoning and road network are effective measures to improve the connectivity of UGI in Beijing. This study is beneficial to extend the research ideas of UGI and promote sustainable urban development.

Valuable Clues for DCNN-Based Landslide Detection from a Comparative Assessment in the Wenchuan Earthquake Area.

Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside detection in the Wenchuan earthquake area, where about 200,000 secondary landslides were available. Multiple structures and layer numbers, including VGG16, VGG19, ResNet50, ResNet101, DenseNet120, DenseNet201, UNet-, UNet+, and ResUNet were investigated with different sample numbers (100, 1000, and 10,000). Results indicate that VGG models have the highest precision (about 0.9) but the lowest recall (below 0.76); ResNet models display the lowest precision (below 0.86) and a high recall (about 0.85); DenseNet models obtain moderate precision (below 0.88) and recall (about 0.8); while UNet+ also achieves moderate precision (0.8) and recall (0.84). Generally, a larger sample set can lead to better performance for VGG, ResNet, and DenseNet, and deeper layers could improve the detection results for ResNet and DenseNet. This study provides valuable clues for designing models' type, layers, and sample set, based on tests with a large number of samples.