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Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ecological conservation red line is an important initiative for ecological civilization construction in China. Studies and works have been conducted to delineate the ecological conservation red line using remote sensing and manual survey methods. However, the traditional ecological assessment methods ignore the impact of dynamic elements on the ecological environment, and a permanent monitoring system for ecological conservation red line is still necessary. Therefore, this paper aimed to build a daily monitoring system of the ecological conservation red line from the perspective of ecological soundscape, combining deep learning and traditional Geographic Information System (GIS) methods. We analyzed the main factors affecting the ecological soundscape to provide a benchmark for selecting sites for permanent soundscape monitoring. Initially, this study developed the first classification standard of soundscape elements for daily monitoring of ecological conservation red line concerning the "ecological-production-living spaces" concept and existing literature on soundscape and built a deep learning model for training, with training and validation accuracies of 89.88% and 72.41%, respectively. The identification results of this model confirmed that the solution of intelligent monitoring through soundscape is reliable in daily monitoring. Then, the deep learning model was employed to classify and predict the soundscape collected from the case sites and effectively identify the areas with severe ecological encroachment by calculating the proportion of ecological soundscape elements, performing the simulation of daily monitoring, which is important guidance for the actual ecological conservation red line monitoring. Finally, a regression model was used to analyze the spatial characteristics of ecologically vulnerable areas, with an R2 of 0.472. The regression model showed that nighttime light intensity had the greatest influence on ecological soundscape, followed by the distance from residential areas, spatial centrality, and road distance. These factors should be considered when implementing permanent soundscape monitoring sites. In addition, Normalized Difference Vegetation Index (NDVI) and topography were not significant in the regression model, which confirmed the shortcomings of considering only the ecological background and ignoring the dynamic biological elements. Daily monitoring should capture dynamic encroachments so that major ecological damage can be detected before it occurs. Therefore, the innovative integration of soundscape elements into the monitoring system meets the basic needs of monitoring ecological conservation red lines daily. [ABSTRACT FROM AUTHOR]

Copyright of Tropical Geography is the property of Tropical Geography Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research on the relationship between social isolation and crime has mainly focused on racial or ethnic social isolation; however, data on the individual activity and emotional and other aspects is relatively insufficient. Based on the questionnaire data, this study constructed an index system of social isolation combining social connection, neighborhood connection, activity connection, and emotional connection, controlled the impacts of basic attributes of the individual, and analyzed the correlation between social isolation and the four crime types, namely violence, burglary, pickpocketing in the community, and pickpocketing outside the community. In this study, 74 typical communities in Guangzhou, China were selected. A total of 1, 568 questionnaires were distributed through a household survey among which 1,148 questionnaires were selected for the final analysis from the residents who had lived in the community for more than three years. Findings indicate that social isolation has a significant positive correlation with the residents' criminal victimization while there is a negative correlation between some indicators and specific crime types. (1) There is only one index with significant correlation between social isolation and violence (negative correlation), which means whether the residents can make suggestions to the neighborhood committees and other departments. (2) The indexes with a significant correlation between social isolation and burglary include the number of family members and friends in the community (negative correlation), community affection (positive correlation), and community management service satisfaction (positive correlation). (3) Indicators of a significant correlation between social isolation and pickpocketing in the community include the number of relatives and friends in the community (negative correlation), whether neighbors visit each other (positive correlation), whether neighbors borrow from each other (negative correlation), and the residents' satisfaction with the community management services (positive correlation). (4) The indicators of a significant correlation between social isolation and pickpocketing include unemployment (negative correlation), harmonious neighborhood relationships (positive correlation), opinions and suggestions (negative correlation), and the residents' feelings toward the community management services (positive correlation). In general, there are differences in the impact of social isolation with different measurement dimensions on the infringement of urban residents, and this difference varies with the type of infringement. The contribution of this study to the existing literature is reflected in the following two aspects: first, the research framework is optimized, and in the process of the research, community connection, neighborhood connection, building environment, and management service evaluation are added to represent individual emotions, and the influence of these factors on the residents' criminal infringement is discussed. Second, based on the index system of social isolation, this study analyzes the correlation between social isolation and the criminal infringement of residents from the perspective of social network relationship, which is also a supplement to the existing literature. [ABSTRACT FROM AUTHOR]

As one of the largest urban agglomerations in China, the Pearl River Delta (PRD) region has entered a transition period since 2010 after experiencing rapid industrial development. Many studies have been conducted on industrial evolution, but less attention has been paid to specific characteristics and paths of industrial evolution during this transition period. Based on the perspective of firm dynamics, an in-depth analysis of the spatial evolution of industries in the PRD from 2010 to 2020 was conducted in this study, using data in relation to the following points of interest. We recognized the aggregating hotspots of firm dynamics, including entry, exit, and succession, represented by points of interest that increase, disappear and change its type on the same location from 2010 to 2020. Moreover, we revealed the rules regarding industrial upgrading paths and relatedness using the apriori data mining method and statistical analysis. The path and patterns of industrial spatial evolution were studied using the above parameters for the PRD. The research showed that the industrial layout of the PRD presented an obvious "Core-Edge" distribution, and the dual core polarization trend of Guangzhou and Shenzhen became obvious between 2010 and 2020. Furthermore, it is understood that three types of industrial upgrading paths are found in the PRD, including evolution from the manufacturing industry to the service industry and internal upgrading of manufacturing and service industries. Generally, industries in the PRD upgrade from lowvalue-added labor-intensive industries, such as garment spinning, printing, and electronic instrument processing, to high-value-added knowledge and technology-intensive industries, such as finance, insurance, and mechanical parts manufacturing. Additionally, industries such as finance and insurance, machinery parts manufacturing, network technology, and business services are more well connected to other industries. Industrial upgrading and industrial relatedness are two major paths of spatial evolution in industries. Industries having a higher market value with stronger and steadier interrelations agglomerate in the core areas of economically developed cities such as Guangzhou and Shenzhen and attract more firms to set up businesses in these areas. On the other hand, industries having a lower market value with minimal relatedness set up businesses in professional towns in Dongguan, Foshan, and other less developed areas such as towns in Jiangmen. The old towns of Guangzhou and Foshan as traditional manufacturing centers witness massive firm succession. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)