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Accurately measuring industrial spatial agglomeration patterns is crucial for promoting regional economic development. However, few studies have considered both agglomeration degrees and cluster locations of industries. Moreover, the traditional multi-scale cluster location mining (MCLM) method still has limitations in terms of accuracy, parameter setting, calculation efficiency, etc. This study proposes a new framework for analyzing industrial spatial agglomeration patterns, which uses the Duranton and Overman (DO) index for estimating agglomeration degrees and a newly developed local DO (LDO) index for mining cluster locations. The MCLM-LDO method was proposed by incorporating the LDO index into the MCLM method, and it was validated via comparisons with three baseline methods based on two synthetic datasets. The results proved that the MCLM-LDO method can achieve accuracies of 0.945 and 1 with computational times of 0.15 s and 0.11 s on two datasets, which are superior to existing MCLM methods. The proposed framework was further applied to analyze the spatial agglomeration patterns of the industry of computer, communication, and other electronic equipment manufacturing in Guangdong Province, China. The results showed that the framework gives a more holistic perspective of spatial agglomeration patterns, which can serve as more meaningful references for industrial sustainable development. [ABSTRACT FROM AUTHOR]

Wetland ecosystems contain rich natural resources and vital ecological functions, and the investigation of spatial and temporal evolution characteristics of wetlands and their driving factors is critical for the management and conservation of wetlands. This study aimed to explore the spatial and temporal dynamics of wetlands in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 1976 to 2019 using multi-source remote sensing data (DISP KH-9, Landsat, and GaoFen-1), combing with the object-based classification method and landscape invasion index, and further analyze the driving forces affecting the spatial and temporal evolution of wetlands. The results showed that: (1) The total area of wetlands in the GBA showed a trend to first increase and then stabilize from 1976 to 2019. (2) The rapid development of aquaculture led to a continuous increase in aquaculture ponds and offshore aquaculture and a flat change in the middle and late stages, the area of mangroves declined substantially before 2000 and has gradually recovered since then, the invasion of various types of wetlands by built-up land is increasing, and wetlands are becoming increasingly fragmented. (3) The wetland changes in the GBA are the result of a combination of natural factors and human activities. Environmental conditions represent the basis for wetland dynamics, while the population, socio-economics, and policies are important drivers of wetland evolution. The findings will be beneficial to the understanding of wetland dynamic changes in the GBA over the past 40 years, and helpful to the scientific management and sustainable development of wetlands. [ABSTRACT FROM AUTHOR]