Accurately identifying the boundary of urban clusters is a crucial aspect of studying the development of urban agglomerations. This process is essential for comprehending and optimizing smart and compact urban development. Existing studies often rely on a single category of data, which can result in coarse identification boundaries, insufficient detail accuracy, and slight discrepancies between the coverage and the actual conditions. To accurately identify the extent of urban clusters, this study proposes and compares the results of three methods for identifying dense urban areas of three major agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Guangdong–Hong Kong–Macao Greater Bay Area. The study then integrates the results of these methods to obtain a more effective identification approach. The social economic method involved extracting a density threshold based on the fused nuclear density of socio-economic vitality data, including population, GDP, and POI, while the remote sensing method evaluated feature indices based on remote sensing images, including the density index, continuity index, gradient index, and development index. The traffic network method utilizes land transportation networks and travelling speeds to identify the minimum cost path and delineate the boundary by 20–30 min isochronous circles. The results obtained from the three methods were combined, and hotspots were identified using GIS overlay analysis and spatial autocorrelation analysis. This method integrates the multi-layered information from the previous three methods, which more comprehensively reflects the characteristics and morphology of urban clusters. Finally, the accuracy of each identification result is verified and compared. The results reveal that the average overall accuracy (OA) of the three areas delineated by the first three methods are 57.49%, 30.88%, and 33.74%, respectively. Furthermore, the average Kappa coefficients of these areas are 0.4795, 0.2609, and 0.2770, respectively. After performing data fusion, the resulting average overall accuracy (OA) was 85.34%, and the average Kappa coefficient was 0.7394. These findings suggest that the data fusion method can effectively delineate dense urban areas with greater accuracy than the previous three methods. Additionally, this method can accurately reflect the scope of urban clusters by depicting their overall boundary contour and the distribution of internal details in a more scientific manner. The study proposes a feasible method and path for the identification of urban clusters. It can serve as a starting point for formulating spatial planning policies for urban agglomerations, aiding in precise and scientific control of boundary growth. This can promote the rational allocation of resources and optimization of spatial structure by providing a reliable reference for the optimization of urban agglomeration space and the development of regional spatial policies.

The paper develops a design of optimal Bonus-Malus System (BMS) based on exact equitable credibility.in which the relative error function is taken as toss function. In BMS,both the frequency and lhe severity componems are considered. This design is compared with traditional BMS derived from classical squared-error loss function. w 1 IntroductionIn automobile insurance, when setting premiums, inequities will necessarily arise when,due to imperfect information,some policyholders are charged more than they should be and others less. At the same time,it will incur a loss to the insurer. To deal with this problem,BMS is widely used.In classical credibility theoryt~:,the loss function is taken to be the traditional squared error. I.emaire >j developed a BMS based only on the number of claims of each policyholder. Each policyholder has to pay a premium proportional to his unknown claim frequency. The use of the estimated claim frequency instead of the true unknown claim frequency will incur a loss to the insurer. To minimize this loss,Lemaire r'~: considered the optimal BMS obtained using the quadratic error loss function,the expected-value premium calculation principle and Poisson-Gamma as 1he claim frequency distribution. Dionne and Vanassa ~el took the characleristics of each individual such as the age variable into consideration. Suppose ~hat age has a negative effect on the expected number of claims,it would imply that insurance premiums should decrease with age. Frangos and Vrontos ::~:designed an optimal BMS with both a frequency and a severity, using Poisson-Gamma as the claim frequency distribution and Exponential-Inveres Gamma as the severity distribution. To minimize the insurer's risk,they used the squared-error loss function and took into account simultaneously the number of claims and the level of severity of each Received : 2003-12-03. M R Sub jeer Crass ifica t ion : 62C 12.

No abstract