Ecological zoning and green–development assessment at the village–town scale in China are significant tasks for sustainable planning in China. In this study, we build an index system to calculate the eco–environmental vulnerability score and divide the results into extreme, heavy, moderate, light, and slight levels based on evidence from 43,046 villages and towns in China from 2000 to 2015; then, we build a sustainable–development score calculation criterion to perform sustainability assessments. The results show that nine indexes (digital elevation model (DEM), slope, net primary productivity (NPP), total rainfall per year, per capita cultivated land, farmland proportion, grassland proportion, forestland proportion, and construction–land proportion) are the main factors in the variation in eco–environmental vulnerability under the conditions of urbanization. The eco–environment is found to have worsened from 2000 to 2015, and the deterioration areas are mainly concentrated in Tibet, the eastern area of Xinjiang and the Xing’an Mountains region. Economic growth and ecological protection can achieve common development when eco–environmental vulnerability is at light and slight levels, while when eco–environmental vulnerability is fragile, the inhibitory effect of economic growth is obvious in rural areas. The results can provide useful information for village–town planning.

Data stream partitioning is a fundamental and important mechanism for distributed systems. However, use of an inappropriate partition scheme may generate a data skew problem, which can influence the execution efficiency of many application tasks. Processing of skewed partitions usually takes a longer time, need more computational resources to complete the task and can even become a performance bottleneck. To solve such data skew issues, this paper proposes a novel partition method to divide on demand the image tiles uniformly into partitions. The partitioning problem is then transformed into a uniform and compact clustering problem whereby the image tiles are regarded as image pixels without spectrum and texture information. First, the equal area conversion principle was used to select the seed points of the partitions and then the image tiles were aggregated in an image layout, thus achieving an initial partition scheme. Second, the image tiles of the initial partition were finely adjusted in the vertical and horizontal directions in separate steps to achieve a uniform distribution among the partitions. Two traditional partition methods were adopted to evaluate the efficiency of the proposed method in terms of the image segmentation testing, data shuffle testing, and image clipping testing. The results demonstrated that the proposed partition method solved the data skew problem observed in the hash partition method. In addition, this method is designed specifically for processing of image tiles and makes the related processing operations for large-scale images faster and more efficient.