Analysis of Rural Labor Transfer Based on Network Data Mining and Financial Time Series Statistics The indepth study of rural labor transfer and rural land transfer is of great significance for promoting economic development, agricultural modernization, and protecting the interests of farmers. Based on the network data mining technology, this study combines the financial time series statistics to analyze the labor transfer in China and simulates the effective strategies of rural labor transfer and management through model simulation. The study shows that after China has jumped over Lewisxe2x80x99 first turning point under the premise of objective development inevitability, our country must not only accelerate the smooth transfer of agricultural labor, but also pay more attention to the improvement of agricultural labor productivity. At the same time, our country needs to improve agricultural labor productivity, in particular, to improve labor technology and promote the transformation of Chinaxe2x80x99s dual economic structure.

Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests Analysis of urban land use dynamics is essential for assessing ecosystem functionalities and climate change impacts. The focus of this study is on monitoring the characteristics of urban expansion in HangJiaHu and evaluating its influences on forests by applying 30m multispectral Landsat data and a machine learning algorithm. Firstly, remote sensed images were preprocessed with radiation calibration, atmospheric correction and topographic correction. Then, the C5.0 decision tree was used to establish classification trees and then applied to make land use maps. Finally, spatiotemporal changes were analyzed through dynamic degree and land use transfer matrix. In addition, average land use transfer probability matrix (ATPM) was utilized for the prediction of land use area in the next 20 years. The results show that: (1) C5.0 decision tree performed with precise accuracy in land use classification, with an average total accuracy and kappa coefficient of more than 90.04% and 0.87. (2) During the last 20 years, land use in HangJiaHu has changed extensively. Urban area expanded from 5.84% in 1995 to 21.32% in 2015, which has brought about enormous impacts on cultivated land, with 198,854 hectares becoming urban, followed by forests with 19,823 hectares. (3) Land use area prediction based on the ATPM revealed that urbanization will continue to expand at the expense of cultivated land, but the impact on the forests will be greater than the past two decades. Rationality of urban land structure distribution is important for economic and social development. Therefore, remotely sensed technology combined with machine learning algorithms is of great significance to the dynamic detection of resources in the process of urbanization.