Farmland protection in China is facing multiple pressures, including urbanization, population growth and ecological degradation. Primary farmland zoning was introduced as a basic state policy to ensure national food security. Previous studies about “primary farmland zoning” have always taken the factors as global linear variables, neglecting the spatial heterogeneity of the study areas. Based on the Development Priority Zoning (DPZ) strategy, we present a zoning approach using Bayesian networks (BNs) for considering regional differences. The networks were developed using quantitative biophysical and economic variables from multi-scale and historical change data of farmland. The simulated results substantiate that this method can ensure the agricultural quality, stability and connectivity of primary farmland. Furthermore, it can further optimize the spatial allocation of primary farmland to meet the development conditions and trends of different sub-regions, promoting the balance between the farmland protection, urban development and ecological protection of certain regions.

Rapid population migration has been viewed as a critical factor impacting urban network construction and regional sustainable development. The supervision and analysis of population migration are necessary for guiding the optimal allocation of urban resources and for attaining the high efficiency development of region. Currently, the explorations of population migration are often restricted by the limitation of data. In the information era, search engines widely collect public attention, implying potential individual actions, and freely provide open, timelier, and large-scope search query data for helping explore regional phenomena and problems. In this paper, we endeavor to explore the possibility of adopting such data to depict population migration. Based on the search query from Baidu search engine, three migration attention indexes (MAIs) are constructed to capture public migration attention in cyber space. Taking three major urban agglomerations in China as case study, we conduct the correlation analysis among the cyber MAIs and population migration in geographical space. Results have shown that external-MAI and local-MAI can positively reflect the population migration inner regions and across regions from a holistic lens and that intercity-MAI can be a helpful supplement for the delineation of specific population flow. Along with the accumulation of cyber search query data, its potential in exploring population migration can be further reinforced.