Taking 171 counties of Beijing-Tiajin-Hebei Region as research units, based on spatial analysis model of GIS and geographic weighted regression model, the spatial-temporal characteristics of GDP per capita and its cause in 1993- 2013 were revealed. Results were as follows. GDP per capita in the Beijing-Tianjin-Hebei Region at county level showed rapid growth trend with expanding difference; GDP per capita at county level showed a significant positive correlation, that is to say, the pattern of high-high concentration and low-low concentration was enhanced. Beijing-Tianjin-Tangshan Region was always the hot economic development zone in Beijing-Tianjin-Hebei Region, the GDP per capita of most counties in Hebei Province was at low level, and cold economic development belt of “Laiyuan County-Gaoyang County-Wuyi County-Zaoqiang Qiu County” was gradually formed. GDP per capita at county level showed spatial pattern of “northeast-southwest”, and the overall trend was enhanced. Wen’an County was the core of GDP per capita gravity, and the centre of economic gravity moved southwest firstly and then northeast, indicating that the economic development function in the northeast of Beijing-Tianjin-Hebei Region further strengthen. Compared with OLS model, the fitting effect of GWR model was improved obviously. The development of GDP per capita in 2013 was mainly promoted by the gross industrial output value per capita, the proportion of value-added of the tertiary industry, the contracted investment actually utilized per capita and urbanization level.

This paper made a comprehensive discussion about the spatial differentiation, evolution trend, correlation and driving factors of the regional agricultural mechanization level by using TOPSIS method, trend surface analysis, ESDA and GWR model. The results show that Hebei county’s agricultural mechanization level presents obvious spatial directivity and topographical distribution differences in 2013. The agricultural mechanization level develops better in central southeast plain, followed by the northwest plateau, and hilly region relatively worse. There exists a significant spatial autocorrelation characteristic and regional convergence phenomenon. The southern area of Hebei major in HH type, and northern area major in LL type. GWR shows great superiority in explaining the spatial non-stationary of elements, and reveals both positive and negative correlations between farmland scale and plant structure (expect for terrain), which is different from OLS result that all the factors are positive. In the future the government can consider such measures as enhancing the cultivated land scale and proportion of planting structure in the plateau area, considering other factors in the plain area to promote county’s agricultural mechanization level.

Taking Tunchang County in Hainan Province as a case study, dual self-organizing model accounting for geographical space as well as attribute space, was proposed. The geographic space information included the longitude and the latitude of the administrative villages. These indices such as the potential, the urgency and the feasibility were combined to construct the attribute space. The results demonstrated that the potential, the urgency and the feasibility of land reclamation were quite different among the villages. The model scores for the villages were significantly higher in the southern region than that in the northern region, and they were higher in the eastern region than that in the western region. The most desired land reclamation projects would be carried out in Poxin Town, Nankun Town, Xichang Town, Tuncheng Town. The 161 villages were divided into 6 project regions through dual self-organizing model. Based on the comprehensive score, the 6 project regions were classified into three types: priority remediation area (near-term), key remediation area (medium-term) and moderate remediation area (long-term). The area percent of three types were 25.14%, 41.83% and 33.03%, respectively. The developing orientations and suggestions for the land reclamation projects were given according to the characteristics of different influence factors. The results provide the scientific foundation in planning and implementing the project of land reclamation in Tunchang County, and is helpful in improving the level of land consolidation planning as well as promoting the land reclamation progress and the sustainable development.