Rural Economic Census Data

At present in China, the socio-economic statistical data collection and dissemination are based on the basic administrative unit, which is unable to meet the analysis needs of the government's economic investment decisions when analyzing the economic situation on the boundary of target area. To solve this problem, we must further obtain statistical data distribution differences within the statistical unit and break up the attribution value of the lower spatial resolution unit into the higher spatial resolution unit.Based on geographic information system (GIS) technology, this paper used land use, road network, elevation data, socio-economic data and statistical data as basic data, proposed an systematical idea which starts from raising an issue, and is followed by analyzing the spatial distribution of the socio-economic data in the study area, and then sets up a geographical elements index system, analyzes the differences of the geographic elements effects in the regions of different economic development levels, ultimately spatializes the rural net income. Taking Fangshan District in Beijing as an example, this paper builds a 500m x 500m spatial database of Fangshan District rural net income in 2006 and the main conclusion is as follows:(1) By spatial autocorrelation analysis, we can find that the rural economy of Fangshan District is obvious spatial clustering, and the aggregation status of the rural economy corresponds to the economic development level distribution. The overall development level of rural economy in Fangshan District is low, and more than half area (mainly in mountainous areas) does not meet the district's average level of rural economic development. The basic spatial distribution of rural economic development in Fangshan District is shaped as "U", which means that the development level of the east and west regions is higher and the middle region is lowest.(2) By analyzing the relationship between the geographic elements and the rural economy in the scale of whole district, it can be found that rural economic development is a result of interaction among the multi-factor. Topography and location factors have negative effects on rural economy, while the effects of traffic, education and business resource factors changes after adding other elements into the equation.(3) By analyzing the relationship between the geographic elements and the rural economy in the scale of different development level regions, we find that the economic development is less affected by geographical elements in the economically developed/sub-developed areas, while the economic development is closely related to the distribution of most geographical elements in the economically underdeveloped areas. This part of research is instructive in amending spatialization model.(4) Use simulated value of regression model based on geographical elements to calculate the modified coefficients, and amend the simulation results. The result shows that the simulation map of spatializaiton is highly precise and more accurately reflects the status quo of distribution of rural residents'net income than other two models.