Abstract: The aim of this paper was to provide methodological support for understanding the response mechanism of soil properties to external factors and the related spatial distribution, which could also serve as a decision-making reference for farmers and agricultural management authorities. Using geostatistical theory, spatial analysis in GIS and geographically weighted regression (GWR) model, the study analyzed the response of soil organic matter to climatic and socio-economic factors in the central Heilongjiang Province in years of 2000 to 2005. For the period 2005, soil organic matter was spatially interpolated along with auxiliary soil type and pH datasets using Co-Kriging in GIS and the temporal variability analyzed. The result showed that in the western region of the study area, organic matter was higher in the east than in the west. Then in the eastern region of the study area, organic matter was higher in the central zone than in the northern and southern zones. Based on conventional regression model and variance inflation factor (VIF), the paper selected suitable variables for GWR model. Spatial autocorrelation analysis of soil organic matter content yielded global Moran's I index of 0.433 (P = 0.00), indicating that significant spatial autocorrelation in soil organic matter. Thus the GWR model was considered to be suitable for local parameter estimation and was used to determine the relationship between organic matter content and its driving factors. The CV method was used to determine the optimal bandwidth and to establish an adaptive kernel-type GWR model. Results showed that the GWR model accounted for over 57% of the total variance in soil organic matter content in the region. The spatial stability of the strength of the influence of each variable on organic matter content was analyzed. It showed that all variables had significant spatial instability. In addition, the minimum, maximum, upper quartile and lower quartile of the regression coefficients of the variables were largely different, and with both positive and negative correlations. This showed that the influence of each variable on soil organic matter content was spatially variable and was either positive or negative. Results from the GWR model showed that precipitation and annual average temperature negatively influenced organic matter content. Annual sunshine hours positively influenced organic matter content in most areas, except southwest Nenjiang Plain and south Songjiang Plain. The influence of mechanized farming level (as a socio-economic factor) on soil organic matter was positive in both north and west Nenjiang Plain and also in some parts of the Kebai Hills. Irrigation areas had relatively large positive effect on soil organic mat-ter in the study area. Fertilizer had negative effect on soil organic matter in areas of south Nenjiang Plain, northwest Songjiang Plain and northeast Sanjiang Plain, but positive effects in other areas. Mulch film consumption had a large positive effect on soil organic matter. The effect of pesticide consumption on soil organic matter was mainly positive in the west of the study area, while it was mainly negative in the east (all significant at the 0.01 level). It was concluded that the effects of climatic factors (which reflect dif-ferences in natural conditions) and socio-economic factors (which reflect agricultural inputs) on soil organic matter were largely het-erogeneous.