This research attempts to understand the relationship between different impact factors and the residential land transaction price of Hangzhou, China. Exploiting various online open data sources and conducting detailed data preprocessing, this study builds a spatial econometric model to identify factors which significantly influence the transaction prices over the year 2012 – 2016. Furthermore, the model exams the spatial heterogeneity of different factors across the city. Adopting points of interest (POI) data from Gaode, one of the largest online map service companies in China, the research first defines the centers of different types of locational factors with K-means clustering. The nearest distances to these urban centers are computed accordingly to be set as potential explanatory variables. Collinearity issue is detected and variable selection is modified by variance inflation factors (VIF) calculation and principal component analysis (PCA). From this, land area, plot ratio, distances to major waterbodies, education centers, company centers, government centers, medical centers, tourism centers, opened subway stations and future subway stations which will be opened within three years are selected as independent variables for regression modeling. Finally, the non-stationarity of spatial distributions of estimated coefficients and pseudo t-values of ten explanatory variables indicate that Geographically Weighted Regression (GWR) is more suitable to analyze the spatial variation of impact factors with land price compared to conventional Ordinary Least Square (OLS) regression. The result of GWR model with fixed Gaussian spatial kernels indicates the spatial non-stationary relationship between residential land transaction price and the selected impact factors. The negative impact of most factors is larger in less developed areas than in highly developed areas. With Inversed Distance Weighting (IDW) interpolation improving the data visualization of coefficients spatial distribution, we can identify that land price change is more sensitive to the selective variables in the periphery regions of the city. Distance to job centers shows stronger influence on land price in the south especially the surrounding areas near the information industry agglomeration identified by K-means clustering. Moreover, two types of subway station factors are both contributing to the land price changes. The stations which will be opened within 3 years are influencing more areas in the city compared to the opened stations, while the magnitude of the coefficient is smaller. It reflects the anticipation of developers about land premium potential. The different spatial patterns of influences from the selected variables on land price implies that urban planners should acquire better understanding about local contexts and conduct location-specific strategies of land price evaluation and land use polices.