国内生产总值（GDP）是衡量一个国家经济状况和发展水平的重要指标，精确核算年度GDP对于一个国家制定发展策略非常重要。然而不同研究用于GDP估算的地理数据均有所差别，且哪种地理数据更有利于核算GDP是尚未明确的问题。文章以我国2848个区县为研究区，采用多元线性回归（OLS）方法及地理加权回归（GWR）方法，结合夜间灯光遥感数据、兴趣点POI数据、腾讯位置大数据和城镇建设用地面积等多源地理数据模拟2020年区县GDP规模，并探讨不同变量的模拟效果。研究发现：①综合运用多源地理数据并结合空间加权回归方法可在区县级和地级市级分别实现74%和87%GDP空间模拟精度；②对比不同地理因子，发现POI数据最能有效反映区县级别GDP总量，效果优于其他地理因子；③相比于POI因子，腾讯位置大数据能较好地反映西部地区GDP总量。因此，结合腾讯位置及兴趣点POI数据，可提高全国GDP建模精度。文章为快速、准确模拟我国GDP空间分布提供了重要数据和方法参考。

GDP is one of the most important indices in measuring the socioeconomic development status of a country, and thus, an accurate estimation of GDP is vital in formulating valid development strategy for a country. However, the geographical data used for GDP simulation in various studies are different, and it is not clear which geographical data is more conducive to GDP simulation. In this study, both ordinary least squares regression ( OLS) and geographical weighted regression ( GWR) methods were conducted in order to simulate the GDP of 2 848 counties in China, in which, the performance and modeling capabilities of multi-sourced open data, including nighttime satellite data, points of interest ( POI) data, Tencent‘s social user location data and built up urban area data, were explored and assessed. The experimental results showed the following findings: ① The overall accuracies of the simulated GDP at counties and cities in China could be achieved above 74% and 87% , respectively, when multi sourced open data, including nightlight satellite data, POI data and Tencent’s user data were utilized by GWR method. ② By comparing different indicators, it is found that the point of interest ( POI) performs better than the other indicators in modelling the actual GDP at the county level in China. ③ The results also indicated that Tencent‘s social user location data has high potential in modeling the GDP distribution in the western part of China, which indicated that the modeling accuracy of GDP in China could be further improved using both Tencent‘s social user data and POI data. The results and findings in this study could provide insights in understanding how to better simulate and model GDP distribution in China.