Information empowerment to the territorial spatial planning has become a hot research field in the new era. However, research on territorial utilization evaluation using big data integration remains to be explored. The purpose of this paper is to carry out an empirical study on the efficiency assessment of residential land use in new urban area employing Tencent location-based big data. Assessment index of residential land use efficiency in each residential area have been proposed, supported by integration of multi-source geospatial data, to reveal the differences in land use efficiency among different residential areas in Changzhou city. The results show that, firstly, population size of hourly particle statistics within the residential area fluctuates periodically, reaching peak value at 21:00 generally, which is in line with the routine of daily going out and returning home for urban residents. There are also expected differences in population agglomeration degree and population size among residential buildings with different capacity rates. Secondly, the 29 residential areas are divided into five groups by year of construction, 1980s, 1990s, 2000s, 2010—2015, and post-2015. The average population size of efficiency index of group 1980s, 1990s, 2000s, 2010—2015, and post-2015 are 1.74, 2.45, 2.31, 0.95, and 0.91 per 100 m2, respectively. Index values of residential areas built before 2010 are significantly higher than those built after 2010. Furthermore, residential areas built after 2010 are lower than the average level (population size of 2.06 per 100 m2in year 2018) of the entire urban residential areas. Thirdly, it is suggested that lower results of efficiency index is not fully equal to poor level of intensive land use. The main reasons of diverse land use efficiency of residential areas constructed in different periods include the growth periodicity of new urban area development in Changzhou city, and urban residents' desire for better living environment to enhance their quality of habitation. Research shows that location-based big data, as a source of population data with high solution, could reflect the temporal and spatial characteristics of resident aggregations objectively. Index constructed to assess urban residential land efficiency using location-based big data is both innovative and scientific, which could provide a new way for the analysis of high-quality land space utilization. In conclusion, regularity recognition of behavior characteristics from urban residents can provide support for spatial policy formulation during the urbanization process based on "putting people first" policy in China. What's more, new data sources, represented by location-based big data in this paper, will play an important role in decision-making mechanism of territorial spatial planning.