[Objectives] Understanding whether Large Language Models (LLMs) possess spatial cognitive abilities and how to quantify them are critical research questions in the fields of large language models and geographic information science. However, there is currently a lack of systematic evaluation methods and standards for assessing the spatial cognitive abilities of LLMs. Based on an analysis of existing LLM characteristics, this study develops a comprehensive evaluation standard for spatial cognition in large language models. Ultimately, it establishes a testing standard framework, SRT4LLM, along with standardized testing processes to evaluate and quantify spatial cognition in LLMs. [Methods] The testing standard is constructed along three dimensions: spatial object types, spatial relations, and prompt engineering strategies in spatial scenarios. It includes three types of spatial objects, three categories of spatial relations, and three prompt engineering strategies, all integrated into a standardized testing process. The effectiveness of the SRT4LLM standard and the stability of the results are verified through multiple rounds of testing on eight large language models with different parameter scales. Using this standard, the performance scores of different LLMs are evaluated under progressively improved prompt engineering strategies. [Results] The geometric complexity of input spatial objects influences the spatial cognition of LLMs. While different LLMs exhibit significant performance variations, the scores of the same model remain stable. As the geometric complexity of spatial objects and the complexity of spatial relations increase, LLMs' accuracy in judging three spatial relations decreases by only 7.2%, demonstrating the robustness of the test standard across different scenarios. Improved prompt engineering strategies can partially enhance LLM's spatial cognitive Question-Answering (Q&A) performance, with varying degrees of improvement across different models. This verifies the effectiveness of the standard in analyzing LLMs' spatial cognitive abilities. Additionally, Multiple rounds of testing on the same LLM indicate that the results are convergent, and score differences between different LLMs exhibit a stable distribution. [Conclusions] SRT4LLM effectively measures the spatial cognitive abilities of LLMs and serves as a standardized evaluation tool. It can be used to assess LLMs' spatial cognition and support the development of native geographic large models in future research.

[Objectives] Population big data, characterized by large sample sizes and high spatiotemporal completeness, has become an essential foundational dataset for research in demography, population geography, and spatial population studies. Despite its widespread application, effective calibration methods for population big data remain lacking. Quantitative research, in particular, requires accurate and reliable population big data. However, common mathematical models struggle to precisely and realistically describe the relationship between big data and the Seventh National Population Census. [Methods] This paper proposes a method to calibrate population big data quantities by using the legally authoritative Seventh National Population Census data as an anchor point, integrating statistical data with 2020 Baidu population big data at a spatial level. Based on the mathematical relationship between population big data and official statistics, an operations-research-based optimization model is constructed to obtain the globally optimal deviation values for calibration. Using resident population data from the Seventh National Population Census for Hunan Province as the anchor point, the method calibrates the 2020 Baidu resident population big data and conducts two validation procedures. [Results] The results show that the deviation ratio between the calibrated resident population big data for Hunan Province and the census data is -1.01% (an improvement of 25.87%), with city-level deviation ratios ranging from -2.05% to +0.92% and county-level ratios from -2.06% to +1.99%, without altering the pre-calibration deviation trends. Validation against National Bureau of Statistics data indicates that the deviation ratio for calculated total urban population ranges from -2.7% to 1.7%. Validation against village-reported data in the "Green Heart" region at the intersection of Changsha, Zhuzhou, and Xiangtan cities shows a deviation ratio of 0.47% between the calculated resident population and the village-reported figures. [Conclusions] The calibration and validation results fully demonstrate the effectiveness of the proposed method, offering a viable approach for estimating population figures in non-census years and generating spatially distributed population datasets based on big data. This method can be applied to calibrate population big data from any provider and can be extended beyond resident population figures to calibrate gender ratios, age structures, working populations, mobile populations, OD flow data, and population profiles in big data.