This study presents a systematic analysis framework for investigating the influencing factors of data acquisition accuracy in road geomagnetic mapping under a crowdsourcing paradigm, to solve the practical problems of uncontrollable data quality, inconsistent acquisition standards and high cost of mapping in crowdsourced geomagnetic mapping. Based on the trajectory data collected by a variety of smartphones according to the crowdsourcing mode, this study constructs a multi-dimensional crowdsourced data quality evaluation system from the perspective of positioning accuracy and geomagnetic accuracy. The impacts of key acquisition parameters-including equipment model, frequency of repeated sampling, acquisition period, and spatial environment-are comprehensively analyzed. To ensure high data quality, an acquisition boundary determination method is proposed, which provides an operational technical pathway and optimization strategy for constructing high-precision road geomagnetic maps in crowdsourced settings, thereby enhancing the reliability and usability of crowdsourced geomagnetic data. Key findings reveal that: (1) prioritizing high-performance mainstream devices (e.g., Huawei, Honor) significantly improves data quality; (2) when the frequency of repeated sampling is about 10 times, it can effectively improve the accuracy of the data, beyond which diminishing returns and saturation effects occur; (3) data acquisition during low-interference periods (e.g., nighttime or early morning) effectively reduces electromagnetic noise and improves data stability; (4) open areas exhibit superior signal conditions and measurement accuracy compared to challenging environments such as urban canyons with significant shading. These insights offer practical guidance for optimizing crowdsourced geomagnetic data acquisition and support the development of robust, low-cost, and wide-coverage data acquisition patterns. The proposed method holds promise for applications in intelligent transportation, underground navigation, and urban infrastructure monitoring, contributing to seamless indoor-outdoor positioning services.
Li et al. (Wed,) studied this question.