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User profiling and region analysis are two tasks of significant commercialvalue. However, in practical applications, modeling different featurestypically involves four main steps: data preparation, data processing, modelestablishment, evaluation, and optimization. This process is time-consuming andlabor-intensive. Repeating this workflow for each feature results in abundantdevelopment time for tasks and a reduced overall volume of task development.Indeed, human mobility data contains a wealth of information. Severalsuccessful cases suggest that conducting in-depth analysis of populationmovement data could potentially yield meaningful profiles about users andareas. Nonetheless, most related works have not thoroughly utilized thesemantic information within human mobility data and trained on a fixed numberof the regions. To tap into the rich information within population movement,based on the perspective that Regions Are Who walk them, we propose a largespatiotemporal model based on trajectories (RAW). It possesses the followingcharacteristics: 1) Tailored for trajectory data, introducing a GPT-likestructure with a parameter count of up to 1B; 2) Introducing a spatiotemporalfine-tuning module, interpreting trajectories as collection of users to derivearbitrary region embedding. This framework allows rapid task development basedon the large spatiotemporal model. We conducted extensive experiments tovalidate the effectiveness of our proposed large spatiotemporal model. It'sevident that our proposed method, relying solely on human mobility data withoutadditional features, exhibits a certain level of relevance in user profilingand region analysis. Moreover, our model showcases promising predictivecapabilities in trajectory generation tasks based on the current state,offering the potential for further innovative work utilizing this largespatiotemporal model.

Regions are Who Walk Them: a Large Pre-trained Spatiotemporal Model Based on Human Mobility for Ubiquitous Urban Sensing