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Data collected about routine human activity and mobility is used in diverse applications to improve our society. Robust models are needed to address the challenges of our increasingly interconnected world. Methods capable of portraying the dynamic properties of complex human systems, such as simulation modeling, must comply to rigorous data requirements. Modern data sources, like SafeGraph, provide aggregate data collected from location aware technologies. Opportunities and challenges arise to incorporate the new data into existing analysis and modeling methods. Our research employs a multiscale spatial similarity index to compare diverse origin-destination mobility datasets. Established distance ranges accommodate spatial variability in the model’s datasets. This paper explores how similarity scores change with different aggregations to address discrepancies in the source data’s temporal granularity. We suggest possible explanations for variations in the similarity scores and extract characteristics of human mobility for the study area. The multiscale spatial similarity index may be integrated into a vast array of analysis and modeling workflows, either during preliminary analysis or later evaluation phases as a method of data validation (e.g., agent-based models). We propose that the demonstrated tool has potential to enhance mobility modeling methods in the context of complex human systems.