This project is an expansion of `piccard`, a Python library to perform longitudinal analyses on data tabulated on unharmonized spatial units. The final library will have three modules: (1) temporal path creation, (2) visualization, and (3) classification. The first module is available on [PyPI]. This module introduces one of `piccard`’s graph-based solution to a frequent problem in spatial data science: identifying temporal trends across noncongruent spatial units of aggregation—e.g., census tracts, dissemination areas, and postal codes from different years. We conceptualize spatial units as nodes, and the edges connecting them as their overlapping geographical areas. Our method creates paths that preserve the original spatial units and their attributes. Thus, `piccard` overcomes some of the limitations of traditional harmonization methods involving labour-intensive apportioning—e.g., defining ad-hoc target units. The selected student will work with the PI and Profesor Daniel Silver (UTSC Sociology) in developing the second and third modules of the library. The visualization module will allow users to subset and inspect network paths. Meanwhile, the classification module will facilitate the classification of paths according to the distribution of the shared attributes across the original geographic units. For example, a user could classify census tracts according to patterns of variation over time.