  As deep learning technology advances and more urban spatial-temporal data
accumulates, an increasing number of deep learning models are being proposed to
solve urban spatial-temporal prediction problems. However, there are
limitations in the existing field, including open-source data being in various
formats and difficult to use, few papers making their code and data openly
available, and open-source models often using different frameworks and
platforms, making comparisons challenging. A standardized framework is urgently
needed to implement and evaluate these methods. To address these issues, we
propose LibCity, an open-source library that offers researchers a credible
experimental tool and a convenient development framework. In this library, we
have reproduced 65 spatial-temporal prediction models and collected 55
spatial-temporal datasets, allowing researchers to conduct comprehensive
experiments conveniently. By enabling fair model comparisons, designing a
unified data storage format, and simplifying the process of developing new
models, LibCity is poised to make significant contributions to the
spatial-temporal prediction field.
