Article citationsMore>>

Zhang, Z., Zhang, H., Zhao, L., Chen, T., Arik, S.Ö. and Pfister, T. (2022) Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding. Proceedings of the AAAI Conference on Artificial Intelligence. 36, 3417-3425.
https://doi.org/10.1609/aaai.v36i3.20252

has been cited by the following article:

• TITLE: “stppSim”: A Novel Analytical Tool for Creating Synthetic Spatio-Temporal Point Data

  AUTHORS: Monsuru Adepeju

  KEYWORDS: Open-Source, Synthetic Data, Crime, Spatio-Temporal Patterns, Data Privacy

  JOURNAL NAME: Open Journal of Modelling and Simulation, Vol.11 No.4, October 12, 2023

  ABSTRACT: In crime science, understanding the dynamics and interactions between crime events is crucial for comprehending the underlying factors that drive their occurrences. Nonetheless, gaining access to detailed spatiotemporal crime records from law enforcement faces significant challenges due to confidentiality concerns. In response to these challenges, this paper introduces an innovative analytical tool named “stppSim,” designed to synthesize fine-grained spatiotemporal point records while safeguarding the privacy of individual locations. By utilizing the open-source R platform, this tool ensures easy accessibility for researchers, facilitating download, re-use, and potential advancements in various research domains beyond crime science.