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LK2AD

local space-time Ripley's K function parameterizes adaptive kernel density estimation

Required modules: numpy

Relevant Literature: Hohl, A., Zheng, M., Tang, W., Delmelle, E., & Casas, I. (2017). Spatiotemporal Point Pattern Analysis Using Ripley’s K Function. In: Karimi, H. A. & Karimi, B. (Eds.) Geospatial Data Science: Techniques and Applications. Taylor & Francis.

Alexander Hohl and Peilin Chen. 2019. Spatiotemporal Simulation: Local Ripley’s K Function Parameterizes Adaptive Kernel Density Estimation. In Proceedings of Geosim’19: 2nd ACM SIGSPATIAL International Workshop on Geospatial Simulation, Chicago, IL, USA, November 5, 2019 (GeoSim’19), 8 pages. https://doi.org/10.1145/3356470.3365528

scripts:

helperFunctions.py - contains utility functions, such as for computing STKDE settings.py - contains parameter settings relevant for all executing scripts

Execute in order

  1. kobs.py - Computes observed local space-time k function.
  2. ksim.py - Computes local space-time k function from n simulated datasets. Can be executed in parallel.
  3. results_collect.py - Gathers results from observed and simulated local k functions, computes simulation envelopes, and differences between observed and simulated.
  4. maxClustScale.py - Finds optimal bandwidths
  5. aSTKDE.py - computes adaptive space-time kernel density estimation using optimal bandwidths determined previousely.
  6. unroll_aSTKDE.py - Out