The repository contains methods of spatial-statistics, so far especially Kernel Density Estimation(KDE) methods.
More specifically, four bandwidth selection methods of KDE are included:
- two fixed KDE bandwidth selection methods : rule of thumb & corss-validation based fixed KDE
- two adaptive KDE bandwidth selection methods: cross-validation based adaptive KDE & QFA-KDE
Further more, two kinds of datasets are included for experiments:
- POI dataset: regional clustered data/Hubei enterprise registration data POI (Li et al.,2018)
- GPS trajectory dataset: linear clustered data
please refer to the upcoming paper of Yuan on the journal of "International Journal of Geographical Information Science" (IJGIS)
A demonstration of the spatial segmentation result computed by QFA-KDE:
The comparison results of the estimated density distribution of GPS points using four different KDE bandwidth selection methods:
note: the bandwidths are calculated using the contained python scripts, and the heat-maps are generated using QGIS software
References:
Li, F., Gui, Z., Wu, H., Gong, J., Wang, Y., Tian, S., & Zhang, J. (2018). Big enterprise registration data imputation: Supporting spatiotemporal analysis of industries in China. Computers, Environment and Urban Systems, 70, 9-23.
Yuan K, Cheng X, Gui Z, et al. A quad-tree-based fast and adaptive Kernel Density Estimation algorithm for heat-map generation[J]. International Journal of Geographical Information Science, 2019, 33(12): 2455-2476.