pip

README.md

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+
+# FastGWR
+A command line tool for fast parallel computation of Geographically Weighted Regression models.
+
+
+### Installation:
+
+1. Install any MPI implementation
+    For Mac:
+
+    OpenMPI: https://www.open-mpi.org
+
+    or `conda install openmpi`
+
+    For PC:
+
+    Microsoft MPI: https://docs.microsoft.com/en-us/message-passing interface/microsoft-mpi
+
+    or `conda install mpich`
+
+
+    Make sure you have the mpi command on your path by running
+    `mpiexec`
+
+
+
+2. Install mpi4py
+
+    `conda install mpi4py`
+
+
+3. Install fastgwr
+
+    `pip install fastgwr`
+
+
+### Example
+Example call to FastGWR which can be called on desktop or HPC:
+```
+fastgwr run -np 4 -data input.csv -out results.csv -adaptive
+```
+```
+where:
+-np 4: using 4 processors.
+-data input.csv: input data matrix.
+-out results.csv: output GWR results matrix including local parameter estimates, standard errors and local diagnostics.
+-adaptive: Adaptive Bisquare kernel.
+-fixed: Fixed Gaussian kernel.
+-constant: Adding a constant column vector of 1 to the design matrix.
+-bw 1000: Pre-defined bandwidth parameter. If missing, it will (golden-section) search for the optimal bandwidth and use that to fit GWR model.
+-minbw 45: Lower bound in golden-section search.
+```
+
+The input needs to be prepared in this order:
+
+| X-coord | Y-coord | y | X1 | X2 | ...| Xk |
+
+|:-------:|:-------:|:-:|:--:|:--:|:--:|:--:|
+
+```
+where:
+X-coord: X coordinate of the location point
+Y-coord: Y coordinate of the location point
+y: dependent variable
+X1...Xk: independent variables
+```
+
+### Citation
+
+[FastGWR](https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1521523)
+
+Li,Z., Fotheringham,A.S., Li,W., Oshan,T. (2019) Fast Geographically Weighted Regression (FastGWR): A Scalable Algorithm to Investigate Spatial Process Heterogeneity in Millions of Observations. International Journal of Geographic Information Science. doi: 10.1080/13658816.2018.1521523