Interpolation and Evaluation

Table of Contents:

• Objective
• Installation
• Library Installation
  • Install pandas
  • Install arcpy
  • Install geopandas
  • Install rasterio
  • Install permetrics
• Functions Used in Libraries
  • Pandas Functions
  • Arcpy Functions
  • Geopandas Functions
  • Rasterio Functions
  • Permetrics Functions
• Example
  • Excel Data from YouTube
  • Variables
  • Output Images
  • Evaluation Metrics
• Code Development
  • Interpolation
  • Evaluation
• Contributing
• Contact

Objective

The goal of this code is to find the best interpolation method for quantitative data and predict other areas in GIS. The selected methods will be evaluated using metrics such as RSME, R, MABE, MAPE, etc. For more details, click here.

Installation

Clone the repository:

git clone https://github.com/yourusername/yourproject.git
cd yourproject

Library Installation

Install Pandas

This library is installed by default and is recognized as pd in the code.

Install Arcpy

If ArcMap or ArcGIS Pro software is installed, arcpy is installed by default.

Install Geopandas

0. Install Anaconda.

1. Remove all environments cloned from Anaconda through the package manager of Anaconda or ArcGIS Pro.

2. Open the Conda prompt for ArcGIS from the following address:

   C:\Program Files\ArcGIS\Pro\bin\Python\Scripts\proenv.bat

3. Clone from arcgispro-py3 using the following command:

   conda create --name gisenv --clone arcgispro-py3

   Note: You can use any name instead of gisenv.

4. Activate the environment created in step 3 using the following command:

   activate gisenv

5. Install geopandas and its required packages using the following commands:

pip install geopandas 

pip install folium

pip install matplotlib 

pip install mapclassify

Install Rasterio

0. Install Anaconda.
1. Remove all environments cloned from Anaconda through the package manager of Anaconda or ArcGIS Pro.
2. Open the Conda prompt for ArcGIS from the following address:

C:\Program Files\ArcGIS\Pro\bin\Python\Scripts\proenv.bat

3. Clone from arcgispro-py3 using the following command:

conda create --name gisenv --clone arcgispro-py3

Note: You can use any name instead of gisenv.

4. Activate the environment created in step 3 using the following command:

activate gisenv

5. Install rasterio using the following command:

pip install rasterio

Install Permetrics

0. Install Anaconda.
1. Remove all environments cloned from Anaconda through the package manager of Anaconda or ArcGIS Pro.
2. Open the Conda prompt for ArcGIS from the following address:

C:\Program Files\ArcGIS\Pro\bin\Python\Scripts\proenv.bat

3. Clone from arcgispro-py3 using the following command:

conda create --name gisenv --clone arcgispro-py3

Note: You can use any name instead of gisenv.

4. Activate the environment created in step 3 using the following command:

activate gisenv

5. Install permetrics using the following commands:

git clone https://github.com/thieu1995/permetrics.git
cd permetrics
python setup.py install

Functions Used in Libraries

Pandas Functions

GitHub Pandas

Website Pandas

Function Name	Input Parameters	Output Type	Link
read_excel	ExcelFile, Sheet Name	DataFrame	Link
iterrows	None	Row in DataFrame	Link
drop	Labels, Axis	DataFrame or None	Link
at	Row, Column	Everything	Link
DataFrame	Data, Index	DataFrame	Link
columns	Origin, Destination	Object	Link
rename	Dictionary	DataFrame	Link
merge	DataFrame, On, How	DataFrame	Link
to_excel	ExcelFile, Index	Excel	Link

Arcpy Functions

GitHub Arcpy

Website Arcpy

Function Name	Input Parameters	Output Type	Link
sa.Idw	Shapefile or Feature Class, Z_Field, Method, Cell_Size	Raster	Link
sa.Kriging	Shapefile or Feature Class, Z_Field, Method, Cell_Size	Raster	Link
sa.Spline	Shapefile or Feature Class, Z_Field, Method, Cell_Size	Raster	Link

Geopandas Functions

GitHub Geopandas

Website Geopandas

Function Name	Input Parameters	Output Type	Link
GeoDataFrame	Data, Geometry	GeoDataFrame	Link
points_from_xy	Longitude, Latitude	Point	Link
set_crs	EPSG, Inplace	Int	Link
to_file	Filename, Driver, Encoding	Everything	Link

Rasterio Functions

GitHub Rasterio

Website Rasterio

Function Name	Input Parameters	Output Type	Link
open	Raster	File	Link
index	Longitude, Latitude	Tuple	Link
read	1, Window	Array	Link

Permetrics Functions

GitHub Permetrics

Website Permetrics

Function Name	Input Parameters	Output Type	Link
RegressionMetric	Y_true, Y_pred	RegressionMetric	Link
mean_bias_error	None	Double	Link
root_mean_squared_error	None	Double	Link
mean_percentage_error	None	Double	Link
pearson_correlation_coefficient	None	Double	Link

Example

Excel Data from YouTube

The table below is based on the YouTube video.

The file is sample.xlsx.

This file is model.xlsx.

Variables

workspace: The address where the interpolation.gdb file should be created.

address_input: The address of the sample Excel file.

zone: The study zone, which is between 38 and 41 for Iran.

raster_address: By default, it is C:\temp.tif. If it gives an error, change the image address, and it will work correctly.

address_model: The address of the model Excel file.

program: Continue the program or not.

Column 2 should be x, and column 3 should be y.

field: Reads from the model Excel.

final_Excel: Asks for the final Excel address.

start_column: Stores the column number of methods, which is 4 by default.

evaluate_Excel: Asks for the evaluation Excel address.

Output Images

Final Excel:

Evaluation Excel:

Evaluation Metrics

Evaluation Methods

The variables are as follows:

Code Development

Interpolation

Other Interpolation Methods

if interpolation_type == "Idw":
 out_raster = arcpy.sa.Idw(temp_address, field, method)       
elif interpolation_type == "Kriging":
 out_raster = arcpy.sa.Kriging(temp_address, method, field)
elif interpolation_type == "Spline":
 out_raster = arcpy.sa.Spline(temp_address, field, spline_type=method)
else:
 return None

Evaluation

Other Evaluation Methods

if method == "MBE":
    res = evaluator.mean_bias_error()
    res_dict[i] = [col, res]  
elif method == "RMSE":
    res = evaluator.root_mean_squared_error()
    res_dict[i] = [col, res]
elif method == "MAPE":
    res = evaluator.mean_percentage_error()
    res_dict[i] = [col, res]
elif method == "R":
    res = evaluator.pearson_correlation_coefficient()
    res_dict[i] = [col, res]
else:
    return None

Contributing

1. Fork the repository.

2. Create a new branch:git checkout -b feature-newfeature.

3. Commit changes: git commit -m 'Add new feature'.

4. Push to the branch: git push origin feature-newfeature.

5. Submit a pull request.

Contact

• Email: [email protected]
• Telegram : mttaherpoor