Geoprocessing is a framework and set of tools for processing geographic and related data. The comprehensive suite of geoprocessing tools can be used to perform spatial analysis or manage GIS data in an automated way. Geoprocessing is for everyone that uses ArcGIS Pro or any GIS software suite. Whether you are a new or advanced user, geoprocessing will likely be an essential part of your day-to-day work.

What is Geoprocessing?

https://pro.arcgis.com/en/pro-app/latest/help/analysis/geoprocessing/basics/python-and-geoprocessing.htm

https://data-flair.training/blogs/python-libraries/

https://gisgeography.com/python-libraries-gis-mapping/

https://www.rsgislib.org/

https://pro.arcgis.com/en/pro-app/2.7/help/analysis/geostatistical-analyst/how-inverse-distance-weighted-interpolation-works.htm

https://gisgeography.com/inverse-distance-weighting-idw-interpolation/

Python and geoprocessing

Python has over 200 standard libraries and thousands of third-party libraries. Here are some of the frequently used libraries for GIS purposes:

1. ArcPy: ESRI's ArcGIS Python library for performing geoprocessing operations.  It is a useful and productive way to perform geographic data analysis, data conversion, data management, and map automation with Python.

2. Geopandas: Python library capable of adding geographic components to tabular analysis, thereby make working with geospatial data in python easier.

3. GDAL: Python library used for translating between GIS data formats and extensions.

4. RSGISLib: It is a Remote Sensing and GIS software Library for raster processing and analysis. It is a collection of tools for processing remote sensing and GIS datasets.

5. PyProj: Library for performing GIS data projection and datum transformation within a range of geographic referencing systems. It can also be used to perform geodetic calculations and distance measurements for any datum.

6. LiDAR: Python library used for processing and visualizing Light Detection and Ranging (LiDAR) dataset. It is a Python package for delineating the nested hierarchy of surface depressions in Digital Elevation Models (DEMs). It is particularly useful for analyzing high-resolution topographic data, such as DEMs derived from LiDAR data.

Some Python Libraries for GIS and Mapping

The Normalized Difference Vegetation Index (NDVI) is a standard band-ratio calculation frequently used to analyze ecological remote sensing data. NDVI indicates whether the remotely-sensed target contains live green vegetation. When sunlight strikes objects, certain wavelengths of this spectrum are absorbed and other wavelengths are reflected. The pigment chlorophyll in plant leaves strongly absorbs visible light (with wavelengths in the range of 400-700 nm) for use in photosynthesis. However, the cell structure of the leaves strongly reflects near-infrared light (wavelengths ranging from 700 - 1100 nm). Plants reflect up to 60% more light in the near-infrared portion of the spectrum than they do in the green portion of the spectrum. By comparing the ratio of Near Infrared (NIR) to Visible (VIS) bands in hyperspectral data, we can obtain a quick look at the vegetation in the region of interest. NDVI is a normalized measure of the difference between reflectance at near-infrared and visible bands of the electromagnetic spectrum.
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Use this tutorial (https://www.battelleecology.org/calc-ndvi-py) to demonstrate how this process can be executed in Python.

Calculate NDVI and Extract Spectra using Masks in Python

https://eos.com/make-an-analysis/ndvi/

https://www.earthdatascience.org/courses/use-data-open-source-python/multispectral-remote-sensing/vegetation-indices-in-python/calculate-NDVI-python/

https://developers.planet.com/planetschool/calculate-an-ndvi-in-python/

https://www.battelleecology.org/calc-ndvi-py

Calculate NDVI in Python

IDW interpolation method is used to predict numerical values of a variable at areas with unknown values. The algorithm uses known variable values surrounding the unknown location to predict the numerical attribute value of that location. The known values closest to the prediction (unknown value) location have more influence on the predicted value than those farther away. IDW assumes that local (surrounding) influence on the predicted value diminishes with distance raised to the power of p. It gives greater weights to points closest to the prediction location, and the weights diminish as a function of distance, hence the name inverse distance weighted.
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The code below demonstrates how the IDW algorithm can be run in Python.

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Description: Interpolationing Unknown Elevation from Known Elevations of Neighbouring Points by IDW

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Variable description: z  = elevation; d = distance; p = power

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print ("Welcome to the IDW Interpolation Method Program")
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print ("===============================================")
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print ("")
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Entering input parameters
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z1 = float (input ("Input the known elevation value of the first location and press ENTER:   "))
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d1 = float (input ("Input the known distance value from the first location and press ENTER:  "))
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print ("")
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z2 = float (input ("Input the known elevation value of the second location and press ENTER:  "))
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d2 = float (input ("Input the known distance value from the second location and press ENTER:  "))
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print ("")
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z3 = float (input ("Input the known elevation value of the third location and press ENTER:   "))
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d3 = float (input ("Input the known distance value from the third location and press ENTER:  "))
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print ("")
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z4 = float (input ("Input the known elevation value of the fourth location and press ENTER:  "))
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d4 = float (input ("Input the known distance value from the fourth location and press ENTER: "))
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print ("")
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p = float(input ("Input the value of power (p) and press ENTER. For optimal results, p usually ranges between 1 and 2:  "))
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print ("")
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Simplifying variables
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A1 = z1/(d1**p)

A2 = z2/(d2**p)

A3 = z3/(d3**p)

A4 = z4/(d4**p)

B1 = 1/(d1**p)

B2 = 1/(d2**p)

B3 = 1/(d3**p)

B4 = 1/(d4**p)
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Calculting unknown elevation from known elevation points
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Z = (A1 + A2 + A3 + A4)/(B1 + B2 + B3 + B4)
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Printing the result rounded up to 3 decimal places
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print ("The Inverse Distance Weighted (IDW) elevation value of the unknown location is", round (Z,3))
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print ("======================================================================================")
print ("")
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End of program
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print ("End of the program ...")


Inverse Distance Weighted (IDW) Interpolation Method

Python is the scripting language of ArcGIS. ArcGIS includes a Python API, ArcPy, that gives users access to all geoprocessing tools as well as scripting functions and specialized modules that help them automate GIS tasks. Users can write scripts that use ArcPy in conjunction with a diverse array of functions provided through Python's standard and third-party libraries. They can execute Python commands and scripts with ArcGIS Notebooks, in the Python window, through script tools, or run Python outside of ArcGIS. However Python is run, the commands work the same way and use the same geoprocessing tools.
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University of Michigan - Ann Arbor

- Virtual Environments
- Package Management
- Python and the Web
- Concurrency and Paralellism
- Deep Learning
- Quantum Computing
- GUI Development
- Popular Tools for Python
- Computer Vision (OpenCV)
- Sorting Algorithms
- Image Processing

Python Programming Advanced Topics

When working on python projects, using "virtual environments" allows for your project to be run using its own isolated python binary. The packages that this project uses also exist separately from the python packages installed locally on one's system. This allows for greater organization while working within projects.

Virtual Environments

Concurrency and parallelism in Python are mainly handled by 2 built-in modules: Threading and multiprocessing. Threading allows Python to make use of multiple threads in order to speed up programs that are I/O dependent. Alternatively, multiprocessing allows Python to use multiple processes to speed up CPU bound scenarios.

Additionally, web servers, databases, and distributed networks also make use of the asyncio module. Asyncio allows for concurrent code so that the aforementioned systems don't have wait on execution

Concurrency and Paralellism in Python

Python is widely used for deep learning  with various reasons.

1. Python offers concise and readable code.

2. To reduce development time, programmers turn to a number of Python frameworks and libraries like kearas, TensorfFlow, and NumPy.

3.  Python is supported by many platforms including Linux, Windows, and macOS.

4. Python is the language that people Google more than any other.


Deep learning with Python

Quantum computing is an emerging field that appears to be more and more difficult to avoid in a vast number of fields including physics, business, mathematics, chemistry, and more notably, data science. One such aspect of quantum computing that attracts the minds of many, especially data scientists, is its potential in advancing our knowledge in optimization problems. As such, it may be invaluable to understand the basics of quantum computing and how to start quantum programming now, even before the widespread availability of quantum computers. Even better, companies like Google, Microsoft, IBM, Rigetti, and more have developed their own open-source development kits to begin learning quantum computing. One such software package is called QuTip within Python.

Quantum Computing with Python

- Anaconda
- Pycharm
- Jupyter Notebook
- Command-Line Interface (CLI)
- pyenv 
- PyInstaller
- pylint

Popular Tools for Python

When developing python applications, you often need to use libraries or packages that aren't included in python's standard library. In order to keep track of the required packages and libraries that your project depends on, you can make use of a package manager to install packages and manage dependencies. Python's documentation officially recommends the use of pipenv, but other package managers exist as well, such as pip and poetry.

Package Management

Python has an assortment of tools that are interwoven with the Internet. The language is frequently used both for web development and for interacting directly with the web.

Python and the Web

Python currently ranks third in usage behind C and Java. The original Python was developed in the 1980s by Guido van Rossum and was first released in 1991. Python 2.0 saw release in 2000. The current Python 3.0 was released in 2008 and represented a major revision emphasizing the design philosophy of code readability. 

Python Usage Statistics and History


OpenCV is a very useful tool for image recognition. It is often used in tandem with machine learning libraries, but also allow for relatively simple image manipulation tools such as rotating, resizing and changing data values. 

The child nodes will break into Basic Tools and Advanced Concepts.

Computer Vision with OpenCV

- Bubble Sort
- Selection Sort
- Merge Sort
- Quick Sort
- Shell Sort
- Radix Sort
- Bitonic Sort
- Bucket Sort
- Quickselect
- Heap Sort

Sorting & Selection Algorithms

Geoprocessing with Python

Python libraries are called "modules". They are a reusable chunk of codes that can be included in your programs to provide various functionalities in the form of different objects or functions. For example, there are modules that have functions you can use in your code to test if certain files exist on your hard drive. Likewise,  there are other modules that have functions for implementing web-server or web-browser functionality or to work with images or create charts and graphs; etc.
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The Python standard library comes with the Python package and installs with it when Python is installed on your computer. 
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Additionally, there is a growing number of Python's third-party libraries that canbe imported into your scripts to executes tasks that outstrip the limits of the standard library.
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Python Libraries

This is a Python port of Google's libphonenumber library It supports Python 2.5-2.7 and Python 3.x (in the same codebase, with no 2to3 conversion needed).
Original Java code is Copyright (C) 2009-2015 The Libphonenumber Authors.
Release HISTORY, derived from upstream release notes.

PyPI: https://pypi.org/project/phonenumbers/

phonenumbers Library

Pillow is a fork of the discontinued Python Imaging Library (PIL) and is the most popular package for image processing. It allows for the creation, editing, and exporting of images, as well as including support for a large number of image file formats. Pillow is not included with a Python installation and must be installed with a package manager such as pip.

Image Processing with Pillow

PyMongo is a Python distribution containing tools for working with MongoDB, and is the recommended way to work with MongoDB from Python. The documentation is referenced below. 

PyMongo Distribution

Python offers many different libraries and frameworks for the development of Graphical User Interfaces (GUIs). Many of these tools can create cross platform GUI applications with varying degrees of customizability. At a base level, these frameworks provide the user the ability to create windows, labels, buttons, and canvases (for drawing things) among other graphical features for their applications.

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