As geospatial technology is slowly embracing data science, deep learning, and artificial intelligence, many programmers and developers have taken it upon themselves to create efficient libraries such as R Libraries, Python Libraries etc that can help in developing simple and effective maps.
Hence, mapping has slowly become an integral part of Python and R studio.
Python programming language is often defined as an interpreted high-level general-purpose programming language. Python’s design philosophy emphasizes code readability with its notable use of significant indentation. Its language constructs as well as its object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.
Inspired by innovators in science, education, government, and industry, RStudio develops free and open tools for R and enterprise-ready professional products for teams who use both R and Python to scale and share their work.
Today, millions of people download and use RStudio open-source products in their daily lives, while thousands of organizations and individuals have the need and ability to pay for commercial products on-premises or online.
According to its website, R Studio’s mission is to create free and open-source software for data science, scientific research, and technical communication.
The usage of R in GIS is growing because of its enhanced capabilities for statistics, data visualization, and spatial analytics while Python is particularly good at working with file systems, networks, web scraping, and automation.
Let’s have a look at the major Python and R libraries specifically meant for mapping.
ArcPy is a Python site package that provides a useful and productive way to perform geographic data analysis, data conversion, data management, and map automation with Python.
This package provides a rich and native Python experience, offering code completion (type a keyword and a dot to get a pop-up list of properties and methods supported by that keyword; select one to insert it) and reference documentation for each function, module, and class.
Working with geospatial data in Python is made easier by the open source project GeoPandas. Pandas extend the datatypes used by pandas to allow spatial operations on geometric types. Geometric operations are performed by Shapely.
The goal of GeoPandas is to make working with geospatial data in Python easier. It combines the capabilities of Pandas and shapely, providing geospatial operations in Pandas and a high-level interface to multiple geometries to Shapely.
GeoPandas enables you to easily do operations in Python that would otherwise require a spatial database such as PostGIS.
GDAL is a translator library for raster and vector geospatial data formats that is released under an X/MIT style Open Source License by the Open Source Geospatial Foundation. As a library, it presents a single raster abstract data model and a single vector abstract data model to the calling application for all supported formats.
It also comes with a variety of useful command-line utilities for data translation and processing.
PySAL is an open-source project designed to support spatial data science. It is released under the modified BSD license. It enhances the visualization of spatial data and works very well when combined with tools such as Matplotlib.
It creates and integrates maps into your R workflow. This package helps to design cartographic representations such as proportional symbols, choropleths, typology, flows, or discontinuity maps. It also offers several features that improve the graphic presentation of maps, for instance, map palettes, layout elements (scale, north arrow, title), labels, or legends.
A collection of functions to visualize spatial data and models on top of static maps from various online sources (e.g., Google Maps and Stamen Maps). It includes tools common to those tasks, including functions for geolocation and routing.
Mapview provides functions to very quickly and conveniently create interactive visualisations of spatial data. Its primary goal is to bridge the gap between quick (not presentation-grade) interactive plotting and visually investigating both aspects of spatial data: the geometries and their attributes.
There is a demand for GIS programming. That is why these libraries are being developed with urgency. Both R and Python developers want to ensure that GIS practitioners get the best when it comes to programming libraries.
If you are interested in learning R studios and Python programming for spatial analysis, there are many online platforms for beginners, intermediates, and experts.
Alternatively, you can visit the tutorials page on this website and begin your journey.
I am almost certain there will be more GIS packages in the near future that incorporate machine learning and artificial intelligence. Watch this space for more details.