With an increase in programming literacy, GIS applications now use programming more than ever. From the mapping of tabular data to the most advanced AI workflows, programming serves to make spatial analysis more efficient and scalable than ever before. Programming alone cannot accomplish many GIS tasks, such as georeferencing and digitization, but it can automate many of the time consuming processes, once that process has been established.
All GIS software is built on programming languages and the GIS libraries which are written in programming languages, such as C++ or Python. For example: when using a GIS software such as ArcGIS Pro or QGIS, you are also using programming libraries such as GDAL/OGR. Instead of using GDAL through traditional software, you can directly use GDAL through your Python code, allowing you to save the time and resources for other tasks. This is very important for repetitive tasks, or tasks which require more computational power than your computer allows. Using Python, you can automate a process, and have this process run on a cluster computer in parallel. If you are doing transformations of thousands of satellite images, or finding the intersections of every waterway and roadway in a country, or running global scale statistics, you will need to use Python.
Other programming languages are quickly becoming popular for mapping. For example: R is a statistics language that has statistical packages for spatial statistics. While spatial statistics can be conducted using traditional GIS software such as GeoDa, cutting edge statistical methods are usually implemented using code, because this allows researchers to modify existing methods and create new ones from scratch, rather than being confined to the methods available in a traditional GIS software. Once a new method is widely understood, refined, and accepted, it may become part of a GIS tool or plugin. However, in order to replicate the latest research, you may need to use a contemporary GIS approach which is centered around programming and GIS.
Modern GIS technology trends and it's future.
Popular programming languages for GIS include: Python, C++, R, and Javascript.
Python is often the best choice for scientific workflows and easy automation, while C++ is better for big data, parallel computing, and faster computing times. Either can be used for creating new algorithms and examining existing methods, but Python is now a leader in the scientific community, making Python the programming language of choice for integrating more advanced analysis methods, such as artificial intelligence. Javascript is often used in the creation of interactive data visualizations such as webmaps and dashboards.
Research reproducibility is another factor that favors programming. When researchers use open source programming, it makes their research output more reproducible and verifiable. Sharing source code increases workflow transparency, and allows other researchers to improve upon your work. Two hallmarks of high quality experiments are replicability and repeatability. Additional benefits to using contemporary GIS methods include integration with version control software such as Git, cross-platform compatibility, greater scalability, and increased collaborative potential.
