How do you make your GIS code faster and more scalable?

1 Choose the right tool

Not all GIS tools are created equal. Some are better suited for certain tasks than others, and some have more features and options than you need. Depending on your project and goals, you should choose the tool that matches your requirements and capabilities. For example, if you are working with raster data, you might want to use a tool that supports parallel processing, such as GDAL or Rasterio. If you are working with vector data, you might want to use a tool that supports spatial indexing, such as GeoPandas or Shapely.

2 Use vector formats

Vector formats are more efficient and flexible than raster formats for storing and processing spatial data. They use less disk space, memory, and bandwidth, and they allow you to perform more operations and transformations on the data. For example, you can easily clip, merge, dissolve, or buffer vector data, while raster data requires more steps and conversions. If you have raster data, you should consider converting it to vector data using tools such as GDAL or QGIS, or using vector tiles instead of raster tiles for web mapping.

3 Simplify your data

One of the easiest ways to make your GIS code faster and more scalable is to simplify your data. Simplifying your data means reducing the number of features, vertices, attributes, or resolution of your data, while preserving the essential information and quality. Simplifying your data can improve your processing speed, storage efficiency, and visualization performance. You can simplify your data using tools such as QGIS, ArcGIS, or GeoPandas, or using algorithms such as Douglas-Peucker or Visvalingam-Whyatt.

4 Use spatial indexes

Spatial indexes are data structures that help you find and access features based on their location. They can speed up your spatial queries, such as finding the nearest neighbor, intersecting features, or calculating distances. Spatial indexes can also reduce the amount of data that you need to scan or load into memory, which can save you time and resources. You can use spatial indexes with tools such as GeoPandas, Shapely, or Spatialite, or using libraries such as Rtree or PyGEOS.

5 Parallelize your code

Parallelizing your code means running multiple tasks or processes at the same time, instead of sequentially. This can make your GIS code faster and more scalable by taking advantage of the multiple cores or threads of your CPU or GPU, or by distributing the workload across multiple machines or clusters. Parallelizing your code can also help you overcome the limitations of single-threaded or single-process tools, such as ArcGIS or QGIS. You can parallelize your code using tools such as GDAL, Rasterio, or Dask, or using libraries such as multiprocessing, concurrent.futures, or PySpark.

6 Profile and test your code

The last step to make your GIS code faster and more scalable is to profile and test your code. Profiling your code means measuring and analyzing the performance and resource usage of your code, such as the execution time, memory consumption, or CPU utilization. Testing your code means checking and verifying the correctness and quality of your code, such as the output, accuracy, or error handling. Profiling and testing your code can help you identify and fix the bottlenecks, bugs, or inefficiencies of your code, and improve your code quality and reliability. You can profile and test your code using tools such as cProfile, memory_profiler, or pytest, or using IDEs such as PyCharm or Visual Studio Code.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?