As we enter 2024, the realm of data science and technology is poised for a transformative shift towards automation. Reports and trends indicate that this year will mark a significant acceleration in the adoption of automation technologies, particularly within the data science domain. Artificial intelligence and machine learning are set to play pivotal roles in streamlining processes, optimizing workflows, and enhancing decision-making across industries. Organizations are increasingly recognizing the efficiency gains and competitive advantages that automation can bring, making 2024 the year where automation takes center stage.
In the field of Geographic Information Systems (GIS), the demand for quicker and more efficient approaches is on the rise. Traditional GIS tools often face challenges in handling large and complex spatial datasets, leading to a growing need for faster solutions. Python, with its robust libraries and scripting capabilities, emerges as a key player in addressing these challenges. This blog explores the evolving landscape of GIS and how Python is becoming an essential tool for developing rapid and effective geospatial workflows.
Map clipping, a crucial operation in GIS, has historically been a time-consuming process in GUI-based software. With the conventional approach taking minutes for a single map, the prospect of dealing with maps for 365 years becomes impractical. This blog advocates for programming-based approaches to automate map clipping, saving time and resources. By exploring scripting languages and automation, the blog demonstrates how this shift can revolutionize the tedious task of map clipping into a swift and efficient process.
In the context of spatial data analysis, specifically with NetCDF files, automation becomes paramount. This blog provides a practical guide on automating NetCDF file clipping using Python and shapefiles. Leveraging popular libraries like xarray and geopandas, the tutorial showcases how to efficiently extract and process spatial data, providing a seamless integration into data analysis pipelines. Finally, the concept of clipping in GIS is demystified in simple terms. Whether cutting out a specific region from a map or extracting spatial data based on defined boundaries, clipping emerges as a fundamental and accessible operation in spatial analysis with diverse applications.
In our previous classes, we covered the topic of handling NetCDF data and many more. If you missed the sessions, you can catch up on it by visiting our website at https://theaireenproject.com/category/python-classes/.
Today, we will explore how to clip NetCDF data using shapefile in python. We’ll also learn plot some customizations along.
The notebook (Clipping_netcdf_using_shapefile.ipynb) can be accessed via the ClimoMarineLab’s GitHub repository here: https://github.com/akashspunnayil/ClimoMarineLab/tree/main/Python and test data can found in the data/ directory.
1. Install necessary packages
ignore if you have already installed these packages
pip install netCDF4
pip install numpy
pip install matplotlib
pip install pandas
pip install xarray
pip install cartopy
pip install geopandas
pip install shapely
pip install rioxarray2. Load packages
2. Load precipitation NetCDF data¶
3. Load shapefile¶
4. Read variables
5. quick look on the shapefile
Now we can see the shapefile as below.
6. Clipping data using shapefile
Now the clipped precipitation map for India will look like below.
7. Subplots for monsoon months
The subplots corresponding to the months of June, July, August, and September is illustrated below.
Having gained the knowledge of how to trim your NetCDF data using a shapefile of your choice, you are now encouraged to apply this code to your specific dataset and shapefile. Feel free to experiment with the code using your own data, allowing you to refine your skills and adapt the process to suit your individual requirements. This hands-on practice will not only reinforce your understanding but also empower you to confidently utilize the code for spatial analysis tasks tailored to your unique datasets and research goals.
Feel free to reach out to the author if you have any questions or need any scientific assistance: Akash (akashspunnayil@gmail.com)
Happy coding!
Cheers!
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