Data Transformations and GIS Analysis Practical Example

In this tutorial, you will learn how to make use of Pandas library of python programming language to transform a dataset using various data wrangling and manipulation techniques, and then use that dataset in QGIS for performing geospatial analysis.

In this example, we download a dataset from the UK police database, which shows crime incidents recorded in the city of London. Usually the freshly downloaded data in its raw format are not in a suitable structure to be used for analyses, hence will require undergoing a number of intermediary data transformation steps.

Finally, we import the cleaned dataset into QGIS using latitude and longitude information provided, to visualize the spatial locations of the crime incidents. Furthermore, by way of employing a categorical symbology type, we also visualize the crime incidents by type, which can provide a much clearer outlook of the data. We also create a heatmap based on the density of the points, which allows us to clearly visualize where the crime hotspots are located at. We use QGIS for performing all of the spatial visualization tasks, including the creation of the spatial heatmaps for identifying crime hotspots.

#dataanalysis #spatialdata #geodeltalabs

[geoempowerinitiative]

I'm always eagerly looking forward to your next tutorial because you never ever disappoint. You are a legend!!

[ТувеЯнссон-оу]

Thank you very much for fantastic tutorial!

[mohanneupane]

will be happy if you support the data working with .las file for x.y.z coordinate of the route.

[hikikomorihachiman]

Thank you.
Sir, how do you have that gui at the top right corner? that r studio also has, that shows us what variables or dataframes are saved.
How to get something similar to that in vs code?