If you have been using the vector data and doing spatial analysis, you know shapefile and geojson. These are two of the most commonly used vector data formats to store data and carry out any spatial analysis. But, both these formats have a lot of disadvantages when you wish to scale your work and build integrated & automated workflows for large-scale deployments. And that’s why you need to use Geopackage files instead of shapefile or GeoJSON. Let’s dive deeper into the details.

If you would like to read more about geospatial data and how it’s changing the field of data…


Map of Tokyo, Japan showing land prices. Photo by Author

QGIS is one of the first tools you come across when you learn about GIS and spatial analysis. You can handle almost every aspect of spatial data and its processing using this open-source software package. Even though it has extensive GUI features to work on, sometimes it’s essential to have a way to deal with scripts. Especially for data scientists and data engineers building workflows, the need to have automated scripts that are scalable is high. QGIS offers a Python API called PyQGIS for this very purpose. You can automate most of the QGIS related actions and spatial algorithms through…


Map of Papa John’s Store locations in the USA. Photo by Author.

The use of location data has been on the rise in recent years. Everything we do is geo-tagged. It might be the restaurant we had a meal at, the jogging circuit we frequent. Companies and industries selling goods & services want to understand where the revenue is high, where their customers are based and many such location-related queries. But the data for places is usually in the form of addresses or a description relating to the location. And when we want to map and visualise, it’s generally not possible. Therefore, we need to assign the geographical coordinates to each of…


Since the beginning of civilization, man has been curious about the fascinating nature of the sky. Even though it looked like the movement of celestial objects was chaotic, he soon realized its unique patterns and how it could help him understand more about the earth and in a sense the universe itself.

One of the earliest applications that used the movements of these objects in the sky was navigation. The sun, the moon and the stars were the guides for man to know where he’s and to travel from a location to another over land or water. The celestial navigation…


LIDAR data showing the 3D visual of the US Capitol area, credits — usgs.gov

In the last 10 years, Data science and analytics has been the talk of the town everywhere. Data analytics has helped many companies make their decision making faster. Its importance is expected to grow even more in the coming years. But, there’s another aspect of data analytics that’s gaining speed quite rapidly that’s known as ‘Geospatial Data Analytics’.

What’s Geospatial Data?

Any conventional data becomes geospatial data when we add location information or a spatial identifier to it that refers to a position on the earth. It can be a building, lake, river, road, mountain and many other features present on the earth…

Nikhil S Hubballi

Data Scientist @Quilt.Ai | Spatial Data Science | GIS & Remote Sensing | Machine Learning | Space Sciences.. Read my blogs at https://samashti.tech

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