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Spatial thinking with Python


Data scientists work with all kinds of data, It could be a text an image or maybe it is a bunch of coordinates. Though computer vision and natural language processing have hit it off spatial data science doesn’t get the attention it deserves. Spatial data has both social and industrial impact. Spatial data is useful in agriculture and for observing weather patterns to predict natural disasters. It is also very important for industries that deal with logistics and supply chain management.

In this talk I would talk about spatial data it’s importance and elaborate on how to store, manipulate and visualize such data. I would talk about the use of Python and several related modules (GDAL, Shapely, Fiona etc) in processing geospatial data. I would also like to discuss QGIS, a desktop based open source GIS platform and a variety of useful operations that can be done with it. I would also like to talk about building geospatial dashboards to share and serve spatial layers and discuss the geospatial support provided by the various open source projects like postgis, geonode, elastic stack V7.0 and much more


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