Fundamentals of Geospatial Data

Explain Live Session –

Developed in collaboration with IBM Research, UK.

This is part of a three-course pathway to learning and understanding the capabilities of geospatial data analytical tools and techniques and how it can be used for industry.

In this course you will first be given an introduction to climate datasets and climate risk assessment, before being led through a series of examples, all related to post-event analysis of an extreme weather event (in this case flooding).  

Following an introduction to climate data science and risk assessment, you will begin the first interactive session, where you will query and explore the datasets used in subsequent parts using the example toolkit, GeoDN. You will use Jupyter notebooks to carry out in-depth analysis of flood risk. You will learn how to calculate anomalies to find unusually high rainfall events using python, and also how to intersect raster and vector datasets to identify properties at risk programmatically. 

In the second part, you will learn about impact functions, which connect the severity of a climate hazard (in this case, flood depth), to the proportion of a building expected to be damaged. You will create and deploy a GeoDN workflow to combine flood risk with impact functions and building locations and categories and complete a full climate risk calculation.  

 In the final part of the course, you will be shown how to train, then develop and onboard an AI model. This will give you the building blocks to be able to use AI modelling at scale. 

 This course is directly relevant to insurance or asset management analysts interested in expanding their knowledge of climate risk and geospatial data science/modeling, and will also be relevant for any python-experienced data scientists who are looking for a general introduction to geospatial analytics and climate risk, or to data analytics with GeoDN. 

In this level course, you will:

• Learn which weather, climate and related datasets are needed for climate risk assessment, and the challenges of working with these datasets 
• Learn how hazard, exposure and vulnerability are combined in a climate risk calculation 
• Discover and explore datasets relevant to flood risk via interactive querying and visualization 
• Learn how to upload local data and combine with large raster datasets 
• Learn how geospatial discovery operations can be used to efficiently find interesting events in a dataset 
• Combine raster and vector data to locate buildings at risk 
• Create an impact function describing the relationship between flood severity and damage 
• Create and run a hazard to impact workflow 
• Use the workflow to calculate the damage exposure of a set of buildings. 

Pre-requisites:

• Completion of the Practical Guide to Geospatial Data Course 
• Experience with Python programming and using Jupyter notebooks 
• Previous geospatial data science experience is not necessary 

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