Over 125 billion dollars were lost in damage due to Hurricane Harvey. Whether you are in the Retail, Logistics, Energy, Insurance or many other sectors, extreme weather events cause major losses due to asset damage, failure in supply chain, etc. In this workshop we will demonstrate how to use a data driven approach for designing a supply chain network that is resilient to weather extremes both for the current climate state and also under future climate scenarios and time horizons. We will also explore how to identify the assets at risk due to real weather events for emergency response planning and impact analysis.

With continuous growth in the size of datasets and declining cost of storage, it’s becoming more common to use a data warehouse like Amazon Redshift for geospatial datasets. With AWS Data Exchange, you can find and subscribe to third-party data sets and seamlessly query them alongside your first party data in Amazon Redshift. In Amazon Redshift you can use spatial joins, built-in spatial functions, and machine learning models on your data directly in Amazon Redshift using SQL which analysts are already familiar with. By keeping the data in Amazon Redshift you can benefit from the best price-performance of any cloud data warehouse. With the geospatial capabilities enabled in Amazon Redshift, combined with the tools from CARTO, it’s possible to solve most spatial use cases natively in the cloud.

Taylor Swift's recent Eras tour is set to generate $4.6 billion in consumer spending - but with only (!) 146 shows, many fans and businesses are set to miss out. In this workshop, Helen will walk you through how to use Spatial Indexes, geosegmentation, spend data and a lot of puns to solve the important spatial problem: which areas are experiencing Taylor Swift deprivation?

This practical workshop will teach geospatial professionals to leverage Snowflake and CARTO capabilities for location-based applications. Using a combination of Snowflake’s built-in functions and CARTO Analytics Toolbox, participants will answer business questions and explore real-life use cases. This session is suitable for geospatial practitioners at all levels, whether they are beginners or advanced developers seeking to explore Snowflake’s latest features.

This hands-on workshop will focus on a variety of contexts where spatial indexes can be used to enhance the performance of spatial analysis and visualizations when using "big data". This session is best for anyone with (at least) some basic SQL knowledge and a desire to learn how to scale their workflows to massive datasets.

Mobility data alone doesn’t provide much value unless it’s analysed and transformed into actionable insights. Technical difficulties for data cleaning and processing, and the expertise needed to extract customer behaviour from mobility data can be a real pain - but once mastered it is a game changer for businesses. In this hands-on workshop attendees will work on a real-world use case and learn about the necessary tools to drive business decisions. During the workshop, attendees will download Echo's mobility data and will work on Python, Geopandas and a Jupyter notebook to successfully master their analysis of mobility insights.

This practical workshop will teach people of all geo backgrounds how to leverage CARTO Workflows to build repeatable low code/no code spatial analytics within a cloud environment. Attendees will build their own Workflows to produce a complete spatial analysis, from raw data to clean results and visualization. This session is good for geo-practitioners at all levels, but a general understanding of spatial analysis and SQL is recommended.

Spatial data that changes over time presents difficulties because of the cost, time, and complexity to process and analyze at scale.  But this data holds valuable, latent and undiscovered insights that can make your analysis deeper (historically), broader (spatially), faster (analytically), and stronger! Join us for a fun and interactive session with Josh and Robert, senior data scientists who specialise in working with global-scale, spatiotemporal datasets. In this session, you'll use CARTO, Python and other familiar technologies to explore and overcome some of the most complex and challenging examples of geospatial analysis, get a glimpse into the future of geospatial data science and pocket some actionable takeaways.

This tutorial provides an introduction to urban spatial analysis using PySAL and geosnap, demonstrating how to use spatial data science to uncover market areas in the city for both residential and employment markets. The tutorial first introduces geodemographic segmentation and regionalization as techniques for understanding the consumer structure of a metropolitan region, and then introduces the combination of spatial autocorrelation measures with computational geometry as a method for uncovering employment market areas in the city. These analytics can be used to better understand the existing and emerging "spatial structure" of a city, identify targeted uses in different portions of the region, and coordinate the co-location of other goods and services like transportation and accommodation.

Thousands of Earth Observation (EO) satellites are flying overhead, thanks to the SmallSat revolution. With space-to-ground bandwidth struggling to keep pace, availability windows come at a premium. In this workshop, you'll explore the benefits of pre-processing data using Python to train a Machine Learning (ML) model that pre-select only high-quality imagery for downlink. The model will be lightweight, and capable of running on off-the-shelf, space-ready hardware.