November 2023 Summaries

The text discusses the analysis of mobility data using a cloud-native approach with MovingPandas and CARTO. It highlights the challenges posed by traditional GIS systems in handling large-scale mobility data, but showcases how leveraging cloud-native tools can provide insights into movement patterns and behavior. The tutorial demonstrates how to work with trajectories, clean location errors, transform data to a spatial index (H3), clip trajectories to polygons, and perform space-time hotspot analysis using CARTO Workflows. By combining these techniques, organizations can gain valuable insights from mobility data and drive decision-making for optimizing resources around spatio-temporal demand.

Pie charts, despite their ubiquity and criticism from prominent figures, can be an effective data visualization tool when used correctly. They have limitations, such as difficulty in interpreting precise values without data labels or being able to compare overall amounts. However, by following five principles - emphasizing key data points, strategic use of color, utilizing data labels, meaningful titles, and maintaining simplicity - pie charts can be made more interpretable. Another type of pie chart, the doughnut chart, is even easier to interpret because it prompts the viewer's gaze to traverse the circumference, allowing for a better judgment of linear distances. Pie charts can also be useful in spatial data visualization, such as in Cannibalization Analysis, where they help compare market sizes and provide additional context with widgets like the Pie Widget.

The home insurance industry is adopting GIS analysis using modern geospatial tech stack, enabling analysts to generate hyper-local insights at scale. Crime data can be used to assess insured risk by analyzing the number of crimes in an area and contextualizing it with population density. By converting crime locations into a hexagonal Spatial Index called H3, insurers can efficiently analyze large datasets and make informed decisions on customized premium pricing, risk mitigation, targeted marketing, and fraud detection. Cloud-native geospatial analysis tools like CARTO are making this process faster and more accessible.

The need for reliable public charging infrastructure is becoming increasingly important as electric vehicles (EVs) make up 14% of global vehicle sales. Accurate data on existing EV charging network infrastructure is critical to answer questions about where and when new infrastructure should be deployed. Eco-Movement has established a comprehensive real-time database of EV charging locations, which contains over 25 attributes at location, EVSE, and connector levels, covering more than a million connectors across over 70 countries. This data can be accessed through CARTO's Spatial Data Catalog and used for various purposes such as optimizing charging infrastructure placement, charging network optimization, and market research and strategy by geospatial analysts and data scientists.