December 2022 Summaries

This article highlights 22 of the best maps, visualizations, and analysis created with CARTO throughout 2022. The maps cover various topics such as retail hospitality, CPG, transport and mobility, real estate and insurance, climate and sustainability, and society and economy. Each map showcases a unique use case for spatial data analysis, from identifying optimal locations for EV charging stations to visualizing the distribution of ride-hailing services in New York. The maps demonstrate the power of geospatial analytics in solving business problems and answering questions, and inspire readers to explore their own spatial data visualizations with CARTO.

Loading geospatial data into Amazon Redshift can be complex, but there are several ways to do it. The post outlines four methods: using CARTO, which allows for easy import of geospatial data into Redshift; loading data with Python, transforming data to a CSV format and then importing it into Redshift; using the native COPY command, which requires formatting the data correctly; and using Airbyte and dbt, an ELT tool that focuses on extracting, loading, and transforming data at scale. These methods can help simplify the process of loading geospatial data into Redshift, making it easier to visualize and analyze.

To calculate spatial hotspots and determine which tools are most suitable, consider using either Getis-Ord* or Local Moran's I. Getis-Ord* identifies clusters of high values relative to the overall dataset, while Local Moran's I not only identifies these clusters but also detects outliers within them. When choosing between the two, consider whether you want to focus on general hotspot analysis or also identify isolated anomalies. If you're dealing with large datasets, Local Moran's I may be more suitable due to its ability to process complex calculations, but it comes at a higher processing time cost. For smaller-scale analyses, Getis-Ord* is often sufficient and faster to run. By understanding the differences between these two hotspot analysis tools and how they can be applied to your specific use case, you can unlock valuable insights into spatial trends and patterns in your location intelligence workflows.