July 2023 Summaries

Visualizing datasets like temperature, wind, and precipitation on maps provides crucial context for understanding global weather patterns and earth surface phenomena. The National Oceanographic and Atmospheric Administration (NOAA) offers extensive observational and modeled data, but translating this information into compelling cartographic visualizations can be complex. This guide demonstrates how to leverage NOAA's data using the Felt API and Python libraries within a Jupyter notebook environment, focusing on creating visualizations such as global wind gusts from the Global Forecast System (GFS) model. The process involves accessing data via the NOAA NOMADS portal, handling data ephemeral challenges, and employing techniques like color mapping for effective visual representation. The tutorial also highlights using tools like rasterio for data handling and matplotlib for visualization, ultimately facilitating the creation of detailed maps on Felt's platform, which supports annotating atmospheric features. Despite the technical challenges, the approach aims to simplify the process of integrating weather data into visual maps, with Felt planning to enhance these capabilities in the future.

Weather visualization using datasets like temperature, wind, and precipitation is crucial for understanding global patterns and conditions. The National Oceanographic and Atmospheric Administration (NOAA) provides a wealth of data through its NOMADS portal, featuring models such as the Global Forecast System (GFS) and High Resolution Rapid Refresh (HRRR). These models offer extensive atmospheric, oceanic, and space data, which can be challenging to extract and visualize effectively. The post describes how to use the Felt API to transform NOAA data into cartographic visualizations, utilizing Python libraries within a Jupyter notebook environment. The process involves accessing NOAA data, converting it into visual formats using color mapping techniques, and uploading it to Felt for display. Additionally, the article highlights the importance of annotating patterns using Felt's tools and encourages exploring the platform's capabilities for enhancing weather visualizations. While the current method is technical, Felt aims to simplify data integration and visualization in the future, fostering a community of mapmakers and developers to share and improve their creations.

The text highlights various mapping projects created using the Felt platform, showcasing diverse applications of geographical data visualization. Jean-Louis Zimmerman mapped tourist attractions in Vaucluse, France, while Chris Soriano depicted the lava flow path from the 2018 Kilauea eruption. Lanrick Bennett Jr. shared a bike tour map in Toronto, and Kaine Robertson visualized taxable property improvement values in Faulkner, Arkansas, which benefits various stakeholders such as insurers and property developers. Alex Alsup examined property ownership in Hancock, Maine, revealing significant out-of-state ownership. Márton Leitold created a map of the 2023 Tour de France stages, and Bobby Feltault mapped public transport options in Astoria, Queens. David Moss visualized a decade of his travels, while Stace Maples georeferenced a historical map of Amsterdam. Mamata Akella used national data to map wind speeds and turbine capacity in the U.S. The text also invites readers to participate in a QGIS mapping challenge to create maps and be featured in the Felt Map Gallery.

Felt, a versatile mapping platform, has been employed by various individuals to create unique and informative maps across diverse subjects and locations, showcasing its capacity to visualize and share geographic data effectively. Notable projects include mapping tourist attractions in Vaucluse, France, the 2018 Kilauea lava flow, and the Tour de France 2023 stages. Other innovative uses involve visualizing property data in Faulkner, Arkansas, and out-of-state property ownership in Hancock, Maine, as well as documenting personal travel experiences and historical georeferencing like the 1747 Plan van Amsterdam. The platform also supports environmental mapping, as demonstrated by a project on wind speeds and turbine capacity in the contiguous US. Additionally, Felt has launched a QGIS mapping challenge to encourage further creative use of its tools, offering participants the chance to have their maps featured in the Felt Map Gallery.