June 2021 Summaries

Mapbox has expanded its aerial imagery coverage in Europe by incorporating high-quality data from PDOK for the Netherlands and SwissTopo for Switzerland, providing resolutions of 25 cm and 10 cm respectively. This enhancement allows users worldwide to utilize GL JS v2.3 for 3D visualization, enabling activities such as trip planning, analyzing runs, or viewing ski slopes. The imagery is part of Mapbox's commitment to offering precise location data and innovative mapping tools, supported by their team of engineers dedicated to processing and enhancing satellite and aerial data.

Mapbox has significantly enhanced its lane guidance coverage by 300% through the use of AI mapping, which processes over 50,000 kilometers of roads using high-resolution aerial imagery from its satellite layer. This advancement is integrated into the Mapbox Navigation SDK and apps such as Dash and Straightaway, providing drivers with detailed lane guidance for safer and more efficient navigation. The AI pipeline identifies road attributes like lane markings and bicycle lanes, enabling the creation of lane guidance indicators, which are further refined by the Mapbox conflation engine that combines multiple data sources for up-to-date road information. These improvements are part of Mapbox's broader initiative to automatically generate richer road data and enhance the navigation experience for users on both Android and iOS platforms.

The Ocean Cleanup is actively addressing the global issue of ocean plastic pollution by focusing on removing existing plastic and preventing further pollution through innovative mapping solutions. Utilizing Mapbox technology, they have developed four interactive maps to enhance public understanding and engagement: a river sources map highlights that 80% of plastic pollution originates from around 1,000 rivers worldwide, allowing for targeted cleanup efforts; a price tag map reveals the $19 billion economic impact of plastic pollution on industries such as fishing, tourism, and government; the Plastic Tracker map demonstrates the journey of a single piece of plastic from its release to its potential ocean destination using a Lagrangian dispersion model; and a citizen science map aggregates data from global contributors to refine cleanup strategies. These efforts are supported by custom map styling and data integration, enabling The Ocean Cleanup to effectively communicate their research and raise awareness.

The Mapbox Mobile Maps SDK v10.0.0-rc1 is now available for Android and iOS, offering developers a high-performance solution for integrating dynamic mobile maps into apps on Google Play or the Apple App Store. Since its public beta launch in January, the SDK has seen significant enhancements, including a 3D terrain and camera system, a transition to Kotlin, Swift, and iOS Metal, and a 25-30% boost in core rendering speeds. The latest release introduces innovative features like a render cache system that reduces CPU and GPU overhead without compromising quality, and an all-new offline system with tile packs that improve download speeds and decrease network overhead. These advancements make the SDK not only more efficient but also easier to use, allowing developers to create fast, visually stunning maps while enhancing the user experience with improved battery life and reduced processing demands. Developers can explore the new features and start building by accessing the Getting Started Guide, Migration Guide, and API Documentation on the Mapbox product page.

Mapbox and Ookla have collaborated to launch the Ookla for Good Global Network Performance map, providing comprehensive access to global broadband performance data to aid researchers, policymakers, journalists, and the public in making informed decisions. This initiative, part of Ookla for Good's mission to improve internet accessibility and speed, aims to increase transparency and accountability among telecommunications providers and policymakers, addressing the digital divide that leaves millions without reliable internet. Utilizing Ookla's Open Datasets and Mapbox's advanced processing and rendering technologies, the map allows for easy visualization and interpretation of data, thus lowering the barrier to entry for users lacking technical expertise. The platform's high performance enables rapid data processing and regular updates to monitor network performance changes, supporting global efforts to understand and mitigate internet access disparities, as exemplified by World Bank research into the effects of COVID-19 lockdowns in Africa. Further insights into the map's development and capabilities will be shared in a forthcoming series by the Ookla for Good team.

Data scientists and analysts can now leverage Mapbox Movement data within BigQuery to integrate geospatial analytics with business data, facilitating insights for strategic decisions like retail site selection. The text demonstrates using Mapbox Movement to evaluate potential restaurant sites by analyzing activity levels during lunch and dinner at various locations. By utilizing H3 hexagons to measure movement scores, the analysis identifies 9225 SW Allen Rd as a prime location for a new restaurant, based on high activity levels during peak dining times. This approach underscores the potential for businesses to use geospatial data to optimize location-based decisions, with sample data provided for those new to BigQuery to experiment with these analyses.

Mapbox GL JS v2.3 introduces new 3D features, performance enhancements, and expanded data-driven styling options, including a fog effect that adds depth to maps and allows designers to simplify styles. Fog can be easily implemented using the map.setFog(..) function, offering controls for color, range, and horizon-blend. The update also includes a new API for client-side elevation querying, which uses Digital Elevation Model tiles to generate terrain elevation profiles quickly and without server requests. Performance improvements involve a reduced GPU memory footprint for terrain, optimized tile cover to minimize DEM tile requests, and per-frame batching of DOM operations to enhance update performance. Additionally, data-driven styling has been expanded to line-dasharray and line-cap properties, allowing for more efficient layer management. The update is available via CDN and NPM, and further details can be found in the v2.3.0 release notes.

The COVID Schools Canada project, initiated by the grassroots group Masks4Canada, aims to provide transparency on the impact of COVID-19 on schools across Canada by compiling and visualizing case and outbreak data. Due to a lack of data transparency from provincial governments, the group launched a data tracker that initially used Google My Maps but faced scaling challenges, prompting a switch to Mapbox with support from the Mapbox Community team. This transition allowed the team to create a dynamic, user-friendly map that updates in real-time with data sourced from crowd-sourced submissions and school board records, processed through a pipeline involving Google Sheets, Perl, Python, and R. The project has documented over 55,600 COVID-19 cases in Canadian schools, gaining significant attention and a large following on social media, and it highlights the importance of community-driven initiatives in public health transparency.