Rendering and exploring large geospatial datasets, especially those generated by mobile air quality monitoring, presents unique challenges due to the sheer volume and granularity of the data. In mobile air quality monitoring, measurements are often captured every second, so even just one hour of data collection can yield 3,600 measurements per pollutant. At scale, these mobile datasets can contain millions of measurements and provide coverage across wide geographic regions.
The size and density of mobile monitoring data create distinct challenges in terms of both data storage and processing speed. For effective analysis and exploration, data scientists and analysts need tools capable of handling rapid data ingestion and near-instantaneous processing to support interactive, iterative exploration. Without high-performance tools, working with these datasets can become slow and cumbersome, limiting analysts’ ability to quickly identify patterns, make comparisons, or drill into the data.
When working with complex geospatial datasets — like those from mobile air quality monitoring — having the right tools to visualize and explore the data efficiently is crucial. Lonboard is an open source Python library developed to address this need, specifically designed for handling and visualizing large geospatial datasets. Designed to work within Jupyter notebooks, Lonboard provides high performance and interactive tools to explore these complex data.
In response to the specific challenges posed by mobile monitoring datasets — characterized by high-frequency measurements across multiple variables — OpenAQ and Development Seed have collaborated to introduce powerful new features to Lonboard. Designed with the needs of high-resolution mobile air quality data in mind, Lonboard’s latest updates provide users with greater control over the filtering and visualization of vast datasets. A features of note is enhancements to dynamic data filtering, allowing users to dynamically filter data by multiple attributes and explore large dataset more easily.
Getting Started with Lonboard
For data scientists, environmental researchers, and urban planners, Lonboard provides a robust and flexible platform for delving into the complexities of mobile air quality datasets. With its capacity to handle large amounts of data and enable instant and iterative exploration, it streamlines the analysis of air quality data from sources like OpenAQ, turning a traditionally slow and cumbersome process into a faster, more insightful one.
To learn more about Lonboard and explore its features and examples, check out the Lonboard documentation.
