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In this paper, we present a newly developed extended reality (XR) environment focused on qualitative and quantitative analysis and visualization of data on deforestation in urban areas and its impact on the area communities. The design and development process followed a user-centric approach that engaged researchers and practitioners. Using off-the-shelf technology such as Meta Quest headsets, the environment was developed in Unity, C#, and Python, and incorporates USGS GIS data layers. Other aspects such as affordability and accessibility were considered by acknowledging individuals with different learning styles and examining a new way to understand data. Compute and storage limitations brought on by the headset were overcome through data sampling and through offloading some of the computing tasks to a separate computer and transmission of the synthesized tasks back to the headset. Initial experiments focused on the ingestion of New York City area data. The region was chosen due to the population density, and the significant socio-economic disparities among various communities, but also due to the availability of ancillary data such as the one provided by the NYC Open Data that can be used to complement the USGS data. Urban and suburban areas were used to find indicators of vegetation and learn about the challenges associated with developing spatial data in different densities. The visualization also showed that while changes in deforestation over the past decade have been fairly uniform in both area types, sub-areas have seen a significant green space decrease. While the current XR environment is envisioned as the first step in the creation of a virtual interactive interface that shows predictive models of urban deforestation, it already constitutes an example of an educational approach to XR development. The code and system description will be made publicly available as Open Source and include mechanisms for community code contributions.
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