USA As Represented by Secretary of the Navy

Elias Ioup

John T Sample

Norman Schoenhardt

Brent Barré

Embodiments relate to visualization of positional geospatial uncertainty. Initially, a map image request for geographic features is received from a client computing device, where the map image request includes an uncertainty type, a distribution shape, and a selected visualization technique. An uncertainty buffer pixel size is determined based on a geographic distance covered by the distribution shape. At this stage, an uncertainty buffer of the uncertainty buffer pixel size is iterated across, and uncertainty is rendered at each position along the uncertainty buffer by determining a corresponding distribution probability from a probability distribution function at a current pixel position, mapping the corresponding distribution probability to a corresponding visual value of the selected visualization technique, rendering an uncertainty feature for the corresponding distribution probability around the geographic feature at the current pixel position and according to the corresponding visual value; and advancing the current pixel position based on the uncertainty type.

Visualization of positional geospatial uncertainty

Embodiments relate to hash-based synchronization of geospatial vector features. Initially, a list of spatial data layers in a source spatial datastore is obtained. For each of the spatial data layers, a source layer hash is determined for a source data layer of the spatial data layers, a destination data layer is identified in a destination spatial datastore that is related to the source data layer, where the destination data layer is associated with a destination layer hash, and in response to determining that the source layer hash and the destination layer hash do not match, source features from the source data layer are selectively synchronized to the destination data layer.

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Elias Ioup