Hyper-locating Places-of-Interest Using Spatial Segmentation
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Solution Overview
Problem
Traditional methods for determining geospatial coordinates of places-of-interest in buildings are inaccurate, especially when multiple places-of-interest share a building structure, leading to overlapping data and noise in location-specific analysis.
Innovation Solution
A hyper-locating system that estimates accurate geospatial coordinates for places-of-interest within building structures by outlining physical boundaries, using building structure data and visitor location data to define blocks associated with each place-of-interest, thereby improving positional accuracy and data specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional centroid-based location methods are used, then data collection is simple, but location accuracy deteriorates due to overlapping boundaries and noise
Solution Approach 1:
The patent segments the building structure into multiple distinct blocks, each associated with a specific place-of-interest. This segmentation allows precise assignment of visitor data to individual places-of-interest within the building, eliminating the overlapping boundary problem inherent in centroid-based methods. Each block is defined by geospatial coordinates that represent the actual physical boundaries of the place-of-interest, not just a central point.
Solution Approach 2:
The patent transitions from two-dimensional centroid coordinates to three-dimensional spatial blocks with defined boundaries. By incorporating building structure data and defining volumetric blocks within the building footprint, the system adds a dimensional layer that enables more accurate spatial differentiation between multiple places-of-interest sharing the same building location.
2Quantity of substance
If multiple places-of-interest share a building structure, then data density increases, but data quality deteriorates due to overlapping circular mappings
Solution Approach 1:
The patent divides the building structure into distinct blocks, each uniquely associated with a place-of-interest. This segmentation ensures that visitor data collected within each block's boundaries is exclusively attributed to that specific place-of-interest, preventing the data contamination and overlap issues that occur with centroid-based circular mappings.
Solution Approach 2:
The patent applies local quality by defining unique boundary characteristics for each place-of-interest within the building. Each block is configured with specific geospatial coordinates and boundary definitions that reflect the actual physical layout and occupancy patterns of that particular place-of-interest, allowing differentiated analysis of visitor behavior at each location.
3Ease of manufacture
If centroid circular mapping is used, then implementation is simple, but boundary definition accuracy deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-defining block boundaries and associating them with places-of-interest before data collection begins. Building structure data is processed in advance to create accurate block definitions, and these pre-configured blocks are then used to filter and attribute visitor data, eliminating the need for complex real-time boundary calculations.
Solution Approach 2:
The patent introduces blocks as intermediary spatial units between the building structure and individual places-of-interest. These blocks serve as mediators that capture the physical boundaries and spatial extent of each place-of-interest, providing a more accurate representation than direct centroid mapping while maintaining systematic organization for data collection.
Data Source
AI summary
The technology disclosed relates to determining visitor behavior. In particular, it relates to receiving preliminary geospatial coordinates of a plurality of places-of-interest, generating (i) contours of the places-of-interest and (ii) hyper-located geospatial coordinates of centroids of the places-of-interest based on processing the preliminary geospatial coordinates of the places-of-interest, and correlating (i) the contours and (ii) the hyper-located geospatial coordinates of the centroids of the places-of-interest with geospatial coordinates of visitor locations to determine the visitor behavior of visitors visiting the places-of-interest, including at least one of detecting whether the visitors visited the places-of-interest, and detecting a duration for which the visitors visited the places-of-interest.