POI Deployment Need Index Calculation Using User Trajectory Data
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Solution Overview
Problem
Traditional methods for determining the deployment of points of interest (POIs) are often based on assumptions and are one-sided, leading to inaccuracies in addressing actual needs in geographical areas.
Innovation Solution
A computer-implemented method that determines POI deployment needs by receiving user geographic locations, identifying target users, calculating POI deployment need indexes based on the number of POIs within a set distance, and providing a total deployment need index for a geographic area, allowing for precise determination of POI deployment locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If POI deployment need is determined based on traditional assumptions about service coverage gaps, then deployment decisions can be made quickly, but the accuracy of identifying actual POI needs deteriorates
Solution Approach 1:
The patent replaces traditional assumption-based deployment analysis with a data-driven approach using user mobility patterns and geographic information systems. User trajectory data and POI distribution data are processed through computational algorithms to objectively determine deployment needs, substituting subjective assumptions with empirical evidence from user behavior data.
Solution Approach 2:
The patent introduces user mobility data and geographic analysis as intermediaries between service providers and deployment decisions. By analyzing user trajectories,停留 times, and interaction patterns with existing POIs, the system mediates the relationship between service coverage and actual user needs, providing an objective basis for deployment decisions.
2Measurement precision
If comprehensive user data analysis is performed to accurately determine deployment needs, then deployment accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent segments the geographic area into grid units and divides user data processing into discrete analytical steps. By calculating deployment need indexes for each grid independently based on user trajectories and POI distributions within specific distance ranges, the system processes large datasets in manageable portions, reducing overall computational burden while maintaining precision.
Solution Approach 2:
The patent focuses analysis on relevant spatial parameters by setting distance thresholds (e.g., analyzing POIs within 500m, 1km, 2km radii) and concentrating computational resources on grids with highest user activity. This partial action approach processes only the most critical data subsets needed for deployment decisions, avoiding unnecessary computation on low-priority areas.
Data Source
AI summary
Implementations for determining deployment need for a point of interest (POI) are disclosed. In one implementation, the deployment need for a POI is determined by: receiving geographical locations of one or more users, determining, based on the geographical locations, one or more target users covered by an area to be inspected, determining one or more POI deployment need indexes of the one or more target users, a POI deployment need index of a target user being determined based on a number of POIs that have the preset function of the POI and were deployed within a set distance from the target user, and providing a total deployment need index for the area to be inspected, the total deployment need index being determined based on the one or more POI deployment need indexes.