Dynamic Random Region Sampling for Venue Data Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing location-based networks face inefficiencies in collecting venue datasets, as they often require exhaustive searches or rely on costly sampling algorithms, which are not effective in large geographic regions or in controlling the locality of sampled users and venues.
Innovation Solution
A dynamic random region sampling algorithm that selects a target location within a geographic region based on venue density predictions, using a weighted average of comparable locations to determine a sub-region for efficient venue data collection, allowing for representative sampling and estimation of statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive search is used to collect venue datasets, then completeness of data collection is improved, but time consumption and computational cost increase significantly
Solution Approach 1:
The patent applies partial action by using sampling algorithms to collect a representative subset of venue data rather than performing exhaustive searches. The system collects data from carefully selected sample regions that statistically represent the entire geographic area, achieving sufficient data completeness for analysis while dramatically reducing time consumption and computational resources.
2Loss of time
If random walk or breath first search sampling algorithms are used, then time consumption is reduced, but control over locality of sampled users and venues is lost
Solution Approach 1:
The patent implements local quality by dividing the geographic region into multiple sub-regions with different characteristics and selectively sampling from specific sub-regions based on the study objectives. This allows the system to maintain control over the locality of sampled data by choosing which geographic areas to sample, while still using efficient sampling methods within those selected regions rather than performing exhaustive searches.
3Productivity
If sampling algorithms are used to reduce time consumption, then efficiency is improved, but representativeness of the sample set may deteriorate
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting sampling parameters such as sample size, region selection criteria, and sampling density based on the characteristics of the geographic area and the specific research objectives. This allows the system to optimize the balance between efficiency and representativeness by modifying sampling parameters rather than using fixed sampling approaches, ensuring that sample sets remain representative while maintaining high collection efficiency.
Data Source
AI summary
Various embodiments provide a method and apparatus for obtaining a representative sample set of venues (i.e., places) within a geographic region in a location based network using a low cost and efficient sampling and estimating algorithm. In particular, a dynamic random region sampling algorithm randomly selects a target location within a geographic region and then determines a sub-region containing the target location within the geographic region based on venue density prediction. Venue density prediction is based on a weighted average of venue densities of two or more comparable locations within the geographic region.