Population Density Mapping via Wireless Signal Detection
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Solution Overview
Problem
Determining population density in crowded areas, such as events or locations, is challenging due to the difficulty in locating unoccupied spaces or avoiding congested areas, as existing methods lack efficient real-time data on person presence and distribution.
Innovation Solution
A system that determines population density by receiving a user's geolocation and detecting nearby wireless signals from devices, such as cell phones, to estimate the number of people in a given area, providing an electronic map highlighting high and low-density areas, and allowing businesses to manage staff and logistics based on this data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine population density, then existing data collection methods are simple, but real-time accuracy and reliability of population density data is insufficient
Solution Approach 1:
The patent uses wireless signals from computing devices as intermediaries to indirectly measure population density. Instead of directly counting people, the system detects wireless signals (cellular, Wi-Fi, Bluetooth) emitted by devices in the area, using signal density as a proxy indicator for population density. This intermediary approach enables real-time accurate measurement without complex direct counting infrastructure.
Solution Approach 2:
The patent leverages the existing wireless signal transmission capability of computing devices themselves as the measurement tool. The devices automatically emit signals for communication purposes, and these same signals are utilized for population density detection. This self-service approach eliminates the need for separate dedicated measurement equipment, reducing system complexity while maintaining measurement accuracy.
2Measurement precision
If real-time population density data is collected using wireless signals, then accuracy of person presence detection is improved, but difficulty in locating unoccupied spaces increases
Solution Approach 1:
The patent inverts the detection approach by measuring the absence of signals rather than the presence of people. Instead of trying to detect unoccupied spaces directly, the system identifies areas with fewer or no wireless signals, which correspond to unoccupied spaces. This inversion simplifies the detection process for unoccupied areas while maintaining high accuracy for occupied area detection.
Solution Approach 2:
The patent uses signal detection from computing devices as a partial indicator of population density. Not all people carry computing devices, and some devices may be inactive, but the signal detection method provides sufficient accuracy for practical applications. This partial action approach balances measurement precision with system simplicity, avoiding the need for comprehensive direct person counting.
3Productivity
If existing methods are used without real-time data, then system complexity is low, but productivity of event management and logistics optimization is reduced
Solution Approach 1:
The patent makes existing wireless communication infrastructure serve multiple functions: both for standard communication purposes and for population density measurement. The same cellular networks, Wi-Fi systems, and Bluetooth protocols used for device communication are simultaneously utilized for event management and logistics optimization. This multi-functionality approach boosts productivity without requiring dedicated complex measurement systems.
Solution Approach 2:
The patent implements real-time feedback mechanisms where population density data is continuously collected and processed to provide immediate information for event management decisions. The system processes wireless signal data to generate current population density maps and insights, enabling dynamic adjustments to staffing, security, and logistics. This feedback loop significantly improves productivity by enabling real-time optimization based on actual conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables users to find less crowded areas and businesses to optimize operations by providing real-time population density information, enhancing user experience and operational efficiency.
Implementation Method 1
determines one or more computing devices in proximity to the determined area of interest of the user based on one or more wireless signals transmitted from the one or more computing devices
Data Source
AI summary
The subject technology determines population density of an area in order to map one or more persons to smaller areas such as bathrooms, stores, and lines. Based on a user's current geolocation, the subject technology utilizes one or more nearby wireless signals transmitted from computing devices to determine a population density of an area near the user's geolocation. The signals from each device can include unique IDs for identifying the device associated with the ID. Mobile devices such as cell phones constantly broadcast a cell signal and/or other types of signals with unique identifiers. These signals are anonymized to protect any personal information associated with the mobile devices transmitting these signals while still being able associate a device to a respective signal. The subject technology therefore can determine an area's population density based on these anonymized signals by detecting one or more associated devices near the geolocation of the user.