Social Media Traffic Mapping for Small-Area Network Capacity Planning
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Solution Overview
Problem
Existing network planning methods struggle to accurately estimate communication service demand in small areas due to unpredictable mobility of people and the uneven distribution of population density among network operators, making it time-consuming and inaccurate.
Innovation Solution
A method utilizing social media data and geolocation information to create a capacity layer for network planning, which classifies places by traffic demand and projects demand across sub-areas using weights and demographic types, without requiring direct network traffic measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If population density is used as input for network planning, then network infrastructure can be planned based on available demographic data, but the estimation accuracy deteriorates because population density does not necessarily correlate with capacity demand and is distributed unevenly amongst customers of different network operators
Solution Approach 1:
The patent introduces social media data as an intermediary indicator that mediates between easily obtainable population density data and the hard-to-measure capacity demand. Social media activity serves as a proxy that correlates with actual network usage patterns, allowing planners to estimate capacity demand without direct measurements while maintaining ease of planning.
Solution Approach 2:
The patent transforms the planning approach by changing from using population density as the primary parameter to using social media activity metrics (number of social media users, activity frequency) as the key parameter for estimating capacity demand. This parameter change enables more accurate correlation with actual network usage while maintaining data accessibility.
2Measurement precision
If direct network traffic measurements are used to estimate capacity demand, then estimation accuracy is improved, but the complexity and time consumption of network planning increases
Solution Approach 1:
The patent uses social media data as a copy or proxy representation of actual network traffic patterns. Instead of measuring real network traffic directly, the system creates a model based on social media activity that replicates the spatial and temporal characteristics of capacity demand, simplifying the planning process while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary estimation of capacity demand using social media data before actual network deployment or modification. This preliminary action allows planners to identify high-demand areas in advance without conducting complex real-time traffic measurements, reducing overall planning complexity and time consumption.
3Productivity
If social media data is used to estimate capacity demand, then estimation accuracy and efficiency are improved, but data integration complexity increases due to combining multiple data sources
Solution Approach 1:
The patent merges social media data from multiple sources (different social media platforms, different data providers) into a unified dataset. By combining these data sources and applying consistent processing rules, the system achieves comprehensive coverage of social media activity while managing integration complexity through standardized methods.
Solution Approach 2:
The patent creates a universal data processing framework that handles multiple social media data sources using the same methodology. The system applies universal rules for data cleaning, normalization, and correlation with geographic information, making the integration process scalable and reducing complexity despite the diversity of input sources.
Data Source
AI summary
This document discloses a solution for estimating network traffic capacity demand in an area of interest. According to an aspect, a computer-implemented method comprises: forming, by using one or more social media applications, a social media layer storing records of a plurality of places in the area of interest; forming, by using at least one source storing real geolocations of the places, a geolocation layer mapping the places to geolocations; classifying the places into a plurality of classes and assigning to each place a weight indicative of a traffic capacity demand dependent on a class of said place; building a capacity layer for the area of interest on the basis of the real geolocations of the places provided by the geolocation layer and the traffic capacity demand per place indicated by the weights, the capacity layer indicating spatial distribution of network traffic capacity demand in a plurality of sub-areas of the area of interest, the plurality of sub-areas comprising sub-areas having at least one of the places and sub-areas between the places.