Heterogeneous Data Fusion for Fine-Scale Tourist Inflow Estimation
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Solution Overview
Problem
Existing methods for estimating visitor numbers in tourist areas lack accuracy and dynamicity at fine spatio-temporal scales, and there is a need for a tool to visualize attendance indicators effectively.
Innovation Solution
A method and device that cross-references business data with open and private data to generate precise measurement indicators for estimating and forecasting attendance, using smart meters for resource consumption data and external socio-economic data to create a predictive model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mobile phone data analysis methods are used to estimate tourist flows, then comprehensive representation of population flows at fine spatio-temporal scales is achieved, but precise instantaneous view of aggregated indicators measuring occupancy rates of specific accommodation types is not provided
Solution Approach 1:
The patent segments the estimation problem into two complementary parts: mobile phone data provides overall population flow patterns, while water consumption data from smart meters provides specific accommodation occupancy information. This segmentation allows each data source to contribute its strengths without requiring the other to provide all information alone.
Solution Approach 2:
The patent merges heterogeneous data sources (mobile phone location data, water consumption data from smart meters, and accommodation registry data) into a unified estimation system. The combination allows cross-validation and complementary information integration, where water data fills gaps in mobile data for occupancy-specific measurements.
2Productivity
If water sector data and waste management data are used combined with field surveys, then retrospective picture of population present is provided, but dynamic assessment of tourist activity is not allowed and accuracy remains uncertain
Solution Approach 1:
The patent implements preliminary action by continuously collecting and pre-processing water consumption data from smart meters in real-time, rather than waiting for retrospective analysis. This allows the system to be ready to provide immediate occupancy estimates when needed, enabling dynamic assessment while maintaining accuracy through continuous data availability.
Solution Approach 2:
The system implements feedback mechanisms where occupancy estimates are continuously updated and validated against multiple data sources. The integration of mobile phone data, water consumption patterns, and accommodation registry information creates a feedback loop that refines measurements and improves accuracy over time while enabling real-time dynamic assessment.
3Adaptability or versatility
If numerous agreements are established with transport operators for flow method data collection, then data aggregation for closed areas is achieved, but adaptability to urban tourism contexts with open territories is difficult
Solution Approach 1:
The patent applies universality by designing a water consumption-based measurement system that can be deployed across diverse urban tourism contexts without requiring location-specific infrastructure or numerous partnerships. The smart meter network serves multiple functions: measuring water consumption for occupancy estimation, providing real-time data, and working across different accommodation types and urban areas uniformly.
Solution Approach 2:
The system enables self-service by utilizing the existing smart meter infrastructure that automatically measures and reports water consumption data without requiring manual intervention, field surveys, or complex coordination with multiple operators. The meters themselves perform the measurement and data transmission functions, reducing system complexity.
Data Source
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AI summary
The invention relates to a method for processing heterogeneous data to determine inflows in time and space. One use of the method is for determining tourist inflows by cross-processing industry data relating to water consumption with open and/or private external data in order to generate different indicators for measuring attendance, on finite time-space scales, for various customer profiles that constitute attendance.