Dynamic Room-Type Classification From Mobile Activity Profiles
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Solution Overview
Problem
Existing methods for analyzing built space are inflexible and unable to capture dynamic, volatile usage patterns due to digitalization, leading to inefficiencies in urban planning and infrastructure management.
Innovation Solution
A computer-implemented method determines spatial types of built space by analyzing location and movement profiles of mobile devices to dynamically assess actual use, assigning room types based on physical conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional spatial analysis methods are used, then measurement precision is maintained, but adaptability to dynamic usage patterns deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static spatial classification to dynamic spatial analysis. The system continuously collects location data from mobile devices and recalculates spatial types based on current activity patterns, allowing the classification to adapt automatically as usage patterns change throughout the day and across different time periods.
Solution Approach 2:
The patent changes key parameters from fixed geometric boundaries to dynamic activity-based definitions. Spatial types are redefined based on variables such as device location, time of day, duration of stay, and movement patterns, allowing the system to capture the volatile nature of modern space usage while maintaining measurement precision through quantitative data analysis.
2Productivity
If manual spatial analysis methods are used, then measurement precision is maintained, but productivity deteriorates
Solution Approach 1:
The system implements self-service by automatically collecting, processing, and analyzing spatial data without requiring manual intervention. Mobile devices continuously provide location information, and the system autonomously processes this data to determine spatial types, calculate activity attributes, and update classifications, dramatically improving productivity while maintaining precision through consistent automated analysis.
Solution Approach 2:
The patent replaces manual mechanical analysis methods with automated computational systems. Instead of human analysts manually measuring and classifying spaces, the system uses algorithms to process mobile device data, calculate activity attributes, and determine spatial types automatically, achieving both high productivity and precise measurement through digital automation.
3Adaptability or versatility
If fixed time budgets are allocated to spaces, then ease of operation is maintained, but adaptability to individual sovereignty deteriorates
Solution Approach 1:
The patent implements dynamic time budgets that automatically adjust based on observed usage patterns. Instead of fixed allocations, the system continuously monitors how individuals actually use spaces and adapts spatial classifications and time budgets accordingly, enabling individual sovereignty while maintaining operational simplicity through automated adaptation rather than manual reconfiguration.
Data Source
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AI summary
A computer-implemented method for determining spatial types of spatial areas of a built space is proposed, comprising: - receiving activity data, wherein the activity data comprises information about location and/or movement profiles of mobile terminals, wherein the mobile terminals have been located and/or logged at spatial points of the built space; - determining the spatial types for the spatial areas of the built space, preferably by means of an analysis unit, wherein one or more activity attributes for one or more spatial points in a spatial area are calculated on the basis of the activity data, wherein the physical nature of the spatial area is determined based on the activity attributes of the spatial points, such that the spatial type of the spatial area is determined depending on the physical nature.