Relocation Analysis Dashboard with Commute Heat Maps
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Solution Overview
Problem
Current digital techniques for informing relocation decisions in enterprises are inefficient and lack accuracy, relying on employee polling that consumes significant computing resources and employee time, and fail to provide detailed or granular analysis or associated visualizations.
Innovation Solution
A relocation analysis system that uses a maps platform API to determine commute times and emissions data, generating a dashboard GUI with interactive visualizations such as heat maps, commute time rankings, and employee density graphs to inform relocation decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If employee polling is used to determine office location preferences, then some relocation decisions can be made, but significant computing resources and employee time are consumed and the accuracy is limited
Solution Approach 1:
The patent replaces the mechanical polling system with an automated data processing system that uses maps platform APIs to calculate commute times and generate visualizations. Instead of manually collecting and analyzing survey responses, the system automatically processes employee location data, calculates commute metrics, and generates interactive dashboards, thereby eliminating the time and resource consumption associated with traditional polling methods
Solution Approach 2:
The system enables self-service relocation analysis by automatically processing employee data and generating insights without requiring manual intervention. The automated pipeline collects employee location information, calculates commute times to various offices, and generates visualizations that employees and stakeholders can interact with to make informed relocation decisions, eliminating the need for manual data collection and analysis
2Loss of information
If polling is used to gather employee preferences, then relocation decisions can be informed, but the data lacks detailed or granular analysis and associated visualizations
Solution Approach 1:
The patent segments the data processing into distinct functional modules: data collection from employee records, commute time calculation using maps APIs, aggregation by office location, visualization generation, and interactive display. This segmentation allows each component to process and analyze specific aspects of the data independently, producing detailed granular analysis while managing complexity through modular architecture
Solution Approach 2:
The system transforms raw employee location data into multiple dimensional visualizations including commute time distributions, office proximity maps, and density heatmaps. By adding visual dimensions to the data presentation, the system reveals patterns and insights that are not apparent in raw data, providing detailed analysis without proportionally increasing processing complexity
3Adaptability or versatility
If current computing systems provide reports on relocation decisions, then some information can be presented, but the systems have limited knowledge regarding available commercial leases and do not interface with external systems
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple data sources and external system integrations through standardized APIs. The system is designed to interface with commercial real estate databases, maps platforms, and employee information systems, making it adaptable to various relocation scenarios and capable of processing diverse data types without requiring system redesign
Solution Approach 2:
The system uses maps platform APIs as intermediaries to bridge the gap between internal employee data and external geographic information. These API intermediaries enable the system to access and process data from external sources such as commercial lease databases and routing services, expanding the system's knowledge base while maintaining a clean architectural boundary
4Ease of operation
If polling data is used for relocation decisions, then some decisions can be made, but the preference data is difficult to interpret and lacks granular analysis
Solution Approach 1:
The patent employs color-coded visualizations including heatmaps with color intensity gradients and colored commute time distributions to represent different levels of employee density and commute characteristics. These color changes provide intuitive visual cues that make the data easy to interpret at a glance while maintaining the ability to drill down into granular details through interactive filtering and grouping
Data Source
AI summary
Methods, non-transitory computer readable media, and relocation analysis systems are disclosed that obtain employee data, a current office address, and relocation data. A first commute time is determined using a maps platform API to the current office address and a second commute time is determined to each of the relocation offices. A dashboard GUI is output for display that includes a commute time ranking of the relocation offices and a heat map reflecting a density of employee locations determined based on home addresses and including selectable locations of the relocation offices. A first commute time distribution is generated based on the first commute times and a second commute time distribution is generated based on the second commute times for one of the relocation offices corresponding to a selected one of the selectable locations. The dashboard GUI is then updated to include a visualization generated based on the commute time distributions.