Age-Friendly Street Auditing Using Multisource Big Data
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Solution Overview
Problem
Conventional auditing methods for age-friendly street environments are time-consuming, inefficient, and limited in scope due to their reliance on manual data collection and professional training, making it difficult to comprehensively assess the impact of built environment elements on elder populations' health and mobility.
Innovation Solution
An auditing system utilizing multisource big data, including a data acquisition module, classification module, summary analysis module, and output module, which uses object detection, semantic segmentation, place perception analysis, and geographic space data analysis to process and visualize urban streetscape, road network, and point-of-interest data, leveraging AI models like YOLOv5 and BiseNet_v2 for efficient and accurate assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual auditing methods are used with professional auditors walking or driving through communities to record built environment characteristics, then detailed on-site observation and recording can be achieved, but the auditing process becomes time-consuming and low in efficiency
Solution Approach 1:
The patent uses image data from map services as copies of the actual built environment, replacing the need for physical presence of auditors. These image copies capture street views, buildings, and environmental features that can be analyzed remotely, maintaining measurement precision while eliminating time-consuming travel and on-site recording
Solution Approach 2:
The patent replaces the mechanical system of manual observation and recording with automated image processing and data analysis algorithms. The system uses computational methods to extract built environment characteristics from image data, substituting human auditors with automated processing that achieves both precision and high efficiency
2Reliability
If professional auditors are required to walk or drive through communities for encoded recording of built environment characteristics, then systematic data collection can be achieved, but the methods become dependent on manpower and greatly limited in applicable auditing range
Solution Approach 1:
The patent creates a universal auditing system that processes image data from multiple sources (map services, different locations, various built environment types) through a single automated platform. The system maintains systematic data collection through standardized processing algorithms while being adaptable to diverse auditing scenarios including different community types, street configurations, and built environment features
Solution Approach 2:
The system performs self-service by automatically acquiring, processing, and analyzing image data without requiring human auditors to physically visit sites. The automated algorithms systematically extract built environment characteristics from remotely obtained images, expanding applicable auditing range to include areas that would be difficult or impossible for human auditors to access
3Loss of information
If conventional auditing methods with manual observation and recording are used, then direct space perception of micro built environment elements can be recorded, but the methods are time-consuming and low in efficiency
Solution Approach 1:
The patent performs preliminary action by obtaining image data of the built environment before the auditing analysis begins. The image data from map services pre-captures street views, building characteristics, and spatial relationships, allowing the system to analyze direct space perception without requiring time-consuming physical visits to each location
Data Source
AI summary
The present invention relates to an auditing system for a built environment of an age-friendly street based on multisource big data. The auditing system includes: a data acquisition module is configured to acquire urban streetscape image data, urban road network data and urban point-of-interest data; a data classification auditing module is configured to acquire the data of the data acquisition module, classify the image data, and process the image data by using a data processing method to acquire evaluated numerical values of different types of indexes; a data summary analysis module is configured to acquire the evaluated numerical values of the data classification auditing module, calculate sub-item index numerical values of each output unit and calculate result data according to the sub-item index numerical values; a audit result output module is configured to acquire the result data of the data summary analysis module and visualize and output the result data.