ArticleComputational intelligence and neuroscience2022
Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Retracted: Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning.Computational intelligence and neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-08-02Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/5AFA0C66F1298F7FB91B915772CE1A
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper integrates classical design theory, multisource urban data, and deep learning to explore an accurate analytical framework in a new data environment, providing a scientific analysis path for the "where" and "how" of greenways in a high-density built environment. The analysis is based on street view data and location service data. Through the integration of multiple data sources such as street scape data, location service data, point-of-interest data, structured web data, and refined built environment data, a systematic measurement of the key elements of density, diversity, design, accessibility to destinations, and distance to transport facilities as defined in the Five Elements of High Quality Built Environment (5D) theory is achieved. The assessment of alignment potential was carried out. The key factors influencing the aesthetics of the street were identified. Based on an extensive landscape perception-based survey, it was found that although different respondents had different views and preferences for the same street scape, their preferences were overwhelmingly influenced by the visual quality of the street scape aesthetics itself, with higher aesthetic quality of the landscape.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.