Bibliographic record
Abstract
This paper reviews spatial computing in digital twins (DTs), highlighting its potential across industries. Spatial computing merges digital information with physical environments, enabling intuitive interactions. Using a systematic literature review, the study constructs an interdisciplinary pool from IEEE Xplore, Web of Science, ScienceDirect, and ACM Digital Library. A Boolean search query with a 2018–2024 timeframe is used to track technological evolution. A three-stage screening process is implemented: initial screening excludes duplicates and non-peer-reviewed papers (2,143 excluded); secondary screening uses the BERT model to retain papers with relevance scores ≥0.75 (1,872 retained); final review identifies 122 core publications through cross-validation. Qualitative analysis combines NVivo 12 for thematic coding (12 main categories, 36 subcategories) and the SWOT-CLPV model for evaluation. It addresses bottlenecks like spatiotemporal alignment errors and interoperability costs in industrial, healthcare, and urban domains. The paper explores the background, trends, and advancements of spatial computing in gaming, healthcare, e-commerce, smart cities, and industrial systems, offering strategic recommendations for integrating spatial computing into DTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".