MétaCan
Menu
Back to cohort
Record W4386778657 · doi:10.1111/tgis.13101

Towards a <scp>spatio‐temporal</scp> multicriteria evaluation method: A suitability analysis of residential units in a <scp>3D</scp> urban environment

2023· article· en· W4386778657 on OpenAlexafffundabout

Bibliographic record

VenueTransactions in GIS · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDowntownComputer scienceDimension (graph theory)Space (punctuation)Depreciation (economics)Transport engineeringUrban planningOperations researchGeographyCivil engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract Spatial multi‐criteria evaluation (MCE) techniques aid urban planning management by analyzing decision problem alternatives for solutions to help inform decision‐making. However, there is a lack of such methods that incorporate the temporal dimension, an important factor when analyzing the dynamic urban landscape and decisions surrounding its changes. A novel spatio‐temporal MCE approach is proposed that operates in three‐dimensional (3D) space and time to identify changing suitability values of decision alternatives. This space–time method is implemented to evaluate the suitability of residential units over a 15‐year period in part of downtown City of Vancouver, Canada. The results indicate that the majority of units exhibit a decrease in suitability with time due to depreciation and reduction of assets like view and privacy from the construction of new buildings. The proposed method can be used by urban planners and developers to assist in long‐term assessments of proposed development scenarios and their impact on existing urban infrastructure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.034
GPT teacher head0.296
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueTransactions in GISSame topicLand Use and Ecosystem ServicesFrench-language works237,207