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Record W2015101735 · doi:10.1068/b2567

Space, Time, and Dynamics Modeling in Historical GIS Databases: A Fuzzy Logic Approach

2001· article· en· W2015101735 on OpenAlexafffundabout
Suzana Dragićević, Danielle J. Marceau, Claude Marois

Bibliographic record

VenueEnvironment and Planning B Planning and Design · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversité de MontréalMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFuzzy logicInterpolation (computer graphics)Computer scienceShoreVisualizationMetropolitan areaSpacetimeSpace (punctuation)Data miningGeographyDatabaseArtificial intelligenceMotion (physics)Geology

Abstract

fetched live from OpenAlex

In this paper, a spatiotemporal interpolation approach for GIS modeling of urban growth dynamics is proposed. It is based on fuzzy logic theory using three different scenarios for temporal simulation, and two techniques for spatial simulation of urban change patterns. The notion of stages in the urban growth is taken into consideration as well as variables describing the speed and the mechanism of change. The simulation results are presented for three study sites from the north shore of the Montreal metropolitan area in Quebec, Canada, covering the period from 1956 to 1986. By comparing the simulation results with aerial photographs of the study area taken in 1958, 1971, 1975, and 1982, the proposed modeling approach is validated. The potential of this approach as a visualization technique is also discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0040.006
Open science0.0020.001
Research integrity0.0010.001
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 head0.049
GPT teacher head0.223
Teacher spread0.174 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations36
Published2001
Admission routes3
Has abstractyes

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