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Record W2128530546 · doi:10.1111/0033-0124.00304

Building Temporal Topology in a GIS Database to Study the Land-Use Changes in a Rural-Urban Environment

2001· article· en· W2128530546 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Professional Geographer · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsZoningComputer scienceDatabaseLand useGeographic information systemSpatial databaseLand use, land-use change and forestryGIS applicationsInterface (matter)GeographyData miningSpatial analysisRemote sensingCivil engineeringEngineering

Abstract

fetched live from OpenAlex

This article addresses the issue of linking temporal and spatial information into a GIS database structure to investigate the land-use changes in a rural-urban region over a thirty-five-year period. More specifically, it describes the application of a programming package developed to build temporal topology in an historical land-use GIS database to efficiently perform spatiotemporal queries. The program was created within the MapInfo environment using MapBasic language. Different types of information, such as the rate of change, the relationship between the change of land use and zoning regulations, and land-use succession were extracted from the database. A user-friendly interface was also developed to easily address spatiotemporal queries to the database. This approach represents a flexible and performing tool for scientists and planners who need to efficiently capture essential spatiotemporal information required for geographical inquiry and decision-making.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.332
Teacher spread0.292 · 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