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Record W2045121856 · doi:10.1068/a40303

A Location Model for Urban Hierarchy Planning with Population Dynamics

2008· article· en· W2045121856 on OpenAlexaff
António Pais Antunes, Oded Berman, João F. Bigotte, Dmitry Krass

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHierarchyUrban hierarchyPlan (archaeology)Urban planningPopulationAggregate (composite)Computer scienceClass (philosophy)GeographyOperations researchDistribution (mathematics)Environmental planningRegional scienceTransport engineeringEngineeringMathematicsCivil engineeringArtificial intelligenceSociologyEconomics

Abstract

fetched live from OpenAlex

Spatial development plans often aim at defining the level of hierarchy to assign to the urban centers of a region, each level being characterized by a class of facilities. The services provided at the facilities typically include education, health care, public safety, and justice. In this paper we present a multiperiod, multilevel location model for urban hierarchy planning. The objective of the model is to maximize the aggregate accessibility of population to the different classes of facilities, taking explicitly into account the fact that location decisions influence the spatial distribution of population growth. The model was applied to a problem dealt with in Portugal during the preparation of the Centro Region Development Plan 1994: the redefinition of the regions' urban hierarchy. The results obtained through the model demonstrate its potential usefulness in real-world applications.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.024
GPT teacher head0.241
Teacher spread0.217 · 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

Citations16
Published2008
Admission routes1
Has abstractyes

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