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Record W2753263736 · doi:10.1177/0042098017712680

Rapid transit, transit-oriented development, and the contextual sensitivity of land value uplift in Toronto

2017· article· en· W2753263736 on OpenAlexaffabout
Christopher D. Higgins, Pavlos Kanaroglou

Bibliographic record

VenueUrban Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTransit-oriented developmentTransit (satellite)Context (archaeology)Land useProxy (statistics)Smart growthGeographyTransport engineeringBusinessEconomic geographyPublic transportComputer scienceCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Rapid transit projects that increase accessibility should result in a localised land value uplift (LVU) benefit for locations near stations. A rich history of research has tested this hypothesis, generally operationalising transit accessibility by proxy through distance from a transit station. However, a growing body of research has also demonstrated LVU effects from transit-oriented development (TOD) as individuals sort themselves into locations that best match their preferences and willingness to pay. Considering the interdependence of transportation and land use in the urban system, we argue that these benefits create a spatial bundle of TOD goods around transit stations and hypothesise that households are willing to pay a premium for locations in more transit-oriented station catchment areas. Utilising latent class analysis, we quantify station area TOD submarkets. Next, interactions between these submarkets and station proximity in spatial hedonic regressions reveal that TOD is capitalised into land values in Toronto, though the maximum amount and spatial impact area of this capitalisation differs by TOD context.

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)0.002
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 score0.397
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.325
Teacher spread0.285 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations76
Published2017
Admission routes2
Has abstractyes

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