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Record W2545551806 · doi:10.2495/tdi-v1-n3-348-358

Relationship between accessibility improvement and residential property appreciation: An observation from shanghai metro

2017· article· en· W2545551806 on OpenAlexaff
Qian Wu, Xuze Ye

Bibliographic record

VenueInternational Journal of Transport Development and Integration · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsProperty (philosophy)Transport engineeringBusinessArchitectural engineeringEngineeringPhilosophy

Abstract

fetched live from OpenAlex

The costly construction and operation of urban rail transit have become escalating problems for cities worldwide, especially in developing countries. Reliable measures of residential property appreciation for urban rail transit can provide suggestions for policy-making of value capture to fund transit improvements. using gIS techniques and residential property price data, the relationships between accessibility improvement value and residential property appreciation are analysed in Shanghai. The impacts of urban rail transit on residential property values are classified into traffic effect and agglomeration effect, both of which are measured by the accessibility improvement model. The results indicate that the goodness-of-fit of the model is greater than 93%. Traffic benefit is greater than agglomeration benefit in the suburb, which is completely different in the city centre. furthermore, the residential property appreciation is about 5 times the accessibility improvement value per year. This study contributes to the evidence of capitalization impacts of public transit from a booming and transitional economy and provides suggestions for land use planning of areas surrounding stations.

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)0.001
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 score0.064
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.004
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 head0.092
GPT teacher head0.348
Teacher spread0.256 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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