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Sustainable Housing Practices: Spatial Analysis of Housing Stress in Corvallis, Oregon

2020· article· en· W3104552371 on OpenAlexaff
Afia Zubair Raja, Zubair Ali Raja

Bibliographic record

VenueJournal of Urban Planning and Development · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsThompson Rivers UniversityUniversity of the Fraser Valley
Fundersnot available
KeywordsAffordable housingSustainabilityRaster graphicsBusinessEnvironmental resource managementEnvironmental economicsGeographyComputer scienceCivil engineeringEngineeringEconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

America's Housing Affordability definition classifies those households as stressed that spend more than 30% of their net income on housing. This paper challenges the traditional economic criteria-based approach that ignores the social and environmental parameters. Authors offer a geographical information science (GIS)-based Multicriteria Decision Analysis, selecting Corvallis, Oregon to prove the applied impact of the proposed methodology. Using experiential literature and interviews with specialists, the research establishes a comprehensive set of housing stress indicators including demographic, housing quality, and commuting time variables. Raster overlay and zonal statistics were deployed to obtain the final housing stress map. The strain was highest in the low-density single-family zone that contained dilapidated housing and longer commuting times, in contrast the stress was lowest for the mixed-use residential. GIS results were then used to make recommendations for affordable housing by channelizing favorable allocation of resources through spatially targeted efforts. This innovative method has a great potential to prioritize improvements based on the accumulated stress scores for each zone and contributes toward the improvements in understanding, examining, and measuring housing stress worldwide.

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 score0.156
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.052
GPT teacher head0.246
Teacher spread0.194 · 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
Published2020
Admission routes1
Has abstractyes

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