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Record W3135713958 · doi:10.5334/bc.82

Urban form and livability: socioeconomic and built environment indicators

2021· article· en· W3135713958 on OpenAlexaffabout
Nicholas Martino, Cynthia Girling, Yuhao Lu

Bibliographic record

VenueBuildings and Cities · 2021
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSocioeconomic statusVitalityGeographyCentralityDiversity (politics)Urban morphologyEconomic geographyBuilt environmentUrban densityPopulationSocioeconomicsUrban planningSociologyDemographyCivil engineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

Spatial relations among urban elements (buildings, streets, etc.) constantly affect the quality of urban spaces, creating more or less livable cities. The study of urban form has been a way of objectively quantifying such relations to understand their dynamics. Urban livability is the ability of urban spaces to fulfill the expectations of its inhabitants for wellbeing and quality of life. Measurable spatial patterns underlie the emergence of livable cities. Still, few researchers have considered if and how these patterns affect socioeconomic conditions across spatial scales. This paper explores the relationships between indicators of socioeconomic livability and cross-scale patterns of demographic and morphological densities within the Metro Vancouver (MV) region (Canada). Indicators of accessibility, social diversity, affordability, and economic vitality were quantified and compared among five population density clusters composed of 3450 census dissemination areas (DAs) in MV. Morphological indicators of intensity, centrality and diversity were aggregated at the DAs using spatial network analysis with five radii from 400 to 4800 m. Socioeconomic indices were regressed on urban form variables to assess the importance of the built environment on predicting livability-related qualities. Overall, indicators of the intensity of urban form were the most significant to predict the socioeconomic metrics. Policy relevance Policies that aim to solve urban issues should consider nonlinear relations among variables. In the case of MV, indicators of accessibility, social diversity and economic vitality are directly correlated with each other and inversely correlated with affordability. Medium to high-density zones presented a fair equilibrium among the different livability qualities analyzed. Attributes aggregated with the 4800 m radius were highly important to predict the livability qualities within a 400 m radius, which potentially means that urban interventions may affect the livability of spaces not immediately close to them. A higher density of buildings with moderate height distributed among parcels with distinct sizes can potentially have a positive impact on economic vitality and housing affordability. The intensity and diversity of the tree canopy was important to predict active accessibility and social diversity. The inclusion of spatial diversity and network centrality measures on urban planning and design practices potentially foster more livable densification processes.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.169
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.162
Teacher spread0.157 · 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 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

Citations87
Published2021
Admission routes2
Has abstractyes

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