MétaCan
Menu
Back to cohort
Record W2763148963 · doi:10.1186/s40503-019-0066-4

Delineating functional territories from outer space

2019· article· en· W2763148963 on OpenAlexfundno aff
Julio A. Berdegué, Tatiana Hiller, Juan M. Ramírez, Santiago Satizábal, Isidro Soloaga, Juan Soto, José Miguel Uribe-Restrepo, Olga Vargas

Bibliographic record

VenueLatin American Economic Review · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsSpace (punctuation)GeographySatelliteEconomic geographyIndex (typography)Regional scienceUnit (ring theory)Cluster analysisComputer scienceArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

The delimitation of functional spatial units or functional territories is an important topic in regional science and economic geography, since the empirical verification of many causal relationships is affected by the size and shape of these areas. This paper proposes a two-step method for the delimitation of functional territories and presents an application for three developing countries: Mexico, Colombia and Chile. The first step of this method uses nighttime satellite images to identify the boundaries of urban continuums (conurbations). When these continuums extend over more than one municipality, we group and redefine them as a new single spatial unit. The second step calculates a dissimilarity index using bidirectional labor-commuting flows between the resulting areas of the first step and then applies a standard clustering procedure to delineate the definitive functional territories. Our results suggest that, using nighttime satellite images, our method can lead to a more accurate definition of functional territories, especially in developing or underdeveloped countries where the official data on labor-commuting flows are often outdated or unreliable.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0410.014

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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

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

Citations29
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueLatin American Economic ReviewSame topicImpact of Light on Environment and HealthFrench-language works237,207