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Enregistrement W2618234375 · doi:10.21433/b3116z0905hw

A Multidirectional Optimal Ecotope-Based Algorithm to Delineate a Commuter Shed

2016· article· en· W2618234375 sur OpenAlexaffabout
Daniel Schleith, Michael J. Widener

Notice bibliographique

RevueInternational Conference on GIScience Short Paper Proceedings · 2016
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueUrban Transport and Accessibility
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMetropolitan areaContext (archaeology)GeographyRegional scienceCensusTransport engineeringCartographyEconomic geographyLibrary scienceArchaeologySociologyComputer scienceEngineeringDemographyPopulation

Résumé

récupéré en direct d'OpenAlex

GIScience 2016 Short Paper Proceedings A Multidirectional Optimal Ecotope-Based Algorithm to Delineate a Commuter Shed D. Schleith, M. J. Widener University of Cincinnati, 401 Braunstein Hall, Cincinnati, OH 45221-0131 Email: schleidk@mail.uc.edu University of Toronto, Sidney Smith Hall, 100 St. George St, Room 5037, Toronto, ON M5S 3G3 Email: michael.widener@utoronto.ca 1. Introduction In commuting research the geographic area under investigation is of crucial importance. When examining commutes occurring in a region of interest, the selection and use of different city, county, or metropolitan region boundaries will have a large impact on analyses of travel times and distances, whether a transit network provides adequate access to jobs, levels of congestion, and so on. This is closely linked to the spatial form of cities (especially in the North American context) where a relatively dense city is surrounded by suburbs with progressively lower densities. Determining what actually constitutes a commuting region (or “commuter shed”) is typically a matter of using administrative boundaries prescribed by the U.S. Census Bureau. In general though, the metropolitan region is often used because it represents a big enough area to capture most of the economic activity occurring inside. The issue with metropolitan boundaries, however, is summarized in Morrill et al. (1999), “… metropolitan areas are widely recognized as far from consistent in meaning or adequate in definition.” The problem is largely attributed to the use of counties as building blocks. Counties that are selected to comprise a metropolitan region are those neighboring the county or counties containing the largest principal city. The neighboring counties are included if they are socially and economically connected to the principal county, as measured by the number of commuters coming into the central county (Office of Management and Budget, 2010). Counties have a large spatial extent, and oftentimes include vast rural spaces with little relationship to the urbanized area of interest to many researchers. A method for providing a more precise measure is warranted. In many ways a commuter shed is like a cluster of commuting activity, where there are significant links between residents moving between relevant, contiguous zones. Here, we use a cluster detection method to delineate the commuter sheds of the counties that make up the Miami, FL metro region. We take census tracts as the building blocks to provide a more precise representation of the commuter shed, and test the spatial interaction of these tracts using the percentage of commutes into the various zones and an advanced spatial clustering statistic.! 2. Relevant Literature As previously mentioned, commuter sheds are the de facto analysis areas of most commuting research, the results of which are sensitive to the definition of the study area. Researchers are therefor interested in these definitions, as they attempt to accurately describe settlement patterns across the country and provide a reasonable assessment of how people move within an urban region. The current method used by the US Census examines the “percent of commutes” in a county to the nearest county containing a central city. So, an outlying county is included if it has at least 15% of its commuters working in that central county (or counties). But an outlying county can only be assigned to one central county and the determination is made based on commutes from the possible central counties added to commutes to the central county.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,632
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,047
Tête enseignante GPT0,342
Écart entre enseignants0,294 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission2
Résumé présentoui

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