MétaCan
Menu
Retour à la cohorte
Enregistrement W6888828447 · doi:10.22108/sppl.2022.127142.1559

Spatial Modeling of Factors Affecting Building Density: A Case Study of Hamedan City

2022· article· en· W6888828447 sur OpenAlexaboutno aff

Notice bibliographique

RevueDOAJ (DOAJ: Directory of Open Access Journals) · 2022
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueLand Use and Ecosystem Services
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInjusticeUrban planningSmart growthInequalityPopulationDistribution (mathematics)Process (computing)Population growthConceptual framework

Résumé

récupéré en direct d'OpenAlex

The process of growth and development of Iranian cities indicates that the unbalanced and uncoordinated growth of the city with the lack of planning and inappropriate design has led to the creation of a heterogeneous structure in the cities. Therefore, from the point of view of urban planning, the category of density is one of the most essential tools for controlling and developing the city. The results show that construction in Hamadan does not have a balanced distribution and a regular pattern. This unbalanced process has caused the population and activity to be concentrated in some specific areas, which has left its negative effects and created conditions where only a few areas and neighborhoods are on the path of development and the rest of the areas remain in stagnation and inactivity. The ever-increasing demand for housing, the high profitability of construction in privileged neighborhoods, and the municipality’s income dependence on construction have caused the formation and continuation of a vicious cycle, the result of which is the increase of spatial inequality and injustice in the city of Hamadan.References- Antoniucci, V., & Marella, G. (2018). Is social polarization related to urban density? Evidence from the Italian housing market. Journal of Landscape and Urban planning, 177, 340-349. - Artmann, M., Kohler, M., Meinel, G., Gan, J., & Ioja, I. C. (2017). How smart growth and green infrastructure can mutually support each other—A conceptual framework for compact and green cities. Journal Ecological Indicators, 96, 10-22.- Balram, Sh., & Dragicevic, S. (2005). Attitudes toward urban land use planning: Integrating questionnaire survey and collaborative GIS techniques to improve attitude measurements. Journal of Landscape and Urban Planning, 71(2-4), 147-162.- Bhiwapurkar, P. (2014). Determinants of urban energy use: Density and urban form. ARCC Conference Repository. Https: // arcc-journal.org/ index.php/ repository/ article/ view /224.- Boyko, C. T., & Cooper, R. (2011). Clarifying and re-conceptualising density. Journal of Progress in Planning, 76(1), 1-61. - Bunting, T., Filion, P., & Priston, H. (2002). Density gradients in Canadian metropolitan regions, 1971–96: Differential patterns of central area and suburban growth and change. Journal of Urban Studies, 39(13), 2531–2552.- Carlino, G. A., Chatterjee, S., & Hunt, R. M. (2007). Urban density and the rate of invention. Journal of Urban Economics, 61(3), 389–419.- Churchman, A. (1999). Disentangling the concept of density. Journal of Planning Literature, 13(4), 389-411.- Cobbinah, P. B., & Darkwah, R. M. (2016). African urbanism: The geography of urban greenery. Journal of Urban Forum, 27(2), 149-165.- Cobbinah, P. B., Erdiaw-Kwasie, M. O., & Amoateng, P. (2015). Africa's urbanisation: Implications for sustainable development. Cities, 47, 62-72.- Cuthbert, A. R. (1985). Architecture, society and space: The high density question reexamined. Journal of Progress in Planning, 24(2), 73–159.- Ewing, R., & Hamidi, S. (2017). Costs of sprawl. New York: Routledge. - Fernandez-Aracil, P., & Ortuno-Padilla, A. (2016). Costs of providing local public services and compact population in Spanish urbanised areas. Journal of Land Use Policy, 58, 234–240.- Flood, J. (1997). Urban and housing indicators. Journal of Urban Studies, 34(10), 1997.- Fortin, M. J., & Dale, M. R. (2005). Spatial analysis, a guide for ecologists. Cambridge: Cambridge University Press.- Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. University of Newcastle Wiley, UK.- He, B. J., Ding, L., & Prasad, D. (2019). Enhancing urban ventilation performance through the development of precinct ventilation zones: A case study based on the greater Sydney, Australia. Journal of Sustainable Cities and Society, 47, 101472.- Hortas-Rico, M., & Sole-Olle, A. (2010). Does urban sprawl increase the costs of providing local public services? Evidence from Spanish municipalities. Journal of Urban Studies, 47(7), 1513–1540.- Hui, S. C. (2001). Low energy building design in high density urban cities. Journal of Renewable Energy, 24(3-4), 624-640.- Jiao, L., Xu, G., Xiao, F., Liu, Y., & Zhang, B. (2017). Analyzing the impacts of urban expansion on green fragmentation using constraint gradient analysis. Journal of the Professional Geographer, 69(4), 553–566.- Lefebvre, H. (1991). The production of space. Oxford, UK; Cambridge, USA: Blackwell.- Li, H., Wei, Y. D., & Korinek, K. (2018). Modeling urban expansion in the transitional greater Mekong region. Urban Studies, 55(8), 1729-1748.- Linard, C., Tatem, A. J., & Gilbert, M. (2013). Modelling spatial patterns of urban growth in Africa. Applied Geography, 44, 23-32.- McFarlane, C. (2016). The geographies of urban density: Topology, politics and the city. Progress in Human Geography, 40(5), 629–648.- Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1-2), 17-23.- Nagendra, H., Bai, X., Brondizio, E. S., & Lwasa, S. (2018). The urban south and the predicament of global sustainability. Journal of Nature Sustainability, 1(7), 341-349.- Pont, M. B., & Haupt, P. (2007). The relation between urban form and density. Journal of Urban Morphology, 11(1), 62.- Rapoport, A. (1975). Toward a redefinition of density. Journal of Environment and Behavior, 7(2), 133-158.- Rapoport, A. (2016). Human aspects of urban form: Towards a man-environment approach to urban form and design. Elsevier.- Sivam, A., Karuppannan, S., & Davis, M. C. (2012). Stakeholder’s perception of residential density-a case study of Adelaide- Australia. Journal of Housing and the Built Environment, 27(4), 473-494. -Sorour, H., Mubaraki, O., & Amiri, S. (2010). Investigating the effects of increasing building density on Tabriz old tissue transport network. Journal of Urban Management Studies Quarterly, 2(4) (in Persian).- Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bulletin of the American Meteorological Society, 93(12), 1879-1900. - Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Journal of Economic Geography, 46(2), 234-240.- Tsai, Y. H. (2005). Quantifying urban form: Compactness versus sprawl. Journal of Urban Studies, 42(1), 141-161.- Um, J., Son, S. W., Lee, S. I., Jeong, H., & Kim, B. J. (2009). Scaling laws between population and facility densities. Proceedings of the National Academy of Sciences, 106(34), 14236–14240.- Wrede, M. (2017). Urban land use, sorting, and population density: A continuous logit model. Transportation Research Part B: Methodological, 101, 283-294.- Xia, C., Zhang, A., Wang, H., Zhang, B., & Zhang, Y., (2019). Bidirectional urban flows in rapidly urbanizing metropolitan areas and their macro and micro impacts on urban growth: A case study of the Yangtze River middle reaches megalopolis, China. Journal of Land Use Policy, 82, 158–168.- Xu, G., Dong, T., Cobbinah, P. B., Jiao, L., Sumari, N. S., Chai, B., & Liu, Y. (2019). Urban expansion and form changes across African cities with a global outlook: Spatiotemporal analysis of urban land densities. Journal of Cleaner Production, 224, 802-810.- Xu, G., Zhou, Z., Jiao, L., & Zhao, R. (2020). Compact urban form and expansion pattern slow down the decline in urban densities: A global perspective. Journal of Land Use Policy, 94, 104563.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,146
Score d'incertitude au seuil0,290

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
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 Opus0,216
Tête enseignante GPT0,484
Écart entre enseignants0,268 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
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

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueDOAJ (DOAJ: Directory of Open Access Journals)Même sujetLand Use and Ecosystem ServicesTravaux en français237 207