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Driving factors of urban sprawl in the Romanian plain. Regional and temporal modelling using logistic regression / Ines Grigorescu in Geocarto international, vol 37 n° 24 ([20/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 24 [20/10/2022] . - pp 7220 - 7246 Titre : Driving factors of urban sprawl in the Romanian plain. Regional and temporal modelling using logistic regression Type de document : Article/Communication Auteurs : Ines Grigorescu, Auteur ; Gheorghe Kucsicsa, Auteur ; Bianca Mitrică, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 7220 - 7246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de données [Termes IGN] analyse spatio-temporelle [Termes IGN] croissance urbaine [Termes IGN] étalement urbain [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] modélisation spatiale [Termes IGN] régression logistique [Termes IGN] Roumanie [Termes IGN] zone urbaine Résumé : (auteur) The paper investigates built-up areas expansion after the 1990 in one of the highly urbanized regions of Romania - Romanian Plain, in order to explore the urban sprawl phenomena and its temporal and regional disparities in relation to some of the main distance driving factors. The research uses Landsat 4/5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM), and Landsat 8 Operational Land Imager (OLI) imagery to derive built-up areas and quantify their expansion over time in relation to fourteen distance explanatory factors: i.e. previous built-up areas, main road infrastructure, Bucharest city’s boundary, location of the urban centres classified according to demographic size and main economic function, forest land and water bodies. To estimate the influence of the predictors, the binary logistic regression was applied. Furthermore, to estimate the effectiveness of the predictor set in the variation of built-up areas expansion, the pseudo R2 was calculated and discussed. Moreover, to understand the future potential trend of urban sprawl and its spatial pattern, the probability maps were generated by integrating the regression coefficients of the statistically significant predictors into the spatial modeling. For the results performance assessment, the statistic Receiver Operating Characteristic and the pixel-based comparison between the real and predicted data were used. To assess possible differences at spatial and temporal scale, the analysis was carried out at regional level, for two periods: 1990–2002 and 2002–2018. In general, our findings show inverse relationship between the distance driving factors and built-up areas expansion, but the estimated predictive power suggests important disparities within the study area over the analysed periods. Overall, the statistical analysis indicate that the distance to previous build-up areas, distance to road infrastructure, distance to Bucharest and other large urban centres, and distance to urban centres with dominant industrial and service functions were more influential to urban sprawl after 1990. Furthermore, the predicted spatial data shows the highest potential of urban sprawl in the future around Bucharest, in the proximity of existing built-up areas and road infrastructure. Because of its predictive character, the present study is to be a useful tool for land managers and policy makers. Numéro de notice : A2022-777 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1967465 Date de publication en ligne : 23/08/2021 En ligne : https://doi.org/10.1080/10106049.2021.1967465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101832 [article]