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A cellular-automata model for assessing the sensitivity of the street network to natural terrain / Jeeno Soa George in Annals of GIS, vol 27 n° 3 (July 2021)  

Public [article]  inAnnals of GIS > vol 27 n° 3 (July 2021) . - pp 261 - 272 Titre : A cellular-automata model for assessing the sensitivity of the street network to natural terrain Type de document : Article/Communication Auteurs : Jeeno Soa George, Auteur ; Saikat Kumar Paul, Auteur ; Richa Dhawale, Auteur Année de publication : 2021 Article en page(s) : pp 261 - 272 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de sensibilité [Termes IGN] automate cellulaire [Termes IGN] Caracas [Termes IGN] croissance urbaine [Termes IGN] données spatiotemporelles [Termes IGN] Inde [Termes IGN] Japon [Termes IGN] modélisation spatiale [Termes IGN] morphologie urbaine [Termes IGN] planification urbaine [Termes IGN] réalité de terrain [Termes IGN] réseau routier [Termes IGN] Singapour Résumé : (auteur) Natural and human-made features are not exclusive in settlements but interact across time and space, placing the context in constant evolution. The purpose of this paper is to search for the influence of terrain, a natural feature, on the configuration of the street network, a human-made feature, by analysing the results of two transition states of cellular automata used to model street networks. This work uses data from open-source projects and open-source applications. The first transition state models the street network considering the neighbourhood rules and randomness, assuming the natural terrain and street are exclusive. The second transition state models the street network as the product of characteristics of the terrain, neighbourhood rules, and randomness, thus assuming the natural terrain and street network interacting with one another. The model is run thirteen times for four different cities by varying the terrain characteristics and calibrated by comparing the simulated street maps with recent street maps. The results are compared and found that the CA model with the second transition state yields better simulation results than the first transition state. In one of the four cities studied, the first transition state results are similar to a specific state of the second transition state, indicating a weak inter-connectedness between the terrain and the street network in the mega-city. Further research can reveal whether the amount of inter-connectedness is specific to the city’s terrain or size. The recognition of the inter-connectedness of the road to terrain can help plan for resilient human settlements. Numéro de notice : A2021-628 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1080/19475683.2021.1936173 Date de publication en ligne : 03/06/2021 En ligne : https://doi.org/10.1080/19475683.2021.1936173 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98269 [article]

Machine learning for inference: using gradient boosting decision tree to assess non-linear effects of bus rapid transit on house prices / Linchuan Yang in Annals of GIS, vol 27 n° 3 (July 2021)  

Public [article]  inAnnals of GIS > vol 27 n° 3 (July 2021) . - pp 273 - 284 Titre : Machine learning for inference: using gradient boosting decision tree to assess non-linear effects of bus rapid transit on house prices Type de document : Article/Communication Auteurs : Linchuan Yang, Auteur ; Yuan Liang, Auteur ; Qing Zhu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 273 - 284 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de la valeur [Termes IGN] apprentissage automatique [Termes IGN] arbre de décision [Termes IGN] bien immobilier [Termes IGN] boosting adapté [Termes IGN] Chine [Termes IGN] Extreme Gradient Machine [Termes IGN] inférence [Termes IGN] logement [Termes IGN] transport public Résumé : (auteur) The adoption of bus rapid transit (BRT) systems has gained worldwide popularity over the past several decades. China is no exception as it has long been aiming at promoting public transportation. Prior studies have provided extensive evidence that BRT has substantial effects on house prices with traditional econometric techniques, such as hedonic pricing models. However, few of those investigations have discussed the non-linear relationship between BRT and house prices. Using the Xiamen data, this study employs a machine learning technique, namely the gradient boosting decision tree (GBDT), to scrutinize the non-linear relationship between BRT and house prices. This study documents a positive association between accessibility to BRT stations and house prices and a negative association between proximity to the BRT corridor and house prices. Moreover, it suggests a non-linear relationship between BRT and house prices and indicates that GBDT has more substantial predictive power than hedonic pricing models. Numéro de notice : A2021-629 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1080/19475683.2021.1906746 Date de publication en ligne : 27/03/2021 En ligne : https://doi.org/10.1080/19475683.2021.1906746 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98270 [article]