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Clustering with implicit constraints: A novel approach to housing market segmentation / Xiaoqi Zhang in Transactions in GIS, vol 26 n° 2 (April 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 2 (April 2022) . - pp 585 - 608 Titre : Clustering with implicit constraints: A novel approach to housing market segmentation Type de document : Article/Communication Auteurs : Xiaoqi Zhang, Auteur ; Yanqiao Zheng, Auteur ; Qiong Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 585 - 608 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algorithme glouton [Termes IGN] analyse de groupement [Termes IGN] Chine [Termes IGN] classification par nuées dynamiques [Termes IGN] contrainte topologique [Termes IGN] hétérogénéité spatiale [Termes IGN] logement [Termes IGN] marché foncier [Termes IGN] programmation par contraintes [Termes IGN] segmentation [Termes IGN] structure spatiale [Termes IGN] zone urbaine Résumé : (auteur) Constrained clustering has been widely studied and outperforms both the traditional unsupervised clustering and experience-oriented approaches. However, the existing literature on constrained clustering concentrates on spatially explicit constraints, while many constraints in housing market studies are implicit. Ignoring the implicit constraints will result in unreliable clustering results. This article develops a novel framework for constrained clustering, which takes implicit constraints into account. Specifically, the research extends the classical greedy searching algorithm by adding one back-and-forth searching step, efficiently coping with the order sensitivity. Via evaluation on both synthetic and real data sets, it turns out that the proposed algorithm outperforms existing algorithms, even when only the traditional pairwise constraints are provided. In an application to a concrete housing market segmentation problem, the proposed algorithm shows its power to accommodate user-specified homogeneity criteria to extract hidden information on the underlying urban spatial structure. Numéro de notice : A2022-362 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12878 Date de publication en ligne : 26/12/2021 En ligne : https://doi.org/10.1111/tgis.12878 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100581 [article]

Spatial modeling of migration using GIS-based multi-criteria decision analysis: A case study of Iran / Naeim Mijani in Transactions in GIS, vol 26 n° 2 (April 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 2 (April 2022) . - pp 645 - 668 Titre : Spatial modeling of migration using GIS-based multi-criteria decision analysis: A case study of Iran Type de document : Article/Communication Auteurs : Naeim Mijani, Auteur ; Davoud Shahpari Sani, Auteur ; Mohsen Dastaran, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 645 - 668 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse multicritère [Termes IGN] approche hiérarchique [Termes IGN] changement climatique [Termes IGN] coefficient de corrélation [Termes IGN] combinaison linéaire ponderée [Termes IGN] données démographiques [Termes IGN] données socio-économiques [Termes IGN] Iran [Termes IGN] migration humaine [Termes IGN] modélisation spatiale [Termes IGN] planification urbaine [Termes IGN] système d'information géographique Résumé : (auteur) Spatial modeling of migration and the identification of the effective parameters are imperative for planning and managing demographic, economic, social, and environmental changes on various geographical scales. The recent climate change stressors as well as inequality in terms of education and life quality have triggered internal mass migrations in Iran, causing pressure on housing, the job market, and potential slums around large cities. This study proposes a new approach to modeling migration patterns in Iran based on multi-criteria decision analysis. For this purpose, a total of 23 individual criteria embedded within four criteria groups (economic, socio-cultural, welfare, and environmental) affecting national migration were used. The analytic hierarchy process was employed to determine weights for the input factors and the weighted linear combination (WLC) model was used for the integration of criteria, based on which maps of migration potential were produced. The model applied was evaluated based on the correlation coefficient between migration potential values obtained from the WLC model and the actual net migration rate. Among the input individual criteria, unemployment, higher education centers, number of physicians, and dust storms were found to influence national migration. Furthermore, our findings reveal that the potential for migration across Iranian provinces is heterogeneous, with the spatial potential for emigration being the highest and lowest in the border and central provinces, respectively. The correlation coefficient calculated between outputs from the WLC model and the net migration rate from 2011 to 2016, was .81, indicating the relatively high performance of the proposed model in producing a migration spatial potential map. Our proposed approach, along with the results achieved, can be useful to decision-makers and planners in designing data-driven policies against inequality- and climate-induced stressors. Numéro de notice : A2022-363 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12873 Date de publication en ligne : 23/11/2021 En ligne : https://doi.org/10.1111/tgis.12873 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100582 [article]

Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction / Jincai Huang in Transactions in GIS, vol 26 n° 2 (April 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 2 (April 2022) . - pp 735 - 754 Titre : Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction Type de document : Article/Communication Auteurs : Jincai Huang, Auteur ; Yunfei Zhang, Auteur ; Min Deng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 735 - 754 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Vedettes matières IGN] Géomatique [Termes IGN] base de données routières [Termes IGN] carrefour [Termes IGN] carte routière [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] données routières [Termes IGN] extraction de traits caractéristiques [Termes IGN] information sémantique [Termes IGN] intégration de données [Termes IGN] navigation automobile [Termes IGN] vitesse [Termes IGN] Wuhan (Chine) Résumé : (auteur) The road map is a fundamental part of a spatial data infrastructure (SDI), and is widely applied in navigation, smart transportation, and mobile location services. Recently, with the ubiquity of positioning devices, crowdsourced trajectories have become a significant data resource for road map construction and updating. However, existing trajectory-based methods mainly place emphasis on extracting road geometry features and may ignore continuous updating of road semantic information. Hence, we propose a divide-and-conquer method to construct a spatial-semantic road map by incorporating multiple data sources (e.g., crowdsourced trajectories and geo-tagged data). The proposed method divides road map construction into two sub-tasks, road structure reconstruction and road attributes inference. The road structure reconstruction process starts to partition raw trajectory data into different cliques of roadways and road intersections, and then extracts various targeted road structures by analyzing the turning modes in different trajectory cliques. The road attributes inference process aims to infer three pieces of crucial semantic information about road speeds, turning rules, and road names from crowdsourced trajectories and geo-tagged data. The case studies in Wuhan were examined to illustrate that the proposed method can construct a routable road map with enhanced geometric structures and rich semantic information, providing a beneficial data solution for car navigation and SDI update. Numéro de notice : A2022-364 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12879 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.1111/tgis.12879 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100583 [article]

Spatially oriented convolutional neural network for spatial relation extraction from natural language texts / Qinjun Qiu in Transactions in GIS, vol 26 n° 2 (April 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 2 (April 2022) . - pp 839 - 866 Titre : Spatially oriented convolutional neural network for spatial relation extraction from natural language texts Type de document : Article/Communication Auteurs : Qinjun Qiu, Auteur ; Zhong Xie, Auteur ; Kai Ma, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 839 - 866 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] appariement sémantique [Termes IGN] apprentissage dirigé [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] exploration de données [Termes IGN] langage naturel (informatique) [Termes IGN] proximité sémantique [Termes IGN] relation spatiale [Termes IGN] relation topologique [Termes IGN] site wiki [Termes IGN] spatial metrics [Termes IGN] système à base de connaissances Résumé : (auteur) Spatial relation extraction (e.g., topological relations, directional relations, and distance relations) from natural language descriptions is a fundamental but challenging task in several practical applications. Current state-of-the-art methods rely on rule-based metrics, either those specifically developed for extracting spatial relations or those integrated in methods that combine multiple metrics. However, these methods all rely on developed rules and do not effectively capture the characteristics of natural language spatial relations because the descriptions may be heterogeneous and vague and may be context sparse. In this article, we present a spatially oriented piecewise convolutional neural network (SP-CNN) that is specifically designed with these linguistic issues in mind. Our method extends a general piecewise convolutional neural network with a set of improvements designed to tackle the task of spatial relation extraction. We also propose an automated workflow for generating training datasets by integrating new sentences with those in a knowledge base, based on string similarity and semantic similarity, and then transforming the sentences into training data. We exploit a spatially oriented channel that uses prior human knowledge to automatically match words and understand the linguistic clues to spatial relations, finally leading to an extraction decision. We present both the qualitative and quantitative performance of the proposed methodology using a large dataset collected from Wikipedia. The experimental results demonstrate that the SP-CNN, with its supervised machine learning, can significantly outperform current state-of-the-art methods on constructed datasets. Numéro de notice : A2022-365 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12887 Date de publication en ligne : 27/12/2021 En ligne : https://doi.org/10.1111/tgis.12887 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100584 [article]