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Development and analysis of land-use/land-cover spatio-temporal metrics in urban environments: Exploring urban growth patterns and linkages to socio-economic factors / Marta Sapena Moll (2021)  

Public Titre : Development and analysis of land-use/land-cover spatio-temporal metrics in urban environments: Exploring urban growth patterns and linkages to socio-economic factors Type de document : Thèse/HDR Auteurs : Marta Sapena Moll, Auteur ; Luis Angel Ruiz Fernandez, Directeur de thèse Editeur : Valencia : Universidad politécnica de Valencia Année de publication : 2021 Importance : 268 p. Format : 21 x 30 cm Note générale : bibliographie PhD in Geomatics Engineering, Universidad politécnica de Valencia Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse discriminante [Termes IGN] analyse spatio-temporelle [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts aléatoires [Termes IGN] croissance urbaine [Termes IGN] données socio-économiques [Termes IGN] implémentation (informatique) [Termes IGN] milieu urbain [Termes IGN] modélisation spatiale [Termes IGN] occupation du sol [Termes IGN] population urbaine [Termes IGN] régression linéaire [Termes IGN] Rhénanie du Nord-Wesphalie (Allemagne) [Termes IGN] utilisation du sol [Termes IGN] ville durable Résumé : (auteur) This thesis addresses the development and analysis of new tools and methods for monitoring and characterizing urban growth using geo-data and land-use/land-cover (LULC) databases, as well as exploring their relationships with socio-economic factors, providing new evidences regarding the use of LULC data for urban characterization at different levels by means of spatial and statistical methods. First, the most common spatio-temporal metrics were compiled and implemented within a software tool, IndiFrag. Then, we present a methodology based on spatio-temporal metrics and propose a new index that quantifies the inequality of growth between population and built-up areas to analyze and compare urban growth patterns at different levels. This allowed for a differentiation of growing patterns, besides, the analysis at various levels contributed to a better understanding of such patterns. Second, we quantified the two-way relationship between the urban structure in cities and their socio-economic status by means of spatial metrics issued from a local climate zone map for 31 cities in North Rhine-Westphalia, Germany. Based on these data, we quantified their relationship with socio-economic indicators by means of multiple linear regression models, explaining a significant part of their variability. The proposed method is transferable to other datasets, levels, and regions. Third, we assessed the use of spatio-temporal metrics derived from LULC maps to identify urban growth spatial patterns. We applied LULC change models to simulate different long-term scenarios of urban growth following various spatial patterns on diverse baseline urban forms. Then, we computed spatio-temporal metrics for the simulated scenarios, selected the most explanatory by applying a discriminant analysis and classified the growth patterns using clustering methods. Finally, we identified empirical relationships between socio-economic indicators and their change over time with the spatial structure of the built and natural elements in up to 600 urban areas from 32 countries. We employed random forest regression models and the spatio-temporal metrics were able to explain substantially the variability of socio-economic variables. This confirms that spatial patterns and their change are linked to socio-economic indicators. This work contributes to a better understanding of urban growth patterns and improves knowledge about the relationships between urban spatial structure and socio-economic factors, providing new methods for monitoring and assessing urban sustainability by means of LULC databases, which could be used by researchers, urban planners and decision-makers to ensure the sustainable future of urban environments. Note de contenu : 1- Introduction 2- Hypotheses and objectives 3- Spatio-temporal analysis of LULC and population in urban areas 4- Relationships between spatial patterns of urban structure and quality of life 5- Spatio-temporal metrics for urban growth spatial pattern categorization 6- Linking spatio-temporal metrics of built-up areas to socio-economic indicators on a semi-global scale 7- Conclusions Numéro de notice : 28308 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Thèse étrangère Note de thèse : PhD Thesis : Geomatics Engineering : Valencia, Spain : 2021 Organisme de stage : German Aerospace Center DOI : 10.4995/Thesis/10251/158626 En ligne : https://doi.org/10.4995/Thesis/10251/158626 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98112

Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London / Nilufer Sari Aslam in Annals of GIS, vol 27 n° 1 (January 2021)  

Public [article]  inAnnals of GIS > vol 27 n° 1 (January 2021) . - pp 29 - 41 Titre : Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London Type de document : Article/Communication Auteurs : Nilufer Sari Aslam, Auteur ; Di Zhu, Auteur ; Tao Cheng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 29 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] carte à puce [Termes IGN] collecte de données [Termes IGN] données socio-économiques [Termes IGN] données spatiotemporelles [Termes IGN] enrichissement sémantique [Termes IGN] loisir [Termes IGN] Londres [Termes IGN] méthode heuristique [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] transport urbain Résumé : (auteur) The large volume of data automatically collected by smart card fare systems offers a rich source of information regarding daily human activities with a high resolution of spatial and temporal representation. This provides an opportunity for aiding transport planners and policy-makers to plan transport systems and cities more responsively. However, there are currently limitations when it comes to understanding the secondary activities of individual commuters. Accordingly, in this paper, we propose a framework to detect and infer secondary activities from individuals’ daily travel patterns from the smart card data and reduce the use of conventional surveys. First, we proposed a ‘heuristic secondary activity identification algorithm’, which uses commuters’ primary locations (home & work) and the direction (from & to) information to identify secondary activities for individuals. The algorithm provides a high-level classification of the activity types as before-work, midday and after-work activity patterns of individuals. Second, this classification is semantically enriched using Points of Interests to provide meaningful insights into individuals’ travel purposes and mobility in an urban environment. Lastly, using the transit data of London as a case study, the model is compared with a volunteer survey to demonstrate its effectiveness and offering a cost-effective method to travel demand research. Numéro de notice : A2021-319 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2020.1783359 date de publication en ligne : 01/08/2020 En ligne : https://doi.org/10.1080/19475683.2020.1783359 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97550 [article]

Using OpenStreetMap data and machine learning to generate socio-economic indicators / Daniel Feldmeyer in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 9 (September 2020) . - 16 p. Titre : Using OpenStreetMap data and machine learning to generate socio-economic indicators Type de document : Article/Communication Auteurs : Daniel Feldmeyer, Auteur ; Claude Meisch, Auteur ; Holger Sauter, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Allemagne [Termes IGN] apprentissage automatique [Termes IGN] arbre aléatoire [Termes IGN] base de données spatiotemporelles [Termes IGN] changement climatique [Termes IGN] chômage [Termes IGN] classification par réseau neuronal [Termes IGN] collectivité territoriale [Termes IGN] données localisées des bénévoles [Termes IGN] données socio-économiques [Termes IGN] inégalité [Termes IGN] limite administrative [Termes IGN] modèle de régression [Termes IGN] modèle de simulation [Termes IGN] OpenStreetMap Résumé : (auteur) Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities. Numéro de notice : A2020-663 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9090498 date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.3390/ijgi9090498 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96139 [article]

Landuse and land cover identification and disaggregating socio-economic data with convolutional neural network / Jingtao Yao in Geocarto international, vol 35 n° 10 ([01/08/2020])  

Public [article]  inGeocarto international > vol 35 n° 10 [01/08/2020] . - pp 1109 - 1123 Titre : Landuse and land cover identification and disaggregating socio-economic data with convolutional neural network Type de document : Article/Communication Auteurs : Jingtao Yao, Auteur ; Xiangbin Kong, Auteur ; Rattan Lal, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1109 - 1123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] densité de population [Termes IGN] désagrégation [Termes IGN] données socio-économiques [Termes IGN] image Landsat-8 [Termes IGN] occupation du sol [Termes IGN] utilisation du sol Résumé : (auteur) Numéro de notice : A2020-427 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1568587 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1568587 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95493 [article]

Los Angeles as a digital place: The geographies of user‐generated content / Andrea Ballatore in Transactions in GIS, Vol 24 n° 4 (August 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 4 (August 2020) . - 23 p. Titre : Los Angeles as a digital place: The geographies of user‐generated content Type de document : Article/Communication Auteurs : Andrea Ballatore, Auteur ; Stefano de Sabbata, Auteur Année de publication : 2020 Article en page(s) : 23 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse spatiale [Termes IGN] centre urbain [Termes IGN] contenu généré par les utilisateurs [Termes IGN] distribution spatiale [Termes IGN] données multisources [Termes IGN] données socio-économiques [Termes IGN] exploration de données géographiques [Termes IGN] Foursquare [Termes IGN] Los Angeles [Termes IGN] modèle de régression [Termes IGN] OpenStreetMap [Termes IGN] participation du public [Termes IGN] représentation géographique [Termes IGN] réseau social [Termes IGN] réseau social géodépendant [Termes IGN] Twitter Résumé : (auteur) Online representations of places are becoming pivotal in informing our understanding of urban life. Content production on online platforms is grounded in the geography of their users and their digital infrastructure. These constraints shape place representation, that is, the amount, quality, and type of digital information available in a geographic area. In this article we study the place representation of user‐generated content (UGC) in Los Angeles County, relating the spatial distribution of the data to its geo‐demographic context. Adopting a comparative and multi‐platform approach, this quantitative analysis investigates the spatial relationship between four diverse UGC datasets and their context at the census tract level (about 685,000 geo‐located tweets, 9,700 Wikipedia pages, 4 million OpenStreetMap objects, and 180,000 Foursquare venues). The context includes the ethnicity, age, income, education, and deprivation of residents, as well as public infrastructure. An exploratory spatial analysis and regression‐based models indicate that the four UGC platforms possess distinct geographies of place representation. To a moderate extent, the presence of Twitter, OpenStreetMap, and Foursquare data is influenced by population density, ethnicity, education, and income. However, each platform responds to different socio‐economic factors and clusters emerge in disparate hotspots. Unexpectedly, Twitter data tend to be located in denser, more deprived areas, and the geography of Wikipedia appears peculiar and harder to explain. These trends are compared with previous findings for the area of Greater London. Numéro de notice : A2020-671 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12600 date de publication en ligne : 02/01/2020 En ligne : https://doi.org/10.1111/tgis.12600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96156 [article]

Exploratory bivariate and multivariate geovisualizations of a social vulnerability index / Georgianna Strode in Cartographic perspectives, n° 95 (July 2020)  

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Spatiotemporally Varying Coefficients (STVC) model: a Bayesian local regression to detect spatial and temporal nonstationarity in variables relationships / Chao Song in Annals of GIS, vol 26 n° 3 (July 2020)  

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Delineating and modeling activity space using geotagged social media data / Lingqian Hu in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

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City-descriptive input data for urban climate models: Model requirements, data sources and challenges / Valéry Masson in Urban climate, vol 31 (March 2020)  

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A comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data / Haiyan Tao in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)  

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Spationomy / Vit Pászto (2020)  

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A thematic mapping method to assess and analyze potential urban hazards and risks caused by flooding / Mohammad Khalid Hossain in Computers, Environment and Urban Systems, vol 79 (January 2020)  

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Analyzing the recent dynamics of wildland fires in Quercus suber L. woodlands in Sardinia (Italy), Corsica (France) and Catalonia (Spain) / Michele Salis in European Journal of Forest Research, vol 138 n° 3 (June 2019)  

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Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level / Jiang Juqin in Cartography and Geographic Information Science, vol 46 n° 3 (May 2019)  

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Ontologies pour représenter l’évolution des découpages territoriaux statistiques / Camille Bernard in Revue internationale de géomatique, vol 28 n° 4 (octobre - décembre 2018)  

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