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Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing / Zhi Zhang in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2262 - 2286 Titre : Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing Type de document : Article/Communication Auteurs : Zhi Zhang, Auteur ; Jing Li, Auteur ; Fung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2262 - 2286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] coût [Termes IGN] hétérogénéité spatiale [Termes IGN] logement [Termes IGN] marché foncier [Termes IGN] Pékin (Chine) [Termes IGN] régression géographiquement pondérée Résumé : (auteur) Geographically and temporally weighted regression (GTWR) has been demonstrated as an effective tool for exploring spatiotemporal data under spatial and temporal heterogeneity. Exploiting the advantages of the two most popular GTWR methods, we propose an alternative GTWR with a good balance between complexity and interpretability via a unilateral temporal weighting scheme called unilateral GTWR (UGTWR). When compared to the other two popular GTWR methods, the simulation experiment shows that UGTWR has comparable estimation accuracy and model fit, but it is more efficient. Furthermore, we propose its multiscale extension, coined multiscale UGTWR (MUGTWR), to characterize the spatiotemporal dynamic regression relationships at multiple scales. The proposed MUGTWR was applied to the analysis of house prices in the period of 2014–2018 in Beijing as a case study. Our analysis reveals that MUGTWR can effectively capture different levels of spatiotemporal heterogeneity in selected factors affecting house prices at different scales. Therefore, this study is useful for the formulation of housing policy in which the spatiotemporal dynamics of house prices with respect to specific factors can be considered. Numéro de notice : A2021-758 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/13658816.2021.1912348 date de publication en ligne : 12/05/2021 En ligne : https://doi.org/10.1080/13658816.2021.1912348 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98773 [article]

Benford’s law and geographical information – the example of OpenStreetMap / Franz-Benjamin Mocnik in International journal of geographical information science IJGIS, vol 35 n° 9 (September 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 9 (September 2021) . - pp 1746 - 1772 Titre : Benford’s law and geographical information – the example of OpenStreetMap Type de document : Article/Communication Auteurs : Franz-Benjamin Mocnik, Auteur Année de publication : 2021 Article en page(s) : pp 1746 - 1772 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] distribution de Benford [Termes IGN] données localisées [Termes IGN] données localisées des bénévoles [Termes IGN] données statistiques [Termes IGN] hétérogénéité spatiale [Termes IGN] OpenStreetMap [Termes IGN] qualité des données Résumé : (auteur) Few laws about geographical information are known, partly because geographical information is inherently complex. Tobler’s first law of Geography and, to a lesser degree, also his second law are among the rare exceptions. In this article, we explore the validity of Benford’s law in the context of the example of OpenStreetMap. More specifically, we compare the distribution of several numerical features of geographical entities to the Benford distribution. It is demonstrated that the numerical features examined are in accordance with Benford’s law to a varying degree with little variation between the types of geographical entities. Spatial patterns in the deviation from Benford’s law are shown to be similar for some aspects but to strongly differ for other ones. We show that many aspects of the data tend to deviate more than average from the Benford distribution in Africa, Greenland, smaller island countries, and, to a lesser degree, in South America. Also, the scale-dependency of Benford’s law is explored. Motivated by the use of Benford’s law to detect indications for fraud in economic and other datasets, future prospects and limitations to systematically develop intrinsic data quality measures are discussed. Numéro de notice : A2021-594 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1829627 date de publication en ligne : 07/04/2021 En ligne : https://doi.org/10.1080/13658816.2020.1829627 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98215 [article]

Cellular automata based land-use change simulation considering spatio-temporal influence heterogeneity of light rail transit construction: A case in Nanjing, China / Jiaming Na in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 5 (May 2021) . - n° 308 Titre : Cellular automata based land-use change simulation considering spatio-temporal influence heterogeneity of light rail transit construction: A case in Nanjing, China Type de document : Article/Communication Auteurs : Jiaming Na, Auteur ; Jie Zhu, Auteur ; Jiazhu Zheng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 308 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] changement d'utilisation du sol [Termes IGN] données spatiotemporelles [Termes IGN] hétérogénéité spatiale [Termes IGN] modèle de simulation [Termes IGN] Nankin (Kiangsou) [Termes IGN] réseau ferroviaire [Termes IGN] train [Termes IGN] transport ferroviaire [Termes IGN] transport public Résumé : (auteur) Light rail transit (LRT), an essential urban public transport system in China, significantly reshaped the urban land-use (LU) pattern. Although the LRT impact and land-use change (LUC) analysis plays an essential role in urban planning policy, the spatiotemporal heterogeneity of LRT impacts have not been considered in LUC simulation studies. This study simulates the urban LU change, considering the spatiotemporal heterogeneity of LRT construction impacts on urban LUC. LUC from 1995 to 2005 in Nanjing, China, is chosen as a case study. At first, the distance decay function is employed to verify the quantitative impact of LRT construction on LU change. Accordingly, the variation trends of each LU type during different stages are described in time and space. A cellular automata model incorporated by the generated LRT impact is established and then implemented for simulation. According to model performance assessment results, the proposed model can produce a realistic urban pattern with Freedom of Movement (FoM) exceeding 24% and a significantly lower relative error than the CA simulation without considering LRT influence. Numéro de notice : A2021-390 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 0.3390/ijgi10050308 date de publication en ligne : 06/05/2020 En ligne : https://doi.org/10.3390/ijgi10050308 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97675 [article]

Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions / Xin-Ming Zhu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2680 - 2697 Titre : Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions Type de document : Article/Communication Auteurs : Xin-Ming Zhu, Auteur ; Xiao-Ning Song, Auteur ; Pei Leng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2680 - 2697 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Chine [Termes IGN] correction atmosphérique [Termes IGN] hétérogénéité spatiale [Termes IGN] image Landsat-8 [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression [Termes IGN] température au sol [Termes IGN] variable biophysique (végétation) Résumé : (Auteur) Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi’an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases. Numéro de notice : A2021-219 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3002821 date de publication en ligne : 26/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3002821 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97211 [article]

Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling / Stefanos Georganos in Geocarto international, vol 36 n° 2 ([01/02/2021])  

Public [article]  inGeocarto international > vol 36 n° 2 [01/02/2021] . - pp 121 -1 36 Titre : Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling Type de document : Article/Communication Auteurs : Stefanos Georganos, Auteur ; Tais Grippa, Auteur ; Assane Niang Gadiaga, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 121 -1 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] autocorrélation spatiale [Termes IGN] classification par forêts aléatoires [Termes IGN] Dakar [Termes IGN] densité de population [Termes IGN] distribution spatiale [Termes IGN] hétérogénéité spatiale [Termes IGN] modèle dynamique [Termes IGN] population [Termes IGN] utilisation du sol Résumé : (auteur) Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditionally geographical topics such as population estimation. Even though RF is a well performing and generalizable algorithm, the vast majority of its implementations is still ‘aspatial’ and may not address spatial heterogenous processes. At the same time, remote sensing (RS) data which are commonly used to model population can be highly spatially heterogeneous. From this scope, we present a novel geographical implementation of RF, named Geographical Random Forest (GRF) as both a predictive and exploratory tool to model population as a function of RS covariates. GRF is a disaggregation of RF into geographical space in the form of local sub-models. From the first empirical results, we conclude that GRF can be more predictive when an appropriate spatial scale is selected to model the data, with reduced residual autocorrelation and lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values. Finally, and of equal importance, GRF can be used as an effective exploratory tool to visualize the relationship between dependent and independent variables, highlighting interesting local variations and allowing for a better understanding of the processes that may be causing the observed spatial heterogeneity. Numéro de notice : A2021-080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595177 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96822 [article]

Deriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 / Helena Bergstedt in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

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Modelling housing rents using spatial autoregressive geographically weighted regression: a case study in cracow, Poland / Mateusz Tomal in ISPRS International journal of geo-information, vol 9 n° 6 (June 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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The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study / Yanan Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

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Streambank topography: an accuracy assessment of UAV-based and traditional 3D reconstructions / Benjamin U. Meinen in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

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Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

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Testing spatial heterogeneity in geographically weighted principal components analysis / Javier Roca-Pardiñas in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)  

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Exposure-related forest-steppe: A diverse landscape type determined by topography and climate / Martin Hais in Journal of Arid Environments, vol 135 (December 2016)  

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Combined effects of area, connectivity, history and structural heterogeneity of woodlands on the species richness of hoverflies (Diptera: Syrphidae) / Pierre-Alexis Herrault in Landscape ecology, vol 31 n° 4 (May 2016)

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Mapping the value of ecosystem services: A case study from the Austrian Alps / Alessandro Paletto in Annals of forest research, vol 58 n° 1 (January 2015)  

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