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Exploring population spatial concentrations in Northern Ireland by community background and other characteristics: an application of geographically weighted spatial statistics / C.D. Lloyd in International journal of geographical information science IJGIS, vol 24 n°7-8 (july 2010)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 24 n°7-8 (july 2010) . - pp 1193 - 1221 Titre : Exploring population spatial concentrations in Northern Ireland by community background and other characteristics: an application of geographically weighted spatial statistics Type de document : Article/Communication Auteurs : C.D. Lloyd, Auteur Année de publication : 2010 Article en page(s) : pp 1193 - 1221 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] autocorrélation spatiale [Termes IGN] géostatistique [Termes IGN] interpolation inversement proportionnelle à la distance [Termes IGN] interpolation par pondération de zones [Termes IGN] Irlande du nord [Termes IGN] pondération [Termes IGN] population [Termes IGN] régression géographiquement pondérée [Termes IGN] religion [Termes IGN] urbanisation Résumé : (Auteur) Information on how populations are spatially concentrated by different characteristics is a key means of guiding government policies in a variety of contexts, in addition to being of substantial academic interest. In particular, to reduce inequalities between groups, it is necessary to understand the characteristics of these groups in terms of their composition and their geographical structure. This article explores the degree to which the population of Northern Ireland is spatially concentrated by a range of characteristics. There is a long history of interest in residential segregation by religion in Northern Ireland; this article assesses population concentration not only by community background ('religion or religion brought up in') but also by housing tenure, employment and other socioeconomic and demographic characteristics. The spatial structure of geographical variables can be captured by a range of spatial statistics including Moran's I. Such approaches utilise information on connections between observations or the distances between them. While such approaches are conceptually an improvement on standard aspatial statistics, a logical further step is to compute statistics on a local basis on the grounds that most real-world properties are not spatially homogenous and, therefore, global measures may mask much variation. In population geography, which provides the substantive focus for this article, there are still relatively few studies that assess in depth the application of geographically weighted statistics for exploring population characteristics individually and for exploring relations between variables. This article demonstrates the value of such approaches by using a variety of geographically weighted statistical measures to explore outputs from the 2001 Census of Population of Northern Ireland. A key objective is to assess the degree to which the population is spatially divided, as judged by the selected variables. In other words, do people cluster more strongly with others who share their community background or others who have a similar socioeconomic status in some respect? The analysis demonstrates how geographically weighted statistics can be used to explore the degree to which single socioeconomic and demographic variables and relations between such variables differ at different spatial scales and at different geographical locations. For example, the results show that there are regions comprising neighbouring areas with large proportions of people from the same community background, but with variable unemployment levels, while in other areas the first case holds true but unemployment levels are consistently low. The analysis supports the contention that geographical variations in population characteristics are the norm, and these cannot be captured without using local methods. An additional methodological contribution relates to the treatment of counts expressed as percentages. Numéro de notice : A2010-326 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810903321321 En ligne : https://doi.org/10.1080/13658810903321321 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30520 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2010041	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2010042	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Deriving ground surface digital elevation models from Lidar data with geostatistics / C.D. Lloyd in International journal of geographical information science IJGIS, vol 20 n° 5 (may 2006)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 20 n° 5 (may 2006) . - pp 535 - 563 Titre : Deriving ground surface digital elevation models from Lidar data with geostatistics Type de document : Article/Communication Auteurs : C.D. Lloyd, Auteur ; P.M. Atkinson, Auteur Année de publication : 2006 Article en page(s) : pp 535 - 563 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données lidar [Termes IGN] géostatistique [Termes IGN] interpolation inversement proportionnelle à la distance [Termes IGN] krigeage [Termes IGN] lissage de données [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] surface du sol Résumé : (Auteur) This paper focuses on two common problems encountered when using Light Detection And Ranging (LiDAR) data to derive digital elevation models (DEMs). Firstly, LiDAR measurements are obtained in an irregular configuration and on a point, rather than a pixel, basis. There is usually a need to interpolate from these point data to a regular grid so it is necessary to identify the approaches that make best use of the sample data to derive the most accurate DEM possible. Secondly, raw LiDAR data contain information on above-surface features such as vegetation and buildings. It is often the desire to (digitally) remove these features and predict the surface elevations beneath them, thereby obtaining a DEM that does not contain any above-surface features. This paper explores the use of geostatistical approaches for prediction in this situation. The approaches used are inverse distance weighting (IDW), ordinary kriging (OK) and kriging with a trend model (KT). It is concluded that, for the case studies presented, OK offers greater accuracy of prediction than IDW while KT demonstrates benefits over OK. The absolute differences are not large, but to make the most of the high quality LiDAR data KT seems the most appropriate technique in this case. Numéro de notice : A2006-175 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810600607337 En ligne : https://doi.org/10.1080/13658810600607337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27902 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-06051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-06052	RAB	Revue	Centre de documentation	En réserve L003	Disponible