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1. |
Record Nr. |
UNINA9910520205303321 |
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Titolo |
Jurnal ilmu keperawatan maternitas |
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Pubbl/distr/stampa |
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Semarang, Jawa Tengah, Indonesia : , : Persatuan Perawat Nasional Indonesia Jawa Tengah, , [2018]- |
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ISSN |
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Descrizione fisica |
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Soggetti |
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Maternity nursing - Indonesia |
Maternity nursing |
Gynecologic nursing |
Pediatric nursing |
Periodicals. |
Indonesia |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Periodico |
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Note generali |
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Sommario/riassunto |
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Articles in the field of maternity nursing which are the results of research, case studies, literature reviews on areas of pregnancy, childbirth, postpartum, healthy newborns, and women's reproductive health. |
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2. |
Record Nr. |
UNINA9910896050703321 |
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Autore |
Cunty Claire |
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Titolo |
Handling and Mapping Geographic Information |
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Pubbl/distr/stampa |
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Newark : , : John Wiley & Sons, Incorporated, , 2024 |
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©2024 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (327 pages) |
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Collana |
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Altri autori (Persone) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Cover -- Title Page -- copyright Page -- Contents -- Foreword -- Introduction -- Chapter 1. Exploring Statistical Relationships with Maps and Charts -- 1.1. Introduction -- 1.2. Mapping the world: which world? what data? -- 1.2.1. Planispheres, projections and centers -- 1.2.2. Grid-related issues -- 1.2.3. Data at global scale: a composite material -- 1.3. Exploring data and relationships with maps and charts -- 1.3.1. Exploring statistical relationships and spatial organizations -- 1.3.2. Formalizing a relationship: from a statistical model to the map of model deviations -- 1.4. Describing statistical relationships between several variables -- 1.5. Conclusion -- 1.6. References -- Chapter 2. Heterogeneous Data Integration and Geoweb Cartographic Representations -- 2.1. Introduction -- 2.2. New data: from production to visualization -- 2.2.1. New data production methods and new scientific applications -- 2.2.2. Data with high spatial and temporal resolution but poorer attributes and quality? -- 2.2.3. Visualizing new data on Web platforms -- 2.3. New data, traditional data: why and how to integrate them? -- 2.3.1. Data heterogeneity, integration, interoperability: preamble to vocabulary development -- 2.3.2. Visual overlay of heterogeneous data, facilitated by advances in technical interoperability -- 2.3.3. Term-to-term matching to assess the quality and enrich the attributes of new data -- 2.3.4. Aggregations for combining heterogeneous data within "pivotal" spatiotemporal units -- 2.3.5. Interpolated data measuring two continuous phenomena to compare them within a common grid -- 2.4. Conclusion -- 2.5. |
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References -- Chapter 3. Environmental Data and Cartographic Objects -- 3.1. Introduction -- 3.1.1. Defining cartographic objects: positioning the problem -- 3.1.2. Specific environmental data. |
3.2. Building cartographic objects: deconstructing to reconstruct -- 3.2.1. From geographic object to cartographic object -- 3.2.2. A few reminders on set theory -- 3.2.3. Defining objects and collecting data -- 3.3. Dealing with disparate and incomplete data: examples from environmental geography -- 3.3.1. Defining cartographic objects from incomplete data -- 3.3.2. Defining cartographic objects at reference scales -- 3.4. Conclusion -- 3.5. References -- Chapter 4. Mapping and Identifying Geographic Configurations: The Example of Segregation -- 4.1. Introduction -- 4.2. Mapping: rendering spatial configurations visible or invisible -- 4.2.1. Defining the study framework -- 4.2.2. The scale of spatial configurations: a question of geographic mesh -- 4.2.3. How the map is created -- 4.3. How to measure a phenomenon so as to reveal its forms -- 4.3.1. The dependence of measures to the definition of categories -- 4.3.2. The indices approach -- 4.3.3. Multivariate analysis approach: qualifying segregated neighborhoods -- 4.4. Capturing spatial forms using dynamic approaches -- 4.4.1. Why a dynamic approach to spatial morphologies? -- 4.4.2. Approaches using spatial indices or spatial autocorrelation -- 4.4.3. Approach based on discontinuities -- 4.4.4. Approach based on population potential -- 4.5. Conclusion -- 4.6. References -- Chapter 5. Map and Statistical Model to Explore Spatial Heterogeneity -- 5.1. Introduction -- 5.2. From raw open-source data to statistical data -- 5.2.1. Open-source data with a complex structure -- 5.2.2. Object of study and scale of analysis -- 5.3. Preliminary explorations of spatial variations -- 5.3.1. Maps to explore the spatial structure of each variable -- 5.3.2. Testing the hypothesis of an administrative or spatial effect in spatial organization -- 5.4. Analyzing relationships statistically and rendering a map. |
5.4.1. Hedonic regression principles -- 5.4.2. Model with no spatial attributes -- 5.4.3. Spatial model estimated by GWR -- 5.5. Conclusion -- 5.6. References -- Chapter 6. Mapping Time -- 6.1. Introduction -- 6.2. Formalization -- 6.2.1. Spatial objects and their temporal component -- 6.2.2. From spatiotemporal objects to spatiotemporal data -- 6.2.3. From geographic data to cartographic data -- 6.2.4. Visualizing time -- 6.3. Monitoring territorial changes -- 6.3.1. Visualizing temporal phenomena -- 6.3.2. Representing changes -- 6.4. Representing phenomena associated with movement -- 6.4.1. Representing movements: from points to trajectories -- 6.4.3. Shape changes -- 6.5. Representing temporality -- 6.5.1. Tracking the spatial organization of events -- 6.5.2. Representing lifespan, duration and change -- 6.5.3. Representing space-time -- 6.6. Conclusion -- 6.7. References -- Chapter 7. Cartograms, Anamorphic Maps: Transformed Territories -- 7.1. Introduction -- 7.2. Cartograms to represent count data associated with areal units -- 7.2.1. Why use cartograms? -- 7.2.2. Links between methods and data -- 7.2.3. Anatomy of the cartogram: methods -- 7.2.4. The piezopleth map to represent rates associated with areal units -- 7.3. Anamorphic map for the representation of space-time -- 7.3.1. Accessibility to a place (unipolar accessibility) -- 7.3.2. Accessibility between all places (multipolar accessibility) -- 7.3.3. Azimuthal transformation -- 7.4. Anamorphic maps, cartograms: cross-cutting reflections on common principles and reading difficulties -- 7.4.1. Some principles underlying all methods -- 7.4.2. Anamorphic maps layout -- 7.4.3. Reading and understanding anamorphic maps: some difficulties -- 7.5. Conclusion -- 7.6. References -- Chapter 8. Exploration, Aggregation and Spatiotemporal |
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Visualization of Big Data -- 8.1. Introduction. |
8.2. Defining the object of study and selecting the corpus -- 8.2.1. Press news: a multidimensional object -- 8.2.2. Defining international news -- 8.2.3. Corpus definition based on RSS news feeds -- 8.3. Crossing the "who" and "what" dimensions -- 8.4. Crossing the "who", "what" and "when" dimensions -- 8.4.1. Detecting trends -- 8.4.2. Analysis of seasonal variations -- 8.4.3. Analysis of weekly variations -- 8.5. Crossing the "who", "what" and "where" dimensions -- 8.5.1. Mapping the distribution of international news by country -- 8.5.2. Identifying and mapping specific national features -- 8.6. Graphs to represent co-location relationships -- 8.6.1. Measuring and visualizing association links -- 8.6.2. From co-citations to global regionalization -- 8.7. Conclusion -- 8.8. References -- Conclusion -- List of Authors -- Index -- EULA. |
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