{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":18,"n_cols":5,"rows_kept":18,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[12,2,12,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910896050703321"]
[null,null,"Autore",null,"Cunty Claire"]
[null,null,"Titolo",null,"Handling and Mapping Geographic Information"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"1-394-32576-2"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (327 pages)"]
[null,null,"Collana",null,"ISTE Consignment Series"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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. References -- Chapter 3. Environmental Data and Cartographic Objects -- 3.1. Introduction -- 3.1.1. Defining cartographic objects: "]
[{"$nulls":4},"positioning the problem -- 3.1.2. Specific environmental data."]
[{"$nulls":4},"3.2. Building cartographic objects: deconstructing to reconstruct -- 3.2.1. From geographic object to cartographic object -- 3.2.2. A few reminders on set theory -- 3.2.3. Defining objects and collecting data -- 3.3. Dealing with disparate and incomplete data: examples from environmental geography -- 3.3.1. Defining cartographic objects from incomplete data -- 3.3.2. Defining cartographic objects at reference scales -- 3.4. Conclusion -- 3.5. References -- Chapter 4. Mapping and Identifying Geographic Configurations: The Example of Segregation -- 4.1. Introduction -- 4.2. Mapping: rendering spatial configurations visible or invisible -- 4.2.1. Defining the study framework -- 4.2.2. The scale of spatial configurations: a question of geographic mesh -- 4.2.3. How the map is created -- 4.3. How to measure a phenomenon so as to reveal its forms -- 4.3.1. The dependence of measures to the definition of categories -- 4.3.2. The indices approach -- 4.3.3. Multivariate analysis approach: qualifying segregated neighborhoods -- 4.4. Capturing spatial forms using dynamic approaches -- 4.4.1. Why a dynamic approach to spatial morphologies? -- 4.4.2. Approaches using spatial indices or spatial autocorrelation -- 4.4.3. Approach based on discontinuities -- 4.4.4. Approach based on population potential -- 4.5. Conclusion -- 4.6. References -- Chapter 5. Map and Statistical Model to Explore Spatial Heterogeneity -- 5.1. Introduction -- 5.2. From raw open-source data to statistical data -- 5.2.1. Open-source data with a complex structure -- 5.2.2. Object of study and scale of analysis -- 5.3. Preliminary explorations of spatial variations -- 5.3.1. Maps to explore the spatial structure of each variable -- 5.3.2. Testing the hypothesis of an administrative or spatial effect in spatial organization -- 5.4. Analyzing relationships statistically and rendering a map."]
[{"$nulls":4},"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 Visualization of Big Data -- 8.1. Introduction."]
[{"$nulls":4},"8.2. Defining the object of study and selecting the corpus -- 8.2.1. "]
[{"$nulls":4},"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."]