Choropleth map

Abstract: The classification of data is an important step towards the elaboration of choropleth maps because the use of colors will be attributed to the basic spatial units in accordance with the classes defined. Another representation for quantitative data analysis is made through the usage of proportional symbols, which are appropriate to the data related to the dimension of the Basic Spatial Unit in relation to the phenomenon under consideration. At a given moment the responsible for the cartographic representation, which will be led to organize the presented data, and for that to happen, it is necessary to define the number of classes to be adopted, which colors or symbols scheme is the most appropriate and, among several classification criteria, which is the best appropriate for the phenomenon under study. In this work, contributions for better understanding and use with methodological basis of the criteria more commonly adopted in the classification and visual representation of geographic data to produce graphs, choropleth maps, maps of proportional symbols or, in addition, to explore the existing relationship between them.

Abstract: In retail business intelligences, and more specifically in an analysis of customer-supermarket relationship, the factors, such as geographic location of customers, demographic distribution, customers' preferences, accessibility to the store are crucial in decision-making tasks. Visualization is an important tool for analysis and decision-making, which should provide means to make informed business decisions. This article presents a novel approach to the hexagonal gridded maps, which integrates diverse information layers with adaptive zoom. These layers are complementary, providing different points of view over the same dataset and various levels of abstraction. Starting with a dot map, which portrays the impact of supermarket localization on customers choices, up to a choropleth map, which depicts population density in an adaptive form depending on the different granularities of administrative units. Ultimately, the presented visualization provides means to: (i) explore and analyze data regarding customer-supermarket relations; (ii) reveal the impact of supermarkets localization on customer preferences; (iii) suggest areas of low coverage by supermarkets. Additionally, the interplay among the complementary graphical layers provided by the visualization increases its exploratory and analytical power.

Abstract: This paper proposes a novel method that leverages georeferenced social media data, together with human assessments of particular words, to estimate population well-being across the U.S. territory. We specifically attempt to learn linear regression models that, by leveraging on simple features that essentially correspond to word counts in lexicons of emotionally-charged words, are capable of approximating a composite well-being index built through traditional surveying methods. Experiments with a large Twitter dataset collected within the year of 2012 attest for the feasibility of the proposed approach (i.e., we approximate the Gallup-Healthways composite well-being index with a mean absolute error of 0.91), and we then produced choropleth maps, either at a state- or at a county-level of detail, that show how well-being varies across the continental U.S. territory.