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A grammar for graphics
Published in Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, Texts in Statistical Science, 2017
Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
Using a map to display data geographically helps both to identify particular cases and to show spatial patterns and discrepancies. In Figure 3.19, the shading of each country represents its oil production. This sort of map, where the fill color of each region reflects the value of a variable, is sometimes called a choropleth map. We will learn more about mapping and how to work with spatial data in Chapter 14.
Spatial analysis of voter turnout in Manila
Published in Shin-ya Nishizaki, Masayuki Numao, Jaime Caro, Merlin Teodosia Suarez, Theory and Practice of Computation, 2019
Ma. Christine Camille P. Chua, Ligaya Leah L. Figueroa, Rommel P. Feria, Ada Angeli D. Cariaga, Ma. Rowena C. Solamo
Choropleth maps are “thematic maps in which areas are distinctly colored or shaded to represent classed values of a particular phenomenon” (Esri 2016). Areal units are colored or shaded in proportion to the values associated to them. This type of map is useful for mapping and visualizing the spatial distribution of a particular phenomenon (Kumar 2003).
Exploratory spatial analysis of food insecurity and diabetes: an application of multiscale geographically weighted regression
Published in Annals of GIS, 2023
The mean for food insecurity ranged from 13% to 20% and it was the highest for Mississippi and the lowest for Tennessee (see Table 1). The choropleth map revealed greater detail by showing individual values at the county-level while the cluster map uncovered adjacent areas for high and low values for food insecurity (see Figures 2 and 3). To expound, the choropleth map represented a thematic map for the variable of interest where the intensity of the colour scheme signified areas with greater values. The Jenks natural breaks classification was utilized since this optimization method allowed for visual appeal (i.e. easier map reading) by minimizing within-group variance while maximizing between-group variance. For example, some counties exhibited much higher values for food insecurity (i.e. 22–36%) near the southern portion of Alabama. Higher values were also found for counties bordering eastern Arkansas with western Mississippi, as depicted by a more intense or darker colour scheme. These same areas also reflected a cluster of high values. To expound, the local indicators of spatial autocorrelation (LISA) cluster map provided a visual representation of high (or low) value counties next to other high (or low) value counties, as well as high-low, low-high, and not statistically significant counties. For example, adjacent counties with food insecurity percentages exceeding 25% were depicted with the red colour. In contrast, a cluster of low values was noted for central Tennessee and this was depicted with the blue colour.
Digital world meets urban planet – new prospects for evidence-based urban studies arising from joint exploitation of big earth data, information technology and shared knowledge
Published in International Journal of Digital Earth, 2020
Thomas Esch, Hubert Asamer, Felix Bachofer, Jakub Balhar, Martin Boettcher, Enguerran Boissier, Pablo d' Angelo, Caroline M. Gevaert, Andreas Hirner, Katerina Jupova, Franz Kurz, Andy Yaw Kwarteng, Emmanuel Mathot, Mattia Marconcini, Alessandro Marin, Annekatrin Metz-Marconcini, Fabrizio Pacini, Marc Paganini, Hans Permana, Tomas Soukup, Soner Uereyen, Christopher Small, Vaclav Svaton, Julian Nils Zeidler
The visualisation and analytics toolbox (VISAT) is one of the most important components since it provides a large scope of functionalities for the visualisation and joint analysis of various data types (e.g. shapefiles, GeoJSON, CSV, GeoTIFF) from global to local scale and for different analytical units (e.g. administrative boundaries such as provided by the Database of Global Administrative Areas – GADM). The visualisation options include interactive charts, tables and (choropleth) maps that can again interactively be used to combine various types of data and layers or normalize the underlying attributes. The system also supports a multi-temporal mode to compare and analyse phenomena at different points in time – for instance in order to document changes or identify trends.
Burden of injuries and its associated hospitalization expenditure in India
Published in International Journal of Injury Control and Safety Promotion, 2021
Jeetendra Yadav, Geetha Menon, Amit Agarwal, Denny John
We provide the annual mean OOPE for each type of injury with the corresponding disease burden for the public and private facilities for major states. The choropleth maps depict the geographical distribution of economic and disease burden by type of injuries. A choropleth map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map. State wise disease burden was compared using the age adjusted DALY rates per 100,000. STATA version 13.0 was used to perform the statistical analysis using sampling weights with clustering and strata). Arc-GIS was used to plot the maps.