Abstract: The unprecedented COVID-19 pandemic has affected human lives at all levels Maps visualizing this pandemic have become a valuable tool for public to retrieve and understand the situation in their areas of interest Some earlier maps visualize information at the level of country or state in the United States, but viewers could not access information at a finer level such as county Motivated by the necessity of visualizing information at a finer level, this study designs a thematic map displaying confirmed COVID-19 cases at the county level in the State of New York The thematic map utilizes a choropleth design with defined data classification and colors for symbolization This study then evaluates this designed thematic map with two other published maps: one from the New York State Department of Health and one from The New York Times The evaluation collects 147 valid responses from public all over the world Results show that choropleth design yields higher accuracy of map understanding In addition, the designed map in this study receives higher preference among participants This study suggests the importance of understanding the data, readers, and mapping purpose in the decision of data classification and symbolization in map design, as it contributes largely to the effectiveness of map interpretation

Abstract: It is acknowledged that various types of thematic maps emphasize different aspects of mapped phenomena and thus support different map users’ tasks. To provide empirical evidence, a user study with 366 participants was carried out comparing three map types showing the same input data. The aim of the study is to compare the effect of using choropleth, graduated symbols, and isoline maps to solve basic map user tasks. Three metrics were examined: two performance metrics (answer accuracy and time) and one subjective metric (difficulty). The results showed that the performance metrics differed between the analyzed map types, and better performances were recorded using the choropleth map. It was also proven that map users find the most commonly applied type of the map, choropleth map, as the easiest. In addition, the subjective metric matched the performance metrics. We conclude with the statement that the choropleth map can be a sufficient solution for solving various tasks. However, it should be remembered that making this type of map correctly may seem easy, but it is not. Moreover, we believe that the richness of thematic cartography should not be abandoned, and work should not be limited to one favorable map type only.

Abstract: COVID-19 maps convey hazard and risk information to the public, which play an important role in the risk communication for individual protection. The aim of this study is to improve the effectiveness and efficiency of communicating the specific risk of COVID-19 maps. By testing 71 subjects from Wuhan, China, this study explored how color schemes (cool, warm, and mixed colors) and data presentation forms (choropleth maps, graduated symbol maps) influence visual cognition patterns, risk perception, comprehension, and subjective satisfaction. The results indicated that the warm scheme (yellow/red) has significant strengths in visual cognition and understanding, and the choropleth map (vs. the graduated symbol map) has significant strengths in risk expression. On subjective satisfaction, the combination of the mixed scheme (blue/yellow/red) and the choropleth map scored highest mean value. These results have implications for enhancing the focused functions of COVID-19 maps that fit different terms: in the early and medium terms of disease transmission, choropleth maps with warm or cool colors should be considered as a priority design for their better risk perception. When the epidemic conditions are on the upturn, a better reading experience combination of choropleth maps with mixed colors can be considered.

Abstract: Dynamic maps are commonly used for the depiction of quantitative information. However, their users often fail to notice changes in the intensity of geographic phenomena. Moreover, if the distributi...

Abstract: Choropleth maps and graduated symbol maps are often used to visualize quantitative geographic data. However, as the number of classes grows, distinguishing between adjacent classes increasingly becomes challenging. To mitigate this issue, this work introduces two new visualization types: choriented maps (maps that use colour and orientation as variables to encode geographic information) and choriented mobile (an optimization of choriented maps for mobile devices). The maps were evaluated in a graphical perception study featuring the comparison of SDG (Sustainable Development Goal) data for several European countries. Choriented maps and choriented mobile visualizations resulted in comparable, sometimes better effectiveness and confidence scores than choropleth and graduated symbol maps. Choriented maps and choriented mobile visualizations also performed well regarding efficiency overall and performed worse only than graduated symbol maps. These results suggest that the use of colour and orientation as visual variables in combination can improve the selectivity of map symbols and user performance during the exploration of geographic data in some scenarios.

Abstract: Thoughtful consideration of the enumeration unit size in choropleth map design is important to ensure the correct communication of spatial information. However, the enumeration unit size and its influence on pattern conveying in choropleth maps have not yet been the subject of in-depth empirical studies. This research aims to address this gap. We focused on the issue concerning whether the ability to recognize spatial patterns on an Equal Area Unit Map is related to the hexagonal enumeration unit size, defined by the number of pixels. The aim is to indicate the range of the enumeration unit sizes, namely, at what point the upper and lower borders of the range where the spatial patterns start, and where the end is visible and recognizable by users. To address this problem, we conducted an empirical study with 488 users. The results show that the enumeration unit size has an impact on the users’ spatial pattern recognition abilities. Choropleth maps with enumeration unit sizes of 26, 52, and 104 pixels were, in the majority, indicated by participants as those most suitable for indicating spatial patterns. This was in contrast to choropleth maps with enumeration unit sizes of 1664 and 3328 pixels, which users indicated as not being useful. However, there were some exceptions to this general finding. Thus, determining the optimal enumeration unit size is a challenging task, and requires further insightful investigations.

Abstract: Numerous lines of evidence have been presented in the literature that show the patterns of pedodiversity and biodiversity are very similar. One of the most corroborated patterns lies in the fits of the relationships between biodiversity and soil diversity to power laws according to the increase in study area. Several authors have analysed the presence of fractal and multifractal features in pedodiversity and biodiversity analyses. Similarly, it has also been found that valuable information can be extracted from the polygons of soil and vegetation maps, which also have surprising similarities. These approaches concern information on the spatial distribution of natural resources. However, other more artificial but interesting maps and their comparison have been neglected by such studies. Land systems maps and their georeferenced databases fall into this latter category, and they include most of the soil‐forming factors. In this paper a georeferenced land system database and map were analysed and the results compared with those obtained in previous pedodiversity and biodiversity studies, primarily the spatial patterns of the polygons. The results showed that the analysed land system map units followed the same patterns that were previously found in pedodiversity and biodiversity studies; that is, the power law concerning richness–area relationships. The same patterns occur with the number of polygons. Some geographers claim there is a “law” that states that there are far more small things/objects than larger ones across several orders of magnitude in geographic space and thus this regularity conforms to scaling laws, independent of the resource involved. The results obtained corroborate this conjecture irrespective of whether natural resources or artificial cartographies were analysed. This paper represents a first test of land use maps; additional work in this area is needed. HIGHLIGHTS: Spatial patterns detected in natural and land system maps seem to be similar Composite GIS maps have the same spatial patterns as their base maps. Soil and land system units conform to long‐tail or heavy tail distributions. Soil maps and land system maps conform to power laws, fingerprints of fractal structures There are more small polygons than large ones across several magnitude orders on choropleth maps.

Abstract: In Bayesian analysis of mortality rates it is standard practice to present the posterior mean rates in a choropleth map, a stepped statistical surface identified by colored or shaded areas. A natural objection against the posterior mean map is that it may not be the "best" representation of the mortality rates. One should really present the map that has the highest posterior density over the ensemble of areas in the map (i.e., the coordinates that maximize the joint posterior density of the mortality rates). Thus, the posterior modal map maximizes the joint posterior density of the mortality rates. We apply a Poisson regression model, a Bayesian hierarchical model, that has been used to study mortality data and other rare events when there are occurrences from many areas. The model provides convenient Rao-Blackwellized estimators of the mortality rates. Our method enables us to construct the posterior modal map of mortality data from chronic obstructive pulmonary diseases (COPD) in the continental United States. We show how to fit the Poisson regression model using Markov chain Monte Carlo methods (i.e., the Metropolis-Hastings sampler), and obtain both the posterior modal map and posterior mean map are obtained by an output analysis from the Metropolis-Hastings sampler. The COPD data are used to provide an empirical comparison of these two maps. As expected, we have found important differences between the two maps, and recommended that the posterior modal map should be used.

Abstract: The line-up task hides a plot of real data amongst a line-up of decoys built around some plausible null hypothesis. It has been proposed as a mechanism for lending greater reliability and confidence to statistical inferences made from data graphics. The proposition is a seductive one, but whether or not line-ups guarantee consistent interpretation of statistical structure is an open question, especially when applied to representations of geo-spatial data. We build on empirical work around the extent to which statistical structure can be reliably judged in map line-ups, paying particular attention to the strategies employed when making line-up judgements. We conducted in-depth experiments with 19 graduate students equipped with a moderate background in geovisualization. The experiments consisted of a series of map line-up tasks with two map designs: choropleth maps and a centroid-dot alternative. We chose challenging tasks in the hope of exposing participants’ sensemaking activities. Through structured qualitative analysis of think-aloud protocols, we identify six sensemaking strategies and evaluate their effects in making judgements from map line-ups. We find five sensemaking strategies applicable to most visualization types, but one that seems particular to map line-up designs. We could not identify one single successful strategy, but users adopt a mix of different strategies, depending on the circumstances. We also found that choropleth maps were easier to use than centroid-dot maps.

Abstract: The use of maps as a complex source of geographical information requires a certain level of mathematical literacy. The lack of such literacy can cause severe failures in map use andthe development of map skills. Therefore, this paper aims to contribute to the discussion about the difficulties in using quantitative thematic maps (specifically choropleth maps and proportional symbol maps), which may result from insufficient level of mathematical literacy at the lower secondary level of education. The paper is structured into two studies: Study 1 focuses on the continuity of mathematics and geography curricula (employing methods of expert cognitive walkthrough and content analysis), while Study 2 examines the relationship between achieved mathematical literacy and map skills (using two achievements tests and a questionnaire). The findings show that the continuity of curricula often fails and that map skills development precedes the development of mathematical literacy. The identified inappropriate chronology might have important consequences, since the correlation of mathematical literacy with the level of thematic map use skills proves to be statistically significant. Their relationship is significant in all aspects of map use (map reading, analysis, and interpretation) and in the use of both types of quantitative thematic maps examined in the study. The results should be of interest to geography teachers, teacher trainers, and curriculumleaders on the national and school levels.

Abstract: This investigation analyzed the impact of place-based inequities on mortality rates in 2014. The team combined mortality data with metrics on health care accessibility, socioeconomic deprivation, and other variables available from publicly available data sets. The investigation team created a centralized database for visualizations that combined mortality data by diagnosis, socioeconomic data, health resource data, and an index of area deprivation. Choropleth maps, scatterplots, and regression analyses were performed to identify the major areas of mortality and how well different measures of the social determinants of health (SDOH) correlate to mortality data. A bivariate color scheme to visually capture both outcomes and SDOH in a choropleth map was shown to be a compact and novel manner to display complex epidemiologic data.

Abstract: We present a system to visualize accessibility to various destinations from essential institutions such as schools and hospitals to common attractions such as beaches. Our visualization system supports real-time computations of driving distances by leveraging the path coherent pairs (PCP) decomposition which allows for fast computation between thousands of points of interest. Our system allows users to import and switch between several datasets without any precomputation of road distances between specific entries in the dataset. We present a case study that demonstrates our visualization system generating Choropleth maps of accessibility to various destinations in the San Francisco Bay Area which could be used to guide tourism and event planning decisions.

Abstract: France has a long tradition of using statistical (choropleth) maps, which use shading to represent the spatial distribution of a variable, such as population, by department. Such maps lead the observer to underestimate the importance of urban areas, especially Paris. A solution that complements the choropleth map is to create a cartogram, which deliberately distorts each department so that the area is in proportion to the variable (such as population). Shading can then be used to show a second variable, typically representing density, on the same map. We illustrate the use of cartograms for the case of metropolitan France, with maps that show the spatial distribution of social housing, unemployment, immigration, suicides, election patterns, and the advance of COVID-19. The maps are relatively straightforward to construct, using ArcMap, but attention is needed to the use of colors and classifications. The cartograms reveal patterns that would not be clear based solely on traditional statistical maps.

Abstract: Among cartographic population density methods, the choropleth method is obviously one of the most used. Two reasons explain this statement: the cartographer’s ease of implementation and the reader’s ease of understanding. Nevertheless, in many cases, this cartographic method may lead to misleading and erroneous results. This happens especially when the case studies reveal high various inner densities, because of the use of calculated density means, the numbers, shapes and sizes of the counting numbers. For spaces observing a high inner heterogeneity, it was proven that the choropleth method has many lacks and deficiencies related in the literature. A few cartographers prefer the dasymetric method based on satellite images since this latter describes more closely the reality of the population distribution even though this method is more difficult to implement. In most cases studies, the dasymetric method was applied to large spaces, whether national or regional, scarcely to urban spaces. The purpose of the chapter book is to give a real idea on the human environment of Tunisia. This will be achieved through the elaboration of a 1:1000000 scale dasymetric map of the population of Tunisia. This map should be the first since similar work on the issue was never achieved on Tunisia. It would portray the great disparities characterizing spaces’ occupation: rural vs urban areas, oasis vs deserts, plains vs mountains, coast vs interior and so on.

Abstract: Data classification, i.e. organising data items in groups (classes), is a general technique widely used in data visualisation and cartography, in particular, for creation of choropleth maps. Conven...

Abstract: This chapter discusses and demonstrates how we can present analysis for presentation to higher education policymakers. The chapter details how to present descriptive statistics in a user-friendly Microsoft Word document format. The chapter also shows how we can use choropleth maps to illustrate data spatially. The chapter also demonstrates how graphs and tables of regression results and marginal effects are created.

Abstract: This paper describes a methodological approach that is able to analyse socio-demographic and -economic data in large-scale spatial detail. Based on the two variables, population density and annual income, one investigates the spatial relationship of these variables to identify locations of imbalance or disparities assisted by bivariate choropleth maps. The aim is to gain a deeper insight into spatial components of socioeconomic nexuses, such as the relationships between the two variables, especially for high-resolution spatial units. The used methodology is able to assist political decision-making, target group advertising in the field of geo-marketing and for the site searches of new shop locations, as well as further socioeconomic research and urban planning. The developed methodology was tested in a national case study in Germany and is easily transferrable to other countries with comparable datasets. The analysis was carried out utilising data about population density and average annual income linked to spatially referenced polygons of postal codes. These were disaggregated initially via a readapted three-class dasymetric mapping approach and allocated to large-scale city block polygons. Univariate and bivariate choropleth maps generated from the resulting datasets were then used to identify and compare spatial economic disparities for a study area in North Rhine-Westphalia (NRW), Germany. Subsequently, based on these variables, a multivariate clustering approach was conducted for a demonstration area in Dortmund. In the result, it was obvious that the spatially disaggregated data allow more detailed insight into spatial patterns of socioeconomic attributes than the coarser data related to postal code polygons.

Abstract: Time series animation of choropleth maps easily exceeds our perceptual limits. In this empirical research, we investigate the effect of local outlier preserving value generalization of animated choropleth maps on the ability to detect general trends and local deviations thereof. Comparing generalization in space, in time, and in a combination of both dimensions, value smoothing based on a first order spatial neighborhood facilitated the detection of local outliers best, followed by the spatiotemporal and temporal generalization variants. We did not find any evidence that value generalization helps in detecting global trends.