Where Maps Lie: Visualization of Perceptual Fallacy in Choropleth Maps at Different Levels of Aggregation

This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the results of application of the method to population density maps of Lithuania. Three spatial aggregation levels were chosen for comparison: the 1 × 1 km statistical grid, elderships (NUTS3), and municipalities (NUTS2). Differences in density class values between the reference grid map and the other two maps were calculated. It is demonstrated that a perceptual fallacy on the municipality level population map of Lithuania leads to a misinterpretation of data that makes such maps frankly useless. The eldership level map is, moreover, also largely misleading, especially in sparsely populated areas. The method proposed is easy to use and transferable to any other field where spatially aggregated data are mapped. It can be used for visual analysis of the degree to which a generalized choropleth map is liable to mislead the user in particular areas.

Kernel density smoothing of composite spatial data on administrative area level

Composite spatial data on administrative area level are often presented by maps. The aim is to detect regional differences in the concentration of subpopulations, like elderly persons, ethnic minorities, low-educated persons, voters of a political party or persons with a certain disease. Thematic collections of such maps are presented in different atlases. The standard presentation is by Choropleth maps where each administrative unit is represented by a single value. These maps can be criticized under three aspects: the implicit assumption of a uniform distribution within the area, the instability of the resulting map with respect to a change of the reference area and the discontinuities of the maps at the borderlines of the reference areas which inhibit the detection of regional clusters.In order to address these problems we use a density approach in the construction of maps. This approach does not enforce a local uniform distribution. It does not depend on a specific choice of area reference system and there are no discontinuities in the displayed maps. A standard estimation procedure of densities are Kernel density estimates. However, these estimates need the geo-coordinates of the single units which are not at disposal as we have only access to the aggregates of some area system. To overcome this hurdle, we use a statistical simulation concept. This can be interpreted as a Simulated Expectation Maximisation (SEM) algorithm of Celeux et al (1996). We simulate observations from the current density estimates which are consistent with the aggregation information (S-step). Then we apply the Kernel density estimator to the simulated sample which gives the next density estimate (E-Step).This concept has been first applied for grid data with rectangular areas, see Groß et al (2017), for the display of ethnic minorities. In a second application we demonstrated the use of this approach for the so-called “change of support” (Bradley et al 2016) problem. Here Groß et al (2020) used the SEM algorithm to recalculate case numbers between non-hierarchical administrative area systems. Recently Rendtel et al (2021) applied the SEM algorithm to display spatial-temporal clusters of Corona infections in Germany.Here we present three modifications of the basic SEM algorithm: 1) We introduce a boundary correction which removes the underestimation of kernel density estimates at the borders of the population area. 2) We recognize unsettled areas, like lakes, parks and industrial areas, in the computation of the kernel density. 3) We adapt the SEM algorithm for the computation of local percentages which are important especially in voting analysis.We evaluate our approach against several standard maps by means of the local voting register with known addresses. In the empirical part we apply our approach for the display of voting results for the 2016 election of the Berlin parliament. We contrast our results against Choropleth maps and show new possibilities for reporting spatial voting results.

Data classification methods for preserving spatial patterns

The primary purpose of choropleth maps is to display or even to emphasize special relationships or patterns in the spatial distribution of attribute values. However, because classification methods commonly used and implemented in software packages (such as equidistance, quantiles, Jenks, etc.) are data-driven, a preservation of such spatial patterns is not guaranteed. Instead of such a data-driven approach in the following a task-oriented procedure is pursued: For typical patterns (local and global extreme values, large value differences to neighbours, spatial clusters, hot/cold spots) specific algorithms have been developed, implemented and tested.

Where there is no local author: a network bibliometric analysis of authorship parasitism among research conducted in sub-Saharan Africa

IntroductionAuthorship parasitism (ie, no authors affiliated with the country in which the study took place) occurs frequently in research conducted in low-income and middle-income countries, despite published recommendations defining authorship criteria. The objective was to compare characteristics of articles exhibiting authorship parasitism in sub-Saharan Africa to articles with author representation from sub-Saharan African countries.MethodsA bibliometric review of articles indexed in PubMed published from January 2014 through December 2018 reporting research conducted in sub-Saharan Africa was performed. Author affiliations were assigned to countries based on regular expression algorithms. Choropleth maps and network diagrams were created to determine where authorship parasitism occurred, and multivariable logistic regression was used to determine associated factors.ResultsOf 32 061 articles, 14.8% (n=4754) demonstrated authorship parasitism, which was most common among studies from Somalia (n=175/233, 75.1%) and Sao Tome and Principe (n=20/28, 71.4%). Authors affiliated with USA and UK institutions were most commonly involved in articles exhibiting authorship parasitism. Authorship parasitism was more common in articles: published in North American journals (adjusted OR (aOR) 1.26, 95% CI 1.07 to 1.50) than in sub-Saharan African journals, reporting work from multiple sub-Saharan African countries (aOR 8.41, 95% CI 7.30 to 9.68) compared with work from upper-middle income sub-Saharan African countries, with <5 authors (aOR 14.46, 95% CI 12.81 to 16.35) than >10 authors, and was less common in articles published in French (aOR 0.60, 95% CI 0.41 to 0.85) than English.ConclusionsAuthorship parasitism was common in articles reporting research conducted in sub-Saharan Africa. There were reliable predictors of authorship parasitism. Investigators and institutions in high-income countries, as well as funding agencies and journals should promote research from sub-Saharan Africa, including its publication, in a collaborative and equitable manner.

Searching for an Optimal Hexagonal Shaped Enumeration Unit Size for Effective Spatial Pattern Recognition in Choropleth Maps

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.

Questions to the Article: A Global Overview of COVID-19 Research in the Pediatric Field (Preprint)

UNSTRUCTURED The article, published on 23 July 2021, is well-written and of interest, but remains several questions that are required for clarifications, such as (1) the static choropleth map of collaboration analysis between countries should be dynamically visualized and highlighted by top three countries on their publications and author collaboration characteristics; (2) the research achievements in authors, institutes, and countries should be quantified by author-weighted scheme considering author order in article bylines; and (3) keyword analysis was too simple to identify the difference in article types between countries. We downloaded 2,268 abstracts from the Pubmed database with a search string of (COVID-19[MeSH Major Topic]) AND (pediatrics[Affiliation]), similar to the mentioned study, and displayed (1) choropleth maps highlighted by the most productive and highly author-collaborated countries, and (2)forest plot to identify differences in article types between two countries. The medical subject headings(MeSH terms) were used to denote the article types in articles. We observed that (1) three top productive countries were the United States, Italy, and India; (2) three top countries collaborated the authors affiliated with the US were Canada, the United Kingdom, and Italy; and (3) only the MeSH term of epidemiology presents the difference in article types between the US and India when the top 10 most frequently occurred MeSH terms were compared. We produced the dashboard-type visualizations to provide valuable information for readers. The novel visual representations make data clear with a better understanding of bibliographic analysis. The methods used in this study are recommended for future studies, not just limited to the field of COVID-19 research.

An Emperical Investigation of Socio-Economic Parameters Associated with Crime in Karnataka Using Predictive Analysis

The crime rate in India has been on a rise with growing population and rapid development. Crimes are a social nuisance and brings disrepute to the society and nation at large. Data mining techniques have enabled models to predict crime. The law enforcement officers and police personnel’s need to spend umpteen time to analyze crime from the crime reports published by National Crime Records Bureau and respective State police departments. Therefore, there is necessity of a mechanism which can predict crime by identifying factors responsible for increase in crime rate. This project is an attempt to address this. Socio-economic variables like poverty, urbanization, literacy, and the demographic and social composition of the population are recorded from Census data. Machine Learning algorithms namely Multiple Linear Regression, Random Forest Regressor and Generalized Linear Models are trained and deployed. These algorithms have been implemented assuming both linear and non-linear ship of data and subsequent results have been compared. Further, choropleth maps have been plotted to represent and understand data in a simple way. The results show that socio-economic factors are good indicator in explaining crime rates and good performance is observed with few models