Abstract: The choropleth map is a device used for the display of socioeconomic data associated with an areal partition of geographic space. Cartographers emphasize the need to standardize any raw count data by an area-based total before displaying the data in a choropleth map. The standardization process converts the raw data from an absolute measure into a relative measure. However, there is recognition that the standardizing process does not enable the map reader to distinguish between low–low and high–high numerator/denominator differences. This research uses concentration-based classification schemes using Lorenz curves to address some of these issues. A test data set of nonwhite birth rate by county in North Carolina is used to demonstrate how this approach differs from traditional mean–variance-based systems such as the Jenks’ optimal classification scheme.

Abstract: This paper describes design and results of an experiment, which was aimed at exploring the influence of colour distance and legend position on map users’ ability to correctly interpret choropleth maps Participants of the experiment were asked to find depicted area on the choropleth map and match it with the corresponding legend class by its colour Experimental stimuli cover five levels of colour distance between neighbouring classes of choropleth maps (ΔE00 = 2, 4, 6, 8 and 10) and six different legend positions within the map sheet The colour distance was determined by the method CIEDE2000 Results of the experiment were based primarily on analysing the accuracy of answers, and analysing duration of fixations in defined areas of interest Research proved that increasing colour distance has a positive influence on the ability of users to interpret choropleth maps correctly Legend position was not found to be significantly important factor of map readability It was also proved, that sequential colour schemes with visually equal steps between classes are not appropriate, because map users have problems to interpret classes in the middle of the colour scheme Based on these observations three optimised colour schemes were designed and evaluated The highest accuracy of answers were observed for the colour scheme in which the lightness of classes if graduated by values of colour distance ΔE00 = 4, 8, 10, 8 and 4

Abstract: The following tutorial describes how to make an interactive choropleth map using the D3 (Data-Driven Documents) web mapping library (d3js.org). This tutorial is based on a laboratory assignment created in the fall of 2014 for an advanced class titled Interactive Cartography and Geovisualization at the University of Wisconsin– Madison. This is the second of two On the Horizon tutorials on web mapping and extends a previous tutorial that used the Leaf let JavaScript library (see Donohue et al. 2013; dx.doi.org/10.14714/CP76.1248). Fully commented source code for both tutorials is available on GitHub (github.com/uwcart/cartographic-perspectives). All code is distributed under a Creative Commons 3.0 license and available for unconditional use, with the exception of the files in the lib directory, for which certain license conditions are required as described in the file LICENSE.txt.

Abstract: This paper outlines a methodology used to disaggregate a census population in order to more accurately determine the population distribution over a regional area or a state scale. Data regarding population distributions are usually accessible at the level of individual census designation places and are usually mapped as aggregated polygons by the choropleth method with the assumption of a homogeneous distribution of population within a cartographic unit. In contrast, dasymetric mapping provides a more reliable view into the allocation of inhabitants, which can be of significant importance when estimating population distributions. Coupling this methodology with the GIS environment and a free open access database of soil sealing facilitates the acquisition of population surface models for human and urban geography applications.

Abstract: Disease mapping, the visualization of disease rates and the clustering of disease data are still one of the most interesting topics in geosciences. This is because of the nature of the data, which are often purely spatial with a rich descriptive part and which are easy to combine with other data (demographic, economic, etc.). This contribution aims to present the usage of empirical Bayesian methods in disease mapping and the subsequent creation of disease maps. Bayesian methods incorporate prior knowledge about the phenomenon (or underlying processes) to provide a more accurate and easily understandable description of the situation. Empirical Bayesian procedures are used for disease rates smoothing in the case of a choropleth map. They also help to identify local clusters of more/less affected areas. The main topic of the case study in this paper is the analysis of the spatial distribution of a disease called campylobacteriosis in the Czech Republic between the years 2008 and 2012 with the usage of global empirical Bayesian estimates based on binomial distribution and local empirical Bayesian estimates based on first order queen contiguity.

Abstract: Urban planners and policy makers often rely on data visualization and spatial data mapping tools to perceive the overall urban trends. The accumulation of historical and real-time urban data from many government and private organizations provides the opportunity for an integrated visual analytic platform. Data management and retrieval for geospatial visualization, correlations, and analysis of multiple data dimensions over a map constitute some of the main challenges when dealing with the heterogeneity of urban data from a variety of sources. In this paper, spatiotemporal aggregation strategies and approaches to accelerate the retrieval of spatial data are presented. The methods are tested on visualizing multivariate urban datasets from two cities in Australia that are aggregated from heterogeneous federated urban data providers. The aggregated spatial or temporal features can be visualized as a choropleth heatmap or extrusion on map. Dynamic spatial window query in our visual analytics tool allows extraction of flat geometry objects optimized through materialized views from a database. Given the robust and scalable orchestration of geometries retrieval, this enables urban planners to perform interactive and dynamic multidimensional visual exploration over a map.

Abstract: Choropleth maps combined with diagram maps are frequently used in geography. For this reason, based on the maps developed by students, the study aims at the following: identifying and analyzing the errors made by the students; establishing and analyzing the competence level of the students; identifying the causes that led to these errors; and finding the best solutions to improve both the educational process aiming at the formation of this kind of competences and the students’ results. The map assessment was accomplished during two academic years (2013-2014 and 2014-2015), in the aftermath of the activities meant to train the competence. We assessed 105 maps prepared by the students in Cartography (Faculty of Geography, University of Bucharest) based on an analytical evaluation grid, with dichotomous scale, comprising 15 criteria. This tool helped us identify the errors made by the students, as well as their competence level. By applying a questionnaire, we identified the source of the errors from the students’ perspective, while by comparing the errors and the competence levels at the end of the two academic years we were able to come up with potential solutions for the improvement of the teaching and learning process.

Abstract: There are many GIS applications available on the internet showing crime statistics all over the world. The usual visualization methods for these statistics are generally pictograms representing individual crime locations. The visualization of individual crimes raise two problems: they are not able to transmit the real crime situations to the users, because it is not easy to interpret the content of it; the other problem is that they may harm the confidentiality of the victims. Therefore in this study the author tested the applicability and effectiveness of 3 visualization methods: contiguous cartogram, choropleth maps and graduated symbols. The results shows that each of them can be applicable for this purpose but for solving most complex problems contiguous cartogram method must be improved with using other cartographic variables, also further investigations should be done with nonprofessional people with the other two methods.

Abstract: . We present an analysis of the influence of colour distance on the user experience with choropleth maps. We systematically evaluated 5 se-quential and 5 qualitative colour schemes in a two-stage user experiment. At first, we conducted an online study to obtain performance metrics accu-racy and response time on a large variety of heterogeneous population. Following this, in a controlled lab study with eye-tracking, we re-examined the findings from the online study for a subset of experimental stimuli and further assessed the user experience through an analysis of their visual be-haviour. In this process, along with accuracy and response time , eye track-ing metrics fixation frequency, fixation duration and scanpath speed as well as a gaze transition analysis were utilized. In both experiments, par-ticipants were asked to compare two areas with controlled colour distances between them, and decide whether these areas are of the same colour. Re-sults suggest that we are able to discriminate narrower colour distances than commonly used in practice, however, not as narrow as suggested in previous literature.

Abstract: Disease maps are powerful tools for depicting spatial variations in disease risk and its underlying drivers. However, producing effective disease maps requires careful consideration of the statistical and spatial properties of the disease data. In fact, the choice of mapping method influences the resulting spatial pattern of the disease, as well as the understanding of its underlying population characteristics. New developments in mapping methods and software in addition to continuing improvements in data quality and quantity are requiring map-makers to make a multitude of decisions before a map of disease burdens can be created. The impact of such decisions on a map, including the choice of appropriate mapping method, not been addressed adequately in the literature. This research demonstrates how choice of mapping method and associated parameters influence the spatial pattern of disease. We use four different disease-mapping methods – unsmoothed choropleth maps, smoothed choropleth maps produced using the headbanging method, smoothed kernel density maps, and smoothed choropleth maps produced using spatial empirical Bayes methods and 5-years of zip code level HIV incidence (2007- 2011) data from Dallas and Tarrant Counties, Texas. For each map, the leading population characteristics and their relative importance with regards to HIV incidence is identified using a regression analysis of a CDC recommended list of socioeconomic determinants of HIV. Our results show that the choice of mapping method leads to different conclusions regarding the associations between HIV disease burden and the underlying demographic and socioeconomic characteristics. Thus, the choice of mapping method influences the patterns of disease we see or fail to see. Accurate depiction of areas of high disease burden is important for developing and targeting appropriate public health interventions.

Abstract: This study investigates the decision criteria of local decision makers (citizens, city councilors and employees of a local authority) within the process of establishing a local budget plan and the factors influencing effective map use within this process. For mapping the local budget plan, the expenditures of the city first had to be georeferenced. A methodical framework was necessary in order to define their spatial impact. Then, the data was presented to the local decision makers by combining different map types (choropleth maps, proportional dot symbol maps, pie chart maps, band signature maps), different data complexity, and variations of legend content (numerical and verbal). The study consists of two empirical parts. An online survey among the local decision makers was conducted in order to get information about their decision criteria and their spatial knowledge. Then, an eye-tracking study was used to compare their gaze behavior within different map and legend types in order to give recommendations for future map use. It was found that spatial thinking plays an important role, but spatial knowledge could be improved. Respondents have shown big interest in using maps in the process of establishing a local budget plan. The gaze parameters show that the respondents’ interest, their emotional addiction to the map topic, influences the average fixation duration more than the complexity of the map content.

Abstract: Data quality should be considered in compiling maps in order to reveal reliable information about the spatial variation of a phenomenon. However, creating classes in a choropleth map by maximizing data reliability (i.e. the statistical differences of observed values between classes) often lead to useless maps with very uneven number of observations in different classes. An interactive mapping system is developed to allow users to adjust class breaks so that the resultant maps are relatively reliable and more effective in spatial pattern detection.

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.