Abstract: Introduction What This Book Is About What You Will Not Find in This Book How to Read This Book R Graphics Packages Software Used to Write This Book About the Author Acknowledgments TIME SERIES Displaying Time Series: Introduction Packages Further Reading Time on the Horizontal Axis Time Graph of Different Meteorological Variables Time Series of Variables with the Same Scale Stacked Graphs Time as a Conditioning or Grouping Variable Scatterplot Matrix: Time as a Grouping Variable Scatterplot with Time as a Conditioning Variable Time as a Complementary Variable Polylines Choosing Colors Labels to Show Time Information Country Names: Positioning Labels A Panel for Each Year Traveling Bubbles About the Data SIAR Unemployment in the United States Gross National Income and CO2 Emissions SPATIAL DATA Displaying Spatial Data: Introduction Packages Further Reading Thematic Maps Proportional Symbol Mapping Choropleth Maps Raster Maps Vector Fields Reference and Physical Maps Physical Maps OpenStreetMap with Hill Shade Layers About the Data Air Quality in Madrid Spanish General Elections CM SAF Land Cover and Population Rasters SPACE-TIME DATA Displaying Spatiotemporal Data: Introduction Contents Packages Further Reading Spatiotemporal Raster Data Introduction Level Plots Graphical Exploratory Data Analysis Space-Time and Time Series Plots Animation Spatiotemporal Point Observations Introduction Data and Spatial Information Graphics with spacetime Animation Bibliography Index

Abstract: Our study has three objectives. We want to investigate (1) whether choropleth maps linked with parallel coordinates help people understand the locations of vulnerable places and the factors making these places vulnerable, (2) whether sparklines that imitate the polylines from a parallel coordinate plot support the understanding of the information provided in that plot, and (3) whether a multiple-view geovisualization approach might be intuitive and useful also for nonexperts. Although we base our work on the functionalities available in the tool called ‘ViewExposed,’ we intend to outline more general conclusions on whether multiple linked views facilitate the understanding of multivariate spatial characteristics. An empirical study with 53 individuals was conducted to obtain insights on these objectives. Our task-based assessment considered the ways in which participants understood the dynamic linking capabilities. Some of the key findings are as follows: (1) even nonexpert users are able to use parallel ...

Abstract: A simple yet a valid question often asked by GIS users is: what is the “optimal” number of choropleth map classes for a given data set? This question is barely addressed in the literature, however. In this present work, a method is therefore proposed and named the “Weighted Number of Classes Index (WNCI)”. It proposes an optimisation approach according to which the resultant class break values are weighted based on the numbers of their occurrences within a set of classification runs. This is followed by normalising the total weight for each classification run; the classification run that has the highest total normalised weight is chosen, and its associated number of classes is considered comparatively the best option for the given data set. Using seven data sets, the results showed that the WNCI method rationale and performance appeared to be unaffected by the types of data sets used, because producing the highest WNCI value for each dataset is possible, regardless of the dataset values and distribution. Further enhancement of the WNCI was proposed from a digital implementation perspective.

Abstract: A choropleth map is a form of thematic map used to portray the structural characteristics of some particular geographical distribution not apparent in data presented in tabular form. Preparation of a choropleth map starts with the assignment of map features to classes based on the value of a specific feature attribute followed by the association of classes of features with appropriate map colors or symbols. Map features are often geographical regions with naturally or artificially defined boundaries, but choropleth maps can also be prepared by segmenting the area to be mapped into a regular grid of regions. Maps prepared with each grid shaded in an intuitive manner such as blue for grids with the lowest attribute values to red for the highest values can be termed “heat maps”. This technical note describes the HeatMap Microsoft Excel application which converts information contained in a worksheet into a heat map, and then converts the heat map into a file suitable for display using mapping systems such as Google Earth. An example illustrates how the application can be used to visualize the seventeenth century frontier between the Polish/Lithuanian Commonwealth and the Ottoman Empire.

Abstract: To address unsolved issues of change detection in animated choropleth maps, we proposed the concept of ‘gross change detection’ and performed an experiment that empirically verifies the incidence of change blindness stems from the ‘magnitude of change (MOC)’, spatial distribution in animated choropleth maps. We generated experimental materials using the change-characterization arrays and the global Moran’s I. Participants had 108 cases of changing maps with time duration (1 to 3 sec) and had questions. The results showed that MOC and duration affect gross change detection, but the most interesting result from our experiment was that different spatial distributions between two adjacent choropleth maps may lead the map reader to under- or over-estimate the level of gross change in the map. It implies that we should consider spatial distribution of change when we design animated choropleth maps.

Abstract: It is common for researchers in the social sciences to be concerned with the distributional aspects of social phenomena – such as rates of unemployment, levels of household income and types of housing tenure – across spatial units that comprise a city, state or nation, and to seek to visualize variations in the patterns of such socio-spatial data in the form of a map. Commonly that involves classifying data to produce a choropleth map. In this chapter we review a number of classification approaches that are commonly used – especially by geographers – to generate map displays of socio-spatial datasets at point and polygon levels. We also discuss the development of a new methodology and tool for enhancing classification through the categorization of data to produce an improved capacity for choropleth display of data in a map. The chapter first discusses standard categorization routines such as equal interval, quantile and natural breaks, and the Location Quotient (LQ) which is a benchmarked approach to categorization. Performances among the various classification approaches may be compared by considering the total within-group variance (TWGV) and the total within-group difference (TWGD), the measure structured in the median clustering objective.

Abstract: The relationship between energy use and climate change is strong and dynamic, but the two are often treated separately and as static phenomena. This affects people’s perception and understanding of climate change. There is therefore the need to show clearly the causal relationship between energy use and climate change. This study developed choropleth maps and bivariate maps in a Geographic Information System (GIS) environment as a new way of visualising the dynamic relationship between energy use and climate change. We observed that the relationship between them is more easily perceived through GIS maps than the customary graphs used in energy and climate change visualisations. This has a role to play in influencing public and stakeholders’ perception and understanding of the relationship between energy use and climate change. KeywordsEnergy Use; Climate change; GIS; Visualisations

Abstract: The invention notably relates to a computer-implemented method of designing a choropleth map, wherein the method comprises the steps of providing (S10) a map, and a number (n) of numerical values (x 1 ,...,x n ) that represent a statistical variable at respective areas of the map; computing (S20) the optimal K-means clustering of the numerical values for a predetermined number of clusters, wherein the computing step (S20) includes iterating, a number of times corresponding to the predetermined number of clusters, a linear-time Row Minima Searching algorithm applied to a square matrix of order equal to the number of numerical values; assigning (S30) a respective coloration to each cluster of the computed clustering; and at all areas of the map at which a respective numerical value is provided, applying (S40) the coloration assigned to the cluster to which the respective numerical value belongs. Such a method improves the design of a choropleth map.

Abstract: The most common means of visualizing data sets with geospatial data is by employing map-based visualization models such as graduated symbol and choropleth. I argue that these models are in fact not the most efficient and effective visualization models for processing geospatial data, especially when the data set holds a notable quantity of location data. To support my argument I designed and developed two alternate models, one that did not use a map, and one that used an abstract version of one: a scatterplot model with geographic references, and a hexagon model. User tests were then performed to evaluate these models. This thesis describes the process, outcomes and future directions of my research. It also provides a literature review that addresses the definition and attributes of data visualization, a taxonomy of data visualization models, and a description of the mechanics of the visual cognition and colour theory employed.

Abstract: To address unsolved issues of change detection in animated choropleth maps, we proposed the concept of 'gross change detection' and performed an experiment that empirically verifies the incidence of change blindness stems from the 'magnitude of change (MOC)', spatial distribution in animated choropleth maps. We generated experimental materials using the change-characterization arrays and the global Moran's I. Participants had 108 cases of changing maps with time duration (1 to 3 sec) and had questions. The results showed that MOC and duration affect gross change detection, but the most interesting result from our experiment was that different spatial distributions between two adjacent choropleth maps may lead the map reader to under- or over-estimate the level of gross change in the map. It implies that we should consider spatial distribution of change when we design animated choropleth maps.