Sarah Battersby

Understanding interactions through movement provides critical insights into urban dynamic, social networks, and wildlife behaviors. With widespread tracking of humans, vehicles, and animals, there is an abundance of large and high-resolution movement data sets. However, there is a gap in efficient GIS tools for analyzing and contextualizing movement patterns using large movement datasets. In particular, tracing space–time interactions among a group of moving individuals is a computationally demanding task, which would uncover insights into collective behaviors across systems. This article develops a Spark-based geo-computational framework through the integration of Esri's ArcGIS GeoAnalytics Engine and Python to optimize the computation of time geography for scaling up movement interaction analysis. The computational framework is then tested using a case study on migratory turkey vultures with over 2 million GPS tracking points across 20 years. The outcomes indicate a drastic reduction in interaction detection time from 14 days to 6 hours, demonstrating a remarkable increase in computational efficiency. This work contributes to advancing GIS computational capabilities in movement analysis, highlighting the potential of GeoAnalytics Engine in processing large spatiotemporal datasets.

This paper explores scholarly literature published since the 1960s that examines peoples’ cognitive and perceptual understanding of map projections. Map projections present challenges to virtually everyone who uses them. Some of the challenges include selecting a projection, specifying projection parameters, and understanding distortion patterns. Broadly speaking, cartographic inquiry has addressed many topics in cognition and perception; yet, research focused on projections remains scarce. We surveyed the body of research studies incorporating projections or a projection-related topic (e.g. distortion) as a variable in the experimental design. Topics included asking participants to estimate areas, conceptualize travel paths, or identify preferred graticule aesthetics. Despite the conclusions reported by these studies, we noted three general concerns that may diminish the accuracy of results from research in this area. First, projection-specific terminology or properties critical to the studies and analysis that may be misunderstood by researchers and/or participants. Second, study participants were largely homogenous. Third, most of the studies were not designed for replication or reproducibility. Given our critique, we offer six suggestions for those who are interested in new cognitive and perceptual projection research.

What other locations are like my neighborhood? How? Why? The heart of many spatial analyses is in finding similarities or dissimilarities between locations. Discovering patterns and interpreting similarity is a complicated process that is based on both the spatial characteristics and the semantics or meaning that we assign to place. Human conceptualization of similarity in locations is multi-faceted and cannot be captured with a simple assessment of single numeric attributes like population density or median income; however, these quantifiable attributes are the basis for an initial pass of sense-making. MixMap facilitates the incorporation of similarity measures and spatial analytics to provide an information reduction (or semantic generalization) that brings the user closer to actionable insights. Through a preliminary evaluation of MixMap, we found that the tool supports the geospatial inquiry of determining similarity between regions, where participants can manipulate individual weights of the various attributes describing these locations. Based on feedback and observations from the study, we discuss potential implications and considerations for exploring the role of context and additional place-specific parameters for computing similarity, as well as understanding the nuances of semantics for place similarity in geospatial analysis tools.

The Mercator effect is the widespread and persistent belief among cartographers and others that people’s global-scale cognitive maps are distorted in a particular way because of their exposure to world maps displayed with the common Mercator projection. In particular, such exposure has been claimed to lead people to believe that polar regions, such as Greenland, are much larger than they really are, relative to equatorial regions. Recent studies, however, have found no evidence for a Mercator effect on recalled areas for world regions. Given that a version of the Mercator projection known as the Web Mercator has been used for Web mapping in the last couple of decades, we carried out a replication with samples at two universities, but we also asked respondents to estimate great-circle directions (“as a jet would fly”) from their home city to several other world cities. We again find no support for a Mercator effect on areas estimated from memory, but our novel collection of spherical direction estimates provides clear evidence of a Mercator effect (or that of a similar rectangular projection) on directional beliefs. These results confirm that cognitive maps are not unitary, analogue mental structures but collections of beliefs stored in different formats in separate mental structures that are not necessarily mutually coordinated and integrated. We also introduce a survey of map use that focuses on digital maps and their use for local versus global geographic inquiries.

Binning is applied to categorize data values or to see distributions of data. Existing binning algorithms often rely on statistical properties of data. However, there are semantic considerations for selecting appropriate binning schemes. Surveys, for instance, gather respondent data for demographic-related questions such as age, salary, number of employees, etc., that are bucketed into defined semantic categories. In this paper, we leverage common semantic categories from survey data and Tableau Public visualizations to identify a set of semantic binning categories. We employ these semantic binning categories in OSCAR: a method for automatically selecting bins based on the inferred semantic type of the field. We conducted a crowdsourced study with 120 participants to better understand user preferences for bins generated by OSCAR vs. binning provided in Tableau. We find that maps and histograms using binned values generated by OSCAR are preferred by users as compared to binning schemes based purely on the statistical properties of the data.

How many crimes occurred in the city center? And exactly which part of town is the ‘city center’? While location is at the heart of many data questions, geographic location can be difficult to specify in natural language (NL) queries. This is especially true when working with fuzzy cognitive regions or regions that may be defined based on data distributions instead of absolute administrative location (e.g., state, country). GeoSneakPique presents a novel method for using a mapping widget to support the NL query process, allowing users to specify location via direct manipulation with data-driven guidance on spatial distributions to help select the area of interest. Users receive feedback to help them evaluate and refine their spatial selection interactively and can save spatial definitions for re-use in subsequent queries. We conduct a qualitative evaluation of the GeoSneakPique that indicates the usefulness of the interface as well as opportunities for better supporting geospatial workflows in visual analysis tasks employing cognitive regions.

The unicorn of map projections is the one that preserves all the spatial relationships from the globe. Topologically, we know this projection doesn’t exist, as it is impossible to preserve both areal and angular relationships from the sphere when projected to the flat map, so we make do the best that we can by balancing distortions to areal and angular relationships to suit the needs of specific mapping projects. Balancing these distortions is a challenge that we all face in spatial analytics or cartographic design. However, people don’t tend to like inaccuracies, at least once they have been pointed out to them.  Combine this with little to no interest in, or ability to, understand / compensate for map projection distortion, and the lure of a single ‘perfect map’ is strong enough to draw many away from the vital consideration of selecting a specific map projection to match the intended purpose of the map.  This paper discusses several map projections that have been introduced in the recent past and noted as being "the world's most accurate" or similar.  I discuss these projection unicorns, their characteristics, limitations, and how they may help, or hinder us, in making better maps to understand the world around us. Since the impact of distortion for global-scale mapping is the most challenging, I will focus on what makes maps work (or not), and some solutions, at this scale.

Multi-Criteria Decision-Making (MCDM) is one of the essential tasks people encounter in their everyday use of information systems. In MCDM, people weigh the relative importance of multiple decision criteria to assess decision candidates. Such an MCDM task is ubiquitous yet can be cognitively taxing without the explicit support of user interfaces (UIs). However, there has been a lack of approaches aiming at systemically understanding how the design of UIs can affect users’ attitudes and behavior in performing their exploratory use of information systems under MCDM scenarios. To better understand the role of UIs in MCDM, we determine two factors in characterizing UI for MCDM; (1) the internal representation, the way that UI frames end-users in determining preferences of decision criteria (i.e., individual, proportional, and pairwise quantifies), and (2) the external representation, the way that UI externalizes user preferences while the users interact with systems (1D and 2D layouts). We conducted two studies to understand how different design choices affect users’ MCDM processes. We found 2D layout improves a set of attitudinal aspects in MCDM scenarios while using different quantifiers introduces a set of trade-offs

We created a global database of glacierized volcanoes, using a projection optimized for each volcano, to identify locations where land ice (glaciers and ice sheets) and volcanoes co-exist on Earth. Our spatial database melds the Smithsonian Global Volcanism Database (SGVD) and the Randolph Glacier Inventory 6.0 (RGI). We identified all Holocene volcanoes within the SGVD that have glacier ice within radii of 1 km, 2.5 km, and 5 km, and thus have the potential to impact or be impacted by surrounding ice. Our analysis shows that 245 Holocene volcanoes have glacier ice within the specified radii, which are covered partly or fully by 2584 unique glaciers or the Antarctic Ice Sheet. The volcanoes are located in all major volcano-tectonic settings, although the majority (72%) are in subduction zones built on continental crust (greater than 25 km thick). They also cover the majority of the typical compositional ranges for igneous rocks (basalt to rhyolite). Twenty-nine volcanoes, or 12%, have at least 90% ice cover within 5 km, which together comprise 36% of global glacier area on volcanoes. About 20,000 people live within 5 km of a glacierized volcano, while 160 million people live within 100 km of a glacierized volcano and could be impacted by lahars and/or disruption of their water sources during future eruptions. By merging our database with existing ice thickness model estimates we find 850 ± 290 km3 of ice within 5 km of volcanic vents globally. We compare the eruption history, ice volume, and nearby population estimates to identify the most dangerous volcanoes on Earth. The combination of volcano locations and ice thickness estimates allows us to identify 20 (out of 245) glacierized volcanoes that are most likely to experience ‘thick’ ice eruptions, while the vast majority are more likely to experience ‘thin’ ice eruptions.

A map projection fundamentally impacts the mapmaking process. Working with Map Projections: A Guide to Their Selection explains why, for any given map, there isn’t a single "best" map projection. Selecting a projection is a matter of understanding the compromises and consequences of showing a 3-D space in two dimensions. The book presents a clear understanding of the processes necessary to make logical decisions on selecting an appropriate map projection for a given data set. The authors discuss the logic needed in the selection process, describe why certain decisions should be made, and explain the consequences of any inappropriate decision made during the selection process. This book also explains how the map projection will impact the map’s ability to fulfill its purpose, uses real-world data sets as the basis for the selection of an appropriate map projection, and provides illustrations of an appropriately and inappropriately selected map projection for a given data set. The authors take a novel approach to discussing map projections by avoiding an extensive inventory of mathematical formulae and using only the mathematics of map projections that matter for many mapping tasks. They also present information that is directly applicable to the process of selecting map projections and not tied to a specific software package. Written by two leading experts, this book is an invaluable resource for anyone studying or working with geospatial data, from students to experienced professionals, and will help readers successfully weigh the pros and cons of choosing one projection over another to suit a map’s intended purpose.

For decades, cartographers and cognitive scientists have speculated about the influence of map projections on mental representations of the world. The development of Web 2.0 and web mapping services at the beginning of the 21st century—such as Google Maps, OpenStreetMap, and Baidu Map—led to an enormous spread of cartographic data, which is available to every Internet user. Nevertheless, the cartographic properties of these map services, and, in particular, the selected map projection or the Web Mercator projection, are questionable. The goal of this study is to investigate if the global-scale mental map of young people has been influenced by the increasing availability of web maps and the Web Mercator projection. An application was developed that allowed participants of Belgium and the US to scale the land area of certain countries and continents compared to Europe or the conterminous United States. The results show that the participants’ estimation of the actual land area is quite accurate. Moreover, an indication of the existence of a Mercator effect could not be discovered. To conclude, the young people’s mental map of the world does not appear to be influenced by a specific map projection but by personal characteristics. These elements are varied and require further analysis.

Distance Cartograms (DC) distort geographical features so that the measured distance between a single location and any other location on a map indicates absolute travel time. Although studies show that users can efficiently assess travel time with DC, distortion applied in DC may confuse users, and its usefulness “in the wild” is unknown. To understand how real world users perceive DC’s benefits and drawbacks, we devise techniques that improve DC’s presentation (preserving topological relationships among map features while aiming at retaining shapes) and scalability (presenting accurate live travel time). We developed a DC-enabled system with these techniques, and deployed it to 20 participants for 4 weeks. During this period, participants spent, on average, more than 50% of their time with DC as opposed to a standard map. Participants felt DC to be intuitive and useful for assessing travel time. They indicated intent in adopting DC in their real-life scenarios.

A distance cartogram (DC) is a technique that alters distances between a user-specified origin and the other locations in a map with respect to travel time. With DC, users can weigh the relative travel time costs between the origin and potential destinations at a glance because travel times are projected in a linearly interpolated time space from the origin. Such glance-ability is known to be useful for travelers who are mindful of travel time when finding their travel destinations. When constructing DC, however, uneven urban traffic conditions introduce excessive distortion and challenge user intuition. In addition, there has been little research focusing on DC’s user interaction design. To tackle these challenges and realize the potential of DC as an interactive decision-making support tool, we derive a set of useful interactions through two formative studies and devise two novel techniques called Geo-contextual Anchoring Projection and Scalable Roadnetwork Construction. We develop an interactive map system using these techniques and evaluate this system by comparing it against an equidistant map (EM), a widely used conventional layout that preserves the geographical reality. Based on the analysis of user behavior and qualitative feedback, we identify several benefits of using DC itself and of the interaction techniques we derived. We also analyze the specific reasons behind these identified benefits

The field of map projections can be described as mathematical, static, and challenging. However, this description is evolving in concert with the development of the Internet. The Internet has enabled new outlets for software applications, learning, and interaction with and about map projections. This chapter examines specific ways in which the Internet has moved map projections from a relatively obscure paper-based setting to a more engaging and accessible online environment. After a brief overview of map projections, this chapter discusses four perspectives on how map projections have been integrated into the Internet. First, map projections and their role in web maps and mapping services is examined. Second, an overview of online atlases and the map projections chosen for their maps is presented. Third, new programming languages and code libraries that enable map projections to be included in mapping applications are reviewed. Fourth, the Internet has facilitated map projection education and research especially with the map reader’s comprehension and understanding of complex topics like map projection distortion is discussed.

Natural language interfaces for visualizations have emerged as a promising new way of interacting with data and performing analytics. Many of these systems have fundamental limitations. Most return minimally interactive visualizations in response to queries and often require experts to perform modeling for a set of predicted user queries before the systems are effective. Eviza provides a natural language interface for an interactive query dialog with an existing visualization rather than starting from a blank sheet and ask- ing closed-ended questions that return a single text answer or static visualization. The system employs a probabilistic grammar based approach with predefined rules that are dynamically updated based on the data from the visualization, as opposed to computationally intensive deep learning or knowledge based approaches. The result of an interaction is a change to the view (e.g., filtering, navigation, selection) providing graphical answers and ambiguity widgets to handle ambiguous queries and system defaults. There is also rich domain awareness of time, space, and quantitative reason- ing built in, and linking into existing knowledge bases for additional semantics. Eviza also supports pragmatics and exploring multi-modal interactions to help enhance the expressiveness of how users can ask questions about their data during the flow of visual analysis.

One method for working with large, dense sets of spatial point data is to aggregate the measure of the data into polygonal containers, such as political boundaries, or into regular spatial bins such as triangles, squares, or hexagons. When mapping these aggregations, the map projection must inevitably distort relationships. This distortion can impact the reader’s ability to compare count and density measures across the map. Spatial binning, particularly via hexagons, is becoming a popular technique for displaying aggregate measures of point data sets. Increasingly, we see questionable use of the technique without attendant discussion of its hazards. In this work, we discuss when and why spatial binning works and how mapmakers can better understand the limitations caused by distortion from projecting to the plane. We introduce equations for evaluating distortion’s impact on one common projection (Web Mercator) and discuss how the methods used generalize to other projections. While we focus on hexagonal binning, these same considerations affect spatial bins of any shape, and more generally, any analysis of geographic data performed in planar space.

Knowledge around geospatial technologies and learning remains sparse, inconsistent, and overly anecdotal. Studies are needed that are better structured; more systematic and replicable; attentive to progress and findings in the cognate fields of science, technology, engineering, and math education; and coordinated for multidisciplinary approaches. A proposed agenda is designed to frame the next generation of research in this field, organized around four foci: (1) connections between GST and geospatial thinking; (2) learning GST; (3) curriculum and student learning through GST; and (4) educators’ professional development with GST. Recommendations for advancing this agenda are included.

Online interactive maps have become a popular means of communicating with spatial data. In most online mapping systems, Web Mercator has become the dominant projection. While the Mercator projection has a long history of discussion about its inappropriateness for general-purpose mapping, particularly at the global scale, and seems to have been virtually phased out for general-purpose global-scale print maps, it has seen a resurgence in popularity in Web Mercator form. This article theorizes on how Web Mercator came to be widely used for online maps and what this might mean in terms of data display, technical aspects of map generation and distribution, design, and cognition of spatial patterns. The authors emphasize details of where the projection excels and where it does not, as well as some of its advantages and disadvantages for cartographic communication, and conclude with some research directions that may help to develop better solutions to the problem of projections for general-purpose, multi-scale Web mapping.

To explore potential differences in food shopping behaviors and healthy food availability perceptions between residents living in areas with low and high food access. A cross-sectional telephone survey to assess food shopping behaviors and perceptions. Data from an 8-county food environment field census used to define the Centers for Disease Control and Prevention (CDC) healthier food retail tract and US Department of Agriculture Economic Research Service food desert measure.

Several recent US policies target spatial access to healthier food retailers. We evaluated 2 measures of community food access developed by 2 different agencies using a 2009 food environment validation study in South Carolina as a reference. Whereas the US Department of Agriculture Economic Research Service’s (USDA ERS) measure designated 22.5% of census tracts as food deserts, the Centers for Disease Control and Prevention’s (CDC) measure designated 29.0% as non-healthier retail tracts; 71% of tracts were designated consistently between USDA ERS and CDC. Our findings suggest a need for greater harmonization of these measures of community food access.

In the United States presidential disaster declarations are typically issued after major disaster events to provide assistance (in the form of monies, staff, geospatial data, etc.) to states when the disaster overwhelms the resources of the state. Geospatial support is one of the forms of assistance and a frequent item noted by Federal agencies in demonstrating their relevance. During the disaster the state is ‘in charge’ of the disaster response while the Federal government provides assistance. Are the geospatial data (including remotely sensed imagery of all types) needs met by the states (based on their experience)? What are the expectations of the states for Federal help in geospatial data? Are states embracing newer paradigms for collecting/exploiting geospatial data, such as volunteered geographic information or crowd-sourced data/information? In the winter of 2011–2012 a nationwide survey of the geospatial data, methods, and problems in all fifty United States emergency management offices (EMOs) was conducted. Responses to the key questions on geospatial data priorities, remotely sensed imagery, timeliness, expectations, staffing, and emerging technologies are presented in this article. This nationwide survey of state EMOs provides a unique view of the EMO director’s view of geospatial methods during emergency response/recovery.

Background: Improving spatial access to healthy food retailers has emerged as a novel focus of public policy in the United States. Various agencies have developed specific measures of community food access but these indicators have not been compared systematically in terms of the areas identified or the populations affected. Methods: We replicated the USDA Economic Research Service’s (ERS) food deserts criteria, the Centers for Disease Control and Prevention’s (CDC) tracts with healthier food retailers, and The Reinvestment Fund (TRF) definition of low access areas using a 2009 food environment validation study conducted in eight South Carolina counties, 2000 US Census data and geographic information systems. Results: According to the USDA ERS criteria, only 10%(n= 15) of 150 Census tracts in the study area were designated as food deserts, compared to 28.7%(n= 43) designated as non-healthier retail tracts by CDC, and 29.3%(n= 44) limited supermarket access areas by TRF. Despite these differences, the geographic overlap was quite high (USDA ERS vs. CDC: 71% of tracts; CDC vs. TRF: 77%; USDA ERS and TRF: 65%). The population estimated to be residing in areas with poor access to healthy food choices ranged from 50,085 to more than 201,300 residents. Conclusions: Marked differences were observed in the underlying methodologies in the estimates of prevalence of areas with low healthy food access, their geographic locations, and the affected populations. The findings suggested a need for clear communication of conceptual differences between the measures of food access.

Several spatial measures of community food access identifying so called “food deserts” have been developed based on geospatial information and commercially-available, secondary data listings of food retail outlets. It is not known how data inaccuracies influence the designation of Census tracts as areas of low access. This study replicated the U.S. Department of Agriculture Economic Research Service (USDA ERS) food desert measure and the Centers for Disease Control and Prevention (CDC) non-healthier food retail tract measure in two secondary data sources (InfoUSA and Dun & Bradstreet) and reference data from an eight-county field census covering 169 Census tracts in South Carolina. For the USDA ERS food deserts measure accuracy statistics for secondary data sources were 94% concordance, 50–65% sensitivity, and 60–64% positive predictive value (PPV). Based on the CDC non-healthier food retail tracts both secondary data demonstrated 88–91% concordance, 80–86% sensitivity and 78–82% PPV. While inaccuracies in secondary data sources used to identify low food access areas may be acceptable for large-scale surveillance, verification with field work is advisable for local community efforts aimed at identifying and improving food access.

Cross-country differences in dietary behaviours and obesity rates have been previously reported. Consumption of energy-dense snack foods and soft drinks are implicated as contributing to weight gain, however little is known about how the availability of these items within supermarkets varies internationally. This study assessed variations in the display of snack foods and soft drinks within a sample of supermarkets across eight countries.

Incorporating Esri’s story maps into the geography undergraduate senior seminar at the University of South Carolina helped students communicate results from their semesterlong projects. The authors discuss the successes they had and the challenges they faced to help other instructors recognize the possibilities—and minimize the limitations—of using story maps in their own classrooms.

In disaster response, timely collection and exploitation of remotely sensed imagery is of increasing importance. Image exploitation approaches during the immediate (first few days) aftermath of a disaster are predominantly through visual analysis rather than automated classification methods. While the temporal needs for obtaining the imagery are fairly clear (within a one- to three-day window), there have only been educated guesses about the spatial resolution requirements necessary for the imagery for visual analysis. In this paper, we report results from an empirical study to identify the coarsest spatial resolution that is adequate for tasks required immediately following a major disaster. The study was conducted using cognitive science experimental methods and evaluated the performance of individuals with varying image interpretation skills in the task of mapping hurricane-related residential structural damage. Through this study, we found 1.5 m as a threshold for the coarsest spatial resolution imagery that can successfully be used for this task. The results of the study are discussed in terms of the likelihood of collection of this type of imagery within the temporal window required for emergency management operations.

The ability to recognize distortions of, for example, areas, angles, and landmass shapes in global-scale map projections, is an important part of critical map reading and use. This study investigates the cues used by individuals when they assess distortion on global-scale map projections. It was hypothesized that landmass shape would be a dominant cue used by individuals with no formal map projection training and that as projection knowledge increased the cues would become more systematic (e.g., use the graticule). Results indicate a tendency for novices to rely on landmass shape as a cue. Some evidence of a systematic evaluation of projections was also found.

Efforts to stem the diabetes epidemic in the United States and other countries must take into account a complex array of individual, social, economic, and built environmental factors. Increasingly, scientists use information visualization tools to "make sense" of large multivariate data sets. Recently, ring map visualization has been explored as a means of depicting spatially referenced, multivariate data in a single information graphic. A ring map shows multiple attribute data sets as separate rings of information surrounding a base map of a particular geographic region of interest. In this study, ring maps were used to evaluate diabetes prevalence among adult South Carolina Medicaid recipients. In particular, county-level ring maps were used to evaluate disparities in diabetes prevalence among adult African Americans and Whites and to explore potential county-level associations between diabetes prevalence among adult African Americans and five measures of the socioeconomic and built environment—persistent poverty, unemployment, rurality, number of fast food restaurants per capita, and number of convenience stores per capita. Although Medicaid pays for the health care of approximately 15 percent of all diabetics, few studies have examined diabetes in adult Medicaid recipients at the county level. The present study thus addresses a critical information gap, while illustrating the utility of ring maps in multivariate investigations of population health and environmental context.

Understanding the causes, effects and geographic patterns of local hazards is important for helping individuals make educated decisions about how to respond to their threat. Unfortunately, it is often difficult to find comprehensive sources of information about local hazards. In this paper, we discuss the development of an online hazards atlas for the state of South Carolina. We have designed this atlas to facilitate awareness about the causes and effects of hazards. An effective method for communicating this information is through education and awareness programs for children, so we have designed this atlas primarily as an educational tool for K-12 students. However, we have also structured the atlas to ensure that it provides details of interest to the general population. In this paper, we focus on educational, methodological and technical aspects of development of the online hazards atlas to highlight both the benefits and challenges of this type of product and to emphasize where it fits as a tool for improving hazards education. In addressing these issues, we present a model that can be adapted for use in other geographic areas.

Animated choropleth maps enable cartographers to visualize time-series data in a way that congruently depicts change over time. However, users have difficulty apprehending information encoded within these displays, and often fail to detect important changes between adjacent scenes. Failures of visual experience, such as change blindness, threaten the effectiveness of dynamic geovisual displays, in which several important changes can occur simultaneously throughout the display. Animated choropleth maps require viewers not only to notice changes but also understand symbolic meanings encoded in rapid transitions between scenes. Graphic interpolation between key frames, also known as “in-betweening” or “tweening”, smoothes transitions and lengthens the duration of the transition between scenes in a dynamic sequence. Previous cartographic literature suggests tweening could be a potential solution for change blindness in the cartographic context. This article examines this issue of change blindness in the cartographic context and reports on a human subjects investigation designed to evaluate the influence of cartographic design variables on map readers' change detection abilities. Our results indicate that 1) map readers have difficulty detecting changes in animated choropleth maps, 2) map readers over-estimate their own change detection abilities, and 3) tweening influences the legibility of change in animated choropleth maps.

Epidemiological research often involves the visual exploration of numerous attributes to help discern patterns between health and characteristics of the physical, socioeconomic, or built environment. Unfortunately, many of the multivariate mapping techniques discussed throughout the cartographic literature can be challenging to create or interpret—particularly for individuals without a cartographic background. In this paper, we present a new style of multivariate map—the ring map—to aid in basic visualization of multivariate datasets. For purposes of example, we focus on the use of the ring map style for exploring county-level epidemiological data for the state of South Carolina to examine patterns of age, race, or gender specific mortality and morbidity within a population.

Maps provide a means for visual communication of spatial information. The success of this communication process largely rests on the design and symbolization choices made by the cartographer. For static mapmaking we have seen substantial research in how our design decisions can influence the legibility of the map’s message, however, we have limited knowledge about how dynamic maps communicate most effectively. Commonly, dynamic maps communicate spatiotemporal information by 1) displaying known data at discrete points in time and 2) employing cartographic transitions that depict changes that occur between these points. Since these transitions are a part of the communication process, we investigate how three common principles of static map design (visual variables, level of measurement, and classed vs. unclassed data representations) relate to cartographic transitions and their abilities to congruently and coherently represent temporal change in dynamic phenomena. In this review we find that many principles for static map design are less than reliable in a dynamic environment; the principles of static map symbolization and design do not always appear to be effective or congruent graphical representations of change. Through the review it becomes apparent that we are in need of additional research in the communication effectiveness of dynamic thematic maps. We conclude by identifying several research areas that we believe are key to developing research-based best practices for communicating about dynamic geographic processes.

One primary utility of animated maps is their ability to depict change over time and space; unfortunately, recent research suggests that humans frequently fail to perceive changes within dynamic graphics. However, different types of dynamic graphics include different manifestations of change. For example, an animated proportional-symbol map possesses different change properties than an animated choropleth map. This article examines issues of change on animated choropleth maps. We identify relevant limitations of the human visual system that pertain to animated map reading, including change blindness and foveal versus peripheral attention, and introduce methods to quantify the magnitude of change that separates individual scenes within choropleth animations. These methods are useful for measuring and describing changes that confront users of animated choropleth maps. We also characterize the transitional behaviours of enumeration units and discuss the influences of data classification and other cartographic controls on change within animated choropleth maps.

For global-scale geographic information, there are relatively few sources that can be used to form or structure a cognitive map. One of the most common sources for this information is maps, the only reference that permits an individual a comprehensive view of the world without having to integrate information from multiple views (e.g., stitching together the two halves of a globe or assembling the pages of an atlas). It has been frequently stated that visual experience with the map projections used for global-scale mapping, specifically the Mercator projection, has a strong influence on the shape and structure of an individual's global-scale cognitive map. In this article, we examine this belief by conducting two studies on the relationship between memory- and inference-derived estimates of land area and the actual land areas of regions on the Earth. Numerical and graphical estimation techniques were used to obtain area estimates. We examined the results for relationships between distortion in estimated area and area as displayed in common map projections such as the Mercator projection, as well as for general trends in the estimation patterns. Nonequal area projections, particularly the Mercator projection, were found not to have much if any influence on the shape of participants' cognitive maps. Instead, we found estimates to be fairly accurate relative to actual area, and that the estimation pattern most clearly reflected standard trends in psychophysical estimation.

The acquisition and conceptualization of spatial knowledge are important topics in human spatial cognition. At the global scale, maps are our primary graphic source of information; however, they distort the size and shape of geographic features. If a distorted reference is used and the reader assumes it to be accurate, it may inappropriately influence decision making and, possibly, the shape of our global-scale cognitive maps. This paper examines trends in perception of land area, using equal-area and non-equal-area references, as well as investigating how map-projection knowledge can influence interpretation of land area. Results from the land-area studies show that map readers attempted compensation for projection distortion only when using the Mercator projection as a reference, and only for certain regions displayed on the Mercator projection. For other reference materials there is no attempted compensation for perceived distortion, even when participants believe that the reference is distorting land area. It is also apparent that most participants have limited projection knowledge and have difficulty transferring this knowledge to other projections or to practical application tasks. Both of these findings have implications for understanding perceptual issues in map reading and for determining where distortions can be introduced at the encoding stage of cognitive map development.

In this article we investigate whether a geospatial task-based framework can be conceptualized and developed to assist in structuring (in a grade-related context) a conceptual framework that could help build a vocabulary and scope and sequence structure for the geospatial thinking that makes the world and its activities legible to us. Our argument is presented in conceptual terms, but we offer preliminary evidence, based on work with local third-grade and sixth-grade students, that a hierarchy of concepts can be developed based on complexity, and we give results from pilot experiments to illustrate the feasibility of the hypothetical framework. The pilot studies show a clear differentiation of vocabulary and concept use between the two sampled grades and provide some substantiation of the potential use of the conceptual framework.

In this paper, we assume that learning to comprehend the geospatial environment would be significantly facilitated by developing a multi-level task ontology that identifies various levels and complexities of geospatial concepts. We suggest that, apart from four spatial ‘primitives’– identity, location, magnitude, and space-time – all geospatial concepts involve ‘inheritance’ characteristics. The more complex and abstract the concept, the larger the inheritance links that need to be appreciated to enhance concept understanding. For example, many basic geospatial concepts – such as direction and distance – are first-order derivatives from the ‘location’ primitive, whereas concepts such as spatial association, map projection or interpolation are high-order concepts that require several layers of geospatial concepts in their derivation. Having offered a five-level ontology for concept organisation, we suggest sets of tasks that could establish an understanding of concepts, thus directly making the environment more legible in a spatial sense. We develop this framework in the context of the teaching of geography in grades from kindergarten to the final years of high school (grade 12 in the United States system). Our conceptualisation is grounded in the US school system – in which geography is usually absent in the curriculum.

As geographic information systems (GIS) are increasingly implemented in K–12 classrooms, the risk becomes one of teaching “buttonlogy” or simply how to point and click to complete certain functions. Through the development of a geospatial concept lexicon and corresponding geospatial task ontology along with simple concept-based tasks completed by students in different grade levels, this research has illuminated grade-related differences in geospatial concept recognition and understanding. In these experiments, simple paper and pencil tasks were given to 6th grade, high school, and undergraduate students to provide insight into different levels of concept understanding, specifically in terms of grade-related abilities to comprehend descriptions of spatial relationships. Results indicate significant differences in geospatial concept recognition, understanding, and use among the grade-based participants tested during the course of the project. These results can be used to inform the development of a “Minimal GIS” in which a pedagogic goal of grade-appropriate concept understanding becomes the driving force behind the GIS, suggesting the structure of an effective support system for spatial thinking.

In this paper, we evaluate map overlay, a concept central to geospatial thinking, to determine how it is naively and technically understood, as well as to identify when it is learned innately. The evaluation is supported by results from studies at three grade levels to show the progression of incidentally learned geospatial knowledge as students mature and develop more complex thinking strategies. Our findings have been useful for understanding when and how geospatial concepts are learned innately. The results of the studies will be discussed in terms of creation of a hierarchy of concepts and use of a “minimal” GIS for geospatial education.