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Manuscript Title:
Representing the Presence of Absence in Cartography
Submitted for review to the Annals of the American Association of Geographers
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Abstract
A key cartographic challenge associated with the rise of big data is to show when spatial data observations are missing and/or to communicate variables that indicate absence. For example, showing where people are tweeting during a disaster may be interesting, but visually identifying where normal signals are missing may in fact highlight the most impacted places. Parcel data may be fully present, but attributes of their observations may convey qualities of absence (abandoned structures, for example). Current geovisualization approaches normally do not show anything at all when data are missing or contain qualities of absence, and only in rare cases may use a specific hue to highlight the presence of absence on maps. Our work argues that people perceive of missingness and absence in a way that is distinct from other spatial data qualities, and we propose a typology of static and dynamic means by which we can draw user attention to the presence of absence. To explore the application of these techniques, we use urban parcel data to visualize patterns of property blight in a Detroit neighborhood. Based on our conceptual developments and case study application, we propose research challenges to evaluate visual representations of missing and absent information on maps.
Keywords: cartography, visual variables, missingness, absence
Introduction
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The big spatial data era promises to provide detailed observations of people and our planet with great density, coverage, and update frequency. Sensor networks, social media sources, and persistent methods for remote sensing are able to provide high resolution, high frequency coverage of vast areas on earth. In this work, we argue that an emerging challenge for cartographers is to develop and evaluate techniques for visually representing and drawing attention to the absence of spatial data observations. For example, where people are tweeting during a disaster may rightfully be the center of a great deal of analytical attention, but the locations in which people have suddenly stopped tweeting may in fact represent the worst impacted place. Sensor network coverage may provide detailed observations on changes in temperature on a farm field, but being able to recognize when one or more sensors has gone offline will become essential in order to properly deliver on the promises of precision agriculture. These scenarios prompt a key question for cartographers to solve: how do we draw user attention to where data is missing/absent? In this paper we propose a new typology of visual techniques that can be applied and evaluated for showing the presence of absence of geospatial data on maps (Figure 1). One way to consider the problem of representing missing or absent information on maps is to reflect on the significant body of previous work in Cartography and GIScience that has focused on describing and evaluating methods for communicating uncertainty (Couclelis, 2003; Kinkeldey, MacEachren, & Schiewe, 2014; A.M. MacEachren, 2015; A.M. MacEachren et al., 2005; Thomson, Hetzler, MacEachren, Gahegan, & Pavel, 2005; J. Zhang & Goodchild, 2002). We argue that our present effort is distinguished from this prior work by virtue of the fact that previous studies of geospatial uncertainty focus largely on the communication and interpretation challenges associated with which ways and to what degree those data are uncertain. In contrast,
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we focus on the development of visual methods for mapping where observations or properties of observations are missing, and when absence itself should be directly conveyed. Although it remains to be evaluated in future work, we hypothesize that map users will interpret the nature of missing data to be distinct from the problem of describing the certainty in present data. Understanding where there are gaps in coverage would appear to be a different analytical scenario than understanding the quality dimensions of data that are already present. For example, as discussed in our crisis management scenario above, the analytical affordances of knowing that a region that normally produces tweets has stopped doing so seem qualitatively different than what can be gleaned from a situation in which tweets do exist across the area of interest and can support deeper analysis. Furthermore, we hypothesize that representation methods for revealing the presence of absence may be more effective if designed using cues that humans routinely perceive to signify absence. For example, although it is possible to simply highlight missing data with a distinct hue, we hypothesize that representing those data with shadows, transparency, blur, or other methods may aid map reading tasks that require users to identify and characterize missing data on maps. Simply put, we seek to explore the extent to which representing absence in ways that look more or less missing may aid map reading. We may find that the opposite is true, that missingness or absence in spatial data is simply yet another discrete attribute – not one that should see special attention through different means of visual representation. Our work here set the stage for this line of inquiry by identifying candidate representation methods and exploring their application in a case study analysis of parcel data, and uses these initial explorations to develop new research questions that require attention in future work in order to test our hypotheses.
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In this work we bring together the concepts of missingness and absence in spatial data. Missingness occurs when data observations contain variables that measure no values. Missingness is often described in terms of three sub-types, indicating the degree to which missing values are due to random processes (Donders, van der Heijden, Stijnen, & Moons). Therefore, missingness in spatial data are those instances in which values are not present for variables in spatial data observations. On the other hand, we also focus on instances in which a quality of absence is present in a spatial data observation. For example, a parcel may have a variable indicating its status as a vacant lot. We argue that both missingness and absence require fresh attention in terms of cartographic representation, and that they should be considered together because they share the common core quality of absence either by virtue of missing an observation, or by having a quality of absence in a present observation. In this article we refer to our overall purpose as characterizing the cartographic needs associated with representing the presence of absence. In the next section we connect our ideas to related previous work to calculate, visualize, and leverage missing or absent data on maps. Then we present a novel typology of visual techniques for representing missing or absent data on maps, demonstrating how common visual variables in cartography as well as newly designed methods that we propose can be applied to points and areas on maps to visually indicate the presence of absence. Next, we propose ideas for dynamic and interactive approaches that could be applied to visualizing missing or absent data on maps. We then explore how static methods from our typology can be applied to the visualization of vacant properties in the city of Detroit. Finally, we propose a set of key research challenges that emerge from our initial work to conceptualize and design representations for mapping missing or absent information.
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Related Work
Our research builds on a body of literature that focuses on methods for measuring missing data, visualization approaches for showing missing data, and efforts to evaluate the effectiveness of visual techniques for showing missing data. It also connects to prior work by geographers to consider concepts of presence and absence in the science of place and space. We review the literature in each of these areas in the sections that follow.
Measuring Missing Data
In the geographical sciences, the problem of computing to measure and account for missing spatial data has received attention for several decades. Early work by Bennett et al. (1984) describes general approaches to address missing data in spatial surface interpolation. A wide range of spatial interpolation methods have been crafted to treat estimation in areas with missing observations in various ways to improve the utility of spatial surfaces (Declercq, 1996; Lam, 1983; Oliver & Webster, 1990). The literature on spatial interpolation is far wider than we can fully review here, and its depth highlights the central role that taking missing information into account plays in geographical analysis. Other subfields of GIScience have also explored the conceptual dimensions associated with measuring and relating missing spatial information. For example, Vasardani and Egenhofer (2008) propose methods for comparing regions that contain holes (Egenhofer, Clementini, & di Felice, 1994), and they highlight the need to characterize regions that have missing information in sensor networks as a major motivation for developing new relational techniques.
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Visualizing Missing Data
Eaton et al. (2005) are among the first to describe the challenge of visualizing missing data as one that the information visualization community must begin to address. The core of their argument is that a key rationale for visualizing information is to provide the means for users to see patterns in data, and failing to show an indication of missing information can lead to false interpretations of patterns in data. Eaton et al. describe five key types of missing data; uncollected data, suppressed confidential data, redefined data categories, mutually exclusive multivariate combinations, and excessive uncertainty that leads to observation culling. We argue that beyond the generic information visualization context, each of these types of missing information can be found in geospatial analysis contexts. Early work by Unwin et al. (1996) is one of the few examples in the literature that highlights this connection. Eaton et al. go on to propose a classification for visualization types to show missing information. Their framework focuses on how the position of a graphical element is calculated, and they propose that this position can be, “…1) dedicated to the data item independently of the attribute values, 2) entirely a function of attribute values, or 3) a function of the attributes values and the values of neighboring items.” (Eaton et al., 2005, p. 3). Eaton et al. mention choropleth maps as an example of a dedicated method, because if data is missing, no symbolization will be used and none of the neighboring areas are impacted by this. Users may see a gap in the coverage of the map. In the attribute dependent case, as in a scatter plot, the user will see no indication that anything is missing, and none of the neighboring observations are impacted by this change. Finally, in the attribute value and value of neighbors case, as found in a pie chart, not only would users not see anything missing, but the visualization itself will be misleading. A pie chart with missing information would be resized to fill the available space. 7
To solve these issues, Eaton et al. propose three visualization approaches; dedicated visual attributes to signify missing values, annotation to call attention to missing values, and animation to show different views of data to users that include missing values. Our research focuses on the first category of visualization methods applied to the cartographic context, to develop and evaluate dedicated visual attributes for showing missing information on maps. Most of the recent literature on visualizing missing data simply treat missing values as any other data type, highlighting and symbolizing those data as a special, discrete category of information. For example, Zhang (2015) use a bright red color in a variety of graph types to show missing observations. In other recent work, a software library has been developed for the R statistics package that allows users to create so-called missingness maps, which use a gridded method to sort variables and observations into observed and missing categories (Cheng, Cook, & Hofmann, 2015). Cheng et al. implement a multiple coordinated-view visualization to show missing dimensions in multivariate data, and once again a single color (bright orange) is used to draw attention to the missing values. Fernstad and Glen (2014) apply the same type of single color technique (bright red) to draw attention to missing data in their exploration of multivariate attribute data describing different types of Iris flowers.
User Evaluation of Visual Methods for Missing Data
In addition to defining key characteristics of missing data in the context of information visualization, Eaton et al. (2005) conducted a user study to test three graph variations in which missing data is recoded as zeroes, missing data is simply not drawn on the graph, and in which it is explicitly coded to draw attention to its existence. Their results suggested that users sometimes
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do not notice when missing data have been recoded by zeroes, and that even when presented with more visually-explicit forms of missing data, users may still try to find trends in partial data. These results complement a body of previous work in psychology to evaluate visual cues that direct attention. Specifically, the subject of visual search asymmetry in perceptual science has focused on how the speed and performance of visual search can depend on whether or not it is the presence or absence of a feature that is the target (Anne Treisman & Gormican, 1988; A. Treisman & Souther, 1985). The literature suggests that identifying features that include the presence of a cue is faster and easier compared to identifying features that are absent a given cue (Wolfe, 2001). An example of search asymmetry is shown in Figure 2. It is easier to identify the target in the left side of the figure than it is to identify the target (which is signified by the absence of a cue) in the right side of the figure. While the precise mechanism for understanding why visual asymmetry occurs remains a source of ongoing research (Moran, Zehetleitner, Liesefeld, Müller, & Usher, 2016), the effect has been found in many visual search experiments, and can be found when manipulating color, orientation, movement, size, and other common visual variables used in cartographic design (Vincent, 2011). The implication of this work for our present research on cartographic representation of the presence of absence is that there is psychological evidence to suggest that map readers will have a difficult time with visual search tasks to find missing/absent information unless we provide distinct cues to draw that attention.
Visualizing Missing Data with Maps
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While most of the existing work on visualizing missing data focuses on non-spatial graph representations, we were able to find some examples of efforts to show missing data with maps. Unwin et al. (1996) represent missing data using white histogram bars and dots on scatterplot axes in coordinate with interactive maps to allow user-driven exploration of missing data with maps. In more recent work, Templ et al. (2012) vary univariate color schemes on proportional circle and choropleth maps to indicate varying levels of missing information. In this paper, we propose new methods designed specifically for showing missing information on maps, and we propose an experimental approach for evaluating those candidate methods. We hypothesize that users will conceive of missing values as more than just another data category, and that drawing attention to missing values will be more effective if we use visual metaphors that suggest absence by design, rather than treating them as simply another standard data category. We characterize our general research aim as the need to visually represent the presence of absence in data. In the geospatial context, this means we need to develop and evaluate techniques for showing missing data on maps. The vast majority of existing research on visualizing missing data has focused on non-geospatial representations, and those that have simply treat it as an element that can be colored or highlighted like any other data category. In the sections that follow we propose a typology of visual methods that can be used to show missing data on maps, use cases in which the application of such methods are likely to be helpful for users, and key research challenges for advancing the state of the art in cartographic techniques for visualizing missing information on maps.
Conceptualizing Absence in Geography
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Our work focuses on cartographic approaches for signifying the presence of absence in missing as well as present data which contains qualities of absence. For the latter, we build on complementary progress made by human geographers to conceptualize and theorize on the definitions and roles of absence in geography. In 1984, Torsten Hägerstrand highlighted the importance of geographic science that could go beyond experimental circumstances in which complexity and situational context are rendered absent in order to craft idealized studies (Hägerstrand, 1984). He argued that presence in geographic science would focus on problems that include the rich context of reality, and absence in geographic science would represent the standard scientific norm in terms of approaches that seek to exclude the complexities of reality. “By shielding off – in other words, making absent – everything except the one or two variables one wants to study, one is also creating conditions which do not exist in the real world and very often cannot exist in any imaginable world.” (p. 377) Recent work has sought to expand on these conceptualizations of absence in geography to propose that our ways of knowing about people and place are fundamentally connected to absences in both aspects, via memories we invoke about the past (Degnen, 2013). This work argues that absence is used as a point of reference just as often as presence, and claims that absence is therefore salient. Complementary work calls for attention to the temporary occupation of space by mobile objects in transportation networks, highlighting the fact that transport infrastructure may shape environments and mobility even though the vehicles themselves may only be present for a brief period of time (Cidell & Lechtenberg, 2016). Other scholars have drawn attention to the challenges associated with geographic frameworks of missing persons, as is found in (Parr & Fyfe, 2013). In their explanation of
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conceptual challenges for understanding missing people, Parr & Fyfte (2013) offer ideas for cartographic approaches that could aid in mapping missing persons as well as predict their spatial behavior. This work proposes tasks for addressing the cartographies of absence but does not propose a focus on visual representation methods to signify absence directly. The presence of absence has also been explored within the context of toponymy. The names we give to places provide an affordance of presence, however sometimes the names themselves tie to concepts of absence (e.g. Nameless Mountain), and the places we choose not to name remain real but may appear absent (Woodman, 2015) on maps. Furthermore, toponymy is always evolving, with new places appearing and old places disappearing as time moves on. Since toponyms are so often encountered on maps, there is a clear need to begin addressing this dynamism through visual representations that can convey absence.
A Typology of Representation Methods for Representing the Presence of Absence on Maps
Two general approaches to visualizing the absence of geospatial data are potential candidates for further study. The first approach would be to adapt previously developed methods for highlighting in geovisualization (Robinson, 2011) to deliberately draw attention to areas where data is missing on maps rather than where data is present (which is how these techniques are normally applied). The second approach is to design new visual representation methods that imply qualities of absence themselves, for example, modifying regions on a map to make them darker, blurrier, and/or change their transparency. We draw here on both approaches to propose a
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set of candidate methods for representing the presence of absence on maps. We structure our typology by categorizing candidate methods as static or interactive techniques. Each method is demonstrated using three prototypical map types. These types include a point observation map, an interpolated surface map, and a choropleth map. Data may be missing in other mapping contexts as well, but we begin here with points, surfaces, and areas as an initial test case for which there are many plausible real-world scenarios in which data will be missing.
Design Criteria
In constructing this typology, we focused on techniques that meet the same criteria as proposed in Robinson (2011); methods must be visually salient, methods must be applicable across a broad range of thematic mapping formats (e.g. points, areas, and interpolated surfaces), and methods must be implementable using existing tools. We also exclude methods that alter the shape, size, or position of observations.
Static Methods
Here we propose a typology of static visual methods that could be readily applied to represent the presence of absence on maps. We draw here upon a great deal of prior work in graphic design and cartography to create and evaluate taxonomies of visual variables (Bertin, 1967; Alan M. MacEachren, 1995; Mackinlay, 1986; Morrison, 1974). Specifically, we focus on those visual variables which do not alter the geometry of observations on maps, so we exclude methods like shape, arrangement, orientation, size, and location. In the sections that follow we describe each proposed method and explore how it may be utilized for representing absence on maps. Each
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method is also demonstrated in Figures 3, 4, and 5 for simulated point, interpolated surface, and choropleth map designs.
Blank
Arguably the simplest and most commonly found method for representing absence in map data is to simply not draw anything for those places. Therefore, one way to represent absence is to simply remove any trace of that information from the map. We do not consider this to be a visual variable, per se, as it cannot be applied in a variable manner. Instead, it is more like a special case of the use of transparency in which transparency is fully applied. This approach has the advantage of being very simple to apply, but we argue that a potential disadvantage of this method is that it gives users no way to evaluate the differences between empty spaces on the map. Distinguishing between an empty area of the map versus an area in which observations have been determined to be missing is impossible. For point symbol and interpolated surface maps on which it is common to include reference layers as a basemap this problem can be especially acute. Hue
Changes to hue can be applied to data on maps to signify absence. This method is easy to apply and supports the goal of providing qualitative discriminability to identify absence versus presence on maps. It is also possible to select hues that resonate with the quality of absence in a given mapping context. For example, one may use an orange color instead of a blue one to represent an area that is missing moisture information.
Saturation
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It is possible to modify the saturation of color to indicate the presence of absence on maps. For example, point symbols may be desaturated to remove much of their color. This approach has the advantage of being easy to apply and that it allows map readers to distinguish between truly empty areas of the map and those that have known missing observations or attributes of observations that indicate absence. Furthermore, strong enough desaturation will lend the appearance of a distinct qualitative category.
Value
Color value can be altered in map symbols to draw attention to missing information on maps. Unlike saturation and hue, variations on value are more likely to look sequentially distinct rather than qualitatively distinct. Therefore, this technique may not be an ideal candidate for drawing attention to the quality of absence compared to presence.
Texture
Symbols can be filled using textures that are intended to represent missing information or attributes of absence in geographic data. As is true with several other methods, the use of texture supports the goal of separating parts of the map by virtue of a qualitative representation. Textures can range from very subtle to very aggressive designs, therefore they may introduce visual clutter in some design variations.
Transparency
The opacity of spatial observations can be varied based on attributes of absence. This approach is easily supported by contemporary mapping tools, but depending on the map context it may be
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difficult to see the effect. This method will be more easily applied in circumstances in which there is a designed basemap that can become selectively more or less visible underneath an observation that indicates absence. When there is no underlying map information, the effect may instead look like a change in color value (as shown in the choropleth example in Figure 5).
Blur
Depth of field blur can also be used as a visual effect to represent missing or absent information on maps. This method has the potential benefit of leveraging our visual ability to easily discriminate between sharp and blurry objects (Kosara, Miksch, & Hauser, 2001). Furthermore, it may offer semantic resonance for showing missing/absent information, as blurriness has been used to convey uncertainty in data visualization (Feng, Kwock, Lee, & Taylor, 2010).
Shadow
We propose the addition of shadow to the otherwise widely agreed upon set of visual variables that can be used for showing the presence of absence on maps. Building on the visual affordances humans experience on a daily basis (peering into an empty container, for example), we propose a blended desaturation, blur, and transparency effect that creates a shadow to signify absence on maps. Drop shadow techniques have been commonly applied in graphic design for decades, and most graphic design and mapping software are capable of rendering this effect. We also draw inspiration here from early work on visualizing missing data which proposed the concept of a “missing value shadow” (Swayne & Buja, 1998).
Dynamic Methods
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In addition to static methods for visualizing the presence of absence on maps, it is also possible to consider a range of dynamic approaches that can be animated or interactively applied. Here we propose a variety of such methods, and provide visual examples to explain how they might work. Several basic techniques can be suggested based on previous research. Lobo et al. (2015) suggest four main categories for map comparison techniques; juxtaposition, translucent overlay, swipe, and lenses. Juxtaposition places two maps, side-by-side, and coordinates their interaction directly based on movement or selection in one or the other view. This method could be used to show the presence of absence on one map while leaving the other in a default basemap view. The translucent overlay technique uses superimposition and a variable control that allows users to gradually move between two map designs. The swipe approach also uses superimposition, but instead of blending transparency, gives users the ability to interactively slide one layer across the other. Finally, lenses can be implemented that allow a region around the input cursor to selectively reveal a layer below the primary map. Each of these interactive methods represent potential candidates for dynamic control over the visualization of missing/absent information on maps. In addition to these basic interactive approaches, we also propose two potential candidates for further consideration. The first approach would use an animated technique to control the disappearance of information on the map with the aim of making it look as though it is vanishing into thin air, or water going down a drain (Figure 6). A second method along these lines would make use of the design patterns used in comic books and graphic novels to create an effect by which information on the map appears to go “poof” and disappear (Figure 6).
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Case Study – Mapping Vacant Properties in Detroit
To explore the utility of our proposed design framework, we apply the techniques we have proposed here to the context of mapping vacant properties in Detroit. The city of Detroit, Michigan has been the locus of dramatic demographic change in the past 100 years, peaking in population around 1950 and shrinking more than 60% in size since that time (Dewar, 2016). This decline has led to a massive number of abandoned homes and vacant lots in the city, with over a quarter of residential properties considered vacant. We use parcel information data for a Detroit urban development district in order to explore the ways in which these abandoned and vacant areas can be visualized to reveal patterns of absence in the city. We focus specifically on an area known as The District Detroit, which is the site of a significant new urban redevelopment project which will create a new hockey and basketball arena as well as a range of new commercial and residential structures in an area that has had a high proportion of vacant lots and abandoned buildings. This project will attempt to resuscitate a “dead zone” in the city and attract new residents to the area (Witsil, Reindl, & Gallagher, 2017). A low-profit limited liability company called Data Driven Detroit (http://portal.datadrivendetroit.org/) has curated a parcel dataset derived from public records that provides lot condition data on 638 parcels in and around The District Detroit (accessible at: http://portal.datadrivendetroit.org/datasets/a4cbc580122e4e5f936a0aa57afb4927_0). The attributes provided in this data source include whether or not each parcel is vacant, the condition of units on each property, the type of use intended for each area, and other attributes such as the presence of boards covering windows and doors on each property. In many contemporary mapping frameworks, such patterns can be difficult to discern. For example, a small portion of a neighborhood in Detroit, Michigan is shown in Figure 7 with 18
default styling in Google Maps as well as using satellite imagery. In the default basemap style, parcels can be seen, but the gaps between them are unclear unless you review the imagery and note that they are in fact vacant lots as a result of the impacts from economic and social turmoil for the past 50 years. Using parcel data from The District Detroit, we generated maps using the static visual techniques we have proposed for representing the presence of absence (Figure 8). In this case study example, we are mapping an attribute that indicates absence, rather than missing data. As a reminder, in this research we are focusing attention on methods that can be used for both missing data as well as attributes of absence on maps. Parcel records from this neighborhood in Detroit indicate whether or not each area is vacant or occupied. Vacancy is therefore the attribute of absence that we map here. The resulting maps can help us understand the design challenges associated with representing absence. Without other accompanying information, simply making those elements disappear makes it quite difficult to discern which parcels may be vacant or not. Changing hue provides a strong visual effect, as does saturation. Lightness (assuming the same hue is used as the non-vacant parcels) is less discriminable. Transparency has an added benefit of revealing a bit of the underlying detail from the basemap. Blur conveys a sense of uncertainty to the vacant areas and is easily separated from the non-vacant parcels. Texture works well to discriminate between both parcel types, but does not seem to suggest presence/absence in the form we utilized here. Finally, the shadow application provides a subtle approach that seems to suggest vacancy in an elegant manner. Much like the examples shown in our design typology (Figures 3, 4, and 5), the Detroit case study application helps us understand quickly that some methods will appear to have a more
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immediate utility than others. A full user evaluation is necessary to characterize those differences of course, but it appears that methods like blank, value, and transparency may pose salience challenges in many realistic mapping scenarios where such effects will cause the observations in question to be impossible or at least more difficult to see. While one could simply opt to use other visual techniques, another approach here could be to use interactivity to switch the visual effect on and off, or to invert the selection such that a visual effect is applied to everything except the observations with qualities of absence.
Conclusion
In this research we have contributed a new design framework to support the implementation and testing of visual methods for representing the presence of absence on maps. In the process we have characterized the problem space associated with showing qualities of absence in cartography, exploring its roots inside as well as outside the geographical sciences, and highlighting its relevance to the age of big data in geography in which understanding missingness in data and attributes of absence is more important than ever. Through a case study analysis, we applied our proposed typology of static representation methods to see what can be shown when attempting to visualize blighted properties in the city of Detroit. Some representation types appear more readily suited for this task than others, but we also recognize that a huge range of design latitude can be had with each of our proposed methods to vary their visual effect. This problem space suggests a wide range of future research challenges to further evaluate the perceptual and functional utility of visual techniques for representing missing and absent data.
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A critical next step is to conduct user evaluations to characterize the extent to which the methods we propose here are in fact useful and usable in support of map reading tasks where understanding the presence of absence is important. A central concern here is to determine whether or not the principle of visual asymmetry is evident in map reading contexts, and to find out if representations of absence should truly be designed distinctly from other data types, or if in fact this kind of information is simply another attribute that can be represented like any other. In addition to perceptual experiments using static stimuli that are designed based on our design typology, we also call for the development of prototype geovisualization tools that implement both static and dynamic methods to serve as test bed systems for future evaluation efforts. Looking ahead, it will also be important to consider the challenges of representing the presence of absence in virtual and augmented reality environments for geovisual analytics. It will be important to explore not only the potential for absence to be controllable with collaborators working together to solve spatial problems (Oprean, Simpson, & Klippel, 2017), but also to identify best practices for signifying the presence of absence in data representations used in immersive analytical experiences. As we showed in Figure 7, remote sensing imagery has the potential to reveal attributes of absence in mapping scenarios. Future work should seek the development of visual approaches that will support comparison of multiple images to highlight the presence of absence. Change detection is a mature area of contemporary remote sensing science (Hussain, Chen, Cheng, Wei, & Stanley, 2013), but to date we have found little work that has focused on the design of effective means for showing where change indicates attributes of absence. The visual complexity of a map is a related concern. In the examples we used here in our case study, we used a relatively simple overall design with low information density. It will
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always be easier in that type of map design to see a particular visual attribute used to signify absence. In many contemporary mapping scenarios the overall information density is very high, and a visual attribute will have to compete with many other salient items to attract reader attention. Future work should consider this challenge and evaluate the extent to which overall information density on maps may impact our ability to correctly attend to and understand the presence of absence in spatial data. A related challenge is to develop methods that will help reveal the presence of absence when the number of missing values or observations with absent qualities is relatively small. In our case study application we had a dataset which included a reasonably large proportion of observations that could be signified. Some cartographic scenarios will only have a very small number of observations that should signify the presence of absence, and we do not yet understand how those maps should be designed. In closing, this work advances the state of the art in cartography by describing new approaches to a geographic information design dimension that we argue will become increasingly relevant in the age of big spatial data. Supporting map readers with the ability to quickly and accurately recognize the presence of absence will be very important once users become accustomed to the notion that spatial data observations are available with very broad coverage and updated at a high frequency. We need to know how maps can direct attention to the presence of absence in spatial data in order to ensure that the gaps in our information can become analytical affordances in their own right.
References 22
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Figure Captions
Figure 1: The map at left represents what might be a normal interpolated surface of observations surrounding a city. The map at right represents the absence of a significant swath of observation density. For example, the map at left could be showing a surface indicating the presence of social media posts on a normal day, and the map at right may show the presence of social media posts following a disaster that has stricken the area closest to the center of the city.
Figure 2: Psychological research on visual asymmetry suggests that it is easier for people to find the target feature in the example at left than it is in the example at right (adapted from Wolfe, 2001).
Figure 3: Static methods for representing the presence of absence in point symbols.
Figure 4: Static methods for representing the presence of absence in interpolated surfaces.
Figure 5: Static methods for representing the presence of absence in choropleth maps.
Figure 6: Dynamic methods for representing missing and absent information on maps could include using drain (at top) and/or explosion (at bottom) effects.
Figure 7: Attributes of absence may become more apparent when viewing imagery, as is shown in this example from a neighborhood in Detroit, Michigan.
Figure 8: Static representation methods for representing the presence of absence are applied here to parcel data for a neighborhood undergoing redevelopment in Detroit. Visually altered parcels are those that are classified as vacant according to public records.
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