Envisioning the future of cartographic research

Figures & data

Table 1. Research questions suggested by ICA Commission Members.

Research questions
How can we take advantage of advances in psychology, cognitive science and neuroscience to improve the effectiveness and efficiency of spatiotemporal decision-making with cartographic displays?
Maps have a potential role to play in big data analysis, we need to know which map representations are suitable as summary maps of big data? How can map design be improved to become an interface to big data?
Can we develop a systematic understanding of human perceptual and cognitive abilities/limitations in relation to the use of geographic visualization?
Many new user research methods and techniques are emerging, so how do we optimally apply and investigate the usability of the variety of methods and techniques of use and user research in cartographic research?
Does the mobile platform require us to revise or rethink time-tested tenets of cartographic design? How should we design our interactive maps to respond across multiple devices, both mobile and non-mobile?
How do we modify our empirical methods to study mobile maps in the wild?
Will the increased mobility of maps alter our sense of place?
One of the biggest problems (but at the same time opportunity) is that everybody can be a cartographer. How can we ensure that resulting products are user friendly, and have significant communicative power?
In which contexts are bottom-up or top-down decision-making circuits more important, and how can we link the design of maps to support whichever of those processes is more important for a given context?
How do we encourage more cartographic experts to share their knowledge and teach beginners? How do we inspire these beginners to learn more?
How can we, as cartographers, start re-establishing cartography by encouraging people inside and out to acknowledge the expertise a cartographer can offer and see them as vital in an organizational context?
So far there is no measurement framework for assessing the validity and limits of cartographic outputs constructed after the process of mapping. What scientific approaches can we use to advance this issue?
Are local mapping communities more effective than larger, national ones?
Can cartography be generalized; that is, can knowledge about conventions, good practices, etc., adapted to the specific constraints of maps, be used, adapted or generalized in the context of geovisualization?
What other spaces aside from Euclidean space can the epistemology of cartography work with?
Can mobile maps be used to help solve society’s most pressing problems?
How do we lower the barriers of entry and minimize technology anxiety within cartography?
Is it time that the epistemology and notion of cartography must be emphasized in the definition of cartography, that is, re-defined to reflect the epistemology and notion of cartography instead?

Table 2. Research themes suggested by ICA Commission Members.

Research themes
Empirical research in an interdisciplinary setting should seek to reveal evidence about the correlation between selected user experience factors and cartographic factors like graphical and interaction design.
Cartographers struggle to create useful spatiotemporal overviews and support analytical reasoning in large, complex datasets. We need new ways to symbolize these data to make their information actionable.
We need to develop cartographic approaches for data coming from different sources, at different scales in space and time, having different quality and reliability.
We need to devise cartographic approaches for massive streaming data: this requires scalable processing and filtering of data, interactive and dynamic representations need to be designed, implemented and evaluated.
We need to develop a new paradigm for visualizing spatiotemporal data to support sensemaking; one that supports space, time and models, from large, complex datasets, including new sources likes sensor networks.
It is important to investigate whether the virtual and augmented reality technology will be user friendly and effective to all, regardless of their literacy and education levels.
We should explore artistic representations; for example, geovisual tools derived from fine art or music, rather than only scientific representations. Art could perhaps be harnessed to support spatiotemporal data mining.
We should focus on increasing cartographic literacy, motivating software vendors to include intelligent cartography modules in their products.
We need to develop a typology of data properties that should help identifying and correcting problems in data and selecting appropriate analysis procedures and cartographic representations for spatiotemporal data.
Working with spatiotemporal data remains problematic. Despite software’s increasing ability to manage, analyze and visualize space-time data, performance for all of these decreases when data sets increase in size.
Choropleth maps mislead the eye for many reasons. Continuous surface models have long been shown to represent the underlying landscape in a more realistic manner, yet surface models are not in the mainstream.