Geospatial big data present a new set of challenges and opportunities for cartographic researchers in technical, methodological and artistic realms. New computational and technical paradigms for cartography are accompanying the rise of geospatial big data. Additionally, the art and science of cartography needs to focus its contemporary efforts on work that connects to outside disciplines and is grounded in problems that are important to humankind and its sustainability. Following the development of position papers and a collaborative workshop to craft consensus around key topics, this article presents a new cartographic research agenda focused on making maps that matter using geospatial big data. This agenda provides both long-term challenges that require significant attention and short-term opportunities that we believe could be addressed in more concentrated studies.

In this contribution, we first focus on maps as landmarks of cartographic innovation in the context of societal and technical developments in history. This historical analysis stimulated us to offer a new definition for the map that reflects our intention to provide a succinct and flexible, yet inclusive and sustainable definition of our discipline for years to come. We then turn to cartography itself, by analyzing the sustainability of past and current disciplinary definitions, as proposed by its leading professional body, the International Cartographic Association (ICA). In the fourth section, we then go on to analyze the structure and activities of the ICA over the recent past, with the aim to further understand the professional development of the cartographic discipline. Finally, we also consider how the geography of the ICA, that is, the geographic origins of the leading members of the organization, including the executive board members and the chairs of ICA Commissions and Working Groups, might have influenced the shaping of the cartographic discipline.

The global population is growing at an incomprehensible rate and with it come complex environmental consequences that often result in social injustices. The United Nations has established a set of Sustainable Development Goals (SDGs) in an attempt to ameliorate inequality and promise safety for the masses. To reach these goals, a set of indicators have been identified and their associated data for each country are publicly available to measure how close each country is to each goal. Multifaceted social and environmental processes that are difficult to understand are causing threats to these goals. Maps help reduce complexity. Now, arguably anyone with access to the Internet and time can make a map. However, not all maps are effective accurate communication vessels. Well-designed maps tell a story that truthfully represents the data available. Here we present a synthesis of the cartographic workflow pointing out specific considerations necessary when mapping SDG indicators. Along the way we illustrate the cartographic workflow as it relates to visualizing SDG indicators. Common mapping pitfalls are described and a range of suggestions to avoid them are also offered. Map makers have a unique opportunity to use these data to illuminate and communicate injustices that are documented therein to inspire creative localized solutions to eradicate inequality.

Epidemics and pandemics are geographical in nature and constitute spatial, temporal, and thematic phenomena across large ranges of scales: local infections with a global spread; short-term decisions by governments and institutions with long-term effects; and diverse effects of the disease on many aspects of our lives. Pandemics pose particular challenges to their visual representation by cartographic means. This article briefly summarizes some of these challenges and outlines ways to approach these. We discuss how to use the information usually available for telling the story of an epidemic, illustrated by the example of the 2019–2020 COVID-19 pandemic. The maps attached to this article demonstrate the discussed cartographic means.

Some map users still experience difficulties when attempting to route plan, navigate and/or wayfind. This paper provides information on the outcomes of a research project that aims to identify the nature of the difficulties/challenges these users experience. The paper draws on findings from studies of strategies used by novice and experienced map users, and users with poor spatial abilities, to identify the skills that those with poor spatial abilities or with little map reading experience lack. Data were collected using Tobii Pro Glasses 2 hardware and associated software. Fourteen participants walked between two predetermined locations while wearing eye-tracking glasses and speaking their thoughts aloud. The data collected in this study were analysed using quantitative and qualitative approaches. Analysis of the results shows three key findings for this particular group: looking at the map incessantly led to missed environmental cues; street names were not labelled at decision-making points on the map; and some individuals having poor spatial abilities prefer small-scale landmarks, for example, buildings with numbers to locate themselves, as opposed to using road names or larger landmarks. We argue that by considering these findings, map designs could be adjusted to meet the needs of map users with poor spatial abilities.

Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.