Promoting the adoption of agent-based modelling for synergistic interventions and decision-making during pandemic outbreaks

Geography has long sought to explain spatial relationships between social and physical processes, including the spread of infectious diseases, within the context of modelling human-environment interactions. The spread of the recent COVID-19 pandemic, and its devastating effects on human activity and welfare, represent but examples of such complex human-environment interactions. In this paper, we discuss the value of agent-based models for simulating the spread of the COVID-19 virus to support decision-making with regards to non-pharmaceutical interventions, e.g., lock-down. We also develop a prototype agent-based model using a minimal set of rules regarding patterns of human mobility within a hypothetical town, and couple that with an epidemiological model of infectious disease spread. The coupled model is used to: (a) create synthetic trajectories corresponding to daily and weekly activities postulated between a set of predefined points of interest (e.g., home, work), and (b) simulate new infections at contact points and their subsequent effects on the spread of the disease. We finally use the model simulations as a means of evaluating decisions regarding the number and type of activities to be limited during a planned lockdown in a COVID-19 pandemic context.

How do green areas influence the temperature of cities? Web map to help local decision making about cities and the climate emergency

The positive effect of urban greenery on the city’s microclimate is well known, as is its ability to reduce the ambient temperature in urban areas. Our results show how the areas with the lowest surface temperature clearly coincide with the vegetated areas in the city of Barcelona. This phenomenon demonstrates the importance of increasing the urban greenery in large compact cities, such as the city of Barcelona, in order to regulate the local temperature and mitigate the effects of global warming on a large scale. The web map presented here can be used as a tool for decision makers to identify the warmest areas in the city of Barcelona and to increase greenery in an efficient manner.

Information-optimal Abstaining for Reliable Classification of Building Functions

In the past decade, major breakthroughs in sensor technology and algorithms have enabled the functional analysis of urban regions based on Earth observation data. It has, for example, become possible to assign functions to areas in cities on a regional scale. With this paper, we develop a novel method for extracting building functions from social media text alone. Therefore, a technique of abstaining is applied in order to overcome the fact that most tweets will not contain information related to a building function albeit they have been sent from a specific building as well as the problem that classification schemes for building functions are overlapping.

Tailoring basemaps for color vision impaired people

Widely used basemaps are not particularly inclusive when it comes to color vision impaired users. The use of colors, even though perfect for the representation of features, does not always account for color vision impairments. Certain combinations of colors in basemaps are indistinguishable for colorblind people. With the awareness that those disabilities should be considered when designing a basemap, Esri Nederland and Kadaster instigated the research on the topic, aiming to create a methodology on how to optimize the basemaps for color vision impaired users. The methodology developed was applied in the assessment of six basemaps and improvements were performed in the two most commonly used basemaps in the Netherlands, the Esri World Topographic Map, and the BRT Achtergrondkaart Standaard. Suitable shades were selected to replace the problematic ones in the original basemaps, and the target group evaluated the improvements through surveys. The results showed that the commonly used basemaps are able to well represent the features in a colorblind-friendly way. However, there is room for improvements in order to achieve more suitable and inclusive basemaps.

Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language. While there has been substantial progress in open-domain question answering, QA systems are still struggling to answer questions which involve geographic entities or concepts and that require spatial operations. In this paper, we discuss the problem of geographic question answering (GeoQA). We first investigate the reasons why geographic questions are difficult to answer by analyzing challenges of geographic questions. We discuss the uniqueness of geographic questions compared to general QA. Then we review existing work on GeoQA and classify them by the types of questions they can address. Based on this survey, we provide a generic classification framework for geographic questions. Finally, we conclude our work by pointing out unique future research directions for GeoQA.

A Geo-Dashboard Concept for the Interactively Linked Visualization of Provenance and Data Quality for Geospatial Datasets

In Earth System Sciences, a data-driven research domain, several communities discuss the importance, guidance and implementation of making research data findable, accessible, interoperable, and reusable. To foster these principles, in particular to support reusability, users need easy-to-use user interfaces with meaningful visualizations for detailed metainformation, e.g. on dataset’s origin and quality. However, visualization tools to facilitate the evaluation of fitness for use of ESS research data on domainspecific metainformation, do hardly exist.We provide a Geo-dashboard concept for user-friendly interactive and linked visualizations of provenance and quality information using standardized geospatial metadata. A provenance graph visualization serves as overview and entry point for further evaluations. Quality information is essential to evaluate the fitness for use of data. Therefore, we developed quality visualizations on several levels of detail to foster evaluation, e.g. by enabling users to choose and classify quality parameters based on their use-case-specific needs.

Group Formation in Shared Spaces

In shared spaces, grouped pedestrians can gain dominance and thus get the right of way from vehicles more easily; grouping can make traffic planning less complicated, e.g. it reduces the number of agents that need to be considered while traffic planning. However, grouping is not well investigated in shared spaces given the dynamic environment and interactions in mixed traffic. In this paper, we apply a dynamic facility location algorithm based on appearance time, origin, and destination of road users before crossing a junction to explore an appropriate grouping strategy in shared spaces, in order to improve the safety and efficiency of traffic.

Helicopter Landing Sites Identification depending on Slope, Landing Site Dimension and Shape

The identification of the Helicopter Landing Sites (HLS) needs complex analysis of the terrain considering a lot of aspects. One of the unconditional aspects in this case is the slope of ground, therefore the HLS identification depending on slope, landing site dimension and shape was conducted. This paper describes the development of the tool for the HLS identification depending only on the relief, but no other objects on the earth’s surface. At the end of the paper, the possible improvements of the tools are stated.

User Participation in the Process of Creating a Citizen Science Portal

Citizen science (CS) relies on cooperation with members of the public. This usually requires a lot of contact to reconcile the needs of the researchers and project staff with the user to retain an active user base. We explore with our CS web portal the potential of including the participants in the creation and design process of the portal. This is done by providing the users with easy access to our design and research staff and approaching people for qualitative feedback. To a large extent, our users do not have obvious ties to academic institutions, however, the vast majority of feedback comes from academics. We categorized the feedback, explored users’ backgrounds, and compared our active user retention to another CS project, which turns out to be comparable.

Using eigen decomposition and sequence-based representation to extract movement patterns from contextualized tracking data

State sequences are a new paradigm to encode and represent contextualised movement data. A state sequence is a temporal succession of characters representing categorical states of the moving entity or its surrounding environment. Eigen decomposition, a principal components analysis method, is an option to reduce and find patterns in such multi-dimensional categorical data through dimensionality reduction. Recurrent patterns can be found by identifying the most relevant eigenbehaviours, which are a set of vectors that characterize the variation in the behaviour of an entity during a time period. Dimensionality reduction techniques have so far not been widely used in movement analytics and in this paper we demonstrate how they could help analyse responses of a moving entity to the dynamic environmental conditions. Specifically, we use sequence-based representation and eigen decomposition to investigate movement patterns of maned wolves (Chrysocyon brachyurus) in relation to vegetation vigour in their habitat. We use a set of GPS-trajectories from a group of maned wolves to which we link multi-source NDVI data as a proxy for the state of vegetation. We find that eigenbehaviours can identify patterns in the wolves’ responses to dynamic environmental conditions that align with the current literature on the species. Our research highlights the potential for dimensionality reduction and sequence-based methods to identify patterns in large tracking databases linked to contextual data.