BackgroundSeveral countries in Southeast Asia are nearing malaria elimination, yet eradication remains elusive. This is largely due to the challenge of focusing elimination efforts, an area where risk prediction can play an essential supporting role. Despite its importance, there is no standard numerical method to quantify the risk of malaria infection. Thus, there is a need for a consolidated view of existing definitions of risk and factors considered in assessing risk to analyse the merits of risk prediction models. This systematic review examines studies of the risk of malaria in Southeast Asia with regard to their suitability in addressing the challenges of malaria elimination in low transmission areas.MethodsA search of four electronic databases over 2010-2020 retrieved 1297 articles, of which 25 met the inclusion and exclusion criteria. In each study, examined factors included the definition of the risk and indicators of malaria transmission used, the environmental and climatic factors associated with the risk, the statistical models used, the spatial and temporal granularity, and how the relationship between environment, climate, and risk is quantified.ResultsThis review found variation in the definition of risk used, as well as the environmental and climatic factors in the reviewed articles. GLM was widely adopted as the analysis technique relating environmental and climatic factors to malaria risk. Most of the studies were carried out in either a cross-sectional design or case-control studies, and most utilized the odds ratio to report the relationship between exposure to risk and malaria prevalence.ConclusionsAdopting a standardized definition of malaria risk would help in comparing and sharing results, as would a clear description of the definition and method of collection of the environmental and climatic variables used. Further issues that need to be more fully addressed include detection of asymptomatic cases and considerations of human mobility. Many of the findings of this study are applicable to other low-transmission settings and could serve as a guideline for further studies of malaria in other regions.

Control of infectious diseases requires insight into transmission dynamics and their relation to relevant spatiotemporal factors. Due to the geographically distributed nature of disease outbreaks, as well as the multidisciplinary teams needed to analyze disease data, the experts needed for analysis and modeling may not all be located in the same place at the same time. There is thus need for an analysis and visualization tool to support distributed teams in upstream and downstream disease modeling tasks. In this paper we present a collaborative platform for visualization and analysis of spatiotemporal data concerning disease incidence and related factors. The platform supports integration of data in a variety of formats and resolutions and creation of derived attributes on the fly. Data can be visualized in terms of 3D choropleth maps, as well as scatter plots which include statistical correlations. Multiple visualizations can be simultaneously displayed and manipulated by all session users. We demonstrate the use of the system with the analysis and modeling of data on dengue incidence and related factors in Thailand. The data includes counts of potential mosquito vector breeding sites extracted from street view images using convolutional neural nets. We show how the visualization supports exploratory data analysis that drives machine learning model development and then show how it helps to understand the model output, which provides insight into how and where the models may be best used.

Background Thailand is among the top five countries with effective COVID-19 transmission control. This study examines how news of presence of COVID-19 in Thailand, as well as varying levels of government restriction on movement, affected human mobility in a rural Thai population along the border with Myanmar. Methods This study makes use of mobility data collected using a smartphone app. Between November 2019 and June 2020, four major events concerning information dissemination or government intervention give rise to five time intervals of analysis. Radius of gyration is used to analyze movement in each interval, and movement during government-imposed curfew. Human mobility network visualization is used to identify changes in travel patterns between main geographic locations of activity. Cross-border mobility analysis highlights potential for intervillage and intercountry disease transmission. Results Inter-village and cross-border movement was common in the pre-COVID-19 period. Radius of gyration and cross-border trips decreased following news of the first imported cases. During the government lockdown period, radius of gyration was reduced by more than 90% and cross-border movement was mostly limited to short-distance trips. Human mobility was nearly back to normal after relaxation of the lockdown. Conclusions This study provides insight into the impact of the government lockdown policy on an area with extremely low socio-economic status, poor healthcare resources, and highly active cross-border movement. The lockdown had a great impact on reducing individual mobility, including cross-border movement. The quick return to normal mobility after relaxation of the lockdown implies that close monitoring of disease should be continued to prevent a second wave.

Counting mosquitoes in the wild is a crucial capability for monitoring, prediction, and control of vector-borne diseases. Current approaches are mainly manual, where specially designed mosquito traps or ovitraps are placed in areas of interest and recovered the next day. The counting itself is performed in an entomological laboratory, where individual mosquitoes are classified into species and counted. This process is costly, slow and inefficient. At the same time, mosquito counting is most relevant in tropical and sub-tropical countries, where mosquitoes spread deadly diseases like malaria, yellow fever and dengue fever. Many countries in these regions have relatively weak public health systems and so cannot support large-scale vector counting efforts. In this paper, we present a system architecture and a prototype to count mosquitoes in the wild with an Internet of Things approach. A sensor board is developed to gather audio data, and models are developed to detect, classify, and count mosquito species. Here, we present our prototype and an extensive background study of classifying mosquitoes based on sound recordings and some preliminary results and discussion.

Malaria elimination remains a major challenge worldwide largely because human mobility can result in importing cases from areas of high incidence to areas of low incidence. Thus, understanding the role of human mobility in malaria transmission is essential. In this study, we collect mobility data from 88 participants over ten months using a smartphone application. Our study area is in northern Thailand along the border with Myanmar, from which malaria may be imported. We analyze amount of time spent in Thailand/Myanmar in areas of various land cover types, spatial distribution of movement, and network patterns of movement. We find significant differences between villages in amounts of time spent in forest areas and in Myanmar, with most travel to Myanmar occurring from two villages. We find significantly higher spatial distribution of movement in the dry season than the wet season. Our results provide important insight to help target surveillance and intervention.

In processing knowledge about spatial situations, spatial concepts are employed for representing objects, properties, and relations in the world. This article presents some fundamental difficulties encountered in representing and processing knowledge about the real geographic world and motivates the need for sophisticated conceptual structures for dealing with spatial knowledge. Consequently, semantic and structural aspects of spatial concepts are discussed. Specific attention is paid to relations among different concepts on one hand and to the relation between conceptual structures and structures in the real world on the other hand. Issues like discrete vs. continuous, crisp vs. fuzzy, fine vs. coarse, and top-down vs. bottom-up concept formation are discussed in the context of spatial representations. The article derives suggestions for task-specific concept formation for the use in future Geographic Information Systems. It concludes with a discussion about fuzzy boundaries of geographic objects. 1 Why is it Difficult to Represent Geographic Knowledge? The geographic world surrounding us is extremely complex. When we want to master a given problem in this world, we need to single out particular aspects of current interest from this multifaceted formation. So at any given time we are only interested in few objects, and concerning these objects again we are regarding only particular properties and/or relations. The capability of isolating the relevant aspects and relating them to one another, results in a unique intellectual efficiency. This efficiency, however, is necessary for successfully operating in the world. To represent knowledge about the world is to make explicit specific aspects of the world. In making explicit certain aspects we ignore others. In representing knowledge, every single aspect of interest can be represented separately. Alternatively, we can aim at representing different aspects within a single structure; this requires that the different aspects of interest must be compatible, i.e., they must fit into one reference system corresponding to a global view. It is ON THE RELATION BETWEEN SPATIAL CONCEPTS AND GEOGRAPHIC OBJECTS 2 impossible to make all potentially interesting aspects of the world simultaneously explicit within one representation medium – be it a conventional map or a single representation structure in a computer. 1.1 The need for different world views There is an information-structural reason why a unified representation of the geographic world is not sufficient for solving all the tasks that can be solved with that information: we are dealing with geographic objects and with relations between these objects. If we could decide once and for all which entities we should view as objects and which as relations, our task would be simpler; however, an important feature of using world knowledge intelligently is the ability to switch between views; depending on the specific task to be solved, certain entities may be viewed as objects (fixed background entities) and others become the relations we manipulate. For other tasks, these roles may change. For example, we may consider roads as relevant geographic objects in some context. For solving certain navigation or transport tasks, we may be interested i n the intersections between these roads; having roads as objects, intersections naturally can be viewed as (connection-) relations between roads. For designing road systems, it may be convenient to view road intersections as the primary objects; the roads then can be viewed as (connection-) relations between these intersections. Of course we could say, we want to view both, roads and their intersections, as objects to obtain a unified representation (after all, our toy train systems contain both, regular tracks and switches as objects). But this does not really solve the general problem: when we view both, the roads and their intersections as (separate) objects, we create a situation in which the roads do not meet the intersecting roads; all the roads meet intersections. To solve navigation tasks, for example, we will have to consider relations between roads and intersections; thus we only have shifted the problem to the next level. W e encounter similar situations when we consider regions and their boundaries, which may be viewed as objects and relations, respectively, or vice versa. Therefore, if we want to make all potentially interesting knowledge accessible, we must make it explicit in different structures (maps or computer representations). Creating many different structures for representing knowledge about the same domain becomes expensive – both computationally and in terms of storage, since a lot of implicit information must be carried along to link the knowledge to its domain. 1.2 The GIS forms the mediating instance between world and user When developing a Geographic Information System (GIS), we must find an adequate compromise between two extreme possibilities: (1) acquiring and storing all knowledge from raw information once and for all before the knowledge is accessed, and (2) providing unprocessed raw information and computing specific knowledge on demand. On one hand we expect a GIS to contain sufficient data about the geographic world, on the other hand we want to ON THE RELATION BETWEEN SPATIAL CONCEPTS AND GEOGRAPHIC OBJECTS 3 obtain a selective view of the relevant aspects and to hide any other data of lesser interest. The entities represented in a GIS stand for the real world objects and their properties. From this point of view, GISs form the mediating instance between world’s reality and the way humans interact with this reality. The user expects the entities represented in the system to show the same properties as the real objects they are standing for. 1.3 Focus on human use of spatial knowledge The human capability of seeing the world as an inexhaustible origin of information may make it desirable to process the knowledge about the world i n such a way that it is instantly available to the human user. But if we process this knowledge about the world in advance, we will create many structures which most likely will never be accessed. On the other hand, if we do not provide structured knowledge to the user, great efforts may be required to compute this knowledge when needed. When we consider human capabilities of dealing with geographic information we can identify two challenges for the development of “intelligent” geographic information systems: (1) How can we model the human ability to focus on relevant information when solving a problem? and (2) How can we overcome the problem that a GIS can not contain all facts about the real world that might become important in a special context? 2 What are Spatial Concepts? When we consider real world objects, we usually are interested in certain properties of these objects, i.e., we regard the objects under certain aspects. For example, when we take a look at a geographic entity, say a lake, we regard it with respect to horizontal extension, depth, shape, or the like. All these notions that describe spatial aspects of a subset of the world, we call spatial concepts. We will use the term ‘concept’ in a rather general sense (c.f. [Church 1956]). Concepts can be anything we have a notion of; thus, “size” can be a concept and “big” can be a concept as well. As we predicate aspects of objects using concepts, it is obvious that spatial concepts will play a crucial role for representing knowledge about the geographic world. 2.1 Properties of spatial concepts In the following subsections we will present several semantic and structural aspects of spatial concepts which are of particular importance for their representation and for the operations that are to be performed on them. Specifically, we will address the relationships between different concepts and the relationships between concepts and real world entities. ON THE RELATION BETWEEN SPATIAL CONCEPTS AND GEOGRAPHIC OBJECTS 4 2.1.1 Concepts have meaning wrt. objects As we have already pointed out, we use concepts to describe aspects or properties of objects. This means, concepts are related to the aspects of the objects that are described. It is impossible to describe qualities of objects without using related concepts. Conversely, concepts have their meaning rooted in their relation to objects, e.g. the concept of a square is related to quadrilateral objects whose sides have equal length and meet at right angles. The mutual relatedness between concepts and objects enforces certain structures upon the concepts; in particular, not every relation between concepts and objects is meaningful. 2.1.2 Concepts have meaning wrt. other concepts Nevertheless, the meaning of spatial concepts is not only given by their relations to physical objects. The meaning of concepts is given to a large extent by their relations to other concepts (c.f. “lateral thinking” [de Bono 1969]). We illustrate this point using the example of the square: we can view the meaning of the concept “square” by its relation to all existing physical objects of square shape; but we also can view the meaning of “square” by relating it to imagined or imaginable objects of square shape. Rather than viewing the meaning to be made up of an infinite number of relationships with imaginable objects, we can view the meaning to consist of a finite number of relationships between related concepts. For example, the concept “square” may be related to the concept “rectangle” by a “special_case” relationship, to the concept “equilateral triangle” and “rhombus” by a “near_miss” relationship, to the concept “shape” by an “isa” relationship, etc. In this way, spatial concepts may have a meaning without reference to physical instances. In particular, spatial concepts have a meaning independent of an envisioned physical materialization: the shape concept “square” abstracts from the instantiation by a section of a checkers board or a marke

Humans solve spatial and abstract problems more easily if these can be visualized and/or physically manipulated. We analyze the domain of geometric problem solving from a cognitive perspective and identify several levels of domain abstraction that interact in the problem solving process. We discuss the roles of physical manifestations of spatial configurations, their manipulation, and their perception for understanding problem solving processes. We propose an extension of the classical problem solving repertoire of constructive geometry to approach certain problems more directly than under the compass-and-straightedge paradigm. Specifically, we introduce strings and pins as helpful metaphors for a generalization of the constructive geometry approach. We present classical problems from spatial problem solving to illustrate the 'strings and pins' paradigm. Three case studies are discussed: strings-and-pins solutions to (i) the ellipse construction problem; (ii) the shortest path problem; and (iii) the angle trisection problem. Comparisons to formal solutions are drawn. Differences and similarities between the compass-and-straightedge paradigm and the strings-and-pins paradigm are analyzed. Features and limitations of constructive and depictive geometry as well as implications for computational approaches are discussed. The strings-and-pins domain is shown to be more general and less restrictive than the compass-and-straightedge domain.

A spatial problem is (1) a question about a given spatial configuration (of arbitrary physical entities) that needs to be answered (e.g. is there wine in the glass?) or (2) the challenge to construct a spatial configuration with certain properties from a given spatial configuration (e.g. add two matchsticks to the given configuration to obtain four squares) (Bertel, 2010). By spatial configurations we mean arrangements of entities in 1-, 2-, or 3-dimensional physical space, where physical space is commonsensically observable Euclidean space and motion, rather than relativistic space-time. Physical space is contrasted here to abstract space of arbitrary dimensionality. Physical space affords certain actions, like (i) rotation (circular motion of objects around a given location); (ii) motion from one location to another; (iii) deformation of objects; (iv) separation of objects into parts; (v) aggregation of objects; and (vi) combinations, i.e. rotation around a changing location.

Information and Communication Technology (ICT) is used to support developing countries in many different ways, such as poverty reduction, public services enhancement, and disaster management and recovery. Mobile4D is a software framework that applies the crowdsourcing paradigm to facilitate information exchange between people during disaster situations. It acts as a disaster reporting and alerting system as well as an information sharing platform. Mobile4D facilitates rapid communication between local citizens and administrative units. Moreover, it allows exchanging experience and knowledge between people to reduce poverty and increase living standards. The Mobile4D framework has been deployed in a pilot study in three provinces in the Lao People's Democratic Republic (Lao PDR). The study was limited to report particular types of disasters, however, it revealed further use cases and identified the required extension of Mobile4D to cover the entire country. This paper presents a report about Mobile4D: initiative, challenges, status, and further extensions.

Natural cognitive agents such as humans and animals may frequently solve spatial problems in their environment by manipulating their environment instead of doing all the computation in their head (e.g., untangling a power cable by inspection and direct interaction: pull here, push there). We call this replacement of computational effort from the central processor by direct manipulation strong spatial cognition. Artificial cognitive agents are currently lacking a comparable ability to exploit their spatio-physical environment for efficient problem solving. One main issue with equipping artificial cognitive agents with strong spatial cognition is that the constraints and properties of this type of problem solving are still insufficiently understood. Being tightly embedded in the spatio-physical and temporal surrounding renders strong spatial cognition difficult to assess by traditional methods. This makes it hard to gain an explicit understanding of its nature and to compare it to existing computational approaches. In this paper, we propose to employ models of strong spatial cognition to gain a deeper understanding of this phenomenon and its nature. We created models of an example application of strong spatial cognition to solve the shortest path problem. By considering different approaches for a computational simulation model, our modeling work revealed that (instantaneous) information propagation constitutes a core characteristic of strong spatial cognition. Moreover, modeling facilitated identifying those questions, which seem of major importance for further deepening our understanding of strong spatial cognition.

The ability to solve spatial tasks is crucial for everyday life and therefore of great importance for cognitive agents. In artificial intelligence (AI) we model this ability by representing spatial configurations and spatial tasks in the form of knowledge about space and time. Augmented by appropriate algorithms, such representations enable the generation of knowledge-based solutions to spatial problems. In comparison, natural embodied and situated cognitive agents often solve spatial tasks without detailed knowledge about underlying geometric and mechanical laws and relationships. They directly relate actions and their effects through physical affordances inherent in their bodies and their environments. Examples are found in everyday reasoning and also in descriptive geometry. In an ongoing research effort we investigate how spatial and temporal structures in the body and the environment can support or even replace reasoning effort in computational processes. We call the direct use of spatial structure Strong Spatial Cognition. Our contribution describes cognitive principles of an extended paradigm of cognitive processing. The work aims (i) to understand the effectiveness and efficiency of natural problem solving approaches; (ii) to overcome the need for detailed representations required in the knowledge-based approach; and (iii) to build computational cognitive systems that make use of these principles.

This book deals with all aspects of intelligent spatial information processing in humans and in technical systems.

Space and time are two of the most fundamental categories any human, animal, or other cognitive agent such as an autonomous robot has to deal with. They need to perceive their environments, make sense of their perceptions, and make interactions as embodied entities with other agents and their environment. The theoretical foundations and practical implications have been investigated from a cognitive perspective (i.e., from an information processing point of view) within the Sonderforschungsbereich/ Transregio SFB/ TR 8 Spatial Cognition (http:// www. sfbtr8. spa tial-cognition. de) over the past 12 years jointly by the Universities of Bremen and Freiburg. The research covered fundamental questions: what are the specific requirements of reasoning about space and time, for acting in space, and for any form of interaction including communication in spatio-temporal domains? It has been a success story in all research lines from foundational research to applications of spatial cognition in robotics, interaction and communication. The SFB/TR 8 actually shaped a new research field by extending a previous subfield of cognitive science with its own interdisciplinary techniques.

Space and time are two of the most fundamental categories any human, animal, or other cognitive agent such as an autonomous robot has to deal with. They need to perceive their environments, make sense of their perceptions, and make interactions as embodied entities with other agents and their environment. The theoretical foundations and practical implications have been investigated from a cognitive perspective (i.e., from an information processing point of view) within the DFG-funded Sonderforschungsbereich/Transregio SFB/TR 8 Spatial Cognition over the past twelve years jointly by the Universities of Bremen and Freiburg. The research covered fundamental questions: what are the specific requirements of reasoning about space and time, for acting in space, and for any form of interaction including communication in spatio-temporal domains? It has been a success story in all research lines from foundational research to applications of spatial cognition in robotics, interaction and communication. The SFB/TR 8 actually shaped a new research field by extending a previous subfield of cognitive science with its own interdisciplinary techniques. M. Ragni & B. Nebel Institut für Informatik, Research Group Foundations of Artificial Intelligence, Albert-Ludwigs-Universität Freiburg, Georges-Köhler-Allee 52, 79110 Freiburg, Germany. E-mail: {ragni, nebel}@informatik.uni-freiburg.de T. Barkowsky & C. Freksa Cognitive Systems, Universität Bremen, P.O. Box 330 440, 28334 Bremen, Germany. E-mail: {barkowsky, freksa}@uni-bremen.de

Frank Dylla studied computer science and psychology at the RWTH Aachen, Germany, where he received his diploma in 2001. Afterwards he deepened his research on cognitive robotics and intelligent behavior at the Knowledge-Based Systems Group at RWTH. In 2003 he moved to the University of Bremen and joined the Cognitive Systems group and the Collaborative Research Center on Spatial Cognition. By combining his expertise on reasoning about action and change with qualitative abstractions of spatial relations Dylla pursued the goal to more intuitive specifications of intelligent agent behavior. For this, besides the pragmatics of qualitative representations, he investigated theoretical properties of such representations and developed new ones to fit application needs. In 2008 he received his doctoral degree for his thesis “An agent control perspective on qualitative spatial reasoning,” which led to abstract domain specifications in, e.g., sea traffic, pedestrian navigation, or architecture. Since 2014 Dylla is a work group leader in the Creative Unit “Intra Operative Information—What surgeons need, when they need it” at the University of Bremen, investigating the role of abstractions in the medical domain and their exploitation for presenting to a surgeon the right information at the right time.

This book constitutes the thoroughly refereed proceedings of the 9th International Conference on Spatial Cognition, Spatial Cognition 2014, held in Bremen, Germany, in September 2014. The 27 revised f