You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Visual analytics of movement: An overview of methods, tools and procedures 
Chapter Title:  Investigation of movement in context 
1. Abstract of Cited Paper (BIB001):  Significant advances in recreation planning have been achieved thanks to the mobile technology and the ubiquitous computation. Today it is possible to know the real time position of a group of individuals participating in an outdoor game, and to obtain a large amount of data about their movements. However, most analyses have focused onindividual movements based on trajectories. In this paper we present a novel form of conceptualising and analysing human movement, based on a metaphor we have called "movement as interaction" which conceives movement as a result of interaction between individuals, collectives and the environment. The implementation of the proposed approach has enabled us to uncover interaction patterns in a group of children participating in an outdoor game based on mobile technology. The first results demonstrate the feasibility of our approach to detect game interactions. 
2. Abstract of Cited Paper (BIB002):  Information foraging and sensemaking with heterogeneous information are context-dependent activities. Thus visual analytics tools to support these activities must incorporate context. But, context is a difficult concept to define, model, and represent. Creating and representing context in support of visually-enabled reasoning about complex problems with complex information is a complementary but different challenge than that addressed in context-aware computing. In the latter, the goal is automated adaptation of the system to meet user needs for applications such as mobile location-based services where information about the location, the user, and the user goals filters what gets presented on a small mobile device. In contrast, for visual analytics-enabled information foraging and sensemaking, the user is likely to take an active role in foraging for the contextual information needed to support sensemaking in relation to some multifaceted problem. In this paper, we address the challenges of constructing and representing context within visual interfaces that support analytical reasoning in crisis management and humanitarian relief. The challenges stem from the diverse forms of information that can provide context and difficulty in defining and operationalizing context itself. Here, we pay particular attention to document foraging to support construction of the geographic and historical context within which monitoring and sensemaking can be carried out. Specifically, we present the concept of geo-historical context (GHC) and outline an empirical assessment of both the concept and its implementation in the Context Discovery Application, a web-based tool that supports document foraging and sensemaking. 
3. Abstract of Cited Paper (BIB003):  Current tracking technologies enable collection of data, describing movements of various kinds of objects, including people, animals, icebergs, vehicles, containers with goods and so on. Analysis of movement data is now a hot research topic. However, most of the suggested analysis methods deal with movement data alone. Little has been done to support the analysis of movement in its spatio-temporal context, which includes various spatial and temporal objects as well as diverse properties associated with spatial locations and time moments. Comprehensive analysis of movement requires detection and analysis of relations that occur between moving objects and elements of the context in the process of the movement. We suggest a conceptual model in which movement is considered as a combination of spatial events of diverse types and extents in space and time. Spatial and temporal relations occur between movement events and elements of the spatial and temporal contexts. The model gives a ground to a generic approach based on extraction of interesting events from trajectories and treating the events as independent objects. By means of a prototype implementation, we tested the approach on complex real data about movement of wild animals. The testing showed the validity of the approach. 
4. Abstract of Cited Paper (BIB004):  We propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.

Literature Review:
The spatio-temporal context of the movement includes the properties of different places (e.g. land cover or road type) and different times (e.g. day or night, working day or weekend) and various spatial, temporal and spatio-temporal objects affecting and/or being affected by the movement. BIB004 Tomaszewski and MacEachren BIB003 consider the notion of context on a more general level and suggest a conceptual model that encompasses three aspects of context, spatial (geographical), temporal (historical) and conceptual. They describe a prototype software system for analysis of text documents where spatial, temporal and conceptual context information can be visually represented to facilitate sense-making. The methods for movement data analysis discussed so far do not address the context in an explicit way. However, movement data are usually visualized using cartographic maps, which serve as very important providers of information about the spatial context. By looking at the maps, the analyst can relate visible spatial patterns to the spatial context (e.g. observe that the highest ship traffic density is near ports). In principle, information about the temporal context can be represented in temporal displays, such as a time graph, together with movement data or their derivatives; however, this is rarely done in practice. It is more usual to represent context information on additional displays, which are linked to displays of movement data by means of visual and/or interactive techniques. Thus, Lundblad et al. BIB001 use a time graph to visualize weather parameters along the routes of selected ships. Besides, multiple weather parameters for all ships at a selected time moment are shown on a parallel coordinate plot. The links between the displays are established through interactive selection of ships or time moments for the additional displays. The mosaic temporal display in Figure 6 conveys context information about temporal cycles through special arrangement of display elements (squares). The colouring of the squares visually links the mosaic display to the display of sea traffic situations. Bouvier and Oates 20 suggest an original interaction technique called 'staining', which may be used for exploring emerging relationships between moving objects and elements of the spatial context. The technique is used with an animated map showing object movements. The user can mark some context item such as area or object on the map by painting (staining) it in a particular colour. Several context items may be stained with the same or different colours. As moving objects move through a stain, they also become stained, that is painted in the colour of the stain. This allows the user to observe easily which objects encountered the marked item, when it happened and how these objects behaved afterwards. Furthermore, it is possible to run the animation backwards in order to see how these objects moved before the encounter. Besides the context items that are explicitly represented on visual displays, the analyst also takes relevant context information from his or her background knowledge. Visual displays, especially maps, help the analyst in doing this because things that are shown can facilitate recall of related things from the analyst's mind. Thus, we noticed in Figure 5 that the spatial situations that occurred on Saturday are characterized by exceptionally high traffic of passenger and ferry boats between the southeast coast of the UK and the Continent. We knew previously from other sources that many residents of the UK visit France and Belgium for shopping. Based on this knowledge, we concluded that the observed pattern of sea traffic may be related to this shopping. So far, we have discussed the cases in which context information is represented visually and relationships between the movement and the context are established solely by the user through observation of the visual displays and interaction with them. At the time of writing, little has been done in the visual analytics research to involve context information in computational processing and analysis of movement data. One existing approach is to produce and visualize dynamic attributes representing certain relationships between positions of moving objects and elements of their spatio-temporal context. BIB004 Many types of relationships can be expressed in terms of spatial and/or temporal distances. BIB005 BIB004 This includes spatial proximity of moving objects to certain locations or types of locations, spatio-temporal proximity to a spatial event, spatio-temporal proximity between moving objects, and so on. Crnovrsanin et al.  compute spatial distances of multiple moving objects to a selected item of spatial context, such as place (point or area) of interest, static object or moving object, and visualize the resulting dynamic attributes of the moving objects on a time graph. Patterns formed by the lines on the graph not only show the movements in relation to the selected context item and allow the user to observe common behaviours and detect outliers, but also indicate various emergent relationships (referred to as 'movement patterns') among the moving objects: spatial concentration (congestion), convergence, divergence, meeting, coincidence, concurrence, etc. Interactive tools allow the user to select the objects participating in these patterns and observe their traces on a map. Two or more time graphs enable comparison of movements from different places. Orellana et al. BIB002 computationally extract occurrences of proximity relationships between moving objects and then visualize them on spatial and temporal displays by means of specially designed techniques. A more generic method for detection and analysis of various types of relationships that can be expressed in terms of spatial and/or temporal distances is suggested by Andrienko et al. BIB004 The main idea is to compute spatial and/or temporal distances from moving objects to context items and represent them as attributes attached to trajectory positions. Then the user interactively filters the positions according to values of one or more of these attributes and creates spatial events from the points and segments satisfying the filter, as described in the 'Looking inside trajectories: attributes, events and patterns' section. The extracted events can be explored visually and with the use of computational methods. We shall present this approach using an example. Unfortunately, for our example dataset with ship trajectories, we have no additional datasets describing elements of the movement context, such as the weather conditions, positions of offshore wind farms and oil platforms, protected zones, and so on. However, various kinds of secondary data produced in the process of analysing movement data can also be considered as context data in the further analyses of the movement data. BIB004 In our previous examples, we have demonstrated derivation of spatial events, event clusters and classes (clusters) of locations and of time moments. Such derived data can be used as context data. From the perspective of the presented approach, the origin of the context data makes no difference. Context data coming from external sources are processed in exactly the same way as context data previously derived from movement data. In our example, the first task is to detect occurrences of near-encounter (close proximity) relationships between ships. We computationally derive a dynamic attribute representing the distance from a ship to the nearest other ship. Then we generate spatial events from the points where this distance is 100 m or less. The events are shown as black circles on a map in Figure 8 (a). Concentrations in areas close to ports are well visible. The next task is to investigate how the nearencounter events affected the movement behaviours of the ships. We compute the statistics of the changes of the movement speed in the spatio-temporal neighbourhood of the events and apply a filter to see only the events associated with speed decrease by at least 10 km/h. Figure 8(b) shows these events. For the areas around the event concentrations, the total numbers of the near-encounter events and the proportions of the events with speed decrease are also shown by pie charts. Spatial and temporal filtering allows us to explore any selected event in detail using an STC (Figure 8(c) ). The traces in the STC are coloured according to the ship types; the meaning of the colours is visible in Figure 8(d) and (e) . To see which types of ships get involved most often in near-encounter events, we apply filtering to the ship trajectories according to the events that had been extracted from them. The table in Figure 8(d) shows the frequencies of different ship types among all trajectories (column 2) and among the trajectories in which near-encounter relationships occurred (column 3). It can be seen that the ships of the type '0 unknown' were disproportionally often involved in such relationships (the type '0 unknown' means that the AIS messages sent from these vessels contained zero in the field meant for the ship type code). Figure 8 (e) shows similar statistics for the subset of near-encounter events accompanied with speed decrease. Again, the relative frequency of the ships of the '0 unknown' type is much higher among the trajectories involved in the events than among all ships. The map in Figure 8(f) shows the trajectories of the ships of this category; an enlarged fragment is given in Figure 8 (g). It is seen that these ships mostly move closely to ports and the movement looks rather chaotic, which can explain the high involvement in near-encounter events. The 'Looking at trajectories', 'Looking inside trajectories: attributes, events and patterns' and 'Bird's-eye view on movement: generalization and aggregation' sections show that movement can be analysed at different levels: whole trajectories, elements of trajectories (points and segments) and high-level summaries (densities, flows, etc.). In principle, analysing movement in context can also be done at these levels. However, a comprehensive set of visual analytics methods addressing all these levels and different types of context items does not yet exist, which necessitates further research in this direction.