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:  Introduction 
1. Abstract of Cited Paper (BIB001):  The described locking mechanism permits two or more rods to be quickly fitted together and adjusted to a fixed, predetermined length. The locking mechanism is of a type such that the rotation of the rods by approximately 45 DEG -90 DEG automatically fixes the rods in place. 
2. Abstract of Cited Paper (BIB002):  The work presented here represents a selection of the contributions made to a workshop coordinated by the International Cartographic Association (ICA) Commission on Geovisualization and the Association of Geographic Information Laboratories in Europe (AGILE) on the Geovisualization of Dynamics, Movement and Change. Theoretical and methodological approaches for exploring and analyzing large datasets with spatial and temporal components were presented, discussed and developed at the meeting in Girona, Catalunya which was held on 5 May 2008 one day before AGILE’s 11th International Conference on Geographic Information Science. 
3. Abstract of Cited Paper (BIB003):  We discuss methods to visualize large amounts of object movements described with so called multivariate trajectories, which are lists of records with multiple attribute values about the state of the object. In this chapter we focus on vessel traffic as one of the examples of this kind of data. The purpose of our visualizations is to reveal what has happened over a period of time. For vessel traffic, this is beneficial for surveillance operators and analysts, since current visualizations do not give an overview of normal behavior, which is needed to find abnormally behaving ships that can be a potential threat. Our approach is inspired by the technique of kernel density estimation and smooths trajectories to obtain an overview picture with a distribution of trajectories: a density map. Using knowledge about the attributes in the data, the user can adapt these pictures by setting parameters, filters, and expressions as means for rapid prototyping, required for quickly finding other types of behavior with our visualization approach. Furthermore, density maps are computationally expensive, which we address by implementing our tools on graphics hardware. We describe different variations of our techniques and illustrate them with real-world vessel traffic data.

Literature Review:
The main idea of visual analytics is to develop knowledge, methods, technologies and practices that exploit and combine the strengths of human and electronic data processing.  Visualization is the means through which humans and computers cooperate using their distinct capabilities for the most effective results. Visualization is particularly essential for analysing phenomena and processes unfolding in geographical space. As the heterogeneity of the space and the variety of properties and relationships occurring within it cannot be adequately represented for fully automatic processing, exploration and analysis of geospatial data and the derivation of knowledge from it needs to rely upon the human analyst's sense of the space and place, tacit knowledge of their inherent properties and relationships, and space-or place-related experiences. BIB003 Analysis of movement is currently a hot research topic in visual analytics. Those in this field of research utilize the legacy of cartography, with its established techniques for representing movements of tribes, armies, explorers, hurricanes, and so on;  time geography, with its revolutionary idea of considering space and time as dimensions of a unified continuum (space-time cube [STC] ) and representation of behaviours of individuals as paths in this continuum; BIB002 information visualization, with its techniques for userdisplay interaction supporting exploratory data analysis; BIB001 and geovisualization, with its interactive maps and associated methods enabling exploration of spatial information.  To meet the challenges posed by today's data deluge and the complexity of the questions to be answered and problems to be solved, the scientists search for ways to augment the power of human visual thinking  with the power of modern computer technologies. In this paper we survey the state of the art in visual analytics concerning the analysis of movement data. We limit the scope of the paper to data about movements of discrete objects whose spatial positions can be represented by points. We do not consider movement of fields, such as ocean currents, and spatially extended objects changing their sizes and shapes, such as clouds. These kinds of moving objects have so far been scarcely addressed in visual analytics, whereas the works dealing with discrete objects are abundant. We divide the relevant works into four categories: 1. Looking at trajectories. The focus is on trajectories of moving objects considered as wholes. The methods support exploration of the spatial and temporal properties of individual trajectories and comparison of several or multiple trajectories. 2. Looking inside trajectories. The focus is on variation of movement characteristics along trajectories. Trajectories are considered at the level of segments and points. The methods support detecting and locating segments with particular movement characteristics and sequences of segments representing particular local patterns of individual movement. 3. Bird's-eye view on movement. The focus is on the distribution of multiple movements in space and time. Individual movements are not of interest; generalization and aggregation are used to uncover overall spatio-temporal patterns. 4. Investigating movement in context. The focus is on relations and interactions between moving objects and the environment (context) in which they move, including various kinds of spatial, temporal, and spatio-temporal objects and phenomena. Movement data are analysed together with other data describing the context. Computational techniques are used to detect occurrences of specific kinds of relations or interactions and visual methods support overall and detailed exploration of these occurrences. The structure of the text corresponds to this categorization. To help the reader better understand the material, we complement the survey of the existing works with illustrated examples showing how different visual analytics techniques can support understanding of various aspects of movement. For the examples, we use a real dataset about movements of ships in the North Sea. The Netherlands Coastguard collects data of shipping movements by radar coverage and automatic identification system (AIS) base stations. MARIN (Maritime Research Institute Netherlands, www.marin.nl) receives the fused data for use in safety assessment studies with respect to shipping. MARIN provided us with an anonymised subset of the data, 8 days' duration, for this research. The collection methods and properties of maritime vessel movement data are described in detail by N Willems in his PhD thesis BIB004 and papers (e.g. refs 9 and 10). The illustrations have been produced by means of software tools that were available to us, which does not mean superiority of these tools over all other existing tools. The illustrations are also not meant to demonstrate capabilities of particular tools, but rather outline possible general approaches, which can be implemented in different ways.