Data driven insights are integral to new forms of knowledge generation in data rich settings. Specifically, in an era of new access to movement data, researchers have opportunities to leverage exis...

Research on movement has increased over the past two decades, particularly in movement ecology, which studies animal movement. Taking context into consideration when analysing movement can contribute towards the understanding and prediction of behaviour. The only way for studying animal movement decision-making and their responses to environmental conditions is through analysis of ancillary data that represent conditions where the animal moves. In GIScience this is called Context-Aware Movement Analysis (CAMA). As ecology becomes more data-oriented, we believe that there is a need to both review what CAMA means for ecology in methodological terms and to provide reliable definitions that will bridge the divide between the content-centric and data-centric analytical frameworks. We reviewed the literature and proposed a definition for context, develop a taxonomy for contextual variables in movement ecology and discuss research gaps and open challenges in the science of movement more broadly. We found that the main research for CAMA in the coming years should focus on: 1) integration of contextual data and movement data in space and time, 2) tools that account for the temporal dynamics of contextual data, 3) ways to represent contextualized movement data, and 4) approaches to extract meaningful information from contextualized data.

Movement analysis has become an integral part of many disciplines, yet with relatively little overlap. A foresight paper in this journal entitled “Towards an integrated science of movement: converging research on animal movement ecology and human mobility science” argued for a better integration of concepts across the divide of animal and human movement, which would lead to the Integrated Science of Movement, but did so from a top-down perspective based on a series of expert workshops. We argue that for a solid establishment of the Integrated Science of Movement, a bottom-up approach is necessary, one based on existing literature which identifies similarities and differences across disciplines. We therefore review, compare, and contrast movement analysis methodologies from GIScience, movement ecology, geography, transportation, public health, computer science, and physics. We structure our review along the dichotomy of individual versus population-based movement or, using terminology from wildlife ecology, between the Lagrangian and Eulerian perspectives. We further introduce a new unifying framework for movement research that is sufficiently general to cover any type of movement study in any discipline and that spans the Lagrangian/Eulerian divide, with the ambitious goal to bridge the gap between disciplines and lay a solid foundation for a new Integrated Science of Movement.

Spatial objects can be interconnected and mutually dependent in complex ways. In Geographical Information Science, spatial objects’ topological relationships are not discussed together with their attributes’ dependencies, and the vagueness of spatial objects is often ignored during the spatial modelling process. To address this, a spatial fuzzy influence diagram (SFID) is introduced. Compared to the traditional statistical or fuzzy modelling approach, the influence diagram brings advantages in helping decision-makers structure complex interdependency problems. A questionnaire was developed to evaluate the applicability of using an influence diagram in modelling spatial objects’ dependencies. As a case study, an SFID is applied to tree-related electric outages. The result of the case study is represented as a vulnerability map of electrical networks. The map shows areas at risk due to tree-related electric outages. The results were first validated by using a visual comparison of the vulnerability map and electricity fault data. In the second validation step, the percentage of fault data, which has received values in different vulnerability categories, was calculated. The results of the case study can be used to support the decision-making process of electrical network maintenance and planning.

Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries, or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. Although this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining, and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age, or gender of the tracked individuals. Further, we explore theorised ‘third places’, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.

Research activity and published literature on the reliability and vulnerability analysis of urban areas for disaster management has grown tremendously in the recent past. Population information has played the most important role during the entire disaster management process. In this article, population information was used as the evaluation criterion, and a fuzzy multiple-attribute decision-making (MADM) approach was used to support a vulnerability analysis of the Helsinki area for disaster management. A kernel density map was produced as a result that showed the vulnerable spatial locations in the event of a disaster. Model results were first validated against the original population information kernel density maps. In the second step, the model was validated by using fuzzy set accuracy assessment and the actual domain knowledge of the rescue experts. This is a novel approach to validation, which makes it possible to see how and if computer decision-making models compare to a real decision-making process in disaster management. The validation results showed that the fuzzy model has produced a reasonably accurate result. By using fuzzy modelling, the number of vulnerable areas was reduced to a reasonable scale and compares to the actual human assessment of these areas, which allows resources to be optimised during the rescue planning and operation.

Geographically weighted spatial statistical methods are a family of spatial statistical methods developed to address the presence of non-stationarity in geographical processes, the so-called spatial heterogeneity. While these methods have recently become popular for analysis of spatial data, one of their characteristics is that they produce outputs that in themselves form complex multi-dimensional spatial data sets. Interpretation of these outputs is therefore not easy, but is of high importance, since spatial and non-spatial patterns in the results of these methods contain clues to causes of underlying non-stationarity. In this article, we focus on one of the geographically weighted methods, the geographically weighted discriminant analysis (GWDA), which is a method for prediction and analysis of categorical spatial data. It is an extension of linear discriminant analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This produces a very complex data set of GWDA results, which include on top of the already complex discriminant analysis outputs (e.g. classifications and posterior probabilities) also spatially varying outputs (e.g. classification function parameters). In this article, we suggest using geovisual analytics to visualise results from LDA and GWDA to facilitate comparison between the global and local method results. For this, we develop a bespoke visual methodology that allows us to examine the performance of global and local classification method in terms of quality of classification. Furthermore, we are also interested in identifying the presence (or absence) of non-stationarity through comparison of the outputs of both methods. We do this in two ways. First, we visually explore spatial autocorrelation in both LDA and GWDA misclassifications. Second, we focus on relationships between the classification result and the independent variables and how they vary over space. We describe our visual analytic system for exploration of LDA and GWDA outputs and demonstrate our approach on a case study using a data set linking election results with a selection of socio-economic variables.

Visual analytics aims to combine the strengths of human and electronic data processing. Visualisation, whereby humans and computers cooperate through graphics, is the means through which this is achieved. Seamless and sophisticated synergies are required for analysing spatio-temporal data and solving spatio-temporal problems. In modern society, spatio-temporal analysis is not solely the business of professional analysts. Many citizens need or would be interested in undertaking analysis of information in time and space. Researchers should find approaches to deal with the complexities of the current data and problems and find ways to make analytical tools accessible and usable for the broad community of potential users to support spatio-temporal thinking and contribute to solving a large range of problems.

Modern positioning and identification technologies enable tracking of almost any type of moving object. A remarkable amount of new trajectory data is thus available for the analysis of various phenomena. In cartography, a typical way to visualise and explore such data is to use a space–time cube, where trajectories are shown as 3D polylines through space and time. With increasingly large movement datasets becoming available, this type of display quickly becomes cluttered and unclear. In this article, we introduce the concept of 3D space–time density of trajectories to solve the problem of cluttering in the space–time cube. The space–time density is a generalisation of standard 2D kernel density around 2D point data into 3D density around 3D polyline data (i.e. trajectories). We present the algorithm for space–time density, test it on simulated data, show some basic visualisations of the resulting density volume and observe particular types of spatio-temporal patterns in the density that are specific to trajectory data. We also present an application to real-time movement data, that is, vessel movement trajectories acquired using the Automatic Identification System (AIS) equipment on ships in the Gulf of Finland. Finally, we consider the wider ramifications to spatial analysis of using this novel type of spatio-temporal visualisation.