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...

Understanding fine-scale dynamics of human mobility patterns is pivotal for effective urban planning, public health strategies, and retail analysis. This study introduces a novel mobility measure – the Local Mobility Index (LMI) – combining geometry-based mobility metrics and accessibility measures. The LMI can be considered a measure of ‘relative localness’ by integrating preferences into the assessment of local mobility patterns, offering a novel measure for understanding mobility behavior in urban contexts. The LMI improves upon existing measures as it captures individual choice for local destinations through measuring whether individuals select nearby destinations; accounting for the unequal spatial distribution of urban amenities. Our contribution is mainly methodological, advancing the field by introducing a metric that captures different aspects of mobility compared to conventional mobility metrics. Leveraging mobile-phone-based GPS data, we examine the LMI using 759 individuals across three cities in England. We found that the LMI captures a new and distinct dimension of urban mobility, as evidenced by its weak correlation with established metrics. Therefore, LMI's capacity to highlight previously undetected aspects of mobility behavior, underscores its importance for advancing research and urban planning.

Comparison of landscapes and patterns is a long-standing challenge in spatial analysis research. Recently, new models and tools developed for non-geographic image data are being used to study geographic problems involving classification or prediction. Specifically, computer vision models and artificial neural networks have been deployed in an ever-growing number of geographical analyses. In this paper, we review the use of these models in geographical analysis, focusing on the representation and comparison of spatial patterns. We review artificial neural networks and provide semantic linking across domains using similar model constructs through the lens of scale. We note that scale, a contextual element in geographical research, is typically considered a model parameter in computer vision. Scale impacts both computer vision techniques and traditional pixel-based or object-oriented analysis, yet computer vision methods such as CNNs are relatively robust to small-scale variations due to their capability to learn multiscale features via spatial filtering and the formation of scale-space tensors across layers. Parameterization of computer vision models to represent multiscale patterns however remains ad hoc. A typology of scales, therefore, provides a framework for mapping model constructs to develop guidelines for parameterizing and evaluating computer vision models in a geographic context.

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.

“We would like to dedicate this special issue to the memory of Professor Rein Ahas (1966 – 2018), a pioneer of computational movement analysis and mobility analytics, who sadly and very unexpectedl...

The analysis of interaction between movement trajectories is of interest for various domains when movement of multiple objects is concerned. Interaction often includes a delayed response, making it difficult to detect interaction with current methods that compare movement at specific time intervals. We propose analyses and visualizations, on a local and global scale, of delayed movement responses, where an action is followed by a reaction over time, on trajectories recorded simultaneously. We developed a novel approach to compute the global delay in subquadratic time using a fast Fourier transform (FFT). Central to our local analysis of delays is the computation of a matching between the trajectories in a so-called delay space. It encodes the similarities between all pairs of points of the trajectories. In the visualization, the edges of the matching are bundled into patches, such that shape and color of a patch help to encode changes in an interaction pattern. To evaluate our approach experimentally, we have implemented it as a prototype visual analytics tool and have applied the tool on three bidimensional data sets. For this we used various measures to compute the delay space, including the directional distance, a new similarity measure, which captures more complex interactions by combining directional and spatial characteristics. We compare matchings of various methods computing similarity between trajectories. We also compare various procedures to compute the matching in the delay space, specifically the Fréchet distance, dynamic time warping (DTW), and edit distance (ED). Finally, we demonstrate how to validate the consistency of pairwise matchings by computing matchings between more than two trajectories.

Mobile tracking technologies are facilitating the collection of increasingly large and detailed data sets on object movement. Movement data are collected by recording an object’s location at discrete time intervals. Often, of interest is to estimate the unknown position of the object at unrecorded time points to increase the temporal resolution of the data, to correct erroneous or missing data points, or to match the recorded times between multiple data sets. Estimating an object’s unknown location between known locations is termed path interpolation. This paper introduces a new method for path interpolation termed kinematic interpolation. Kinematic interpolation incorporates object kinematics (i.e. velocity and acceleration) into the interpolation process. Six empirical data sets (two types of correlated random walks, caribou, cyclist, hurricane and athlete tracking data) are used to compare kinematic interpolation to other interpolation algorithms. Results showed kinematic interpolation to be a suitable interpolation method with fast-moving objects (e.g. the cyclist, hurricane and athlete tracking data), while other algorithms performed best with the correlated random walk and caribou data. Several issues associated with path interpolation tasks are discussed along with potential applications where kinematic interpolation can be useful. Finally, code for performing path interpolation is provided (for each method compared within) using the statistical software R.

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.

Time geography represents a powerful framework for the quantitative analysis of individual movement. Time geography effectively delineates the space–time boundaries of possible individual movement by characterizing movement constraints. The goal of this paper is to synchronize two new ideas, probabilistic time geography and kinetic-based time geography, to develop a more realistic set of movement constraints that consider movement probabilities related to object kinetics. Using random-walk theory, the existing probabilistic time geography model characterizes movement probabilities for the space–time cone using a normal distribution. The normal distribution has a symmetric probability density function and is an appropriate model in the absence of skewness – which we relate to an object’s initial velocity. Moving away from a symmetric distribution for movement probabilities, we propose the use of the skew-normal distribution to model kinetic-based movement probabilities, where the degree and direction of skewness is related to movement direction and speed. Following a description of our model, we use a set of case-studies to demonstrate the skew-normal model: a random walk, a correlated random walk, wildlife data, cyclist data, and athlete movement data. Our results show that for objects characterized by random movement behavior, the existing model performs well, but for object movement with kinetic properties (e.g., athletes), the proposed model provides a substantial improvement. Future work will look to extend the proposed probabilistic framework to the space–time prism.

The collection, visualization, and analysis of movement data is at the forefront of geographic information science research. Movement data are generally collected by recording an object's spatial location (e.g., XY coordinates) at discrete time intervals. Methods for extracting useful information, for example space–time patterns, from these increasingly large and detailed datasets have lagged behind the technology for generating them. In this article we review existing quantitative methods for analyzing movement data. The objective of this article is to provide a synthesis of the existing literature on quantitative analysis of movement data while identifying those techniques that have merit with novel datasets. Seven classes of methods are identified: (1) time geography, (2) path descriptors, (3) similarity indices, (4) pattern and cluster methods, (5) individual–group dynamics, (6) spatial field methods, and (7) spatial range methods. Challenges routinely faced in quantitative analysis of movement data include difficulties with handling space and time attributes together, representing time in GIS, and using classical statistical testing procedures with space–time movement data. Areas for future research include investigating equivalent distance comparisons in space and time, measuring interactions between moving objects, developing predictive frameworks for movement data, integrating movement data with existing geographic layers, and incorporating theory from time geography into movement models. In conclusion, quantitative analysis of movement data is an active research area with tremendous opportunity for new developments and methods.