AM-90 - Computational Movement Analysis

Figure 1. Group movement patterns as illustrated in this coordinated escape behavior of a group of mountain goat (Rubicapra rubicapra) evading approaching hikers on the Fuorcla Trupchun near the Italian/Swiss border are at the core of computational movement analysis. Once the trajectories of moving objects are collected and made accessible for computational processing, CMA aims at a better understanding of the characteristics of movement processes of animals, people or things in geographic space.
 
Computational Movement Analysis (CMA) develops and applies analytical computational tools aiming at a better understanding of movement data. CMA copes with the rapidly growing data streams capturing the mobility of people, animals, and things roaming geographic spaces. CMA studies how movement can be represented, modeled, and analyzed in GIS&T. The CMA toolbox includes a wide variety of approaches, ranging from database research, over computational geometry to data mining and visual analytics.

Topic Contents: 
  1. Definitions
  2. Why study movement in GIS&T?
  3. Conceptual models of movement and movement spaces
  4. Movement data>
  5. Trajectory operations
  6. Context
  7. Movement patterns
  8. Visual analytics of movement

 

1. Definitions

Computational Movement Analysis (CMA) is the interdisciplinary research field studying the development and application of computational techniques for capturing, processing, managing, structuring, and ultimately analyzing data describing movement phenomena, both in geographic and abstract spaces, aiming for a better understanding of the processes governing that movement (Laube 2014, p. 4).

After Laube (2014, p. 5), CMA investigates the scientific fundamentals related to:

  • the specific characteristics and peculiarities of the geographic phenomenon of movement and the spatio-temporal data describing it, including data quality (uncertainty, accuracy), scale issues, and spatio-temporal autocorrelation,
  • the peculiarities of established and emerging integrated spatial systems serving as direct or indirect tracking systems capturing raw or enriched movement data,
  • capturing, (pre-)processing, integrating, storing, managing, and querying the rapidly growing data streams describing movement phenomena,
  • the conceptual models for moving objects and movement processes, and the spaces embedding that movement, the data structures implementing these models, and the implications of models and structures on the CMA process,
  • the development and evaluation of analysis techniques and operations structuring low-level movement data and deriving high-level process knowledge from that data. This draws on methods from spatio-temporal analysis, geography, computational geometry, scientific visualization, data mining and KDD, and statistics,
  • the characteristics and semantics of the wide range of current applications of CMA, and the assessment of the potential of prospective applications areas, and
  • the societal issues, including ethics and privacy, as well as issues around user-generated and open data.

 

2. Why study movement in GIS&T?

Understanding how and why people, animals, goods, and more generally things move about in space-time is one of the fundamental questions in geography. Early work on movement analysis in GIS&T was very much data and technology driven, mainly triggered by technological progress in GPS tracking technology (Laube 2015). Whereas GPS data of a dozen caribou offered previously unseen tracking data two decades ago, today we are not that far away from tracking populations of entire countries via mobile phone data. Movement data is inherently spatio-temporal and hence allows adding true dynamics to the otherwise predominantly static GIS&T environment. Major achievements in CMA have emerged from the field of movement ecology, where biologists strive for a better understanding of animal movement. Other important application areas of CMA include transportation research, surveillance and security, marketing and consumer behavior, and increasingly also sports analytics.

 

3. Conceptual models of movement and movement spaces

Movement always happens in a space enabling and possibly constraining that movement. Be it animals that move through their habitats, cars driving along streets, people commuting through public transit, or shoppers browsing a mall – the characteristics of the spaces containing the movement have a big influence on the movement itself. From a computer science perspective, both the movement and its embedding space must be modeled using conceptual models such that movement analysis becomes possible in a computational environment (Laube 2014).

Within any GIS&T environment, the classic entity- and field-based conceptual models for representing space lend themselves as basic movement spaces. These two are then complemented with network spaces for street networks and public transit. Often, such 2D-spaces are extended to include a third temporal dimension (x, y and t for time) making reference to Hägerstrand’s time geography. The model for the movement space then rules how the movement itself is modeled, and subsequently what analysis techniques are possible: Whereas, for example, a 2D Euclidean field-space produces trajectories as sequences of x, y and t-tuples, a transportation network rather produces trajectories in the form of sequences of visited network edges or nodes (Andrienko et al. 2008, Laube 2017). Movement in the space-time cube, by contrast, can be modelled as space-time paths or lifelines or then space-time prisms representing the potential movement given some constraints (e.g., maximal speed). These three different forms of movement illustrate, how subsequently different algorithmic approaches for analyzing the movement are required. Trajectories can be tackled with time-enabled approaches for line analysis (e.g. polyline similarity measures). Space-time prisms analysis requires rather complex 3D volume intersections. Finally, node sequence data can be analyzed using sequence mining algorithms, akin to association rule mining.

 

4. Movement data

In any CMA project, a substantial fraction of time will be spent on preprocessing, cleaning, and structuring the raw movement data. Given the multitude of systems able to track movement, movement data comes in a plethora of different forms, each with its peculiarities worth studying when aiming for efficient and effective processing in GIS&T and supporting environments.

An increasingly popular characterization of different tracking perspectives is borrowed from physics, the distinction between the Lagrangian and Eulerian observation perspective. The Lagrangian perspective considers changes in a moving object’s position, where the moving object produces a sequence of position fixes as it moves across space (e.g. GPS tracking). The Eulerian perspective, by contrast, describes movement as changes in position of moving objects relative to known, fixed locations in space. These check-points can be GSM antennas, traffic gantries, or smart-card readers at train stations. Whereas Lagrangian tracking data is mostly limited to smaller samples, it typically offers finer spatio-temporal granularities and a higher reliability. Eulerian tracking systems, however, promise access to much larger samples, but for the price of coarser spatio-temporal granularity and often much less control over the sampling regime. The two perspectives also correspond to different conceptual models of movement and movement spaces, again resulting in different analytical approaches for the resulting movement data.

Obviously, movement data is spatial, so it comes with all the known issues of spatial data, including questions about reference systems, data quality issues (inaccuracy, uncertainty), scale issues, and autocorrelation. Importantly, movement data is also temporal, still challenging rather static GIS&T with the inclusion of temporal reference systems, temporal relations, temporal granularity or sampling issues, and questions about temporal autocorrelation. Tracking data quality is clearly an issue for CMA in GIS&T, especially when inaccurate positional data is used for computing descriptive parameters such as speed, turning angle or sinuosity. Equally important when deriving such movement attributes is scale, or here the spatio-temporal sampling granularity (Laube and Purves 2011).

 

5. Trajectory operations

This reference article uses the simple notion of a trajectory as a “time-stamped sequence of visited locations representing a moving object’s trace in space-time.” This may hence include time-enriched polylines, 3D life lines, network edges sequences, and sequences of check-ins and –outs of public transit smart card system. Irrespective of its peculiar form, a first set of GIS&T operations focuses on the trajectory entities alone; that is shape or arrangement characteristics of the trajectories not yet embedding them in their geographic context (see Section 6, below. Whereas some trajectory operations can be performed in GIS environments, the temporal dimension often requires the use of additional data processing tools (e.g. R packages).

A first crucial task is enriching trajectories with descriptive variables such as speed, step length, acceleration, turning angle, or sinuosity. A second key trajectory operation is segmentation, decomposing the trajectory into self-similar segments (Buchin et al. 2011). Very often a first segmentation will separate moves from stops, that is periods when the object is not moving at all. This may be important with tracking systems that keep recording positions whilst the object is static, potentially producing pseudo-movement resulting from inaccurate measurements rather than actual movement.

Comparing trajectories is a further important trajectory operation. Comparing trajectories is important for grouping or clustering objects that express similar or dissimilar movement. This is another good example of the importance of the modeling process in CMA, as the different conceptual models used for movement will require rather different trajectory similarity measures (Toohey and Duckham, 2015). The catalogue of proposed approaches reaches from geometry-based line similarity measures to time series analysis to edit distance concepts borrowed from string analysis and genetics (Dodge et al. 2012). Once a suitable similarity metric has been found, conventional clustering techniques allow grouping of objects according to their movement properties.

 

6. Context

The GIS&T core strength of integrating spatial variables through a spatial reference system (overlay) is the basis for contextualizing trajectories. The key to a better understanding of movement processes very often lies in putting the trajectories in context to the underlying geography, hence studying relations between the trajectories and the enabling and constraining geographic context (Nathan et al 2008). In the simplest case this may mean adding a land-use category to every observed position of a moving entity. More complex forms of such semantic enrichment of trajectories may involve buffer operations or topological operations. Dodge et al. (2013) present a framework for annotating animal movement data with environmental variables (e.g. weather data) in the widely used Movebank system.

Another challenge for CMA comes in the form of additional sensor data further characterizing mobility behavior (Shamoun-Baranes et al. 2012, Williams et al. 2014). The most prominent such sensor is the accelerometer, nowadays-standard equipment even on mobile phones. Also many GPS trackers are complemented with accelerometers, adding further data dimensions to the mobility monitoring exercise. Combined with speed readings accelerometer data may help qualifying different behaviors when monitoring animals or detecting travel modes when monitoring commuters. Further sensors allowing such semantic enrichment of trajectories include sensors tracking physiological parameters such as heart rate or body temperature, acoustic sensors, or light sensors.

 

7. Movement patterns

The search for salient movement patterns clearly is one of the key promoters of CMA within GIS&T. Movement patterns can refer to frequently used routes by commuters, spatially-explicit manifestations of animal behavior such as hunting excursions of feral animals, or arrangement and coordination patterns relating sets of mobile objects such as flocking, convoys, or leadership (Andrienko et al. 2008, Long and Nelson 2013).

The GIS&T community has been very successful in adopting a range of tools from related disciplines for this challenging task. First of all, data mining algorithms were deemed to be a natural fit, with borrowings from spatio-temporal association rule mining known from market basket analysis or time series analysis from stock exchange analysis. Then, movement data and its processing also led to significant developments in the database community, with moving object database (MOD) specifically being tailored for managing movement data and querying patterns. Furthermore, the computer science discipline of computational geometry produced a wide range of movement pattern approaches focusing mostly on the geometric aspects of arrangements of points and lines representing movement. Although the GIS&T community has produced some attempts aiming for a generic categorization of movement patterns, the diverse nature of the involved application areas keeps challenging such noble efforts (Dodge, Weibel, Lautenschütz, 2008).

Detecting movement patterns in essence means structuring data: Be it finding spatio-temporal hot-spots of space-use, clusters of objects expressing similar movement or coordination events in groups of moving objects. The next knowledge discovery step of attaching meaning and process understanding to those same patterns, is much harder. Galton (2005) highlights this difficult analysis task as “bridging the semantic gap.”

 

8. Visual analytics of movement

Visualization and visual analytics is another GIS&T field that has enthusiastically adopted the challenge of CMA. With its natural affinity to large noisy spatio-temporal datasets, information visualization and exploratory data analysis are a natural fit for movement data (Andrienko and Andrienko, 2012, Demšar et al. 2015). The oft-cited ideal match between the human ability to perceive patterns and trends with the computer’s power to process and present large datasets also works well for CMA – adhering to Shneiderman’s mantra “overview first, zoom and filter, then details-on-demand.” Linked views and animation are especially prominent elements in the many visualization settings put forward for movement data. Typical views include maps, 3D space-time spaces, and parameter spaces typically depicting time series of derived movement parameters such as speed or acceleration. Also, the interactive visualization of previously mined movement patterns in map views – hence the combination of data mining and visualization promoted as visual analytics – works well for exploring large streams of movement data.

References: 

Andrienko, N., & Andrienko, G. (2012). Visual analytics of movement: An overview of methods, tools and procedures. Information Visualization, doi: 10.1177/1473871612457601.
 
Andrienko, N., Andrienko, G., Pelekis, N., & Spaccapietra, S. (2008). Basic concepts of movement data. In Mobility, data mining and privacy (pp. 15-38). Springer Berlin Heidelberg.
 
Buchin, K., Buchin, M., Van Kreveld, M., & Luo, J. (2011). Finding long and similar parts of trajectories. Computational Geometry, 44(9), 465-476. doi: 10.1016/j.comgeo.2011.05.004
 
Demšar, U., Buchin, K., Cagnacci, F., Safi, K., Speckmann, B., Van de Weghe, N., ... & Weibel, R. (2015). Analysis and visualisation of movement: an interdisciplinary review. Movement ecology, 3(1), 1-24. doi: 10.1186/s40462-015-0032-y.
 
Dodge, S., Bohrer, G., Weinzierl, R., Davidson, S. C., Kays, R., Douglas, D., ... & Wikelski, M. (2013). The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data. Movement Ecology, 1(1), 3. doi: 10.1186/2051-3933-1-3.
 
Dodge, S., Laube, P., & Weibel, R. (2012). Movement similarity assessment using symbolic representation of trajectories. International Journal of Geographical Information Science, 26(9), 1563-1588. doi: 10.1080/13658816.2011.630003.
 
Dodge, S., Weibel, R., & Lautenschütz, A. K. (2008). Towards a taxonomy of movement patterns. Information visualization, 7(3-4), 240-252. doi: 10.1057/palgrave.ivs.9500182
 
Galton, A. (2005). Dynamic collectives and their collective dynamics. In International Conference on Spatial Information Theory (pp. 300-315). Springer Berlin Heidelberg. doi: 10.1007/11556114_19.
 
Laube, P. (2014). Computational movement analysis. New York: Springer.
 
Laube, P. (2015). The low hanging fruit is gone: achievements and challenges of computational movement analysis. SIGSPATIAL Special, 7(1), 3-10. doi: 10.1145/2782759.2782762.
 
Laube, P. (2017, in press). Representation, trajectories. D. Richardson, N. Castree, M. Goodchild, W. Liu, A. Kobayashi, & R. Marston (Eds.) The International Encyclopedia of Geography: People, the Earth, Environment, and Technology. Wiley/AAG, Hoboken, NJ.
 
Laube, P., & Purves, R. S. (2011). How fast is a cow? cross‐scale analysis of movement data. Transactions in GIS, 15(3), 401-418. doi: 10.1111/j.1467-9671.2011.01256.x
 
Long, J. A., & Nelson, T. A. (2013). A review of quantitative methods for movement data. International Journal of Geographical Information Science, 27(2), 292-318. doi: 10.1080/13658816.2012.68257.
 
Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., & Smouse, P. E. (2008). A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, 105(49), 19052-19059. doi: 10.1073/pnas.0800375105
 
Shamoun-Baranes, J., Bom, R., van Loon, E. E., Ens, B. J., Oosterbeek, K., & Bouten, W. (2012). From sensor data to animal behaviour: an oystercatcher example. PloS one, 7(5), e37997. doi:10.1371/journal.pone.0037997
 
Toohey, K., & Duckham, M. (2015). Trajectory similarity measures. SIGSPATIAL Special, 7(1), 43-50. doi: 10.1145/2782759.2782767
 
Williams, T. M., Wolfe, L., Davis, T., Kendall, T., Richter, B., Wang, Y., ... & Wilmers, C. C. (2014). Instantaneous energetics of puma kills reveal advantage of felid sneak attacks. Science, 346(6205), 81-85.doi: 10.1126/science.1254885

Author and Citation Info: 

Laube, P. (2017). Computational Movement Analytics. The Geographic Information Science & Technology Body of Knowledge (2nd Quarter 2017 Edition), John P. WIlson (ed.), doi: 10.22224/gistbok/2017.2.6.  
This entry was first published on June 25, 2017. No earlier editions exist. 

Learning Objectives: 
  • Sketch the most important conceptual data models and data structures for movement spaces and respective movement traces. Discuss opportunities and limitations of the above models and structures for the conceptualization of specific movement patterns.
  • Discuss the importance and implications of the classic spatial data quality concepts precision, accuracy, and vagueness for the movement analysis process.
  • Explain and apply basic trajectory operations.
    • Discuss and apply the crucial task of segmentation in Computational Movement Analysis, especially for separating stops from moves.
    • Explain at least three different trajectory similarity measures in their own words and by drawing simple sketches. Assess the suitability of different trajectory similarity measures for different types of given movement data.
  • List several movement patterns and conceptualize their patterns. Sketch patterns with pen and paper and devise algorithms for detecting movement patterns in movement data.
  • Discuss why movement should be analyzed in its geographic context. Conceptualize and sketch computational approaches for relating movement data to geographic context data, considering different conceptual models for the movement and the movement spaces.
  • Explain why complementing raw movement trajectories with auxiliary sensor data is key for understanding movement processes. List at least 5 additional sensor types that are often used together with location sensors.
  • Name key visual analytics approaches for analyzing movement data, and list their properties and limitations.
Instructional Assessment Questions: 
  1. Make a list of all tracking systems for capturing the movement of mobile objects you know. Extend your list by characterizing the type and the properties of the movement data that the systems in the list produce.
  2. Given the movement of a commuter between home and work: Sketch with pen and paper the movement trajectory of the commuter using different conceptual models for the movement space embedding the commuter’s movement: (a) the field of the underlying digital terrain model, (b) the transportation network used, and (c) the irregular tessellation of space spanned by the proximity zones of mobile phone antennas.
  3. Draw a couple of trajectories of the form of polylines connecting time-stamped (x,y,t)-tuples. Now add sketch drawings illustrating your ideas about different approaches for computing the similarity between the drawn trajectories. Make sure that some of your measures consider space and time, and not only space.
  4. Assume you have the trajectories of all staff of an IT company. Two employees see each other socially. How could you detect these two employees when the movement of all staff is given with three types of movement data: (a) GPS-tracks from a tracking device with a 24/7 sampling rate of 1 minute, (b) the cell phone logs with the sequence of all antennas all staff are connected to, (c) the logs of the public transport loyalty systems all staff of this company use.
  5. Find a free smart phone app that allows the tracking of your movement whilst going on a hike or a bike ride. Record a couple of your own movement trajectories. Then map your trips in any mapping environment or GIS available to you. Study (a) data quality issues of your tracks, especially when comparing to the street or path network used, (b) come up with an algorithmic approach for detecting stops in your trajectories, (c) compute speed and sinuosity for your trajectory, (d) resample your trajectory reducing significantly the temporal granularity of your data and then re-compute speed and sinuosity. Discuss your findings from (a) – (d).
Additional Resources: 

The following list features useful textbooks on various aspects of CMA:

  • Zheng, Y., & Zhou, X. (Eds.). (2011). Computing with spatial trajectories. Springer Science & Business Media.
  • Andrienko, G., Andrienko, N., Bak, P., Keim, D., & Wrobel, S. (2013). Visual analytics of movement. Springer Science & Business Media.
  • Urbano, F., & Cagnacci, F. (2014). Spatial Database for GPS Wildlife Tracking Data (pp. 978-3). Springer Cham Heidelberg New York Edition. doi: 10.1007/978-3-319-03743-1.
  • Güting, R. H., & Schneider, M. (2005). Moving objects databases. Elsevier.