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AAG_2011
- 1. Geovisual Analytics and the Space-Time Cube Otto Huisman, Menno-Jan Kraak and Bas Retsios huisman@itc.nl
- 2. Introduction We are currently witnessing rapid advances in integrated positioning technologies such as GPS, cellular positioning, and RFID tracking, resulting in the increasing availability of large datasets describing human movement. As scientists, we are constrained by our tools for scientific inquiry. This is evidenced by increasing ‘implementations’ of various space-time analytical toolkits. In the ongoing search to better understand our environment and the impacts that human activities have upon it, we need flexible environments for the integration of visualization, analysis and models. These should help to create knowledge and understanding through facilitating the transition from raw geodata to contextualisedgeoinformation The Space-Time Cube is both a concept and an interactive environment for data mamangement, visualisation and analysis (plugins) Here we demonstrate Geovisual Analytics for spatio-temporal data in a range of application settings involving human movement and dynamics.
- 4. Unknowns relating to accuracy and quality
- 5. Scale / resolution / granularity
- 6. ‘Data integration’Given increases in data volumes and focus on ‘automated’ approaches, these issues require appropriate strategies – and appropriate tools.
- 8. The Space-Time Cube Software environment Concept Data model
- 9. Data management, visualisation and analysis Context map scatterplot PCP ???
- 12. Space-time data describing movement / behaviour Key issues: different ‘levels’ of investigation – which represent different levels of interest, and at the same time, complexity: Examining behaviour in small to medium datasets Identifying patterns from small to massive datasets Pattern analysis and interpretation – a ‘hot’ frontier of research. Applications: Post-earthquake movement behaviour ‘Moving flock’ patterns (pedestrians)
- 13. Examining behaviour: post-earthquake evacuation paths Kawaguchi town – hit with 6.8 on 23rd October 2004 at 17:56
- 14. Identifying patterns in trajectory data: ‘moving flocks’ in Dwingelerveld Park
- 15. Identifying patterns in trajectory data: ‘moving flocks’ in Dwingelerveld Park Moving flock algorithm source: based upon source code from KDD Laboratory, University of Pisa.
- 18. Interpretation?
- 19. Possible movement: alternative data models Incorporation of (loosely-coupled) simulation models to generate movement possibilities using Time-Geographic concepts from Hagerstrand (1970). Uses taxel/voxel data model for data management and analysis developed by Forer (1998), and methodology in Huisman (2006)
- 20. Possible movement: discrete space-time
- 21. Space-time ‘events’ and ‘phenomena’ When we do not have {x,y,t,id}, but instead the data refer to some (unique) event happening at x,y,t. Examples include (reported) disease cases, accidents, and so forth. Here will look at 2 very different examples: Synthetic data on illegal migrant arrests [VAST2008] Archaeological inquiry
- 22. VAST2008 Challenge dataset: illegal migrant arrests The data include interdiction by U.S. Coast Guard cutters as well as information about successful landings. Some attribute information attached. Objective: to understand more about the migration during these years. Solution (stage 1 only) : find clusters of common interdictions/landings Algorithm: extended from WEKA opensourceimplementation
- 23. Identifying patterns: space-time clustering with OPTICS
- 24. Identifying patterns: space-time clustering with OPTICS
- 25. Identifying patterns: space-time clustering with OPTICS Interpretation?
- 27. Identification of clusters of related observations or finds (represented as nodes in a linked graph); and
- 31. Summary Space-time data capturing aspects of human dynamics is highly differentiated, in terms of: Resolution, granularity, sampling interval Accuracy / quality Compare ‘cellphone tracking’ datasets with space-time ‘event’ datasets, and GPS tracking data. These differ enormously in their sampling properties, can be interpolated to different degrees, and hence require appropriate methods to handle them. The Space-Time Cube is one tool to aid in management, (interactive) analysis and visualisation– as such, it is one component of a complete visual analytics toolkit. In progress: everything, but focus on space-time ‘sampling’ methodology to handle massive datasets.
Editor's Notes
- We are currently witnessing rapid advances in integrated positioning technologies such as GPS,cellular positioning, and RFID tracking, resulting in the increasing availability of large datasets describing human movement. In the ongoing search to better understand our environment and the impacts that human activities have upon it, dealing with these large, complex, heterogeneous datasets increasingly calls for adequate tools - in the form of flexible environments for the integration of visualization, analysis and models.The Space-Time Cube (STC) originates from Hägerstrand's space-time aquarium concept (Hägerstrand 1970). It is being increasingly deployed to analyze trajectory data in analyses of 'moving objects'.Various computational implementations of the STC exist, each with a different balance of analytical, display, and rendering capabilities. This paper describes and illustrates the functionality of the Space-Time Cube software environment developed by the authors.
- Work with MatteoGismondi @ Univ. of Tsukuba in Japan.Illustrates ‘local level’ and small sample sizeKawaguchi town – hit with 6.8 on 23rd October 2004 at 17:56
- From work with Monica Wachowicz which has been in progress for some time (my apologies!)GPS data collected in Dwingelerveld park in the NetherlandsInterested in moving flock patterns : notion that a collective of objects that move together over a minimum time interval.
- Note that if a collective simply stays together in one place for a time interval, we do not consider this as a flock and hence, our implementation prunes out these type of patterns.
- NOTE: source for optics algorithm – existing WEKA opensource implementation extended to work on space-time data.This requires
- NOTE: source for optics algorithm – existing WEKA opensource implementation extended to work on space-time data.Ongoing testing and implementation of improved ‘distance function’ – relationship between space and time. - constrained movement
- 3 years of different data. Illustrates major and minor clusters
- All interdictions collapsed into a single yr --
- All interdictions collapsed into a single yr --
- In Figure 5, archaeological sites are represented as nodes in the graph. These have been sorted (offset in the z-dimension) by two different temporal attributes, and graphic linkages have been generated for the classified variable “Culture.” Figure 5 shows specific known cultures associated with finds at an archaeological site with: (a) the first known period during which a particular culture was known to exist there, or, as in (b), the last known period for which a given culture was known to exist there. These could be combined into a single cube to give an insight into the periods with which specific finds are associated, or alternatively, they could be viewed as stations (see Figure 4) to illustrate potential associations between cultures (in the form of temporal overlap), as viewed in their immediate spatial context.We can see in Figure 5 a series of network clusters. Each has a color classification representing the culture variable. In (a) these are sorted by Start_time, a variable which represents the first known association of [object,time_period,culture]. In (b), the same relationships are sorted by End_time, the last known association of [object,time_period,culture]. The relationships between artefacts found at certain sites, and the cultures to which they belong, are clearly shown. It is also indicated that a certain degree of cultural interaction is likely to have taken place, as the ordering of the [object,time_period,culture] association has changed after being sorted by End_time. This suggests the dominance of one culture over another, possibly revealed as the emergence of one culture and the displacement or eventual disappearance of another. These kinds of spatiotemporal patterns are difficult to extract from large datasets using traditional methods.