Why just?
The topic "Visualization of spatio-temporal patterns in public transport data" has two perspectives: Transportation and Geoinformation science.
Transportation domain involves planning, monitoring, management and evaluation of different modes of transit and enabling infrastructure such as road network, traffic signals. Transport geography can be defined as "sub-discipline of geography concerned about movements of freight, people and information along with their physical and transactional context. It tries to link spatial constraints and attributes with the origin, the destination, the extent, the nature and the purpose of movements" [Rodrigue et al., 2004].
Geo-information science (GIScience) concerns the development of theory and methods for capturing, exploring, processing, analyzing and disseminating geographic information. Geovisualization is a part of GIScience that uses the visualization techniques to explore and present geographic information. Geovisualization stems from the field of cartography among others. Geovisualization is defined as "the use of concrete visual representations, whether on paper or through computer displays or other media, to make spatial contexts and problem visible, so as to engage the most powerful of human information processing abilities, those associated with vision" [Maceachren et al., 1999].
The applications in the transportation domain as stated above may require applying spatial constraints in attempting to solve transportation-related problems. The part of GIScience that addresses this issue of spatial component in the transportation domain is Geographic Information Science for Transportation (GISci-T). GISci-T is defined as "a subset of GIScience that develops theory and methods for capturing, processing, analyzing, and disseminating digital transportation information" [Miller and Shaw, 2001]. The development of GISci-T is identified in three stages [Goodchild, 2000]: map view, navigation view and behavior view. The map view with static perspective relates to the application of inventory and description in transportation. The navigation view address the network connectivity and topology with the storage of time-dependent attributes. The behavior view attempts to deal with dynamic transportation objects and events such as transport systems, people, and activity pattern occurring within static transportation space. Each of these views has potential challenges or problems to be solved [Goodchild, 2000].
The behavior view in GISci-T has evolved from the work of people like Hagerstrand [Goodchild, 2000]. The concept of representing the moving objects in time and hence understand the behavior of the moving objects has evolved from time-geography. This concept has been applied by human geographers to model the behavior of people and understand the activity pattern in space and time. The time-geography concept is also applied in human geography to get insight into socio-economic condition, migration and urban growth, political geography and other people-related activities [Pred, 1977].
In this decade, the transport geographers are interested in considering the activity pattern and other social science factors into transportation planning and analysis [Fox, 1995]. Currently, simulation models and statistical approach are used to model the transportation systems (inclusive of transport systems, people travel in them, traffic flows) and results are sometimes linked spatially in Geographic Information Systems for Transportation (GIS-T). An opportunity to tie-up the time-geography concept and transport geography will lead to model the behavior of moving objects like transport systems, people in transport system, traffic flows and get insight into them.
It is interesting how GIScience can help in realizing this opportunity. �To what effect geovisualization can provide methods and tools to model transportation systems, in this case, Public Transport System (PTS) and to explore to get insight into them?� is the basic motivation for formulating this research work.
`just' is a research prototype developed to visualize spatio-temporal patterns in public transport data. It displays the geovisualization methods and tools that help visual exploration of public transport data.
Visual tasks such as locate, identify, associate and compare in just help PTS planners to do spatio-temporal reasoning. Multiple views for the PTS data are created by studying its spatial, temporal characteristics and ontology. These multiple views are then linked to space-time cube and geographic map.
Reference:
[Rodrigue et al., 2004] Rodrigue, J. P., Andrey, J., Comtois, C., Slack, B., Litman, T. A., Pouliot, M., and Shaw, S.-L. (2004). Transport geography on the web. Hofstra University Department of Economics & Geography http://people.hofstra.edu/geotrans.
[Maceachren et al., 1999] Maceachren, A. M., Wachowicz, M., Edsall, R., Haug, D., and Masters, R. (1999). Constructing knowledge from multivariate spatiotemporal data: integrating geographical visualization with knowledge discovery in database methods. International Journal of Geographical Information Science (Taylor and Francis Ltd), 13(4):311-334.
[Miller and Shaw, 2001] Miller, H. J. and Shaw, S. (2001). Geographic Information Systems for Transportation: Principles and Applications. Oxford University Press, Inc.
[Goodchild, 2000] Goodchild, M. F. (2000). GIS and transportation: Status and challenges. GeoInformatica, 4(2):127-139.
[Pred, 1977] Pred, A. (1977). The choreography of existence: Comments on Hagerstrand's time geography and its usefulness. Economic Geography, 53:207-221.
[Fox, 1995] Fox, M. (1995). Transport planning and the human activity approach.
Journal of Transport Geography, 3(2):105-116.
