Understanding Spatiotemporal Patterns: Visual Ordering of Space and Time

Menno-Jan Kraak, Daniël E. van de Vlag
2007 Cartographica  
Deriving patterns and relations from large multivariate and multi-temporal datasets to acquire knowledge about real world processes is not trivial. To understand the content of such datasets current analytical tools do offer interesting solutions, but an integrated approach is lacking. Here we introduce a visual integrated solution that allows one to explore and analyze the data at hand. The approach introduced consists of a dynamically linked multi view environment that offers different
more » ... rs different interactive visual representations to 'look at and play with' the data. For the time component the temporal ordered space matrix (TOSM), which schematizes the temporal nature of the data set, is introduced. The rows of the matrix represent time and the columns the geographic units. A preliminary usability test has been conducted to see how the multi-view approach in general performs when considering specific tasks oriented towards the understanding of spatio-temporal patterns. The TOSM functions well for naturally (linear) ordered phenomena such as rivers and coastlines. The paper also discusses the use of the TOSM for non linear ordered phenomena such as administrative units. The method is based on directional ordering and is compared with other ordering approaches, such as space filling curves, traveling salesman problem and plane sweeping algorithms. Abstract Deriving patterns and relations from large multivariate and multi-temporal datasets to acquire knowledge about real world processes is not trivial. To understand the content of such datasets current analytical tools do offer interesting solutions, but an integrated approach is lacking. Here we introduce a visual integrated solution that allows one to explore and analyze the data at hand. The approach introduced consists of a dynamically linked multi view environment that offers different interactive visual representations to 'look at and play with' the data. For the time component the temporal ordered space matrix (TOSM), which schematizes the temporal nature of the data set, is introduced. The rows of the matrix represent time and the columns the geographic units. A preliminary usability test has been conducted to see how the multi-view approach in general performs when considering specific tasks oriented towards the understanding of spatio-temporal patterns. The TOSM functions well for naturally (linear) ordered phenomena such as rivers and coastlines. The paper also discusses the use of the TOSM for non linear ordered phenomena such as administrative units. The method is based on directional ordering and is compared with other ordering approaches, such as space filling curves, traveling salesman problem and plane sweeping algorithms.
doi:10.3138/carto.42.2.153 fatcat:u6sqrpsttjfpjpefgiumbfnzsy