Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self-Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be cons...
Previous research exploring space–time patterns has focused on the relative merits and drawbacks of...
Large quantities of spatiotemporal (ST) data can be easily collected from various domains such as tr...
Abstract Spatio-temporal clustering is a process of grouping objects based on their spatial and temp...
Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to th...
Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to th...
We suggest a visual analytics framework for the exploration and analysis of spatially and temporally...
We suggest a visual analytics framework for the exploration and analysis of spatially and temporally...
Spatial sciences are confronted with increasing amounts of high-dimensional data. These data commonl...
Dynamic space-time pattern and potential interesting events in space and time have in practice a muc...
To support analysis and modelling of large amounts of spatio-temporal data having the form of spatia...
We focus on visual analysis of space- and time-referenced categorical data, which describe possible ...
We focus on visual analysis of space- and time-referenced categorical data, which describe possible ...
The research reported in this paper focuses on integrating analytical and visual methods in order to...
Origin-destination (OD) movement data describe moves or trips between spatial locations by specifyin...
Crime continues to cast a shadow over citizen well-being in big cities today, while also imposing hu...
Previous research exploring space–time patterns has focused on the relative merits and drawbacks of...
Large quantities of spatiotemporal (ST) data can be easily collected from various domains such as tr...
Abstract Spatio-temporal clustering is a process of grouping objects based on their spatial and temp...
Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to th...
Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to th...
We suggest a visual analytics framework for the exploration and analysis of spatially and temporally...
We suggest a visual analytics framework for the exploration and analysis of spatially and temporally...
Spatial sciences are confronted with increasing amounts of high-dimensional data. These data commonl...
Dynamic space-time pattern and potential interesting events in space and time have in practice a muc...
To support analysis and modelling of large amounts of spatio-temporal data having the form of spatia...
We focus on visual analysis of space- and time-referenced categorical data, which describe possible ...
We focus on visual analysis of space- and time-referenced categorical data, which describe possible ...
The research reported in this paper focuses on integrating analytical and visual methods in order to...
Origin-destination (OD) movement data describe moves or trips between spatial locations by specifyin...
Crime continues to cast a shadow over citizen well-being in big cities today, while also imposing hu...
Previous research exploring space–time patterns has focused on the relative merits and drawbacks of...
Large quantities of spatiotemporal (ST) data can be easily collected from various domains such as tr...
Abstract Spatio-temporal clustering is a process of grouping objects based on their spatial and temp...