S
Serge Rey
STARS: Space Time Analysis of Regional Systems
Version 0.7.2
Released 2004-July-09
STARS is an exploratory data analysis package designed for variables measured for
geographical units over time. It combines a series of statistical graphics and
mapping views together with some newly developed measures for space-time
statistical analysis. The views support several types of dynamic
visualization:
- Linking different views to examine a given observation across different
dimensions (spatial, temporal, distributional)
- Conditioning (brushing) one or more destination views on the selection of
a set of observations on an origin view
- Animations of of maps, distributions, box plots, scatter plots, parallel
coordinate plots and other views over time
- Spatial traveling
- Roaming windows
- View generated views (creating new views from existing views)
STARS includes a suite of computational modules:
- Classic descriptive statistics
- Exploratory Spatial Data Analysis (Local and Global Spatial
Autocorrelation)
- Inequality Analysis
- Distributional mixing and mobility
- Classic Markov and Spatial Markov methods
STARS also includes utility methods for creating different types of
geographical weight matrices, variable transformations, and for reading
ArcView shapefiles to develop STARS projects.
STARS is written in Python, making heavy use of Numpy and Tkinter, and is
released under the GPL.
Source tarballs, a Windows binary installer, as well as user guides,
QuickTime tutorials, background papers and other documentation can be found at
the project's homepage: http://stars-py.sf.net
Version 0.7.2
Released 2004-July-09
STARS is an exploratory data analysis package designed for variables measured for
geographical units over time. It combines a series of statistical graphics and
mapping views together with some newly developed measures for space-time
statistical analysis. The views support several types of dynamic
visualization:
- Linking different views to examine a given observation across different
dimensions (spatial, temporal, distributional)
- Conditioning (brushing) one or more destination views on the selection of
a set of observations on an origin view
- Animations of of maps, distributions, box plots, scatter plots, parallel
coordinate plots and other views over time
- Spatial traveling
- Roaming windows
- View generated views (creating new views from existing views)
STARS includes a suite of computational modules:
- Classic descriptive statistics
- Exploratory Spatial Data Analysis (Local and Global Spatial
Autocorrelation)
- Inequality Analysis
- Distributional mixing and mobility
- Classic Markov and Spatial Markov methods
STARS also includes utility methods for creating different types of
geographical weight matrices, variable transformations, and for reading
ArcView shapefiles to develop STARS projects.
STARS is written in Python, making heavy use of Numpy and Tkinter, and is
released under the GPL.
Source tarballs, a Windows binary installer, as well as user guides,
QuickTime tutorials, background papers and other documentation can be found at
the project's homepage: http://stars-py.sf.net