SPatial INTeraction models (SPINT)

Installation¶

spint supports python 3.5 and 3.6 only. Please make sure that you are operating in a python 3 environment.

Installing released version¶

spint is available on the Python Package Index. Therefore, you can either install directly with pip from the command line:

pip install -U spint

or download the source distribution (.tar.gz) and decompress it to your selected destination. Open a command shell and navigate to the decompressed folder. Type:

pip install .

Installing development version¶

Potentially, you might want to use the newest features in the development version of spint on github - pysal/spint while have not been incorporated in the Pypi released version. You can achieve that by installing pysal/spint by running the following from a command shell:

pip install https://github.com/pysal/spint/archive/master.zip

You can also fork the pysal/spint repo and create a local clone of your fork. By making changes to your local clone and submitting a pull request to pysal/spint, you can contribute to the mgwr development.

API reference¶

Gravity-type spatial interaction models¶

`spint.gravity.BaseGravity`(flows, cost[, …])	Base class to set up gravity-type spatial interaction models and dispatch estimaton technqiues.
`spint.gravity.Gravity`(flows, o_vars, d_vars, …)	Unconstrained (traditional gravity) gravity-type spatial interaction model
`spint.gravity.Production`(flows, origins, …)	Production-constrained (origin-constrained) gravity-type spatial interaction model
`spint.gravity.Attraction`(flows, …[, …])	Attraction-constrained (destination-constrained) gravity-type spatial interaction model
`spint.gravity.Doubly`(flows, origins, …[, …])	Doubly-constrained gravity-type spatial interaction model

Tests for overdispersion¶

`spint.dispersion.alpha_disp`(model[, alt_var])	Test the hypothesis that var[y] = mu (equidispersion) against the alternative hypothesis that var[y] = mu + alpha * alt_var(mu) where mu is the expected value of y, alpha is an estimated coefficient, and alt_var() specifies an alternative variance as a function of mu.
`spint.dispersion.phi_disp`(model)	Test the hypothesis that var[y] = mu (equidispersion) against the alternative hypothesis (quasi-Poisson) that var[y] = phi * mu where mu is the expected value of y and phi is an estimated overdispersion coefficient which is equivalent to 1+alpha in the alternative alpha dispersion test.