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README.md

1GeospatIal Distribution DYnamics (giddy) in PySAL
2=================================================
3
4![.github/workflows/unittests.yml](https://github.com/pysal/giddy/workflows/.github/workflows/unittests.yml/badge.svg?branch=master)
5[![codecov](https://codecov.io/gh/pysal/giddy/branch/master/graph/badge.svg)](https://codecov.io/gh/pysal/giddy)
6[![Gitter room](https://badges.gitter.im/pysal/giddy.svg)](https://gitter.im/pysal/giddy)
7[![PyPI version](https://badge.fury.io/py/giddy.svg)](https://badge.fury.io/py/giddy)
8[![DOI](https://zenodo.org/badge/91390088.svg)](https://zenodo.org/badge/latestdoi/91390088)
9[![badge](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pysal/giddy/master)
10
11Giddy is an open-source python library for the analysis of dynamics of
12longitudinal spatial data. Originating from the spatial dynamics module
13in [PySAL (Python Spatial Analysis Library)](http://pysal.org/), it is under active development
14for the inclusion of newly proposed analytics that consider the
15role of space in the evolution of distributions over time.
16
17*Below are six choropleth maps of US state per-capita incomes from 1929 to 2004 at a fifteen-year interval.*
18
19![us_qunitile_maps](figs/us_qunitile_maps.png)
20
21Documentation
22-------------
23
24Online documentation is available [here](http://pysal.org/giddy/).
25
26
27Features
28--------
29- Directional LISA, inference and visualization as rose diagram
30
31[![rose_conditional](figs/rose_conditional.png)](notebooks/DirectionalLISA.ipynb)
32
33*Above shows the rose diagram (directional LISAs) for US states incomes across 1969-2009 conditional on relative incomes in 1969.*
34
35- Spatially explicit Markov methods:
36    - Spatial Markov and inference
37    - LISA Markov and inference
38- Spatial decomposition of exchange mobility measure (rank methods):
39    - Global indicator of mobility association (GIMA) and inference
40    - Inter- and intra-regional decomposition of mobility association and inference
41    - Local indicator of mobility association (LIMA)
42        - Neighbor set LIMA and inference
43        - Neighborhood set LIMA and inference
44
45[![us_neigborsetLIMA](figs/us_neigborsetLIMA.png)](notebooks/RankBasedMethods.ipynb)
46
47- Income mobility measures
48
49Examples
50--------
51
52* [Directional LISA](notebooks/DirectionalLISA.ipynb)
53* [Markov based methods](notebooks/MarkovBasedMethods.ipynb)
54* [Rank Markov methods](notebooks/RankMarkov.ipynb)
55* [Mobility measures](notebooks/MobilityMeasures.ipynb)
56* [Rank based methods](notebooks/RankBasedMethods.ipynb)
57* [Sequence methods (Optimal matching)](notebooks/Sequence.ipynb)
58
59Installation
60------------
61
62Install the stable version released on the [Python Package Index](https://pypi.org/project/giddy/) from the command line:
63
64```
65pip install giddy
66```
67
68Install the development version on [pysal/giddy](https://github.com/pysal/giddy):
69
70```
71pip install https://github.com/pysal/giddy/archive/master.zip
72```
73
74#### Requirements
75
76- scipy>=1.3.0
77- libpysal>=4.0.1
78- mapclassify>=2.1.1
79- esda>=2.1.1
80- quantecon>=0.4.7
81
82Contribute
83----------
84
85PySAL-giddy is under active development and contributors are welcome.
86
87If you have any suggestion, feature request, or bug report, please open a new [issue](https://github.com/pysal/giddy/issues) on GitHub. To submit patches, please follow the PySAL development [guidelines](https://github.com/pysal/pysal/wiki) and open a [pull request](https://github.com/pysal/giddy). Once your changes get merged, you’ll automatically be added to the [Contributors List](https://github.com/pysal/giddy/graphs/contributors).
88
89Support
90-------
91
92If you are having issues, please talk to us in the [gitter room](https://gitter.im/pysal/giddy).
93
94License
95-------
96
97The project is licensed under the [BSD license](https://github.com/pysal/giddy/blob/master/LICENSE.txt).
98
99
100BibTeX Citation
101---------------
102
103```
104@software{wei_kang_2020_3887050,
105  author       = {Wei Kang and
106                  Sergio Rey and
107                  Philip Stephens and
108                  Nicholas Malizia and
109                  James Gaboardi and
110                  Stefanie Lumnitz and
111                  Levi John Wolf and
112                  Charles Schmidt and
113                  Jay Laura and
114                  Eli Knaap},
115  title        = {pysal/giddy: Release v2.3.1},
116  month        = jun,
117  year         = 2020,
118  publisher    = {Zenodo},
119  version      = {v2.3.1},
120  doi          = {10.5281/zenodo.3887050},
121  url          = {https://doi.org/10.5281/zenodo.3887050}
122}
123```
124
125Funding
126-------
127
128<img src="figs/nsf_logo.jpg" width="50"> Award #1421935 [New Approaches to Spatial Distribution Dynamics](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421935)
129