segregation 1.0.4

Methods for estimating and performing inference for spatial and non-spatial segregation in PySAL.

Segregation Measures Framework in PySAL

Methods for estimating and performing inference for spatial and non-spatial segregation.

Easily estimate several segregation measures:

Perform comparative segregation:

What is segregation?

The PySAL segregation module allow users to estimate several segregation measures and perform inference for single measures and comparative inference in a concise way.

It can be divided into two frameworks: point estimation and inference wrappers. The first framework could be, in turn, subdivided in non-spatial indexes and spatial indexes. The inference approach present functions to perform inference for a single measure or for comparison between two measures.

Installation

i) pip directly running in the prompt:

pip install segregation

ii) Another recommended method for installing segregation is with anaconda. Clone this repository or download it manually then cd into the directory and run the following commands (this will install the development version):

$ conda env create -f environment.yml
$ source activate segregation
$ python setup.py develop

iii) pip directly from this repository running in the prompt:

$ pip install git+https://github.com/pysal/segregation

Segregation uses:

• libpysal
• pandas
• geopandas
• numpy
• scipy
• scikit-learn

Getting started

All input data for this module rely on pandas DataFrames for the non-spatial measures and geopandas DataFrames for spatial ones. In a nutshell, the user needs to pass the pandasDataFrame as its first argument and then two string that represent the variable name of population frequency of the group of interest (variable group_pop_var) and the total populationof the unit (variable total_pop_var).

So, for example, if a user would want to fit a dissimilarity index (D) to a DataFrame called df to a specific group with frequency freq with each total population population, a usual call would be something like this:

>>> from segregation.non_spatial_indexes import Dissim
>>> index = Dissim(df, "freq", "population")

If a user would want to fit a spatial dissimilarity index (SD) to a geopandas DataFrame called gdf to a specific group with frequency freq with each total population population, a usual call would be something like this:

>>> from segregation.spatial_indexes import Spatial_Dissim
>>> spatial_index = Spatial_Dissim(gdf, "freq", "population")

Every class of segregation has a statistic and a core_data attributes. The first is a direct access to the point estimation of the specific segregation measure and the second attribute gives access to the main data that the module uses internally to perform the estimates. To see the estimated D in the first generic example above, the user would have just to run index.statistic to see the fitted value.

For point estimation, all the measures available can be summarized in the following table:

Measure	Class/Function	Spatial?	Function Inputs
Dissimilarity (D)	Dissim	No	-
Gini (G)	Gini_Seg	No	-
Entropy (H)	Entropy	No	-
Isolation (xPx)	Isolation	No	-
Exposure (xPy)	Exposure	No	-
Atkinson (A)	Atkinson	No	b
Correlation Ratio (V)	Correlation_R	No	-
Concentration Profile (R)	Con_Prof	No	m
Modified Dissimilarity (Dct)	Modified_Dissim	No	iterations
Modified Gini (Gct)	Modified_Gini_Seg	No	iterations
Bias-Corrected Dissimilarity (Dbc)	Bias_Corrected_Dissim	No	B
Density-Corrected Dissimilarity (Ddc)	Density_Corrected_Dissim	No	-
Spatial Proximity Profile (SPP)	Spatial_Prox_Prof	Yes	m
Spatial Dissimilarity (SD)	Spatial_Dissim	Yes	w, standardize
Boundary Spatial Dissimilarity (BSD)	Boundary_Spatial_Dissim	Yes	standardize
Perimeter Area Ratio Spatial Dissimilarity (PARD)	Perimeter_Area_Ratio_Spatial_Dissim	Yes	standardize
Spatial Isolation (SxPx)	Spatial_Isolation	Yes	alpha, beta
Spatial Exposure (SxPy)	Spatial_Exposure	Yes	alpha, beta
Spatial Proximity (SP)	Spatial_Proximity	Yes	alpha, beta
Relative Clustering (RCL)	Relative_Clustering	Yes	alpha, beta
Delta (DEL)	Delta	Yes	-
Absolute Concentration (ACO)	Absolute_Concentration	Yes	-
Relative Concentration (RCO)	Relative_Concentration	Yes	-
Absolute Centralization (ACE)	Absolute_Centralization	Yes	-
Relative Centralization (RCE)	Relative_Centralization	Yes	-
Spatial Information Theory (SIT)	Spatial_Information_Theory	Yes	w, unit_in_local_env, original_crs

Once the segregation indexes are fitted, the user can perform inference to shed light for statistical significance in regional analysis. The summary of the inference framework is presented in the table below:

Inference Type	Class/Function	Function main Inputs	Function Outputs
Single Value	Infer_Segregation	seg_class, iterations_under_null, null_approach, two_tailed	p_value, est_sim, statistic
Two Value	Compare_Segregation	seg_class_1, seg_class_2, iterations_under_null, null_approach	p_value, est_sim, est_point_diff

If you are new to segregation and PySAL you will best get started with our documentation! We encourage you to take a look at some examples of this module in the notebooks repo!

Contribute

PySAL-segregation is under active development and contributors are welcome.

If you have any suggestion, feature request, or bug report, please open a new issue on GitHub. To submit patches, please follow the PySAL development guidelines and open a pull request. Once your changes get merged, you’ll automatically be added to the Contributors List.

Support

If you are having issues, please talk to us in the gitter room.

License

The project is licensed under the BSD license.

Funding

Award #1831615 RIDIR: Scalable Geospatial Analytics for Social Science Research

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Process number 88881.170553/2018-01