segregation.aspatial.ConProf¶
-
class
segregation.aspatial.
ConProf
(data, group_pop_var, total_pop_var, m=1000)[source]¶ Concentration Profile Index
- Parameters
- dataa pandas DataFrame
- group_pop_varstring
The name of variable in data that contains the population size of the group of interest
- total_pop_varstring
The name of variable in data that contains the total population of the unit
- mint
a numeric value indicating the number of thresholds to be used. A large value of m creates a smoother-looking graph and a more precise concentration profile value but slows down the calculation speed.
Notes
Based on Hong, Seong-Yun, and Yukio Sadahiro. “Measuring geographic segregation: a graph-based approach.” Journal of Geographical Systems 16.2 (2014): 211-231.
Reference: [HS14].
Examples
In this example, we will calculate the concentration profile (R) for the Riverside County using the census tract data of 2010. The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
Firstly, we need to perform some import the modules and the respective function.
>>> import pandas as pd >>> import geopandas as gpd >>> import segregation >>> from segregation.aspatial import ConProf
Secondly, we need to read the data:
>>> # This example uses all census data that the user must provide your own copy of the external database. >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below. >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv' >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
Then, we filter only for the desired county (in this case, Riverside County):
>>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
The value is estimated below.
>>> conprof_index = ConProf(df, 'tractid', 'pop10') >>> conprof_index.statistic 0.06393365660089256
You can plot the profile curve with the plot method.
>>> conprof_index.plot()
- Attributes
- statisticfloat
Concentration Profile Index
- core_dataa pandas DataFrame
A pandas DataFrame that contains the columns used to perform the estimate.
Methods
plot
()Plot the Concentration Profile
-
__init__
(data, group_pop_var, total_pop_var, m=1000)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(data, group_pop_var, total_pop_var)Initialize self.
plot
()Plot the Concentration Profile