geosnap.analyze package¶
Submodules¶
Module contents¶
-
geosnap.analyze.
linc
(labels_sequence)[source]¶ Local Indicator of Neighborhood Change
- Returns
- lincs: array
local indicator of neighborhood change over all periods
Notes
The local indicator of neighborhood change defined here allows for singleton neighborhoods (i.e., neighborhoods composed of a single primitive area such as a tract or block.). This is in contrast to the initial implementation in [RAF+11] which prohibited singletons.
Examples
Time period 0 has the city defined as four neighborhoods on 10 tracts:
>>> labels_0 = [1, 1, 1, 1, 2, 2, 3, 3, 3, 4]
Time period 1 in the same city, with slight change in composition of the four neighborhoods
>>> labels_1 = [1, 1, 1, 1, 1, 2, 3, 3, 3, 4] >>> res = linc([labels_0, labels_1]) >>> res[4] 1.0 >>> res[1] 0.25 >>> res[7] 0.0 >>> res[-1] 0.0
And, in period 2, no change
>>> labels_2 = [1, 1, 1, 1, 1, 2, 3, 3, 3, 4] >>> res = linc([labels_1, labels_2]) >>> res[0] 0.0
We can pass more than two time periods, and get a “time-wise global linc” for each unit
>>> res = linc([labels_0, labels_1, labels_2]) >>> res[0] 0.25
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geosnap.analyze.
sequence
(gdf, cluster_col, seq_clusters=5, subs_mat=None, dist_type=None, indel=None, time_var='year', id_var='geoid')[source]¶ Pairwise sequence analysis and sequence clustering.
Dynamic programming if optimal matching.
- Parameters
- gdf(geo)DataFrame
Long-form (geo)DataFrame containing neighborhood attributes with a column defining neighborhood clusters.
- cluster_colstring or int
Column name for the neighborhood segmentation, such as “ward”, “kmeans”, etc.
- seq_clustersint, optional
Number of neighborhood sequence clusters. Agglomerative Clustering with Ward linkage is now used for clustering the sequences. Default is 5.
- dist_typestring
“hamming”: hamming distance (substitution only and its cost is constant 1) from sklearn.metrics; “markov”: utilize empirical transition probabilities to define substitution costs; “interval”: differences between states are used to define substitution costs, and indel=k-1; “arbitrary”: arbitrary distance if there is not a strong theory guidance: substitution=0.5, indel=1. “tran”: transition-oriented optimal matching. Sequence of transitions. Based on [Bie11].
- subs_matarray
(k,k), substitution cost matrix. Should be hollow ( 0 cost between the same type), symmetric and non-negative.
- indelfloat, optional
insertion/deletion cost.
- time_varstring, optional
Column defining time and or sequencing of the long-form data. Default is “year”.
- id_varstring, optional
Column identifying the unique id of spatial units. Default is “geoid”.
Examples
>>> from geosnap.data import Community >>> columbus = Community.from_ltdb(msa_fips=columbusfips) >>> columbus1 = columbus.cluster(columns=['median_household_income', ... 'p_poverty_rate', 'p_edu_college_greater', 'p_unemployment_rate'], ... method='ward', n_clusters=6) >>> gdf = columbus1.gdf >>> gdf_new, df_wide, seq_hamming = Sequence(gdf, dist_type="hamming") >>> seq_hamming.seq_dis_mat[:5, :5] array([[0., 3., 4., 5., 5.], [3., 0., 3., 3., 3.], [4., 3., 0., 2., 2.], [5., 3., 2., 0., 0.], [5., 3., 2., 0., 0.]])
-
geosnap.analyze.
transition
(gdf, cluster_col, time_var='year', id_var='geoid', w_type=None, permutations=0)[source]¶ (Spatial) Markov approach to transitional dynamics of neighborhoods.
- Parameters
- gdf(geo)DataFrame
Long-form (geo)DataFrame containing neighborhood attributes with a column defining neighborhood clusters.
- cluster_colstring or int
Column name for the neighborhood segmentation, such as “ward”, “kmeans”, etc.
- time_varstring, optional
Column defining time and or sequencing of the long-form data. Default is “year”.
- id_varstring, optional
Column identifying the unique id of spatial units. Default is “geoid”.
- w_typestring, optional
Type of spatial weights type (“rook”, “queen”, “knn” or “kernel”) to be used for spatial structure. Default is None, if non-spatial Markov transition rates are desired.
- permutationsint, optional
number of permutations for use in randomization based inference (the default is 0).
Examples
>>> from geosnap.data import Community >>> columbus = Community.from_ltdb(msa_fips=columbusfips) >>> columbus1 = columbus.cluster(columns=['median_household_income', ... 'p_poverty_rate', 'p_edu_college_greater', 'p_unemployment_rate'], ... method='ward', n_clusters=6) >>> gdf = columbus1.gdf >>> a = transition(gdf, "ward", w_type="rook") >>> a.p array([[0.79189189, 0.00540541, 0.0027027 , 0.13243243, 0.06216216, 0.00540541], [0.0203252 , 0.75609756, 0.10569106, 0.11382114, 0. , 0.00406504], [0.00917431, 0.20183486, 0.75229358, 0.01834862, 0. , 0.01834862], [0.1959799 , 0.18341709, 0.00251256, 0.61809045, 0. , 0. ], [0.32307692, 0. , 0. , 0. , 0.66153846, 0.01538462], [0.09375 , 0.0625 , 0. , 0. , 0. , 0.84375 ]]) >>> a.P[0] array([[0.82119205, 0. , 0. , 0.10927152, 0.06622517, 0.00331126], [0.14285714, 0.57142857, 0.14285714, 0.14285714, 0. , 0. ], [0.5 , 0. , 0.5 , 0. , 0. , 0. ], [0.21428571, 0.14285714, 0. , 0.64285714, 0. , 0. ], [0.18918919, 0. , 0. , 0. , 0.78378378, 0.02702703], [0.28571429, 0. , 0. , 0. , 0. , 0.71428571]])
-
geosnap.analyze.
cluster
(gdf, n_clusters=6, method=None, best_model=False, columns=None, verbose=False, time_var='year', id_var='geoid', return_model=False, scaler=None, **kwargs)[source]¶ - Create a geodemographic typology by running a cluster analysis on the
study area’s neighborhood attributes
- Parameters
- gdfpandas.DataFrame
long-form (geo)DataFrame containing neighborhood attributes
- n_clustersint
the number of clusters to model. The default is 6).
- methodstr
the clustering algorithm used to identify neighborhood types
- best_modelbool
if using a gaussian mixture model, use BIC to choose the best n_clusters. (the default is False).
- columnslist-like
subset of columns on which to apply the clustering
- verbosebool
whether to print warning messages (the default is False).
- time_var: str
which column on the dataframe defines time and or sequencing of the long-form data. Default is “year”
- id_var: str
which column on the long-form dataframe identifies the stable units over time. In a wide-form dataset, this would be the unique index
- scaler: str or sklearn.preprocessing.Scaler
a scikit-learn preprocessing class that will be used to rescale the data. Defaults to StandardScaler
- Returns
- pandas.DataFrame with a column of neighborhood cluster labels appended
- as a new column. Will overwrite columns of the same name.
-
geosnap.analyze.
cluster_spatial
(gdf, n_clusters=6, spatial_weights='rook', method=None, columns=None, threshold_variable='count', threshold=10, time_var='year', id_var='geoid', return_model=False, scaler=None, **kwargs)[source]¶ Create a spatial geodemographic typology by running a cluster analysis on the metro area’s neighborhood attributes and including a contiguity constraint.
- Parameters
- gdfgeopandas.GeoDataFrame
long-form geodataframe holding neighborhood attribute and geometry data.
- n_clustersint
the number of clusters to model. The default is 6).
- weights_typestr ‘queen’ or ‘rook’
spatial weights matrix specification` (the default is “rook”).
- methodstr
the clustering algorithm used to identify neighborhood types
- columnslist-like
subset of columns on which to apply the clustering
- threshold_variablestr
for max-p, which variable should define p. The default is “count”, which will grow regions until the threshold number of polygons have been aggregated
- thresholdnumeric
threshold to use for max-p clustering (the default is 10).
- time_var: str
which column on the dataframe defines time and or sequencing of the long-form data. Default is “year”
- id_var: str
which column on the long-form dataframe identifies the stable units over time. In a wide-form dataset, this would be the unique index
- scaler: str or sklearn.preprocessing.Scaler
a scikit-learn preprocessing class that will be used to rescale the data. Defaults to StandardScaler
- Returns
- geopandas.GeoDataFrame with a column of neighborhood cluster labels
- appended as a new column. Will overwrite columns of the same name.