geosnap.analyze.dynamics module

Transition and sequence analysis of neighborhood change.

geosnap.analyze.dynamics.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.dynamics.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]])