crankshaft/doc/04_markov.md

Spatial Markov

CDB_SpatialMarkov(subquery text, column_names text array)

This function takes time series data associated with geometries and outputs likelihoods that the next value of a geometry will move up, down, or stay static as compared to the most recent measurement. For more information, read about Spatial Dynamics in PySAL.

Arguments

Name	Type	Description
subquery	TEXT	SQL query that exposes the data to be analyzed (e.g., SELECT * FROM real_estate_history). This query must have the geometry column name the_geom and id column name cartodb_id unless otherwise specified in the input arguments
column_names	TEXT Array	Names of column that form the history of measurements for the geometries (e.g., Array['y2011', 'y2012', 'y2013', 'y2014', 'y2015', 'y2016']).
num_classes (optional)	INT	Number of quantile classes to separate data into.
weight type (optional)	TEXT	Type of weight to use when finding neighbors. Currently available options are 'knn' (default) and 'queen'. Read more about weight types in PySAL's weights documentation.
num_ngbrs (optional)	INT	Number of neighbors if using k-nearest neighbors weight type. Defaults to 5.
permutations (optional)	INT	Number of permutations to check against a random arrangement of the values in column_name. This influences the accuracy of the output field significance. Defaults to 99.
geom_col (optional)	TEXT	The column name for the geometries. Defaults to 'the_geom'
id_col (optional)	TEXT	The column name for the unique ID of each geometry/value pair. Defaults to 'cartodb_id'.

Returns

A table with the following columns.

Column Name	Type	Description
trend	NUMERIC
trend_up	NUMERIC
trend_down	NUMERIC	The statistical significance (from 0 to 1) of a cluster or outlier classification. Lower numbers are more significant.
volatility	NUMERIC	A measure of the variance of the probabilities returned from the Spatial Markov predictions
rowid	NUMERIC	id of the row that corresponds to the id_col (by default cartodb_id of the input rows)

Example Usage

SELECT
  c.the_geom,
  m.trend,
  m.trend_up,
  m.trend_down,
  m.volatility
FROM CDB_SpatialMarkov('SELECT * FROM nyc_real_estate'
                       Array['m03y2009','m03y2010','m03y2011','m03y2012','m03y2013','m03y2014','m03y2015','m03y2016']) As m
JOIN nyc_real_estate As c
ON c.cartodb_id = m.rowid;