2.8 KiB
2.8 KiB
Spatial Markov
CDB_SpatialMarkovTrend(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 | The probability that the measure at this location will move up (a positive number) or down (a negative number) |
| trend_up | NUMERIC | The probability that a measure will move up in subsequent steps of time |
| trend_down | NUMERIC | The probability that a measure will move down in subsequent steps of time |
| 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.cartodb_id,
c.the_geom,
m.trend,
m.trend_up,
m.trend_down,
m.volatility
FROM CDB_SpatialMarkovTrend('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;