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doc/04_markov.md
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@ -1,6 +1,6 @@
## Spatial Markov
### CDB_SpatialMarkov(subquery text, column_names text array)
### 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](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/dynamics.html).
@ -23,9 +23,9 @@ 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. |
| 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) |
@ -34,13 +34,14 @@ A table with the following columns.
```sql
SELECT
c.cartodb_id,
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
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;
```