Merge branch 'develop' into add-spatial-markov
This commit is contained in:
Andy Eschbacher
commit
971c8f75d8
272
doc/02_moran.md
272
doc/02_moran.md
@ -1,169 +1,185 @@
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## Name
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## Areas of Interest Functions
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CDB_AreasOfInterest -- returns a table with a cluster/outlier classification, the significance of a classification, an autocorrelation statistic (Local Moran's I), and the geometry id for each geometry in the original dataset.
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||||
### CDB_AreasOfInterestLocal(subquery text, column_name text)
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||||
|
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## Synopsis
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This function classifies your data as being part of a cluster, as an outlier, or not part of a pattern based the significance of a classification. The classification happens through an autocorrelation statistic called Local Moran's I.
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||||
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||||
```sql
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table(numeric moran_val, text quadrant, numeric significance, int ids, numeric column_values) CDB_AreasOfInterest(text query, text column_name)
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#### Arguments
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||||
|
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table(numeric moran_val, text quadrant, numeric significance, int ids, numeric column_values) CDB_AreasOfInterest(text query, text column_name, int permutations, text geom_column, text id_column, text weight_type, int num_ngbrs)
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||||
```
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||||
| Name | Type | Description |
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||||
|------|------|-------------|
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||||
| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
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||||
| column_name | TEXT | Name of column (e.g., should be `'interesting_value'` instead of `interesting_value` without single quotes) used for the analysis. |
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||||
| 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](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html). |
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| num_ngbrs (optional) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
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| 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. |
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| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
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||||
| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
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## Description
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#### Returns
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||||
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CDB_AreasOfInterest is a table-returning function that classifies the geometries in a table by an attribute and gives a significance for that classification. This information can be used to find "Areas of Interest" by using the correlation of a geometry's attribute with that of its neighbors. Areas can be clusters, outliers, or neither (depending on which significance value is used).
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A table with the following columns.
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Inputs:
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| Column Name | Type | Description |
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|-------------|------|-------------|
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| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the geometry with id of `rowid` |
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| quads | TEXT | Classification of geometry. Result is one of 'HH' (a high value with neighbors high on average), 'LL' (opposite of 'HH'), 'HL' (a high value surrounded by lows on average), and 'LH' (opposite of 'HL'). Null values are returned when nulls exist in the original data. |
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| significance | NUMERIC | The statistical significance (from 0 to 1) of a cluster or outlier classification. Lower numbers are more significant. |
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| rowid | INT | Row id of the values which correspond to the input rows. |
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| vals | NUMERIC | Values from `'column_name'`. |
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* `query` (required): an arbitrary query against tables you have access to (e.g., in your account, shared in your organization, or through the Data Observatory). This string must contain the following columns: an id `INT` (e.g., `cartodb_id`), geometry (e.g., `the_geom`), and the numeric attribute which is specified in `column_name`
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* `column_name` (required): column to perform the area of interest analysis tool on. The data must be numeric (e.g., `float`, `int`, etc.)
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* `permutations` (optional): used to calculate the significance of a classification. Defaults to 99, which is sufficient in most situations.
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* `geom_column` (optional): the name of the geometry column. Data must be of type `geometry`.
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* `id_column` (optional): the name of the id column (e.g., `cartodb_id`). Data must be of type `int` or `bigint` and have a unique condition on the data.
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* `weight_type` (optional): the type of weight used for determining what defines a neighborhood. Options are `knn` or `queen`.
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* `num_ngbrs` (optional): the number of neighbors in a neighborhood around a geometry. Only used if `knn` is chosen above.
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Outputs:
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* `moran_val`: underlying correlation statistic used in analysis
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* `quadrant`: human-readable interpretation of classification
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* `significance`: significance of classification (closer to 0 is more significant)
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* `ids`: id of original geometry (used for joining against original table if desired -- see examples)
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* `column_values`: original column values from `column_name`
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Availability: crankshaft v0.0.1 and above
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## Examples
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#### Example Usage
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```sql
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SELECT
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t.the_geom_webmercator,
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t.cartodb_id,
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c.the_geom,
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aoi.quads,
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aoi.significance,
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aoi.quadrant As aoi_quadrant
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FROM
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observatory.acs2013 As t
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JOIN
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crankshaft.CDB_AreasOfInterest('SELECT * FROM observatory.acs2013',
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'gini_index')
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c.num_cyclists_per_total_population
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FROM CDB_GetAreasOfInterestLocal('SELECT * FROM commute_data'
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'num_cyclists_per_total_population') As aoi
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JOIN commute_data As c
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ON c.cartodb_id = aoi.rowid;
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```
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## API Usage
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### CDB_AreasOfInterestGlobal(subquery text, column_name text)
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Example
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This function identifies the extent to which geometries cluster (the groupings of geometries with similarly high or low values relative to the mean) or form outliers (areas where geometries have values opposite of their neighbors). The output of this function gives values between -1 and 1 as well as a significance of that classification. Values close to 0 mean that there is little to no distribution of values as compared to what one would see in a randomly distributed collection of geometries and values.
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```text
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http://eschbacher.cartodb.com/api/v2/sql?q=SELECT * FROM crankshaft.CDB_AreasOfInterest('SELECT * FROM observatory.acs2013','gini_index')
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#### Arguments
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| Name | Type | Description |
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|------|------|-------------|
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| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
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| column_name | TEXT | Name of column (e.g., should be `'interesting_value'` instead of `interesting_value` without single quotes) used for the analysis. |
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| 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](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html). |
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| num_ngbrs (optional) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
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| 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. |
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| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
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| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
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#### Returns
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A table with the following columns.
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| Column Name | Type | Description |
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|-------------|------|-------------|
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| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the entire dataset. Values closer to one indicate cluster, closer to -1 mean more outliers, and near zero indicates a random distribution of data. |
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| significance | NUMERIC | The statistical significance of the `moran` measure. |
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#### Examples
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```sql
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SELECT *
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FROM CDB_AreasOfInterestGlobal('SELECT * FROM commute_data', 'num_cyclists_per_total_population')
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```
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Result
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```json
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{
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time: 0.120,
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total_rows: 100,
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rows: [{
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moran_vals: 0.7213,
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quadrant: 'High area',
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significance: 0.03,
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ids: 1,
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column_value: 0.22
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},
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{
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moran_vals: -0.7213,
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quadrant: 'Low outlier',
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significance: 0.13,
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ids: 2,
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column_value: 0.03
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},
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...
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]
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}
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### CDB_AreasOfInterestLocalRate(subquery text, numerator_column text, denominator_column text)
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Just like `CDB_AreasOfInterestLocal`, this function classifies your data as being part of a cluster, as an outlier, or not part of a pattern based the significance of a classification. This function differs in that it calculates the classifications based on input `numerator` and `denominator` columns for finding the areas where there are clusters and outliers for the resulting rate of those two values.
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#### Arguments
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| Name | Type | Description |
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|------|------|-------------|
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| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
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| numerator | TEXT | Name of the numerator for forming a rate to be used in analysis. |
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| denominator | TEXT | Name of the denominator for forming a rate to be used in analysis. |
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| 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](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html). |
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| num_ngbrs (optional) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
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| 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. |
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| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
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| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
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#### Returns
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A table with the following columns.
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| Column Name | Type | Description |
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||||
|-------------|------|-------------|
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| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the geometry with id of `rowid` |
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| quads | TEXT | Classification of geometry. Result is one of 'HH' (a high value with neighbors high on average), 'LL' (opposite of 'HH'), 'HL' (a high value surrounded by lows on average), and 'LH' (opposite of 'HL'). Null values are returned when nulls exist in the original data. |
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| significance | NUMERIC | The statistical significance (from 0 to 1) of a cluster or outlier classification. Lower numbers are more significant. |
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| rowid | INT | Row id of the values which correspond to the input rows. |
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| vals | NUMERIC | Values from `'column_name'`. |
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#### Example Usage
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```sql
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SELECT
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c.the_geom,
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aoi.quads,
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aoi.significance,
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c.cyclists_per_total_population
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FROM CDB_GetAreasOfInterestLocalRate('SELECT * FROM commute_data'
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'num_cyclists',
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'total_population') As aoi
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JOIN commute_data As c
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ON c.cartodb_id = aoi.rowid;
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```
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## See Also
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### CDB_AreasOfInterestGlobalRate(subquery text, column_name text)
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crankshaft's areas of interest functions:
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This function identifies the extent to which geometries cluster (the groupings of geometries with similarly high or low values relative to the mean) or form outliers (areas where geometries have values opposite of their neighbors). The output of this function gives values between -1 and 1 as well as a significance of that classification. Values close to 0 mean that there is little to no distribution of values as compared to what one would see in a randomly distributed collection of geometries and values.
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* [CDB_AreasOfInterest_Global]()
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* [CDB_AreasOfInterest_Rate_Local]()
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* [CDB_AreasOfInterest_Rate_Global]()
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#### Arguments
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| Name | Type | Description |
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|------|------|-------------|
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| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
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| numerator | TEXT | Name of the numerator for forming a rate to be used in analysis. |
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||||
| denominator | TEXT | Name of the denominator for forming a rate to be used in analysis. |
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||||
| 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](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html). |
|
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| 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. |
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| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
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| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
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PostGIS clustering functions:
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#### Returns
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* [ST_ClusterIntersecting](http://postgis.net/docs/manual-2.2/ST_ClusterIntersecting.html)
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* [ST_ClusterWithin](http://postgis.net/docs/manual-2.2/ST_ClusterWithin.html)
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A table with the following columns.
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| Column Name | Type | Description |
|
||||
|-------------|------|-------------|
|
||||
| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the entire dataset. Values closer to one indicate cluster, closer to -1 mean more outliers, and near zero indicates a random distribution of data. |
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| significance | NUMERIC | The statistical significance of the `moran` measure. |
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-- removing below, working into above
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#### Examples
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#### What is Moran's I and why is it significant for CartoDB?
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```sql
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SELECT *
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FROM CDB_AreasOfInterestGlobalRate('SELECT * FROM commute_data',
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'num_cyclists',
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'total_population')
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```
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Moran's I is a geostatistical calculation which gives a measure of the global
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clustering and presence of outliers within the geographies in a map. Here global
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means over all of the geographies in a dataset. Imagine mapping the incidence
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rates of cancer in neighborhoods of a city. If there were areas covering several
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neighborhoods with abnormally low rates of cancer, those areas are positively
|
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spatially correlated with one another and would be considered a cluster. If
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there was a single neighborhood with a high rate but with all neighbors on
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average having a low rate, it would be considered a spatial outlier.
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## Hotspot, Coldspot, and Outlier Functions
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|
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While Moran's I gives a global snapshot, there are local indicators for
|
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clustering called Local Indicators of Spatial Autocorrelation. Clustering is a
|
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process related to autocorrelation -- i.e., a process that compares a
|
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geography's attribute to the attribute in neighbor geographies.
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These functions are convenience functions for extracting only information that you are interested in exposing based on the outputs of the `CDB_AreasOfInterest` functions. For instance, you can use `CDB_GetSpatialHotspots` to output only the classifications of `HH` and `HL`.
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|
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For the example of cancer rates in neighborhoods, since these neighborhoods have
|
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a high value for rate of cancer, and all of their neighbors do as well, they are
|
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designated as "High High" or simply **HH**. For areas with multiple neighborhoods
|
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with low rates of cancer, they are designated as "Low Low" or **LL**. HH and LL
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naturally fit into the concept of clustering and are in the correlated
|
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variables.
|
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### Non-rate functions
|
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|
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"Anticorrelated" geogs are in **LH** and **HL** regions -- that is, regions
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where a geog has a high value and it's neighbors, on average, have a low value
|
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(or vice versa). An example of this is a "gated community" or placement of a
|
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city housing project in a rich region. These deliberate developments have
|
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opposite median income as compared to the neighbors around them. They have a
|
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high (or low) value while their neighbors have a low (or high) value. They exist
|
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typically as islands, and in rare circumstances can extend as chains dividing
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**LL** or **HH**.
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#### CDB_GetSpatialHotspots
|
||||
This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocal` except that the outputs are filtered to be only 'HH' and 'HL' (areas of high values). For more information about this function's use, see `CDB_AreasOfInterestLocal`.
|
||||
|
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Strong policies such as rent stabilization (probably) tend to prevent the
|
||||
clustering of high rent areas as they integrate middle class incomes. Luxury
|
||||
apartment buildings, which are a kind of gated community, probably tend to skew
|
||||
an area's median income upwards while housing projects have the opposite effect.
|
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What are the nuggets in the analysis?
|
||||
#### CDB_GetSpatialColdspots
|
||||
This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocal` except that the outputs are filtered to be only 'LL' and 'LH' (areas of low values). For more information about this function's use, see `CDB_AreasOfInterestLocal`.
|
||||
|
||||
Two functions are available to compute Moran I statistics:
|
||||
#### CDB_GetSpatialOutliers
|
||||
This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocal` except that the outputs are filtered to be only 'HL' and 'LH' (areas where highs or lows are surrounded by opposite values on average). For more information about this function's use, see `CDB_AreasOfInterestLocal`.
|
||||
|
||||
* `cdb_moran_local` computes Moran I measures, quad classification and
|
||||
significance values from numerial values associated to geometry entities
|
||||
in an input table. The geometries should be contiguous polygons When
|
||||
then `queen` `w_type` is used.
|
||||
* `cdb_moran_local_rate` computes the same statistics using a ratio between
|
||||
numerator and denominator columns of a table.
|
||||
### Rate functions
|
||||
|
||||
The parameters for `cdb_moran_local` are:
|
||||
#### CDB_GetSpatialHotspotsRate
|
||||
|
||||
* `table` name of the table that contains the data values
|
||||
* `attr` name of the column
|
||||
* `signficance` significance threshold for the quads values
|
||||
* `num_ngbrs` number of neighbors to consider (default: 5)
|
||||
* `permutations` number of random permutations for calculation of
|
||||
pseudo-p values (default: 99)
|
||||
* `geom_column` number of the geometry column (default: "the_geom")
|
||||
* `id_col` PK column of the table (default: "cartodb_id")
|
||||
* `w_type` Weight types: can be "knn" for k-nearest neighbor weights
|
||||
or "queen" for contiguity based weights.
|
||||
This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocalRate` except that the outputs are filtered to be only 'HH' and 'HL' (areas of high values). For more information about this function's use, see `CDB_AreasOfInterestLocalRate`.
|
||||
|
||||
The function returns a table with the following columns:
|
||||
#### CDB_GetSpatialColdspotsRate
|
||||
|
||||
* `moran` Moran's value
|
||||
* `quads` quad classification ('HH', 'LL', 'HL', 'LH' or 'Not significant')
|
||||
* `significance` significance value
|
||||
* `ids` id of the corresponding record in the input table
|
||||
This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocalRate` except that the outputs are filtered to be only 'LL' and 'LH' (areas of low values). For more information about this function's use, see `CDB_AreasOfInterestLocalRate`.
|
||||
|
||||
Function `cdb_moran_local_rate` only differs in that the `attr` input
|
||||
parameter is substituted by `numerator` and `denominator`.
|
||||
#### CDB_GetSpatialOutliersRate
|
||||
|
||||
This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocalRate` except that the outputs are filtered to be only 'HL' and 'LH' (areas where highs or lows are surrounded by opposite values on average). For more information about this function's use, see `CDB_AreasOfInterestLocalRate`.
|
||||
|
||||
@ -1,89 +1,233 @@
|
||||
-- Moran's I (global)
|
||||
-- Moran's I Global Measure (public-facing)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_AreasOfInterest_Global (
|
||||
CDB_AreasOfInterestGlobal(
|
||||
subquery TEXT,
|
||||
attr_name TEXT,
|
||||
column_name TEXT,
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id',
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5)
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS TABLE (moran NUMERIC, significance NUMERIC)
|
||||
AS $$
|
||||
plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
|
||||
from crankshaft.clustering import moran_local
|
||||
# TODO: use named parameters or a dictionary
|
||||
return moran(subquery, attr, num_ngbrs, permutations, geom_col, id_col, w_type)
|
||||
return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
|
||||
-- Moran's I Local
|
||||
-- Moran's I Local (internal function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_AreasOfInterest_Local(
|
||||
_CDB_AreasOfInterestLocal(
|
||||
subquery TEXT,
|
||||
attr TEXT,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id',
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5)
|
||||
RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, ids INT, y NUMERIC)
|
||||
column_name TEXT,
|
||||
w_type TEXT,
|
||||
num_ngbrs INT,
|
||||
permutations INT,
|
||||
geom_col TEXT,
|
||||
id_col TEXT)
|
||||
RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
|
||||
from crankshaft.clustering import moran_local
|
||||
# TODO: use named parameters or a dictionary
|
||||
return moran_local(subquery, attr, permutations, geom_col, id_col, w_type, num_ngbrs)
|
||||
return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
|
||||
-- Moran's I Rate (global)
|
||||
-- Moran's I Local (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_AreasOfInterest_Global_Rate(
|
||||
CDB_AreasOfInterestLocal(
|
||||
subquery TEXT,
|
||||
column_name TEXT,
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
|
||||
SELECT moran, quads, significance, rowid, vals
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col);
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
|
||||
-- Moran's I only for HH and HL (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_GetSpatialHotspots(
|
||||
subquery TEXT,
|
||||
column_name TEXT,
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
|
||||
SELECT moran, quads, significance, rowid, vals
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('HH', 'HL');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
|
||||
-- Moran's I only for LL and LH (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_GetSpatialColdspots(
|
||||
subquery TEXT,
|
||||
attr TEXT,
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
|
||||
SELECT moran, quads, significance, rowid, vals
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('LL', 'LH');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
|
||||
-- Moran's I only for LH and HL (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_GetSpatialOutliers(
|
||||
subquery TEXT,
|
||||
attr TEXT,
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
|
||||
SELECT moran, quads, significance, rowid, vals
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('HL', 'LH');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
|
||||
-- Moran's I Global Rate (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_AreasOfInterestGlobalRate(
|
||||
subquery TEXT,
|
||||
numerator TEXT,
|
||||
denominator TEXT,
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id',
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5)
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS TABLE (moran FLOAT, significance FLOAT)
|
||||
AS $$
|
||||
plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
|
||||
from crankshaft.clustering import moran_local
|
||||
# TODO: use named parameters or a dictionary
|
||||
return moran_rate(subquery, numerator, denominator, permutations, geom_col, id_col, w_type, num_ngbrs)
|
||||
return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
|
||||
|
||||
-- Moran's I Local Rate
|
||||
-- Moran's I Local Rate (internal function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_AreasOfInterest_Local_Rate(
|
||||
_CDB_AreasOfInterestLocalRate(
|
||||
subquery TEXT,
|
||||
numerator TEXT,
|
||||
denominator TEXT,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id',
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5)
|
||||
w_type TEXT,
|
||||
num_ngbrs INT,
|
||||
permutations INT,
|
||||
geom_col TEXT,
|
||||
id_col TEXT)
|
||||
RETURNS
|
||||
TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, ids INT, y NUMERIC)
|
||||
TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
|
||||
from crankshaft.clustering import moran_local_rate
|
||||
# TODO: use named parameters or a dictionary
|
||||
return moran_local_rate(subquery, numerator, denominator, permutations, geom_col, id_col, w_type, num_ngbrs)
|
||||
return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
|
||||
-- -- Moran's I Local Bivariate
|
||||
-- CREATE OR REPLACE FUNCTION
|
||||
-- cdb_moran_local_bv(
|
||||
-- subquery TEXT,
|
||||
-- attr1 TEXT,
|
||||
-- attr2 TEXT,
|
||||
-- permutations INT DEFAULT 99,
|
||||
-- geom_col TEXT DEFAULT 'the_geom',
|
||||
-- id_col TEXT DEFAULT 'cartodb_id',
|
||||
-- w_type TEXT DEFAULT 'knn',
|
||||
-- num_ngbrs INT DEFAULT 5)
|
||||
-- RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric)
|
||||
-- AS $$
|
||||
-- from crankshaft.clustering import moran_local_bv
|
||||
-- # TODO: use named parameters or a dictionary
|
||||
-- return moran_local_bv(t, attr1, attr2, permutations, geom_col, id_col, w_type, num_ngbrs)
|
||||
-- $$ LANGUAGE plpythonu;
|
||||
-- Moran's I Local Rate (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_AreasOfInterestLocalRate(
|
||||
subquery TEXT,
|
||||
numerator TEXT,
|
||||
denominator TEXT,
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS
|
||||
TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
|
||||
SELECT moran, quads, significance, rowid, vals
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col);
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
|
||||
-- Moran's I Local Rate only for HH and HL (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_GetSpatialHotspotsRate(
|
||||
subquery TEXT,
|
||||
numerator TEXT,
|
||||
denominator TEXT,
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS
|
||||
TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
|
||||
SELECT moran, quads, significance, rowid, vals
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('HH', 'HL');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
|
||||
-- Moran's I Local Rate only for LL and LH (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_GetSpatialColdspotsRate(
|
||||
subquery TEXT,
|
||||
numerator TEXT,
|
||||
denominator TEXT,
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS
|
||||
TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
|
||||
SELECT moran, quads, significance, rowid, vals
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('LL', 'LH');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
|
||||
-- Moran's I Local Rate only for LH and HL (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_GetSpatialOutliersRate(
|
||||
subquery TEXT,
|
||||
numerator TEXT,
|
||||
denominator TEXT,
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5,
|
||||
permutations INT DEFAULT 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS
|
||||
TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
|
||||
SELECT moran, quads, significance, rowid, vals
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('HL', 'LH');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
|
||||
@ -1,248 +1,277 @@
|
||||
\pset format unaligned
|
||||
\set ECHO all
|
||||
\i test/fixtures/ppoints.sql
|
||||
-- test table (spanish province centroids with some invented values)
|
||||
CREATE TABLE ppoints (cartodb_id integer, the_geom geometry, the_geom_webmercator geometry, code text, region_code text, value float);
|
||||
INSERT INTO ppoints VALUES
|
||||
( 1,'0101000020E6100000A8306DC0CBC305C051D14B6CE56A4540'::geometry,ST_Transform('0101000020E6100000A8306DC0CBC305C051D14B6CE56A4540'::geometry, 3857),'01','16',0.5),
|
||||
( 4,'0101000020E6100000E220A4362DC202C0FD8AFA5119994240'::geometry,ST_Transform('0101000020E6100000E220A4362DC202C0FD8AFA5119994240'::geometry, 3857),'04','01',0.1),
|
||||
( 5,'0101000020E610000004377E573AC813C0CB5871BB17494440'::geometry,ST_Transform('0101000020E610000004377E573AC813C0CB5871BB17494440'::geometry, 3857),'05','07',0.3),
|
||||
( 2,'0101000020E610000000F49BE19BAFFFBF639958FDA6694340'::geometry,ST_Transform('0101000020E610000000F49BE19BAFFFBF639958FDA6694340'::geometry, 3857),'02','08',0.7),
|
||||
( 3,'0101000020E61000005D0B7E63C832E2BFDB63EB00443D4340'::geometry,ST_Transform('0101000020E61000005D0B7E63C832E2BFDB63EB00443D4340'::geometry, 3857),'03','10',0.2),
|
||||
( 6,'0101000020E61000006F3742B7FB9018C0DD967DC4D95A4340'::geometry,ST_Transform('0101000020E61000006F3742B7FB9018C0DD967DC4D95A4340'::geometry, 3857),'06','11',0.05),
|
||||
( 7,'0101000020E6100000E4BB36995F4C0740EAC0E5CA9FC94340'::geometry,ST_Transform('0101000020E6100000E4BB36995F4C0740EAC0E5CA9FC94340'::geometry, 3857),'07','04',0.4),
|
||||
( 8,'0101000020E61000003D43CC6CAFBEFF3F6B52E66F91DD4440'::geometry,ST_Transform('0101000020E61000003D43CC6CAFBEFF3F6B52E66F91DD4440'::geometry, 3857),'08','09',0.7),
|
||||
( 9,'0101000020E61000003CC797BD99AF0CC0495A87FA312F4540'::geometry,ST_Transform('0101000020E61000003CC797BD99AF0CC0495A87FA312F4540'::geometry, 3857),'09','07',0.5),
|
||||
(13,'0101000020E61000001CAA00A9F19F0EC05DF9267B7A764340'::geometry,ST_Transform('0101000020E61000001CAA00A9F19F0EC05DF9267B7A764340'::geometry, 3857),'13','08',0.4),
|
||||
(16,'0101000020E6100000D8208F3CBC9001C065638DC1B1F24340'::geometry,ST_Transform('0101000020E6100000D8208F3CBC9001C065638DC1B1F24340'::geometry, 3857),'16','08',0.4),
|
||||
(17,'0101000020E6100000E9E6A94A71630540AD7A0CB062104540'::geometry,ST_Transform('0101000020E6100000E9E6A94A71630540AD7A0CB062104540'::geometry, 3857),'17','09',0.6),
|
||||
(18,'0101000020E6100000719792D59E240AC098AC548E00A84240'::geometry,ST_Transform('0101000020E6100000719792D59E240AC098AC548E00A84240'::geometry, 3857),'18','01',0.3),
|
||||
(19,'0101000020E6100000972C878B50FD04C0123C881D1F684440'::geometry,ST_Transform('0101000020E6100000972C878B50FD04C0123C881D1F684440'::geometry, 3857),'19','08',0.7),
|
||||
(21,'0101000020E6100000F7893E9934511BC0EAA4BF03E1C94240'::geometry,ST_Transform('0101000020E6100000F7893E9934511BC0EAA4BF03E1C94240'::geometry, 3857),'21','01',0.1),
|
||||
(22,'0101000020E6100000572C2123B2A8B2BF7ED7FABAFD194540'::geometry,ST_Transform('0101000020E6100000572C2123B2A8B2BF7ED7FABAFD194540'::geometry, 3857),'22','02',0.4),
|
||||
(25,'0101000020E6100000461B67D688C4F03FD990EEC3A0054540'::geometry,ST_Transform('0101000020E6100000461B67D688C4F03FD990EEC3A0054540'::geometry, 3857),'25','09',0.4),
|
||||
(26,'0101000020E6100000A139FB06E82204C0539D84F62E234540'::geometry,ST_Transform('0101000020E6100000A139FB06E82204C0539D84F62E234540'::geometry, 3857),'26','17',0.6),
|
||||
(27,'0101000020E6100000A92E54E618C91DC00D3A947B81814540'::geometry,ST_Transform('0101000020E6100000A92E54E618C91DC00D3A947B81814540'::geometry, 3857),'27','12',0.3),
|
||||
(28,'0101000020E6100000971DC8B682BC0DC016D0E8055F3F4440'::geometry,ST_Transform('0101000020E6100000971DC8B682BC0DC016D0E8055F3F4440'::geometry, 3857),'28','13',0.8),
|
||||
(30,'0101000020E6100000A2DC1964A8C5F7BF19299C994D004340'::geometry,ST_Transform('0101000020E6100000A2DC1964A8C5F7BF19299C994D004340'::geometry, 3857),'30','14',0.1),
|
||||
(31,'0101000020E6100000DCA1FCC87B56FABF9B88E9D866554540'::geometry,ST_Transform('0101000020E6100000DCA1FCC87B56FABF9B88E9D866554540'::geometry, 3857),'31','15',0.9),
|
||||
(32,'0101000020E6100000E1517AFCD15E1EC0A18D8D4825194540'::geometry,ST_Transform('0101000020E6100000E1517AFCD15E1EC0A18D8D4825194540'::geometry, 3857),'32','12',0.3),
|
||||
(33,'0101000020E6100000A7FF33825AF917C0FABE7DFB6BA54540'::geometry,ST_Transform('0101000020E6100000A7FF33825AF917C0FABE7DFB6BA54540'::geometry, 3857),'33','03',0.4),
|
||||
(34,'0101000020E6100000FB4E4EBEB72412C0898E7240982F4540'::geometry,ST_Transform('0101000020E6100000FB4E4EBEB72412C0898E7240982F4540'::geometry, 3857),'34','07',0.3),
|
||||
(35,'0101000020E6100000224682B01B1A2DC011091656CC5C3C40'::geometry,ST_Transform('0101000020E6100000224682B01B1A2DC011091656CC5C3C40'::geometry, 3857),'35','05',0.3),
|
||||
(36,'0101000020E6100000F7C9447110EC20C04C5D4823C7374540'::geometry,ST_Transform('0101000020E6100000F7C9447110EC20C04C5D4823C7374540'::geometry, 3857),'36','12',0.2),
|
||||
(37,'0101000020E610000053D6A26DFB4218C09D58FAE209674440'::geometry,ST_Transform('0101000020E610000053D6A26DFB4218C09D58FAE209674440'::geometry, 3857),'37','07',0.5),
|
||||
(38,'0101000020E6100000B1D1B5FC910431C03C0C89BA03503C40'::geometry,ST_Transform('0101000020E6100000B1D1B5FC910431C03C0C89BA03503C40'::geometry, 3857),'38','05',0.4),
|
||||
(39,'0101000020E610000086E6FEE1BD1E10C00417096748994540'::geometry,ST_Transform('0101000020E610000086E6FEE1BD1E10C00417096748994540'::geometry, 3857),'39','06',0.6),
|
||||
(40,'0101000020E6100000FB51C33F733710C038D01729E4954440'::geometry,ST_Transform('0101000020E6100000FB51C33F733710C038D01729E4954440'::geometry, 3857),'40','07',0.5),
|
||||
(41,'0101000020E6100000912D6FDA28BB16C031321F08C4B74240'::geometry,ST_Transform('0101000020E6100000912D6FDA28BB16C031321F08C4B74240'::geometry, 3857),'41','01',0.4),
|
||||
(42,'0101000020E6100000554432EABEB504C069ECD78775CF4440'::geometry,ST_Transform('0101000020E6100000554432EABEB504C069ECD78775CF4440'::geometry, 3857),'42','07',0.2),
|
||||
(43,'0101000020E6100000157F117C1A2EEA3F027CD1F2368B4440'::geometry,ST_Transform('0101000020E6100000157F117C1A2EEA3F027CD1F2368B4440'::geometry, 3857),'43','09',0.3),
|
||||
(44,'0101000020E610000051AA5B1BD718EABFEE67613BA4544440'::geometry,ST_Transform('0101000020E610000051AA5B1BD718EABFEE67613BA4544440'::geometry, 3857),'44','02',0.2),
|
||||
(45,'0101000020E610000022C5C01BB69710C08563BC1499E54340'::geometry,ST_Transform('0101000020E610000022C5C01BB69710C08563BC1499E54340'::geometry, 3857),'45','08',0.3),
|
||||
(46,'0101000020E6100000D5FCF78A11A0E9BFDEA46F8E64AF4340'::geometry,ST_Transform('0101000020E6100000D5FCF78A11A0E9BFDEA46F8E64AF4340'::geometry, 3857),'46','10',0.2),
|
||||
(47,'0101000020E61000003AE63525866313C02100050B2BD14440'::geometry,ST_Transform('0101000020E61000003AE63525866313C02100050B2BD14440'::geometry, 3857),'47','07',0.3),
|
||||
(48,'0101000020E610000030F187FD1FD206C0C767E1496C9E4540'::geometry,ST_Transform('0101000020E610000030F187FD1FD206C0C767E1496C9E4540'::geometry, 3857),'48','16',0.5),
|
||||
(49,'0101000020E61000009C22867B12EC17C006C5F40C14DD4440'::geometry,ST_Transform('0101000020E61000009C22867B12EC17C006C5F40C14DD4440'::geometry, 3857),'49','07',0.2),
|
||||
(50,'0101000020E6100000F7D5EFC62D08F1BF69D1231D68CF4440'::geometry,ST_Transform('0101000020E6100000F7D5EFC62D08F1BF69D1231D68CF4440'::geometry, 3857),'50','02',0.6),
|
||||
(51,'0101000020E61000005B0E1F8DAA5F15C0530BFE285BF24140'::geometry,ST_Transform('0101000020E61000005B0E1F8DAA5F15C0530BFE285BF24140'::geometry, 3857),'51','18',0.01),
|
||||
(10,'0101000020E61000000FD65D82AEA418C06192D1351FDB4340'::geometry,ST_Transform('0101000020E61000000FD65D82AEA418C06192D1351FDB4340'::geometry, 3857),'10','11',0.04),
|
||||
(11,'0101000020E6100000B305531DAB0A17C0DEAFCD4EE5464240'::geometry,ST_Transform('0101000020E6100000B305531DAB0A17C0DEAFCD4EE5464240'::geometry, 3857),'11','01',0.08),
|
||||
(12,'0101000020E610000059721A7297C9C2BF9EBE383BE51E4440'::geometry,ST_Transform('0101000020E610000059721A7297C9C2BF9EBE383BE51E4440'::geometry, 3857),'12','10',0.2),
|
||||
(14,'0101000020E610000000C86313AF3C13C0E530879C10FF4240'::geometry,ST_Transform('0101000020E610000000C86313AF3C13C0E530879C10FF4240'::geometry, 3857),'14','01',0.2),
|
||||
(15,'0101000020E61000002A475497B6ED20C06643D4131A904540'::geometry,ST_Transform('0101000020E61000002A475497B6ED20C06643D4131A904540'::geometry, 3857),'15','12',0.3),
|
||||
(20,'0101000020E6100000F975566FAD8D01C0E840C33F67924540'::geometry,ST_Transform('0101000020E6100000F975566FAD8D01C0E840C33F67924540'::geometry, 3857),'20','16',0.8),
|
||||
(23,'0101000020E610000025FA13E595880BC022BB07131D024340'::geometry,ST_Transform('0101000020E610000025FA13E595880BC022BB07131D024340'::geometry, 3857),'23','01',0.1),
|
||||
(24,'0101000020E61000009C5F91C5095C17C0C78784B15A4F4540'::geometry,ST_Transform('0101000020E61000009C5F91C5095C17C0C78784B15A4F4540'::geometry, 3857),'24','07',0.3),
|
||||
(29,'0101000020E6100000C34D4A5B48E712C092E680892C684240'::geometry,ST_Transform('0101000020E6100000C34D4A5B48E712C092E680892C684240'::geometry, 3857),'29','01',0.3),
|
||||
(52,'0101000020E6100000406A545EB29A07C04E5F0BDA39A54140'::geometry,ST_Transform('0101000020E6100000406A545EB29A07C04E5F0BDA39A54140'::geometry, 3857),'52','19',0.01)
|
||||
\i test/fixtures/ppoints2.sql
|
||||
-- test table (spanish province centroids with some invented values)
|
||||
CREATE TABLE ppoints2 (cartodb_id integer, the_geom geometry, code text, region_code text, numerator float, denominator float);
|
||||
INSERT INTO ppoints2 VALUES
|
||||
( 1,'0101000020E6100000A8306DC0CBC305C051D14B6CE56A4540'::geometry,'01','16',0.5, 1.0),
|
||||
( 4,'0101000020E6100000E220A4362DC202C0FD8AFA5119994240'::geometry,'04','01',0.1, 1.0),
|
||||
( 5,'0101000020E610000004377E573AC813C0CB5871BB17494440'::geometry,'05','07',0.3, 1.0),
|
||||
( 2,'0101000020E610000000F49BE19BAFFFBF639958FDA6694340'::geometry,'02','08',0.7, 1.0),
|
||||
( 3,'0101000020E61000005D0B7E63C832E2BFDB63EB00443D4340'::geometry,'03','10',0.2, 1.0),
|
||||
( 6,'0101000020E61000006F3742B7FB9018C0DD967DC4D95A4340'::geometry,'06','11',0.05, 1.0),
|
||||
( 7,'0101000020E6100000E4BB36995F4C0740EAC0E5CA9FC94340'::geometry,'07','04',0.4, 1.0),
|
||||
( 8,'0101000020E61000003D43CC6CAFBEFF3F6B52E66F91DD4440'::geometry,'08','09',0.7, 1.0),
|
||||
( 9,'0101000020E61000003CC797BD99AF0CC0495A87FA312F4540'::geometry,'09','07',0.5, 1.0),
|
||||
(13,'0101000020E61000001CAA00A9F19F0EC05DF9267B7A764340'::geometry,'13','08',0.4, 1.0),
|
||||
(16,'0101000020E6100000D8208F3CBC9001C065638DC1B1F24340'::geometry,'16','08',0.4, 1.0),
|
||||
(17,'0101000020E6100000E9E6A94A71630540AD7A0CB062104540'::geometry,'17','09',0.6, 1.0),
|
||||
(18,'0101000020E6100000719792D59E240AC098AC548E00A84240'::geometry,'18','01',0.3, 1.0),
|
||||
(19,'0101000020E6100000972C878B50FD04C0123C881D1F684440'::geometry,'19','08',0.7, 1.0),
|
||||
(21,'0101000020E6100000F7893E9934511BC0EAA4BF03E1C94240'::geometry,'21','01',0.1, 1.0),
|
||||
(22,'0101000020E6100000572C2123B2A8B2BF7ED7FABAFD194540'::geometry,'22','02',0.4, 1.0),
|
||||
(25,'0101000020E6100000461B67D688C4F03FD990EEC3A0054540'::geometry,'25','09',0.4, 1.0),
|
||||
(26,'0101000020E6100000A139FB06E82204C0539D84F62E234540'::geometry,'26','17',0.6, 1.0),
|
||||
(27,'0101000020E6100000A92E54E618C91DC00D3A947B81814540'::geometry,'27','12',0.3, 1.0),
|
||||
(28,'0101000020E6100000971DC8B682BC0DC016D0E8055F3F4440'::geometry,'28','13',0.8, 1.0),
|
||||
(30,'0101000020E6100000A2DC1964A8C5F7BF19299C994D004340'::geometry,'30','14',0.1, 1.0),
|
||||
(31,'0101000020E6100000DCA1FCC87B56FABF9B88E9D866554540'::geometry,'31','15',0.9, 1.0),
|
||||
(32,'0101000020E6100000E1517AFCD15E1EC0A18D8D4825194540'::geometry,'32','12',0.3, 1.0),
|
||||
(33,'0101000020E6100000A7FF33825AF917C0FABE7DFB6BA54540'::geometry,'33','03',0.4, 1.0),
|
||||
(34,'0101000020E6100000FB4E4EBEB72412C0898E7240982F4540'::geometry,'34','07',0.3, 1.0),
|
||||
(35,'0101000020E6100000224682B01B1A2DC011091656CC5C3C40'::geometry,'35','05',0.3, 1.0),
|
||||
(36,'0101000020E6100000F7C9447110EC20C04C5D4823C7374540'::geometry,'36','12',0.2, 1.0),
|
||||
(37,'0101000020E610000053D6A26DFB4218C09D58FAE209674440'::geometry,'37','07',0.5, 1.0),
|
||||
(38,'0101000020E6100000B1D1B5FC910431C03C0C89BA03503C40'::geometry,'38','05',0.4, 1.0),
|
||||
(39,'0101000020E610000086E6FEE1BD1E10C00417096748994540'::geometry,'39','06',0.6, 1.0),
|
||||
(40,'0101000020E6100000FB51C33F733710C038D01729E4954440'::geometry,'40','07',0.5, 1.0),
|
||||
(41,'0101000020E6100000912D6FDA28BB16C031321F08C4B74240'::geometry,'41','01',0.4, 1.0),
|
||||
(42,'0101000020E6100000554432EABEB504C069ECD78775CF4440'::geometry,'42','07',0.2, 1.0),
|
||||
(43,'0101000020E6100000157F117C1A2EEA3F027CD1F2368B4440'::geometry,'43','09',0.3, 1.0),
|
||||
(44,'0101000020E610000051AA5B1BD718EABFEE67613BA4544440'::geometry,'44','02',0.2, 1.0),
|
||||
(45,'0101000020E610000022C5C01BB69710C08563BC1499E54340'::geometry,'45','08',0.3, 1.0),
|
||||
(46,'0101000020E6100000D5FCF78A11A0E9BFDEA46F8E64AF4340'::geometry,'46','10',0.2, 1.0),
|
||||
(47,'0101000020E61000003AE63525866313C02100050B2BD14440'::geometry,'47','07',0.3, 1.0),
|
||||
(48,'0101000020E610000030F187FD1FD206C0C767E1496C9E4540'::geometry,'48','16',0.5, 1.0),
|
||||
(49,'0101000020E61000009C22867B12EC17C006C5F40C14DD4440'::geometry,'49','07',0.2, 1.0),
|
||||
(50,'0101000020E6100000F7D5EFC62D08F1BF69D1231D68CF4440'::geometry,'50','02',0.6, 1.0),
|
||||
(51,'0101000020E61000005B0E1F8DAA5F15C0530BFE285BF24140'::geometry,'51','18',0.01, 1.0),
|
||||
(10,'0101000020E61000000FD65D82AEA418C06192D1351FDB4340'::geometry,'10','11',0.04, 1.0),
|
||||
(11,'0101000020E6100000B305531DAB0A17C0DEAFCD4EE5464240'::geometry,'11','01',0.08, 1.0),
|
||||
(12,'0101000020E610000059721A7297C9C2BF9EBE383BE51E4440'::geometry,'12','10',0.2, 1.0),
|
||||
(14,'0101000020E610000000C86313AF3C13C0E530879C10FF4240'::geometry,'14','01',0.2, 1.0),
|
||||
(15,'0101000020E61000002A475497B6ED20C06643D4131A904540'::geometry,'15','12',0.3, 1.0),
|
||||
(20,'0101000020E6100000F975566FAD8D01C0E840C33F67924540'::geometry,'20','16',0.8, 1.0),
|
||||
(23,'0101000020E610000025FA13E595880BC022BB07131D024340'::geometry,'23','01',0.1, 1.0),
|
||||
(24,'0101000020E61000009C5F91C5095C17C0C78784B15A4F4540'::geometry,'24','07',0.3, 1.0),
|
||||
(29,'0101000020E6100000C34D4A5B48E712C092E680892C684240'::geometry,'29','01',0.3, 1.0),
|
||||
(52,'0101000020E6100000406A545EB29A07C04E5F0BDA39A54140'::geometry,'52','19',0.0, 1.01)
|
||||
-- Areas of Interest functions perform some nondeterministic computations
|
||||
-- (to estimate the significance); we will set the seeds for the RNGs
|
||||
-- that affect those results to have repeateble results
|
||||
SELECT cdb_crankshaft._cdb_random_seeds(1234);
|
||||
_cdb_random_seeds
|
||||
-------------------
|
||||
|
||||
(1 row)
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
_cdb_random_seeds
|
||||
|
||||
SELECT ppoints.code, m.quads
|
||||
FROM ppoints
|
||||
JOIN cdb_crankshaft.CDB_AreasOfInterest_Local('SELECT * FROM ppoints', 'value') m
|
||||
ON ppoints.cartodb_id = m.ids
|
||||
ORDER BY ppoints.code;
|
||||
code | quads
|
||||
------+-------
|
||||
01 | HH
|
||||
02 | HL
|
||||
03 | LL
|
||||
04 | LL
|
||||
05 | LH
|
||||
06 | LL
|
||||
07 | HH
|
||||
08 | HH
|
||||
09 | HH
|
||||
10 | LL
|
||||
11 | LL
|
||||
12 | LL
|
||||
13 | HL
|
||||
14 | LL
|
||||
15 | LL
|
||||
16 | HH
|
||||
17 | HH
|
||||
18 | LL
|
||||
19 | HH
|
||||
20 | HH
|
||||
21 | LL
|
||||
22 | HH
|
||||
23 | LL
|
||||
24 | LL
|
||||
25 | HH
|
||||
26 | HH
|
||||
27 | LL
|
||||
28 | HH
|
||||
29 | LL
|
||||
30 | LL
|
||||
31 | HH
|
||||
32 | LL
|
||||
33 | HL
|
||||
34 | LH
|
||||
35 | LL
|
||||
36 | LL
|
||||
37 | HL
|
||||
38 | HL
|
||||
39 | HH
|
||||
40 | HH
|
||||
41 | HL
|
||||
42 | LH
|
||||
43 | LH
|
||||
44 | LL
|
||||
45 | LH
|
||||
46 | LL
|
||||
47 | LL
|
||||
48 | HH
|
||||
49 | LH
|
||||
50 | HH
|
||||
51 | LL
|
||||
52 | LL
|
||||
(1 row)
|
||||
code|quads
|
||||
01|HH
|
||||
02|HL
|
||||
03|LL
|
||||
04|LL
|
||||
05|LH
|
||||
06|LL
|
||||
07|HH
|
||||
08|HH
|
||||
09|HH
|
||||
10|LL
|
||||
11|LL
|
||||
12|LL
|
||||
13|HL
|
||||
14|LL
|
||||
15|LL
|
||||
16|HH
|
||||
17|HH
|
||||
18|LL
|
||||
19|HH
|
||||
20|HH
|
||||
21|LL
|
||||
22|HH
|
||||
23|LL
|
||||
24|LL
|
||||
25|HH
|
||||
26|HH
|
||||
27|LL
|
||||
28|HH
|
||||
29|LL
|
||||
30|LL
|
||||
31|HH
|
||||
32|LL
|
||||
33|HL
|
||||
34|LH
|
||||
35|LL
|
||||
36|LL
|
||||
37|HL
|
||||
38|HL
|
||||
39|HH
|
||||
40|HH
|
||||
41|HL
|
||||
42|LH
|
||||
43|LH
|
||||
44|LL
|
||||
45|LH
|
||||
46|LL
|
||||
47|LL
|
||||
48|HH
|
||||
49|LH
|
||||
50|HH
|
||||
51|LL
|
||||
52|LL
|
||||
(52 rows)
|
||||
_cdb_random_seeds
|
||||
|
||||
SELECT cdb_crankshaft._cdb_random_seeds(1234);
|
||||
_cdb_random_seeds
|
||||
-------------------
|
||||
|
||||
(1 row)
|
||||
code|quads
|
||||
01|HH
|
||||
02|HL
|
||||
07|HH
|
||||
08|HH
|
||||
09|HH
|
||||
13|HL
|
||||
16|HH
|
||||
17|HH
|
||||
19|HH
|
||||
20|HH
|
||||
22|HH
|
||||
25|HH
|
||||
26|HH
|
||||
28|HH
|
||||
31|HH
|
||||
33|HL
|
||||
37|HL
|
||||
38|HL
|
||||
39|HH
|
||||
40|HH
|
||||
41|HL
|
||||
48|HH
|
||||
50|HH
|
||||
(23 rows)
|
||||
_cdb_random_seeds
|
||||
|
||||
SELECT ppoints2.code, m.quads
|
||||
FROM ppoints2
|
||||
JOIN cdb_crankshaft.CDB_AreasOfInterest_Local_Rate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
|
||||
ON ppoints2.cartodb_id = m.ids
|
||||
ORDER BY ppoints2.code;
|
||||
code | quads
|
||||
------+-------
|
||||
01 | LL
|
||||
02 | LH
|
||||
03 | HH
|
||||
04 | HH
|
||||
05 | LL
|
||||
06 | HH
|
||||
07 | LL
|
||||
08 | LL
|
||||
09 | LL
|
||||
10 | HH
|
||||
11 | HH
|
||||
12 | HL
|
||||
13 | LL
|
||||
14 | HH
|
||||
15 | LL
|
||||
16 | LL
|
||||
17 | LL
|
||||
18 | LH
|
||||
19 | LL
|
||||
20 | LL
|
||||
21 | HH
|
||||
22 | LL
|
||||
23 | HL
|
||||
24 | LL
|
||||
25 | LL
|
||||
26 | LL
|
||||
27 | LL
|
||||
28 | LL
|
||||
29 | LH
|
||||
30 | HH
|
||||
31 | LL
|
||||
32 | LL
|
||||
33 | LL
|
||||
34 | LL
|
||||
35 | LH
|
||||
36 | HL
|
||||
37 | LH
|
||||
38 | LH
|
||||
39 | LL
|
||||
40 | LL
|
||||
41 | LH
|
||||
42 | HL
|
||||
43 | LL
|
||||
44 | HL
|
||||
45 | LL
|
||||
46 | HL
|
||||
47 | LL
|
||||
48 | LL
|
||||
49 | HL
|
||||
50 | LL
|
||||
51 | HH
|
||||
(1 row)
|
||||
code|quads
|
||||
03|LL
|
||||
04|LL
|
||||
05|LH
|
||||
06|LL
|
||||
10|LL
|
||||
11|LL
|
||||
12|LL
|
||||
14|LL
|
||||
15|LL
|
||||
18|LL
|
||||
21|LL
|
||||
23|LL
|
||||
24|LL
|
||||
27|LL
|
||||
29|LL
|
||||
30|LL
|
||||
32|LL
|
||||
34|LH
|
||||
35|LL
|
||||
36|LL
|
||||
42|LH
|
||||
43|LH
|
||||
44|LL
|
||||
45|LH
|
||||
46|LL
|
||||
47|LL
|
||||
49|LH
|
||||
51|LL
|
||||
52|LL
|
||||
(29 rows)
|
||||
_cdb_random_seeds
|
||||
|
||||
(1 row)
|
||||
code|quads
|
||||
02|HL
|
||||
05|LH
|
||||
13|HL
|
||||
33|HL
|
||||
34|LH
|
||||
37|HL
|
||||
38|HL
|
||||
41|HL
|
||||
42|LH
|
||||
43|LH
|
||||
45|LH
|
||||
49|LH
|
||||
(12 rows)
|
||||
_cdb_random_seeds
|
||||
|
||||
(1 row)
|
||||
code|quads
|
||||
01|LL
|
||||
02|LH
|
||||
03|HH
|
||||
04|HH
|
||||
05|LL
|
||||
06|HH
|
||||
07|LL
|
||||
08|LL
|
||||
09|LL
|
||||
10|HH
|
||||
11|HH
|
||||
12|HL
|
||||
13|LL
|
||||
14|HH
|
||||
15|LL
|
||||
16|LL
|
||||
17|LL
|
||||
18|LH
|
||||
19|LL
|
||||
20|LL
|
||||
21|HH
|
||||
22|LL
|
||||
23|HL
|
||||
24|LL
|
||||
25|LL
|
||||
26|LL
|
||||
27|LL
|
||||
28|LL
|
||||
29|LH
|
||||
30|HH
|
||||
31|LL
|
||||
32|LL
|
||||
33|LL
|
||||
34|LL
|
||||
35|LH
|
||||
36|HL
|
||||
37|LH
|
||||
38|LH
|
||||
39|LL
|
||||
40|LL
|
||||
41|LH
|
||||
42|HL
|
||||
43|LL
|
||||
44|HL
|
||||
45|LL
|
||||
46|HL
|
||||
47|LL
|
||||
48|LL
|
||||
49|HL
|
||||
50|LL
|
||||
51|HH
|
||||
(51 rows)
|
||||
_cdb_random_seeds
|
||||
|
||||
(1 row)
|
||||
code|quads
|
||||
03|HH
|
||||
04|HH
|
||||
06|HH
|
||||
10|HH
|
||||
11|HH
|
||||
12|HL
|
||||
14|HH
|
||||
21|HH
|
||||
23|HL
|
||||
30|HH
|
||||
36|HL
|
||||
42|HL
|
||||
44|HL
|
||||
46|HL
|
||||
49|HL
|
||||
51|HH
|
||||
(16 rows)
|
||||
_cdb_random_seeds
|
||||
|
||||
(1 row)
|
||||
code|quads
|
||||
01|LL
|
||||
02|LH
|
||||
05|LL
|
||||
07|LL
|
||||
08|LL
|
||||
09|LL
|
||||
13|LL
|
||||
15|LL
|
||||
16|LL
|
||||
17|LL
|
||||
18|LH
|
||||
19|LL
|
||||
20|LL
|
||||
22|LL
|
||||
24|LL
|
||||
25|LL
|
||||
26|LL
|
||||
27|LL
|
||||
28|LL
|
||||
29|LH
|
||||
31|LL
|
||||
32|LL
|
||||
33|LL
|
||||
34|LL
|
||||
35|LH
|
||||
37|LH
|
||||
38|LH
|
||||
39|LL
|
||||
40|LL
|
||||
41|LH
|
||||
43|LL
|
||||
45|LL
|
||||
47|LL
|
||||
48|LL
|
||||
50|LL
|
||||
(35 rows)
|
||||
_cdb_random_seeds
|
||||
|
||||
(1 row)
|
||||
code|quads
|
||||
02|LH
|
||||
12|HL
|
||||
18|LH
|
||||
23|HL
|
||||
29|LH
|
||||
35|LH
|
||||
36|HL
|
||||
37|LH
|
||||
38|LH
|
||||
41|LH
|
||||
42|HL
|
||||
44|HL
|
||||
46|HL
|
||||
49|HL
|
||||
(14 rows)
|
||||
|
||||
@ -1,21 +1,11 @@
|
||||
\i test/fixtures/polyg_values.sql
|
||||
CREATE TABLE values (cartodb_id integer, value float, the_geom geometry);
|
||||
INSERT INTO values(cartodb_id, value, the_geom) VALUES
|
||||
(1,10,'0106000020E61000000100000001030000000100000005000000E5AF3500C03608C08068629111374440C7BC0A00C00F02C0AC0551523B414440C7BC0A00C0A700C0CAF23B6E74FB4340A7267FFFFF5206C0FBB7E41B7EE74340E5AF3500C03608C08068629111374440'::geometry),
|
||||
(2,20,'0106000020E610000001000000010300000001000000050000002439EC00804AF7BF07D6CCB5C3064440C7BC0A00C0A700C0CAF23B6E74FB4340C7BC0A00C00F02C0AC0551523B414440E20CD5FFFF30FABFBE4F76AFEA4B44402439EC00804AF7BF07D6CCB5C3064440'::geometry)
|
||||
SELECT round(cdb_crankshaft.cdb_overlap_sum(
|
||||
'0106000020E61000000100000001030000000100000004000000FFFFFFFFFF3604C09A0B9ECEC42E444000000000C060FBBF30C7FD70E01D44400000000040AD02C06481F1C8CD034440FFFFFFFFFF3604C09A0B9ECEC42E4440'::geometry,
|
||||
'values', 'value'
|
||||
), 2);
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
round
|
||||
-------
|
||||
4.42
|
||||
(1 row)
|
||||
|
||||
SELECT round(cdb_crankshaft.cdb_overlap_sum(
|
||||
'0106000020E61000000100000001030000000100000004000000FFFFFFFFFF3604C09A0B9ECEC42E444000000000C060FBBF30C7FD70E01D44400000000040AD02C06481F1C8CD034440FFFFFFFFFF3604C09A0B9ECEC42E4440'::geometry,
|
||||
'values', 'value', schema_name := 'public'
|
||||
), 2);
|
||||
round
|
||||
-------
|
||||
4.42
|
||||
|
||||
2
src/pg/test/fixtures/polyg_values.sql
vendored
2
src/pg/test/fixtures/polyg_values.sql
vendored
@ -1,3 +1,5 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
CREATE TABLE values (cartodb_id integer, value float, the_geom geometry);
|
||||
INSERT INTO values(cartodb_id, value, the_geom) VALUES
|
||||
(1,10,'0106000020E61000000100000001030000000100000005000000E5AF3500C03608C08068629111374440C7BC0A00C00F02C0AC0551523B414440C7BC0A00C0A700C0CAF23B6E74FB4340A7267FFFFF5206C0FBB7E41B7EE74340E5AF3500C03608C08068629111374440'::geometry),
|
||||
|
||||
2
src/pg/test/fixtures/ppoints.sql
vendored
2
src/pg/test/fixtures/ppoints.sql
vendored
@ -1,3 +1,5 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
-- test table (spanish province centroids with some invented values)
|
||||
CREATE TABLE ppoints (cartodb_id integer, the_geom geometry, the_geom_webmercator geometry, code text, region_code text, value float);
|
||||
INSERT INTO ppoints VALUES
|
||||
|
||||
2
src/pg/test/fixtures/ppoints2.sql
vendored
2
src/pg/test/fixtures/ppoints2.sql
vendored
@ -1,3 +1,5 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
-- test table (spanish province centroids with some invented values)
|
||||
CREATE TABLE ppoints2 (cartodb_id integer, the_geom geometry, code text, region_code text, numerator float, denominator float);
|
||||
INSERT INTO ppoints2 VALUES
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
\pset format unaligned
|
||||
\set ECHO all
|
||||
\i test/fixtures/ppoints.sql
|
||||
\i test/fixtures/ppoints2.sql
|
||||
|
||||
@ -8,14 +10,70 @@ SELECT cdb_crankshaft._cdb_random_seeds(1234);
|
||||
|
||||
SELECT ppoints.code, m.quads
|
||||
FROM ppoints
|
||||
JOIN cdb_crankshaft.CDB_AreasOfInterest_Local('SELECT * FROM ppoints', 'value') m
|
||||
ON ppoints.cartodb_id = m.ids
|
||||
JOIN cdb_crankshaft.CDB_AreasOfInterestLocal('SELECT * FROM ppoints', 'value') m
|
||||
ON ppoints.cartodb_id = m.rowid
|
||||
ORDER BY ppoints.code;
|
||||
|
||||
SELECT cdb_crankshaft._cdb_random_seeds(1234);
|
||||
|
||||
-- Spatial Hotspots
|
||||
SELECT ppoints.code, m.quads
|
||||
FROM ppoints
|
||||
JOIN cdb_crankshaft.CDB_GetSpatialHotspots('SELECT * FROM ppoints', 'value') m
|
||||
ON ppoints.cartodb_id = m.rowid
|
||||
ORDER BY ppoints.code;
|
||||
|
||||
SELECT cdb_crankshaft._cdb_random_seeds(1234);
|
||||
|
||||
-- Spatial Coldspots
|
||||
SELECT ppoints.code, m.quads
|
||||
FROM ppoints
|
||||
JOIN cdb_crankshaft.CDB_GetSpatialColdspots('SELECT * FROM ppoints', 'value') m
|
||||
ON ppoints.cartodb_id = m.rowid
|
||||
ORDER BY ppoints.code;
|
||||
|
||||
SELECT cdb_crankshaft._cdb_random_seeds(1234);
|
||||
|
||||
-- Spatial Outliers
|
||||
SELECT ppoints.code, m.quads
|
||||
FROM ppoints
|
||||
JOIN cdb_crankshaft.CDB_GetSpatialOutliers('SELECT * FROM ppoints', 'value') m
|
||||
ON ppoints.cartodb_id = m.rowid
|
||||
ORDER BY ppoints.code;
|
||||
|
||||
|
||||
SELECT cdb_crankshaft._cdb_random_seeds(1234);
|
||||
|
||||
-- Areas of Interest (rate)
|
||||
SELECT ppoints2.code, m.quads
|
||||
FROM ppoints2
|
||||
JOIN cdb_crankshaft.CDB_AreasOfInterest_Local_Rate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
|
||||
ON ppoints2.cartodb_id = m.ids
|
||||
JOIN cdb_crankshaft.CDB_AreasOfInterestLocalRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
|
||||
ON ppoints2.cartodb_id = m.rowid
|
||||
ORDER BY ppoints2.code;
|
||||
|
||||
SELECT cdb_crankshaft._cdb_random_seeds(1234);
|
||||
|
||||
-- Spatial Hotspots (rate)
|
||||
SELECT ppoints2.code, m.quads
|
||||
FROM ppoints2
|
||||
JOIN cdb_crankshaft.CDB_GetSpatialHotspotsRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
|
||||
ON ppoints2.cartodb_id = m.rowid
|
||||
ORDER BY ppoints2.code;
|
||||
|
||||
SELECT cdb_crankshaft._cdb_random_seeds(1234);
|
||||
|
||||
-- Spatial Coldspots (rate)
|
||||
SELECT ppoints2.code, m.quads
|
||||
FROM ppoints2
|
||||
JOIN cdb_crankshaft.CDB_GetSpatialColdspotsRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
|
||||
ON ppoints2.cartodb_id = m.rowid
|
||||
ORDER BY ppoints2.code;
|
||||
|
||||
SELECT cdb_crankshaft._cdb_random_seeds(1234);
|
||||
|
||||
-- Spatial Outliers (rate)
|
||||
SELECT ppoints2.code, m.quads
|
||||
FROM ppoints2
|
||||
JOIN cdb_crankshaft.CDB_GetSpatialOutliersRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
|
||||
ON ppoints2.cartodb_id = m.rowid
|
||||
ORDER BY ppoints2.code;
|
||||
|
||||
@ -14,7 +14,7 @@ import crankshaft.pysal_utils as pu
|
||||
# High level interface ---------------------------------------
|
||||
|
||||
def moran(subquery, attr_name,
|
||||
permutations, geom_col, id_col, w_type, num_ngbrs):
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I (global)
|
||||
Implementation building neighbors with a PostGIS database and Moran's I
|
||||
@ -56,7 +56,7 @@ def moran(subquery, attr_name,
|
||||
return zip([moran_global.I], [moran_global.EI])
|
||||
|
||||
def moran_local(subquery, attr,
|
||||
permutations, geom_col, id_col, w_type, num_ngbrs):
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I implementation for PL/Python
|
||||
Andy Eschbacher
|
||||
@ -96,7 +96,7 @@ def moran_local(subquery, attr,
|
||||
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
|
||||
|
||||
def moran_rate(subquery, numerator, denominator,
|
||||
permutations, geom_col, id_col, w_type, num_ngbrs):
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I Rate (global)
|
||||
Andy Eschbacher
|
||||
@ -137,7 +137,7 @@ def moran_rate(subquery, numerator, denominator,
|
||||
return zip([lisa_rate.I], [lisa_rate.EI])
|
||||
|
||||
def moran_local_rate(subquery, numerator, denominator,
|
||||
permutations, geom_col, id_col, w_type, num_ngbrs):
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I Local Rate
|
||||
Andy Eschbacher
|
||||
|
||||
@ -11,7 +11,7 @@ def construct_neighbor_query(w_type, query_vals):
|
||||
@param query_vals dict: values used to construct the query
|
||||
"""
|
||||
|
||||
if w_type == 'knn':
|
||||
if w_type.lower() == 'knn':
|
||||
return knn(query_vals)
|
||||
else:
|
||||
return queen(query_vals)
|
||||
@ -22,13 +22,13 @@ def get_weight(query_res, w_type='knn', num_ngbrs=5):
|
||||
Construct PySAL weight from return value of query
|
||||
@param query_res dict-like: query results with attributes and neighbors
|
||||
"""
|
||||
# if w_type == 'knn':
|
||||
# row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
|
||||
# weights = {x['id']: row_normed_weights for x in query_res}
|
||||
# else:
|
||||
# weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
|
||||
# if len(x['neighbors']) > 0
|
||||
# else [] for x in query_res}
|
||||
if w_type.lower() == 'knn':
|
||||
row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
|
||||
weights = {x['id']: row_normed_weights for x in query_res}
|
||||
else:
|
||||
weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
|
||||
if len(x['neighbors']) > 0
|
||||
else [] for x in query_res}
|
||||
|
||||
neighbors = {x['id']: x['neighbors'] for x in query_res}
|
||||
print 'len of neighbors: %d' % len(neighbors)
|
||||
|
||||
@ -57,7 +57,7 @@ class MoranTest(unittest.TestCase):
|
||||
data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1234)
|
||||
result = cc.moran_local('subquery', 'value', 99, 'the_geom', 'cartodb_id', 'knn', 5)
|
||||
result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
result = [(row[0], row[1]) for row in result]
|
||||
expected = self.moran_data
|
||||
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
|
||||
@ -69,7 +69,7 @@ class MoranTest(unittest.TestCase):
|
||||
data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1234)
|
||||
result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 99, 'the_geom', 'cartodb_id', 'knn', 5)
|
||||
result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
print 'result == None? ', result == None
|
||||
result = [(row[0], row[1]) for row in result]
|
||||
expected = self.moran_data
|
||||
@ -81,7 +81,7 @@ class MoranTest(unittest.TestCase):
|
||||
data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1235)
|
||||
result = cc.moran('subquery', 'value', 99, 'the_geom', 'cartodb_id', 'knn', 5)
|
||||
result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
print 'result == None?', result == None
|
||||
result_moran = result[0][0]
|
||||
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user