Appendix K: Geographic functions
editAppendix K: Geographic functions
editThe geographic functions detect anomalies in the geographic location of the input data.
The machine learning features include the following geographic function: lat_long
.
You cannot create forecasts for anomaly detection jobs that contain geographic functions. You also cannot add rules with conditions to detectors that use geographic functions.
Lat_long
editThe lat_long
function detects anomalies in the geographic location of the
input data.
This function supports the following properties:
-
field_name
(required) -
by_field_name
(optional) -
over_field_name
(optional) -
partition_field_name
(optional)
For more information about those properties, see the create anomaly detection jobs API.
Example 1: Analyzing transactions with the lat_long function.
PUT _ml/anomaly_detectors/example1 { "analysis_config": { "detectors": [{ "function" : "lat_long", "field_name" : "transaction_coordinates", "by_field_name" : "credit_card_number" }] }, "data_description": { "time_field":"timestamp", "time_format": "epoch_ms" } }
If you use this lat_long
function in a detector in your anomaly detection job, it
detects anomalies where the geographic location of a credit card transaction is
unusual for a particular customer’s credit card. An anomaly might indicate
fraud.
A "typical" value indicates a centroid of a cluster of previously observed locations that is closest to the "actual" location at that time. For example, there may be one centroid near the person’s home that is associated with the cluster of local grocery stores and restaurants, and another centroid near the person’s work associated with the cluster of lunch and coffee places.
The field_name
that you supply must be a single string that
contains two comma-separated numbers of the form latitude,longitude
, a
geo_point
field, a geo_shape
field that contains point values, or a
geo_centroid
aggregation. The latitude
and longitude
must be in the range
-180 to 180 and represent a point on the surface of the Earth.
For example, JSON data might contain the following transaction coordinates:
{ "time": 1460464275, "transaction_coordinates": "40.7,-74.0", "credit_card_number": "1234123412341234" }
In Elasticsearch, location data is likely to be stored in geo_point
fields. For more
information, see geo_point
data type. This data type is
supported natively in machine learning features. Specifically, when pulling data from a
geo_point
field, a datafeed will transform the data into the appropriate
lat,lon
string format before sending to the anomaly detection job.
For more information, see Altering data in your datafeed with runtime fields.