Geomancer is a geospatial feature engineering library. It allows you to query from a geospatial data warehouse in order to create features for downstream tasks (analysis, modelling, visualization, etc.). Its features include:

  • Feature primitives for geospatial feature engineering
  • Ability to switch out data warehouses
  • Compilation and sharing your features

Feature Primitives

The basic building blocks in Geomancer are called Spells. These are SQL queries that were packaged in logical groups. Given a set of coordinates, you can obtain features such as the distance to the nearest point-of-interest (POIs), number of POIS within a certain range, and etc.

For example, we wish to obtain the distance to the nearest embassy given a sample of coordinates:

In [1]: # Load the sample_points as a dataframe
In [2]: from tests.conftest import sample_points
In [3]: df = sample_points
In  [4]: df.head()
Out [4]:

                              WKT  code
0  POINT (121.0042183 14.6749145)  2082
1  POINT (121.0052375 14.6767411)  2110
2     POINT (121.009712 14.68067)  2082
3  POINT (121.0093311 14.6799482)  2082
4  POINT (121.0073296 14.6783498)  2082

The geometries are encoded as a str inside a column named WKT. In addition, there is a code column that represents any arbitrary column in your data. What Geomancer will do is just add another column for your chosen feature while retaining the columns you originally have.

In [5]: from geomancer.spells import DistanceToNearest

In [6]: # Configure and cast the spell
In [7]: spell = DistanceToNearest("embassy",
In [8]: df_with_features = spell.cast(df, dburl="bigquery://geospatial")

It then returns a DataFrame with an added column, dist_embassy:

In  [9]: df_with_features.head()
Out [9]:
                              WKT  code  dist_embassy
0  POINT (121.0042183 14.6749145)  2082   4948.580211
1  POINT (121.0052375 14.6767411)  2110   5084.787270
2     POINT (121.009712 14.68067)  2082   5319.746371
3  POINT (121.0093311 14.6799482)  2082   5256.165257
4  POINT (121.0073296 14.6783498)  2082   5162.177598

Data Warehouse Flexibility

Geomancer is powered by a data warehouse backend for engineering features. It is then possible to compile features from different sources through this flexible API. So far, we’ve supported (and planning to support) the following database backends:

  • Google BigQuery, an analytics data warehouse from the Google Cloud Platform
  • PostGIS, a geospatial extension for PostGreSQL
  • SpatiaLite, a geospatial extension for SQLite

Most of our examples harness the power of Open Data, particularly of Open Street Maps. For our geographical columns we depend on Geofabrik’s OSM data.

Geomancer architecture


First you need to setup your data warehouse in order to accommodate Geomancer. For more instructions, please see the Setup instructions in this documentation.

Compile and share features

Once you’ve created a good set of features (or transformations), you can then compile them into a SpellBook and share it to others. For example, if I identified from my experiments that the number of supermarkets and distance to primary roads are good economic indicators, I can bind them together and share with other researchers to try on their own data.

from geomancer.spells import DistancetoNearest, NumberOf
from geomancer.spellbook import SpellBook

# Create a spellbook
spellbook = SpellBook(

# Export SpellBook into a file
spellbook.author = "Juan dela Cruz"
spellbook.description = "Good Features for Economic Indicators"

You can then share this to other people so that they can cast it on their own datasets

from geomancer.spellbook import SpellBook
from tests.conftest import sample_points

spellbook = SpellBook.read_json("features_dela_cruz.json")
df = sample_points() # load your own data

# Cast someone's Spells into your own data
df_with_features = spellbook.cast(df)