FastGeospatial is a PostGIS geospatial api to enable geospatial analyses on geographical data within a spatial database. FastGeospatial is written in Python using the FastAPI web framework.
Source Code: https://github.com/mkeller3/FastGeospatial
FastGeospatial requires PostGIS >= 2.4.0.
In order for the api to work you will need to edit the config.py file with your database connections.
Example
DATABASES = { "data": { "host": "localhost", "database": "data", "username": "postgres", "password": "postgres", "port": 5432, } }
To run the app locally uvicorn main:app --reload
Build Dockerfile into a docker image to deploy to the cloud.
| Method | URL | Description |
|---|---|---|
GET |
/api/v1/analysis/status/{process_id} |
Analysis Status |
POST |
/api/v1/analysis/buffer |
Buffer |
POST |
/api/v1/analysis/dissolve |
Dissolve |
POST |
/api/v1/analysis/dissolve_by_value |
Dissolve By Value |
POST |
/api/v1/analysis/square_grids |
Square Grids |
POST |
/api/v1/analysis/hexagon_grids |
Hexagon Grids |
POST |
/api/v1/analysis/bounding_box |
Bounding Box |
POST |
/api/v1/analysis/k_means_cluster |
K Means Cluster |
POST |
/api/v1/analysis/center_of_each_polygon |
Center Of Each Polygon |
POST |
/api/v1/analysis/center_of_dataset |
Center Of Dataset |
POST |
/api/v1/analysis/find_within_distance |
Find Within Distance |
POST |
/api/v1/analysis/convex_hull |
Convex Hull |
POST |
/api/v1/analysis/aggregate_points_by_grids |
Aggregate Points By Grid |
POST |
/api/v1/analysis/aggregate_points_by_polygons |
Aggregate Points By Polygons |
POST |
/api/v1/analysis/select_inside |
Select Inside |
POST |
/api/v1/analysis/select_outside |
Select Outside |
POST |
/api/v1/analysis/clip |
Clip |
Any time an analysis is submitted it given a process_id to have the analysis run in the background using FastAPI's Background Tasks. To check the status of an analysis, you can call this endpoint with the process_id.
/api/v1/analysis/status/472e29dc-91a8-41d3-b05f-cee34006e3f7
{
"status": "PENDING"
}{
"status": "SUCCESS",
"new_table_id": "shnxppipxrppsdkozuroilkubktfodibtqorhucjvxlcdrqyhh",
"completion_time": "2022年07月06日T19:33:17.950059",
"run_time_in_seconds": 1.78599
}{
"status": "FAILURE",
"error": "ERROR HERE",
"completion_time": "2022年07月08日T13:39:47.961389",
"run_time_in_seconds": 0.040892
}
Buffer an geometric table with a buffer in kilometers.
Example: Buffer zip centroids by one kilometer.
{
"table": "zip_centroids",
"database": "data",
"distance_in_kilometers": 1
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Dissolve any geometric table into one single geometry.
Example: Dissolve all the US States into one single geometry.
{
"table": "states",
"database": "data"
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Dissolve any geometric table into geometries based off a column in the table.
Example: Dissolve US States based off a column in the table called sub_region.
{
"table": "states",
"database": "data",
"column": "sub_region"
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Generate square grids of any size based off of a tables geometry.
Example: Generate 100 kilometers square grids based off of a table containing US States.
{
"table": "states",
"database": "data",
"grid_size_in_kilometers": "100"
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Generate hexagon grids of any size based off of a tables geometry.
Example: Generate 100 kilometers hexagon grids based off of a table containing US States.
{
"table": "states",
"database": "data",
"grid_size_in_kilometers": 100
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Generate a bounding box of a table.
Example: Find the bounding box of a table that contains all of the US States.
{
"table": "states",
"database": "data",
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Example: Group all US zip centroids into 5 groups based off of k means clusters.
Use K Means Clustering to group points based on their location.
{
"table": "zip_centroids",
"database": "data",
"number_of_clusters": 5
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Find the center of each polygon for a given table.
Example: Find the center of each US State.
{
"table": "states",
"database": "data"
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Find the center of all geometries based off a given table.
Example: Find the geomeric center of a table that contains all of the US States.
{
"table": "states",
"database": "data"
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Find all geometries within a given distance from a given point.
Example: Find all states within 500 kilometers of latitude 40.45 and latitude -88.95.
{
"table": "states",
"database": "data",
"latitude": 40.45,
"longitude": -88.95,
"distance_in_kilometers": 500
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Find the smallest convex hull around a given table.
Example: Find the smallest convex hull around all the US States.
{
"table": "states",
"database": "data"
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}Aggregate Points By Grid Image
Aggregate a table of points into grids and determine how points are in each grid.
Example: Determine how many zip centroids are each 1000 kilometer hexagon grid.
{
"table": "zip_centroids",
"database": "data",
"distance_in_kilometers": 1000,
"grid_type": "hexagon"
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}Aggregate Points By Polygons Image
Aggregate a table of points into a table of polygons and determine how points are in each polygon.
Example: Determine how many zip centroids are within each US State.
{
"table": "zip_centroids",
"database": "data",
"polygons": "states"
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Find all geometries within a given polygon table.
Example: Find all zip centroids within the US States table.
{
"table": "zip_centroids",
"database": "data",
"polygons": "states"
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Find all geomtries outside a given polygon table.
Example: Find all the zip centroids outside of the table with US States.
{
"table": "zip_centroids",
"database": "data",
"polygons": "states"
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}
Clip any geometric table based of a polygon table.
Example: Clip the US States table to a large polygon.
{
"table": "states",
"database": "data",
"polygons": "big_polygon"
}{
"process_id": "c8d7b8d8-3e82-4f93-b441-55a5f51c4171",
"url": "http://127.0.0.1:8000/api/v1/analysis/status/c8d7b8d8-3e82-4f93-b441-55a5f51c4171"
}