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": "2022-07-06T19:33:17.950059", "run_time_in_seconds": 1.78599 }{ "status": "FAILURE", "error": "ERROR HERE", "completion_time": "2022-07-08T13:39:47.961389", "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 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 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" }