Geospatial analysis is not limited to a single desktop software or a python kernel; if you use massive spatial analysis or if you work on a team that want some specific spatial output the use of a Rest Api might be convenient. We have developed a basic, introductory but clear tutorial of a geospatial Rest Api that implements the Post method and Get method and returns the centroid of a polygon and the list of elements with their coordinates respectively. Tutorial is done in Windows, however a real Rest server that runs several high energy spatial queries is expected to run in Linux.

Tutorial

Code

This is the code of the rest api:

# app.py
from flask import Flask, request, jsonify
from shapely.geometry import shape
import json 

app = Flask(__name__)

polygons = [
]

def _find_next_id():
    print(polygons)
    if len(polygons) > 0:
        return max(polygon["id"] for polygon in polygons) + 1
    else:
        return 1
    
@app.get("/centroids")
def get_countries():
    return jsonify(polygons)

@app.post("/centroids")
def add_polygon():
    if request.is_json:
        polygon = json.loads(request.get_json())
        polyCoords = polygon["features"][0]["geometry"]["coordinates"]
        polyShape = shape({
            "type": "MultiPolygon",
            "coordinates":polyCoords
            })
        polygon["centroidx"] = polyShape.centroid.x
        polygon["centroidy"] = polyShape.centroid.y
        polygon["id"] = _find_next_id()
        polygon["name"] = polygon["features"][0]["properties"]["name"]
        
        #remove unwanted keys
        polygon.pop("features")
        polygon.pop("type")
        
        polygons.append(polygon)
        
        return polygon, 201
    return {"error": "Request must be JSON"}, 415

Jupyter notebook that calls and represents data from the Rest api:

import requests, json
import folium
import geopandas as gpd

#open geospatial data
facDf = gpd.read_file('../In/schoolsHospitalsPoliceFire.gpkg')
#plot polygon
facDf.plot()

<AxesSubplot:>

#show dataframehead
facDf.head()

	amenity	name	geometry
0	school	Pioneer Elementary School	MULTIPOLYGON (((-116.34768 43.64752, -116.3475...
1	school	Centennial High School	MULTIPOLYGON (((-116.33940 43.65290, -116.3389...
2	school	Joplin Elementary School	MULTIPOLYGON (((-116.33274 43.65530, -116.3300...
3	school	Lowell Scott Middle School	MULTIPOLYGON (((-116.35409 43.65206, -116.3539...
4	school	Cecil D. Andrus Elementary School	MULTIPOLYGON (((-116.34585 43.66075, -116.3447...

#get the first row
firstRow = facDf.loc[[0]]
type(firstRow)

geopandas.geodataframe.GeoDataFrame

#first row as json
rowJson = firstRow.to_json()
rowJson

'{"type": "FeatureCollection", "features": [{"id": "0", "type": "Feature", "properties": {"amenity": "school", "name": "Pioneer Elementary School"}, "geometry": {"type": "MultiPolygon", "coordinates": [[[[-116.347681, 43.6475172], [-116.3475058, 43.6475166], [-116.346734, 43.6475155], [-116.3466762, 43.6475261], [-116.3466453, 43.6475518], [-116.3466085, 43.6475852], [-116.3465764, 43.6475987], [-116.3465489, 43.6476038], [-116.3465174, 43.6476076], [-116.3464713, 43.6476106], [-116.346431, 43.6476106], [-116.3463823, 43.6476086], [-116.3463207, 43.6476059], [-116.346322, 43.6474886], [-116.3444521, 43.6474892], [-116.3444149, 43.6474889], [-116.3444291, 43.6458487], [-116.3445183, 43.6458486], [-116.3477109, 43.6458464], [-116.347681, 43.6475172]]]]}}]}'

#url of the rest api
apiUrl = "http://127.0.0.1:5000/centroids"

#get all polygons, none to the moment
response = requests.get(apiUrl)
response.json()

[{'centroidx': -116.34606811481463,
  'centroidy': 43.64667797982817,
  'id': 1,
  'name': 'Pioneer Elementary School'},
 {'centroidx': -116.33714292646543,
  'centroidy': 43.65049255824704,
  'id': 2,
  'name': 'Centennial High School'}]

#add one poly
response = requests.post(apiUrl,json=rowJson)

responseDict = response.json()
responseDict

{'centroidx': -116.34606811481463,
 'centroidy': 43.64667797982817,
 'id': 3,
 'name': 'Pioneer Elementary School'}

import folium

m = folium.Map(
    location=[responseDict['centroidy'],responseDict['centroidx']],
    tiles="cartodbpositron",
    zoom_start=16,
)

centroidMarker = folium.Marker(location=[responseDict['centroidy'],responseDict['centroidx']])
centroidMarker.add_to(m)
polyJson = folium.GeoJson(data=rowJson,
                           style_function=lambda x: {'fillColor': 'orange'})
polyJson.add_to(m)

m

#get all polygons, one to the moment
response = requests.get(apiUrl)
response.json()

[{'centroidx': -116.34606811481463,
  'centroidy': 43.64667797982817,
  'id': 1,
  'name': 'Pioneer Elementary School'},
 {'centroidx': -116.33714292646543,
  'centroidy': 43.65049255824704,
  'id': 2,
  'name': 'Centennial High School'},
 {'centroidx': -116.34606811481463,
  'centroidy': 43.64667797982817,
  'id': 3,
  'name': 'Pioneer Elementary School'}]

### All step involved for second polygon

#get the second row
secondRow = facDf.loc[[1]].to_json()
#url of the rest api
apiUrl = "http://127.0.0.1:5000/centroids"
#add one poly
response = requests.post(apiUrl,json=secondRow)
#get respones
responseDict = response.json()
print(responseDict['centroidx'],responseDict['centroidy'])

-116.33714292646543 43.65049255824704

#get all polygons, two to the moment
response = requests.get(apiUrl)
response.json()

[{'centroidx': -116.34606811481463,
  'centroidy': 43.64667797982817,
  'id': 1,
  'name': 'Pioneer Elementary School'},
 {'centroidx': -116.33714292646543,
  'centroidy': 43.65049255824704,
  'id': 2,
  'name': 'Centennial High School'},
 {'centroidx': -116.34606811481463,
  'centroidy': 43.64667797982817,
  'id': 3,
  'name': 'Pioneer Elementary School'},
 {'centroidx': -116.33714292646543,
  'centroidy': 43.65049255824704,
  'id': 4,
  'name': 'Centennial High School'}]

Input data

You can download the input data from this link.

Blog

How to make a geospatial Rest Api web service with Python, Flask and Shapely - Tutorial

Tutorial

Code

Input data

Hatari Labs

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