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Introduction: The Power of Web Maps and Geospatial Data Centers
Web maps and geospatial data centers are crucial for industries like urban planning, environmental management, transportation, and emergency services. They offer real-time, interactive access to spatial data that helps governments, businesses, and organizations make informed decisions. With the rise of cloud computing and web technologies, it has never been easier to create and host these powerful tools.
In this guide, we’ll walk you through the step-by-step process of setting up a web mapping platform and building a geospatial data center. This blog is designed for geospatial professionals, GIS developers, and data scientists who want to bring their data online, create interactive maps, and manage their geospatial data in an organized, scalable manner. We’ll showcase how we at Avakaza Geoscience successfully implemented this, and you can view the live example here: Manchitra Prajna Web Mapping Platform.
Technologies Covered:
Python & Django: Backend framework for building web applications, including geospatial services.
PostGIS: An extension to PostgreSQL that adds support for geographic objects, enabling spatial queries.
Leaflet.js: A lightweight, open-source JavaScript library for creating interactive web maps.
Mapbox: A platform for customizable map creation with powerful styling options.
PyDeck & Deck.gl: Libraries for 3D terrain visualization and advanced map rendering.
AWS EC2 & RDS: Cloud hosting infrastructure for scalable and reliable geospatial services.
GDAL/OGR: Tools for processing and converting geospatial data formats
Django REST Framework with GIS Extensions: For building APIs to expose geospatial data.
Step 1: Setting Up the Development Environment
1.1 Install Django and Create Your Project
-Start by setting up a Python virtual environment and installing Django:
>>Create and activate a virtual environment
python3 -m venv myenv
source myenv/bin/activate
>>Install Django
pip install django
-Create a new Django project and app:
django-admin startproject myproject
cd myproject
django-admin startapp myapp
1.2 Set Up PostgreSQL and PostGIS
-PostGIS extends PostgreSQL to handle geospatial data. Install PostgreSQL and PostGIS:
sudo apt-get install postgresql postgis
-In PostgreSQL, create your geospatial database:
-CREATE DATABASE geospatial_db;
\c geospatial_db
-CREATE EXTENSION postgis;
-Next, configure the Django settings to use PostGIS in settings.py:
DATABASES = {
'default': {
'ENGINE': 'django.contrib.gis.db.backends.postgis',
'NAME': 'geospatial_db',
'USER': 'postgres',
'PASSWORD': 'your_password',
'HOST': 'localhost',
'PORT': '5432',
}
}
1.3 Install GDAL and Geospatial Tools
GDAL is an essential tool for working with geospatial data. Install GDAL on your system:
sudo apt-get install gdal-bin
Set up the path to GDAL in your settings.py file:
import os
GDAL_LIBRARY_PATH = os.getenv('GDAL_LIBRARY_PATH', '/usr/local/lib/libgdal.so')
Step 2: Building the Geospatial Data Backend
2.1 Creating Geospatial Models
Django’s django.contrib.gis module allows you to work with spatial data types. Let’s define a model to store point data (like locations) using the PointField:
from django.contrib.gis.db import models
class Location(models.Model):
name = models.CharField(max_length=100)
point = models.PointField()
def _str_(self):
return self.name
2.2 Creating Geospatial APIs
To expose your geospatial data via APIs, use Django Rest Framework and the GIS extension:
pip install djangorestframework djangorestframework-gis
Now, create a serializer and view for the Location model:
from rest_framework_gis.serializers import GeoFeatureModelSerializer
from .models import Location
class LocationSerializer(GeoFeatureModelSerializer):
class Meta:
model = Location
geo_field = 'point'
fields = ['name']
And create an API view to expose the geospatial data:
from rest_framework import viewsets
from .models import Location
from .serializers import LocationSerializer
class LocationViewSet(viewsets.ModelViewSet):
queryset = Location.objects.all()
serializer_class = LocationSerializer
In your urls.py:
from django.urls import path, include
from rest_framework.routers import DefaultRouter
from .views import LocationViewSet
router = DefaultRouter()
router.register(r'locations', LocationViewSet)
urlpatterns = [
path('api/', include(router.urls)),
]
Step 3: Adding Interactive Web Maps
3.1 Setting Up Leaflet.js
Leaflet.js is a simple yet powerful library for creating interactive maps. Add Leaflet.js to your Django template:
<head>
<link rel="stylesheet" href="https://unpkg.com/leaflet@1.7.1/dist/leaflet.css" />
</head>
<body>
<div id="map" style="height: 400px;"></div>
<script>
var map = L.map('map').setView([51.505, -0.09], 13);
L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
maxZoom: 19
}).addTo(map);
</script>
</body>
3.2 Integrating Mapbox for Custom Maps
Mapbox offers high-quality map visualizations. Add it to your template:
<link href='https://api.tiles.mapbox.com/mapbox-gl-js/v2.0.0/mapbox-gl.css' rel='stylesheet' />
<script>
mapboxgl.accessToken = 'your_mapbox_access_token';
var map = new mapboxgl.Map({
container: 'map',
style: 'mapbox://styles/mapbox/streets-v11',
center: [77.5946, 12.9716], // Coordinates of Bengaluru, India
zoom: 12
});
</script>
3.3 Building 3D Terrain Models with PyDeck
For advanced 3D visualizations, use PyDeck with Deck.gl. Here’s an example of a simple 3D column layer:
import pydeck as pdk
# Define the map view
view_state = pdk.ViewState(
latitude=12.9716,
longitude=77.5946,
zoom=12,
pitch=45
)
# Define a 3D column layer
layer = pdk.Layer(
'ColumnLayer',
data=[{'lat': 12.9716, 'lon': 77.5946, 'elevation': 100}],
get_position=['lon', 'lat'],
get_elevation='elevation',
get_fill_color='[255, 0, 0]',
radius=200,
elevation_scale=100
)
# Render the deck.gl map
r = pdk.Deck(layers=[layer], initial_view_state=view_state)
r.to_html('terrain_model.html')
Step 4: Deploying the Application on AWS
4.1 Hosting the Application with AWS EC2
Create an AWS EC2 instance to host your application. After launching the instance, connect to it via SSH:
ssh -i your-key.pem ec2-user@your-ec2-public-ip
-Set up the environment by installing required packages:
sudo apt-get update
sudo apt-get install python3-pip python3-venv nginx postgresql postgis
-Clone your project and set up the virtual environment:-
git clone https://github.com/your-repo.git
cd your-project
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
4.2 Setting Up PostgreSQL and PostGIS on AWS RDS
You can also use AWS RDS for your database. Set up an RDS instance with PostgreSQL and PostGIS, then update your settings.py:
DATABASES = {
'default': {
'ENGINE': 'django.contrib.gis.db.backends.postgis',
'NAME': 'postgres',
'USER': 'your_rds_username',
'PASSWORD': 'your_rds_password',
'HOST': 'your_rds_endpoint',
'PORT': '5432',
}
}
4.3 Configuring Gunicorn and Nginx
Set up Gunicorn to serve your Django application:
gunicorn --workers 3 --bind 0.0.0.0:8000 myproject.wsgi:application
Then configure Nginx to reverse proxy to Gunicorn:
server {
listen 80;
server_name your_domain.com;
location / {
proxy_pass http://127.0.0.1:8000;
}
}
Enable the configuration and restart Nginx:
sudo ln -s /etc/nginx/sites-available/myproject /etc/nginx/sites-enabled
sudo systemctl restart nginx
Conclusion
By following this guide, you’ll be able to set up a comprehensive web mapping platform and geospatial data center. At Avakaza Geoscience, we specialize in delivering geospatial solutions, from data center development to advanced mapping platforms. For more information or personalized training, visit our website at Avakaza Geoscience Research Technologies (AGSRT)- https://www.agsrt.com/
Or explore our live demo at Manchitra Prajna Private Limited:- https://manchitraprajna.com/
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