Creating Smart Maps with Python and Leaflet Windows Version

Create a spatial database in postgres with pgAdmin, including a new database and a login role with privileges. Install PostGIS extensions to enable spatial data storage.

Download and load the water consumption dataset by suburb. Install Excel reading and data manipulation libraries and analyze monthly kilolitres to identify high-use suburbs for potential alerts or distribution control.

Register the geo admin for a Django model, read Excel data into a pandas DataFrame, convert rows to point geometries, and load them into spatial back-end with Leaflet map verification.

Add the django geo json serializer and leaflet configuration to read json spatial data, set a default center and zoom, and configure base maps like open street map.

Develop the front end by adding static files in a Django project, downloading the base template, and integrating the static folder into the water watch app.

Create and wire the index view by mapping the root URL to the home view, render the index HTML on request, and load the leaflet map in the browser.

Apply leaflet badges and styling to the index page, add left and right sidebars with toggle buttons, initialize the map, and test styling so the map fills the screen.

Create data endpoints for a map app, exposing two JSON datasets—water consumption and top 10 consumers—serialized from the data model and served via HTTP responses.

Display water consumption data on the map by fetching JSON via Ajax from the water consumption endpoint, render circle markers scaled by kilolitre value, and show suburb popups.

Create a bottom-right legend for the map that defines three circle markers by kilolitre ranges (0–4, 5–10, 11–20), with labels and a title to describe data size.

Create a bar chart of top 10 water consumers in kilolitres by fetching JSON data via ajax, parsing suburb labels and kilolitre values, and rendering with chalkboard js on canvas.