Skip to content

Latest commit

 

History

History
158 lines (97 loc) · 2.2 KB

lecture11.md

File metadata and controls

158 lines (97 loc) · 2.2 KB
Raw
title layout
Lecture 11
lecture

Data Visualization

Matthew Turk

Fall 2019

Lecture 11

ipyleaflet

conda install -c conda-forge ipyleaflet
jupyter labextension install jupyter-leaflet
jupyter labextension install @jupyter-widgets/jupyterlab-manager

Documentation and Github Repo

Creating a Map

import ipyleaflet
m = ipyleaflet.Map()
display(m)

Now, zoom waaaaay out.

Layers

We will refer to all of our "data objects" as Layers with ipyleaflet. These can include:

  • Tiles
  • Markers
  • Image / video overlays
  • Polyline / MultiPolyline / Polygon / MultiPolygon
  • Rectangle / Circle
  • Marker Cluster
  • Heatmap

Add Control

m.add_control(ipyleaflet.LayersControl())

This will let us choose and manipulate individual layers.

Adding some data

import json 
with open("champaign_trees.geojson") as f:
    gd = json.load(f)

layer = ipyleaflet.GeoJSON(data = gd)
m.add_layer(layer)

Experiment 1: Champaign Public Transit

Retrieve either the CUMTD data yourself from developer.cumtd.com or on Whole Tale in "Code Along."

Let's visualize the routes.

Experiment 1: Step 1

This data is in the GTFS format. It has routes, trips, stops, etc.

Step 1: load the stop and route data files

Experiment 1: Step 2

Place markers for stops on the map.

Experiment 1: Step 3

Add a layer for a heatmap of stop locations.

Kepler

First, try out kepler.gl

Kepler in Jupyter

We can display the map in a jupyter notebook.

import keplergl
k = keplergl.KeplerGL()
display(k)

Kepler: Adding Data in Jupyter

gd = pd.read_csv(" ... ")
k.add_data(data = gd, name = "my data")

Experiment 2: Step 1

Load the tree data

Experiment 2: Step 2

Display heatmap, with varying values

Voila

conda install voila

Now, let's take one of our notebooks and make it a dashboard.

Meeting Time

Time for you to meet with your groups.