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Visualize Geospatial Data Using Plotly and Mapbox

In this project, we'll learn how to load and preprocess datasets in Python. We'll explore the Plotly library, and Mapbox to visualize and analyze geospatial data in Python.

Visualize Geospatial Data Using Plotly and Mapbox

You will learn to:

Import data into pandas DataFrame.

Preprocess data using Python libraries.

Visualize and analyze data via the Plotly library.

Use the Mapbox library to plot the geospatial data.


Data Extraction

Data Manipulation

Data Visualization


Basic understanding of Python

Basic understanding of data preprocessing

Basic understanding of data visualization

Basic understanding of data analysis





Project Description

Data visualization is a method to represent textual information in a graphical format. It includes the use of plots, charts, and animation. Data visualization helps us identify behaviors and trends while ensuring no information gets overlooked, even in large datasets. For this reason, visualization is generally preferred over textual data for information communication, data analysis, and decision making.

Geospatial data is any data with information regarding the geographical locations around the sphere. It can be either in latitude-longitude (Decimal Degree) format or Degree, Minute, Second (DMS) format. Geospatial data visualization is an important tool for analysis in social and environmental science.

Python provides many visualization libraries. One of these is Plotly which provides us means and ways for data visualization via its modules and functions. Mapbox is a web service that provides tools to work with maps, directions, and navigation. The Mapbox is integrated with Plotly to offer support for plotting data on the map.

In this project, we’ll use a dataset of taxi trips to learn about the processing and visualization of geospatial data. We’ll start with cleaning and preprocessing the dataset using NumPy and Pandas. Then, we will use Plotly and Mapbox to observe patterns in taxis’ trip times and visualize traffic flow with each passing minute on the map.

Project Tasks



Task 0: Get Started

Task 1: Import Libraries

Task 2: Load the Dataset

Task 3: Provide the Mapbox Access Token


Data Preparation

Task 4: Clean the Data

Task 5: Create a New DataFrame


Spatiotemporal Information Extraction

Task 6: Extract Coordinates per Minute

Task 7: Store Time per Coordinate


Data Visualization

Task 8: Analyze Trip Lengths

Task 9: Geospatial Visualization