Visualize Geospatial Data Using Plotly and Mapbox

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.