Heat Maps

Understanding Heat Maps on Locale

⬣ Introduction

This console helps us analyze/visualize the distribution of metrics across different areas and times. The main idea comes from the fact that any moving asset changes its properties/behavior in both location and time.

As a result, when you want to understand how a certain area behaves over time, you can use heat maps. They make it simple to display and understand complex data at a glance.

The values are depicted in different colors. The variation in color may be by hue or intensity, i.e., different shades of the same color or different shades.

You can use the legend to understand how the data is depicted and what it represents. Read more about it here:

pageHow can we filter to different areas?

Why do you use hexagon heat maps on Locale?

The grid system brings granularity to the platform. It does an excellent job of grouping all of your lat-longs into "cells." These cells can also be clustered to represent a specific neighborhood or area and can be aggregated at various levels.

If you are a hyperlocal, on-demand company, grids as small as 0.5 sq. km can be very useful to run models that work in context to location and in real-time. Examples include surge pricing in high demand areas, promotions in low-demand areas & distribution models of delivery folks on the ground.

Click here to learn more about why we chose hexagons in particular and the benefits they provide:

❓What problems can I solve with heat maps?

Heat maps can be used to visualize how a specific metric is spread across an area. It helps us understand the patterns, behavior, and characteristics of a particular area and the associated metrics.

Click here to learn more about metrics and what they are:


It helps us understand the patterns, behavior, and characteristics of a particular area and the associated metrics.

πŸ’¬ Heat maps help us answer questions like:

  • Which areas do users cancel the most?

  • On what days of the week should we send promotions to users and in which areas?

  • In which area do users order the most?

  • Which areas are facing high cancellations on Mondays?

  • Which areas are most of my delivery partners idle and at what time?

  • What are the areas where the shipments are always delayed?

  • At 6:00 PM, where are the most number of bookings coming from?

  • Where is the maximum number of bookings coming from, in the northern part of the city or southern part of the city?

If you find yourself asking questions similar to these, you can use hexagon heat maps.

More information on the use cases for mobility and hyperlocal delivery businesses can be found here:

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