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Dashboard Week Day 3 —— London Fire Risk

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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Dashboard week is one of the most signature sessions in The Data School training. In this session, the cohort will be given one or several specific datasets and create a corresponding dashboard. The aiming of dashboard week is to develop excellent time management and data visualisation skills.

 

Introduction

Today is the Third day of dashboard week and the topic is London Fire Brigade Incident Records. London Fire Brigade is the busiest fire and rescue service in the country and one of the largest firefighting and rescue organisations in the world. This dataset contains the details of every incident which the LFB attended from 2019 to 2022, such as date, call time, location and cost.

 

Data Preparation

Most attributes of the dataset are very clean so there were not too many data cleaning tasks to do. The only issue is the GIS location. The GIS information is stored in three different pairs of attributes, which are latitude and longitude, easting m and northing m, easting around and northing around. I have to select the proper attributes to generate the correct points. Moreover,  I also applied Statistical GIS Boundary Files for London to this project so that I can analyse the data at the borough level. The final Alteryx workflow is below:

 

Dashboard

Below is the screenshot of my dashboard:

 

The dashboard contains four parts. Firstly, on the left-hand side, there is a London map, working as a big filter. You can select a specific borough to check the corresponding information. Secondly, on the top right corner, there is a matrix evaluating the risk of fire. Then it comes to a heatmap, which shows the number of incidents by time and weekday. The last session is a bar chart and a pie chart that provide data in different aspects regarding incidents.

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