Duration of Hackathon: February 17th-27th, 2020

Team: Reispar Analytics Academy (links to our data visualisations are in reference)

Team Reispar Analytics Academy

Problem Statement: In a densely populated city such as Lagos State, with 20 local governments, there is a major concern for accidents, security and fire outbreaks. There is a need to improve the efficiency of Emergency control services and the response time to help minimise the loss of lives and properties due to these situations. Hence the incentive for this project.

Aim of the Hackathon Project: To Improve Emergency Response Time in Lagos State, Nigeria.


  1. To identify existing fire emergency elements in Lagos.

2. To identify towns and local government areas that were more sensitive to fire outbreaks

3. To identify LASEMA response units within these local government areas.

4. To map out the proximity of LASEMA response units to these susceptible local government areas.

5. To identify possible areas for sighting these fire service stations

1 Team, 5 Tools, 5 Unique Skills

How we started!

17th February 2020, the hackathon commenced at CCHUB, Sabo, Lagos State, Nigeria, with 20 selected teams. Areas of focus for the hackathon were; Health, Transportation, Education, Agriculture, Urban planning and Citizen Accountability. Each team was required to brainstorm and choose a sector to work with.

Our team chose Health sector, with a variety of skilled individuals across Data Collection, Data Mining, Wrangling, Engineering and Visualisation as well as Geo-spatial design, we embarked on the 8-Days “Data4Governace visualisation” hackathon implemented by CCHub in partnership with The World Bank to accelerate the utilisation of data for good governance and improved Public service delivery.

Use Case: Lagos State

Jan. 24th, 2020 fire outbreak at Balogun Market, Lagos State

In recent years, there has been a significant increase in the number of lives and properties lost to fire outbreaks in Nigeria. Few examples of states that have been greatly affected by this are:

  1. Abia State: Pipeline explosion in Aba in October 2010 which recorded about 150 deaths
  2. Lagos State: Fire explosion in Balogun market on January 29th, 2020 and Apongbo fire explosion on February 16th, 2020.
  3. Bauchi State: 2 lives lost, 147 lives saved, N948,025,000 properties destroyed in the span of a year (between June 2018 and June 2019) as a result of home and industrial fire outbreaks.
  4. Lagos State: Truck explosion on Lagos-Ibadan expressway, 25th February 2020

Response Stations

Lagos State Emergency Management Agency (LASEMA)

The Lagos State Emergency Management Agency is an agency under the Lagos State Government that coordinates the Lagos Response Unit. They are charged with the responsibility to provide an adequate and prompt response as well as sustaining intervention in all forms of emergency/disaster situations in the State within the territorial boundary called “Lagos”.

With the increase in urbanization in Lagos State, there has been a rise in fire outbreaks occurring in homes and public places. Most of the incidents have witnessed slow response time from the emergency control centers. The number of lives and properties lost to fire outbreak has hence heightened the need for improving the efficiency of these Emergency response stations within Lagos State, Nigeria.

A visualisation on Emergency response stations in Lagos State per Local Governments and Towns in each LGAs

Data Sources

With the specified guidelines given, we optimised the use of the data found in the KOBO TOOLBOX platform and GRID3 website. Our major data source was the GRID3 website owned and managed by the Federal Government of Nigeria. The data collected were on hamlets, markets, fire and ambulance services, etc. Our engineered and mined data can be found here.

Collect → Clean → Visualize

With the data collected from Grid3, we identified relevant data required for our target sector and use-case, then modified the data using DataScience tools such Python and its rich libraries one of which is the “Geopandas Libary” thereby ensuring the data is credible and fit for the intended purpose and stored as a CSV/Excel file for visualization purposes.

Tools Used

For visualisation, we utilised data visusalisation and spatial tools such Microsoft PowerBI and ArcGis. View our data visualization on Power Bi Here

Data Insights

The data we used to draw insight can be found here

  • Response Elements in Lagos (Fire Stations and Public Health Centers):
  • Towns in Lagos State
  • Local Government Areas in Lagos State
  • Markets in Lagos State


  1. Using historical data (past occurrences ) to gather highly emergency scenarios areas, we grouped by their Local Government Area (LGAs).
  2. Map-out case study areas based as assessed from their LGAs
  3. Research and list out Fire Service Stations based on a field research visit/data research.
  4. Network Analysis to show the shortest route between Fire Service Stations to the Towns (under Wards) wherein they are located at.
  5. Based on the result from Step 4, map-out possible areas for sighting Fire Service Stations in a Ward.
Visualisation on Power Bi ( click here to view)

No doubt fire can occur anywhere, however, from network analysis considering Fire Station-Town bases, we can infer that Alimosho is the LGA most affected and will require more sightings of Fire Stations”

Expected Outcomes

  1. Using Power BI and ARCGIS to show less focused areas and the relationship between Towns and Fire Service Stations.
ArcGis Visualization for Market Cluster and Risk areas

2. Hotspot Cluster Map/ Heat Map: (market and Fire station services): The heat map for market data focuses on high-risk areas using market places in Lagos as a case study. The baseline for this data was the number of market days, as expected higher chances of fire. Also, a heat map for locations of Fire service stations was done.

ArcGis visualization for Hotspot Cluster Map

3. Using Power BI report to show less concentrated areas (with fewer or now Fire Service stations/ response centers). Automatically, this will increase the number of response time from the Fire service stations.

4. Buffering & Analysing Fire Service Areas-Towns: We used a buffer feature on ARCGIS with a coverage range of 4000 metres to create a spatial visual of Fire Stations in Lagos and proximity to Towns. The results made us aware of areas with more clusters of Fire Service points (high concentrated) to Towns. That is, some areas in Lagos have fewer or no Fire Stations close by.

400 Meters Spatial Visuals on Fire Stations — ArcGis (click to view)

5. Network Analysis and Table of Response Time: The network analysis shows the time it will take from a Fire Service Stations to a Town in less concentrated area (areas with fewer Fire service points).

Click to view on ArcGis

This data and project enable us to proffer solutions in improving response time in Lagos.


  1. Presence of response units in largely dense areas such as Alimosho and Ajeromi-Ifelodun
  2. Citing fire service stations in less clustered areas within a local government area to facilitate faster response time
  3. Readily available fire hydrants in areas prone to fire outbreaks.

Potential to scale

This solution has wide scale applicability across the country. We believe the government is very interested in protecting lives and preventing loss of property wherever applicable to the optimum. Hence, there is a need to reduce significantly the losses incurred from fire incidents. The key to this solution is information on prone areas and how response times can be improved. Our project has been able to not only provide insights on these but also better inform how they can be solved. This solution is also applicable across multiple cities in the country.

Links to Our ARCGiS Geospatial and Power BI data interactive visualisation

Route map for the less clustered areas → https://arcg.is/KSq4

Heat map for the fire service → https://arcg.is/1yr4GH

A buffer of fire stations → https://arcg.is/10Wjqa

Power BI data visualisation

GitHub Project Link












building people and empowering industries with insights from data!