To support the progress towards SDG goal 11 of sustainable cities and communities- contributing to reducing the destruction rates and scale of secondary impacts from earthquakes and major emergencies among the population of Dhaka City.

The focus was to mainly demarcate the zone that is more likely to suffer from secondary impacts such as Fire Hazards and Damage from Infrastructure on a micro-level from the selected wards. This project aims to demarcate the spatially risky zones, locate the resources, and determine the best route by analyzing the road network to reach the impaired location, subdue the hazard, and evacuate the residents. Simultaneously introduce an App that will allow the affected people to get notified during such an outbreak.

The data collection for this project is done by using KoBo Toolbox, paper maps, and by integrating that data in ArcGIS. At the beginning of the study, there were some countermeasures required for the smooth operation of the project. various data sets were collected through Google Earth Pro, Google Satellite Imagery, Open Street Map, and through collecting and digitizing official maps of Dhaka South City Corporation. These data sets were compiled and then integrated via ArcGIS to prepare the maps that the volunteers were used to identify and specify the characteristics of the feature (Educational Institution, Open Space, Healthcare Facility, Fire Hydrant, Water Pump, Place of Storing Flammable Materials, Transformers, Fire Service and Civil Defense, Vulnerable Structure, Ward Councilor Office, Water Logged Area)

The KoBo Questionnaire was also prepared beforehand. Regarding these steps, a training session was held to make the volunteers familiar with the tools and techniques that were used later on the field. 

In each ward, a team of two volunteers was sent to collect data. They were given paper maps as well as access to the questionnaire in KoBo Toolbox because- while the data could be collected in real-time showing the location, the accuracy would have been less than 10 meters in the KoBo Questionnaire. Each ward was divided into 8 sections therefore the two volunteers would be provided 8 maps. When they visited the study area they had to reach out to a suitable respondent who would help the volunteers provide the details about the feature. The data was saved offline when there was no internet connection and after it is restored the data was uploaded to the main online database.

After the respondent had given input to help fill out the specifics of the feature, the volunteers had to plot the exact location of the feature in the paper map. This process was carried out keeping in mind the position of the volunteers, and if the demarcation and identification made a line with reality, the features were marked and identified either in point or area and were Given an ID (“Ward No” e.g., W1 and then Map id M1) beside the marking location. Then the name of the feature was written down on white paper. Each feature would have a feature ID therefore by joining the ID of the digitized features to the datasets acquired from the KoBo Questionnaire via Spatial Join in ArcGIS, we would be able to see a complete attribute table of the variables. Any distortion in the output maps was cross-checked by comparing it to the data retrieved from the hard copy maps. Thus if any errors were to remain it would have been to a Minimum. The final step consisted of a workshop to validate and approve the work done and documentation of the final process. 

The data was collected throughout 5 days (Day-1 (27/02/2021), Day-2 (28/02/2021), Day-3 (01/03/2021), Day-4 (04/03/2021), and Day-5 (05/03/2021). A total of 28 volunteers were sent to avail data within the 14 wards.

One of the results was a risk and resource map of each ward. In the total study area (14 Wards). A total of 1164 risk and resource features were identified in the 14 wards. it was assessed that risk features are predominant.

Another outcome was a map that one can use to gain a general understanding of the individual standpoint of each variable in every ward. Therefore, this was an interesting and informative find.