DOI: 10.5281/zenodo.18312567
title: Synthetic data generator to determine the best location and quantity of ambulances for the 30 largest cities in the state of São Paulo-Brazil
title: A database for determining the location and quantity of ambulances that must be available in a emergency medical care

description: This dataset was developed to support research and decision-making regarding the ideal location and quantity of ambulances in emergency medical services. It is primarily intended for discrete event simulation (DES) applications and was generated using the NotebookLM tool, which was used to define the emergency response flowchart and probability distributions for response times.

Synthetic data generated to represent the cities of Sorocaba and Campinas, two of the largest cities in the state of São Paulo, Brazil, are shared here. Data for the 30 largest municipalities in São Paulo can be generated through the Google Colab environment using the link provided in the article.

Care Flowchart and Time Markers (T1 to T5)



T1 - Interval between successive call arrivals

T2 - Dispatch time (time between receiving the call and the ambulance leaving for the call)

T3 - Travel Time: Calculated from the latitude and longitude of the call

T4 - Local Response Time and Dispatch to Hospital (minutes)

T5 - Time for vehicle cleaning and release


Probability Distributions Used

Data can be generated for any of the 30 largest cities in the state of São Paulo - Brazil using any of the following probability distributions:



Normal

Lognormal

Gamma

Exponential

Triangular

Uniform


Using the parameters p1, p2, or p3 according to the chosen distribution.

 

- Daily demand pattern:

·        00:00–08:00: 20% of daily calls

·        08:00–16:00: 35% of daily calls

·        16:00–00:00: 45% of daily calls

 

CSV File Format






Column



Type



Description





Municipio



string



Name of chosen city Simulation day number (starting from 0).





latitude



float64



Generated latitude of the incident.





longitude



float64



Generated longitude of the incident.





T1



float64



 Interval between successive call arrivals





T2



float64



 Dispatch time (time between receiving the call and the ambulance leaving for the call)





T4



float64



On-site care time in minutes.





T5



float64



Transport and hospital release time in minutes.





Arrival_Time



string



date and time of call





Arrival_Date



date



date of call





Arrival_Hour



float64



the hour of the day of the call





Arrival_Day_Offset



float64



day of the call





Arrival_Time_Str



hour



hour of day of call





Care_type



string



Type of care ("basic" or "advanced").





 

Dataset Applications

·        Modeling and simulation of healthcare systems.

·        Studies on ambulance allocation.

·        Impact analysis of response times.

·        Support for public policy and management decisions.

Access and Reproducibility

·        Download .CSV from Sorocaba:

https://drive.google.com/file/d/1p7L_xVoSPbdlMX2fG4YuehSjkKI9teA0/view?usp=sharing

·        Regenerate data on Google Colab: 

https://colab.research.google.com/drive/14SsawsTOpDNSQEfFcj3vRT-Bgt6Zikkc?usp=sharing

Related Publications

Gigante, R. L., & Azevedo, A. T. (2022). Study of the impact of the start time of work shift on the efficiency of an emergency system through a simulation model of discrete events. Gestão & Produção, 29, e4421. DOI: https://doi.org/10.1590/1806-9649-2022v29e4421

Gigante, R. L., Azevedo, A. T. de, & Ohishi, T. (2024). Optimizing ambulance base locations: A clustering-based approach. In 5th South American IEOM Conference. DOI: https://doi.org/10.46254/SA05.20240171