Emergency demand forecast (Joint joint research with Yokohama City University)

In fiscal 2017, we have been conducting research with the goal of modeling the number of emergency cases and calculating the predicted value. We announce about the result in the mayor regular press conference and are compiled in press release document.

The following is the data used for modeling.

Regarding each item of the data

• Year of understanding: The year in which City of Yokohama, Fire Bureau recognized an ambulance request (4 digits)
• The month in which City of Yokohama, Fire Bureau recognized an ambulance request (1-12)
• Date of detection: The date when City of Yokohama, Fire Bureau noticed an ambulance request (1 to 31)
• When City of Yokohama, Fire Bureau detects an ambulance request (0-23)
• Number of victims: Includes those who were transported, not only those who transported, but also those who did not transport for some reason.

※If the data has not been entered, it will be "unfilled".

Regarding each item of the data

• Year of understanding: The year in which City of Yokohama, Fire Bureau recognized an ambulance request (4 digits)
• The month in which City of Yokohama, Fire Bureau recognized an ambulance request (1-12)
• Residential category: Address of the victim (city, out-of-city, foreign country, etc.)
  • City: Injuries living in Yokohama City
  • Outside of Yokohama City: Injuries living outside of Yokohama City (in Japan)
  • Foreign: Victims residing outside Japan
  • Others: Injuries whose Address is unknown, such as uncertain address, unknown address, etc.
• Number of victims: Includes those who were transported, not only those who transported, but also those who did not transport for some reason.

※If the data has not been entered, it will be "unfilled".

Regarding each item of the data

• Year of understanding: The year in which City of Yokohama, Fire Bureau recognized an ambulance request (4 digits)
• The month in which City of Yokohama, Fire Bureau recognized an ambulance request (1-12)
• 18 Administrative districts (Tsurumi Ward, Kanagawa Ward, Nishi Ward, Naka Ward, Minami Ward, Konan Ward, Hodogaya Ward, Asahi Ward, etc. Isogo Ward, Kanazawa Ward, Kohoku Ward, Midori Ward, Aoba Ward, Tsuzuki Ward  , Totsuka Ward, Sakae Ward, Izumi Ward, Seya Ward) and suburbs
• Number of victims: Includes those who were transported, not only those who transported, but also those who did not transport for some reason.

※If the data has not been entered, it will be "unfilled".

Regarding each item of the data

• Year of understanding: The year in which City of Yokohama, Fire Bureau recognized an ambulance request (4 digits)
• The month in which City of Yokohama, Fire Bureau recognized an ambulance request (1-12)
• Accident type: Reason for the first aid request (sudden illness, general injury, traffic accident)
  • Sudden illness: Due to illness and treated as emergency services
  • General injuries: Accidents that are not classified as fires, natural disasters, water floods, work related accidents, athletics, injuries, self-harm, etc.
  • Traffic accidents: Accidents caused by collisions or contact with other transportation or pedestrians
• Age classification: Age classification of the victim (elderly, adult, boy or younger)
  • Elderly: 65 years old or older
  • Adults: 18 to 64 years old
  • Juvenile and younger: 17 years old
• Number of victims: Includes those who were transported, not only those who transported, but also those who did not transport for some reason.

※If the data has not been entered, it will be "unfilled".

It is data for each fiscal year that extracted the budget amount for proper emergency use from the public relations expenses of the City of Yokohama, Fire Bureau Emergency Planning Division.

Yokohama-shi emergency telephone consultation was service that began in 2016, and since there was still little data, it was fixed at 3.07%, which was the utilization rate of 2016 (city population ratio).

Using "Chapter 2_ Population_5_ Age (each age, 5 years old class), administrative district, population according to gender" of Yokohama-shi statistics book, we obtained data of city population.

Similarly, using "Chapter 2_Population_19_Cortion Status of Commuting / School Population_(1) Day / Night Population, inflowing Population and Population Density" in the Yokohama City Statistical Report, there will be no significant change in the inflowing population until 2030 Based on the assumption that there is no basic data.

We used future population estimation (at that time, 2010 standard) calculated by Yokohama-shi based on national census.

From past weather data (outside site) released by the Japan Meteorological Agency (outside site), we used weather data for each date and time during 2016.

We acquired the total number of foreign guests from 2010 to 2015 from the annual report published by the Culture and Tourism Bureau of Yokohama.

Based on this data and the inbound target value (outside site) announced by the Japanese government at the Tourism Vision Initiative Conference to Support Tomorrow's Japan tomorrow", the total number of foreign guests from overseas to 2030 is estimated, and used as basic data for emergency demand forecast.

In addition to Saturday and Sunday, national holidays (outside site) are referred to as "holidays", and other days are weekdays.

The number of cases per day (number of cases / day) and the number of cases per hour (number of cases / hour) were calculated for each of the following categories:

• Housing Category
• Residential category (per hour)
• Administrative district
• Cross classification of age classification and accident type

We searched for the optimal model using multiple regression analysis with the number of cases and days obtained above as response variables and factor candidates as explanatory variables. In other words, the following model was applied to the data when y is a vector of the number of cases/day (response variable) and X is the plan matrix of factor candidates (explanatory variables).

y=Xβ+ε, but ε-Nn(0,σ2P)

Here, ε is an error vector that follows the n dimension normal distribution Nn with an average vector 0 and a covariation matrix σ2P (P is a correlation matrix), and n is the number of days to be analyzed (number of specimens). The error term is based on the self-regression (AR) structure, and model selection was based on AIC. For the model number/time model, we used a model that added variables related to demographics and alternate interactions during perception were added to the model selected by the number of cases/day.

The estimated values of the coefficient parameter β of each model obtained by the above procedure are as follows.

Using the optimal model obtained from exploration, we calculated the estimated number/day and number/times in 2017-2030 was calculated by giving explanatory variables with estimates for future years.

Regarding each item of the data

•Year of awareness: The year in which City of Yokohama, Fire Bureau recognizes an ambulance request (four digits AD)
•Average number of emergency participations in the city per day (prediction value)
•Average number of emergency participations per day to residents outside the city (prediction value)
•Average number of emergency participations (forecast value) for foreign residents

Regarding each item of the data

• Year of awareness: The year in which City of Yokohama, Fire Bureau recognizes an ambulance request (four digits AD)
• When City of Yokohama, Fire Bureau detects an ambulance request (0-23)
• Average number of emergency participations in the city per day (prediction value)
• Average number of emergency participations per day to residents outside the city (prediction value)
• Average number of emergency participations (forecast value) for foreign residents

Regarding each item of the data

• Year of awareness: The year in which City of Yokohama, Fire Bureau recognizes an ambulance request (four digits AD)
• Average daily participation in Tsurumi Ward.
• Average daily participation in Kanagawa Ward.
• Average daily participation in Nishi Ward.
• Average daily participation in Naka Ward.
• Average daily participation in Minami Ward.
• Average daily participation in Konan Ward.
• Average daily participation in Hodogaya Ward.
• Average daily participation in Asahi Ward.
• Average daily participation in Isogo Ward.
• Average daily participation in Kanazawa Ward.
• Average daily participation in Kohoku Ward.
• Average daily participation in Midori Ward.
• Average daily participation in Aoba Ward.
• Average daily participation in Tsuzuki Ward  .
• Average daily participation in Totsuka Ward.
• Average daily participation in Sakae Ward.
• Average daily participation in Izumi Ward.
• Average daily participation in Seya Ward.
• Average number of daily participation outside the city (estimated value)

Regarding each item of the data

• Year of awareness: The year in which City of Yokohama, Fire Bureau recognizes an ambulance request (four digits AD)
• Average number of injuries per day: Sudden illness (prediction value)
• Average number of injuries per day: General injuries under juveniles and younger (estimated value)
• Average number of injuries per day: Traffic accidents for boys and younger (estimated values)
• Average number of injuries per day: Adult sudden illness (predictable value)
• Average number of injuries per day: General injury in adults (estimated value)
• Average number of injuries per day: Adult traffic accidents (forecasts)
• Average number of injuries per day: Suddenly ill (predictable value) of elderly people
• Average number of injuries per day: General injury of elderly people (estimated value)
• Average number of injuries per day: Traffic accidents for elderly people (predictions)