# Statistical and Machine Learning Models for Predicting Fire and Other
Emergency Events

Dilli Prasad Sharma, Nasim Beigi-Mohammadi, Hongxiang Geng, Alberto Leon-
Garcia
The Edward S. Rogers Sr. Department of Electrical and Computer Engineering
University of Toronto, 10 King’s College Road, Toronto, ON M5S 3G4, Canada.
{dilli.sharma<EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>
&Dawn Dixon, Rob Madro
Fire Rescue Services, City of Edmonton, Alberta, Canada
{dawn.dixon<EMAIL_ADDRESS>
Phil Emmenegger, Carlos Tobar, Jeff Li
TELUS Communications Inc, Canada
{phil.emmenegger, carlos.tobar<EMAIL_ADDRESS>
###### Abstract

Emergency events in a city cause considerable economic loss to individuals,
their families, and the community. Accurate and timely prediction of events
can help the emergency fire and rescue services in preparing for and
mitigating the consequences of emergency events. In this paper, we present a
systematic development of predictive models for various types of emergency
events in the City of Edmonton, Canada. We present methods for (i) data
collection and dataset development; (ii) descriptive analysis of each event
type and its characteristics at different spatiotemporal levels; (iii) feature
analysis and selection based on correlation coefficient analysis and feature
importance analysis; and (iv) development of prediction models for the
likelihood of occurrence of each event type at different temporal and spatial
resolutions. We analyze the association of event types with socioeconomic and
demographic data at the neighborhood level, identify a set of predictors for
each event type, and develop predictive models with negative binomial
regression. We conduct evaluations at neighborhood and fire station service
area levels. Our results show that the models perform well for most of the
event types with acceptable prediction errors for weekly and monthly periods.
The evaluation shows that the prediction accuracy is consistent at the level
of the fire station, so the predictions can be used in management by fire
rescue service departments for planning resource allocation for these time
periods. We also examine the impact of the COVID-19 pandemic on the occurrence
of events and on the accuracy of event predictor models. Our findings show
that COVID-19 had a significant impact on the performance of the event
prediction models.

_K_ eywords Fire and other emergency events $\cdot$ spatio-temporal event
prediction models $\cdot$ statistical analysis $\cdot$ machine learning
$\cdot$ emergency management and planning $\cdot$ resource allocation

00footnotetext: This work has been submitted to the IEEE for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessible.

##  1 Introduction

Emergency response management is a crucial problem faced by fire and emergency
departments around the world. First responders must respond to various events
such as fires, rescue, traffic accidents, and medical emergencies. In recent
years, researchers have developed statistical, analytical, and artificial
intelligence, specifically, machine learning approaches for designing
emergency response management (ERM) systems [1, 2]. Emergency incidents in
urban areas are often caused by natural disasters (e.g., flooding, earthquake,
tsunami), accidents (e.g., fire, explosion, traffic), or pandemic situations.
There is a huge socioeconomic impact of fire and emergency incidents due to
the considerable economic losses to individuals, families, and communities.
Machine learning (ML) models with data obtained from multiple sources such as
census bureau, police department, historical fire and rescue events, property
inspections and violations, property assessments, property characteristics,
parcel, tax lien, and community survey data can help predict the fire risk of
the commercial buildings in a city [3, 4].

A good emergency event predictive model needs to capture spatial and temporal
correlations. Neural network-based models such as Convolutions Neural Networks
(CNNs), and Recurrent Neural Networks (RNNs) can represent spatio-temporal
relationships for the event incident locations and rates [5, 6]. However,
these models are limited to specific events (e.g., structural fire, traffic
accidents) and lose their accuracy for small spatial regions of a city and
short time intervals. Predicting the events occurring in neighborhoods, wards,
or suburbs of the city for a certain time duration (e.g., daily, weekly, or
monthly) would help with emergency planning and risk mitigation. The
characteristics of the city such as population density, land and
property/building structure types and occupancy, household income, length of
residency, education, age group, and point of interest can influence the
likelihood of occurrence of the events. Identifying the key features/variables
and assessing their association with the events can be useful for predicting
the risk of event occurrence in the city.

In this paper, we present our approach using statistical analysis and machine
learning to predict the likelihood of fire and other emergency events in the
City of Edmonton, Alberta. We strive to answer the following research
questions:

  1. 1.

What data and sources are relevant to fire and emergency event occurrence?

  2. 2.

How can we engineer features from various data sources across various temporal
and spatial granularities?

  3. 3.

What features are highly correlated with fire and other emergency events?

  4. 4.

Can we develop a model that predicts fire and other emergency events with good
accuracy given the features?

  5. 5.

Can the prediction model predict all types of events? or can it predict some
better than others?

  6. 6.

Can the model predict events at any level of spatial and temporal granularity?
For instance, can the model be used across all fire stations?

  7. 7.

Can we predict the events at any temporal level (i.e., hourly, daily, monthly,
etc.)?

  8. 8.

How does the COVID-19 pandemic impact the prediction of emergency events?

To address the above questions, we present a systematic methodology for (i)
dataset development and feature extraction, (ii) feature analysis and
selection, and (iii) model development and evaluations for predicting the
Edmonton Fire Rescue Service (EFRS) events at neighborhood and fire-station
levels for the City of Edmonton. We collected the city’s data from multiple
sources, extracted relevant features by applying computational methods to
prepare the datasets, and analyzed them with state-of-the-art statistical and
machine-learning models. We collected neighborhood demographic and socio-
economic information, and EFRS event data from the city’s open data portal [7,
8], and map data (points of interest) from OpenStreetMap[9]). We made the
following key contributions in this work:

  * •

Development of a systematic approach to collect data from different sources
(city, maps, fire, and emergency events, etc.) and preparation of datasets at
different temporal and spatial levels for training and validating ML models
designed to predict fire and other emergency events.

  * •

Identifying the key predictor variables that are strongly associated with EFRS
event types; we identified highly correlated features for each of the EFRS
event types and used them for building the ML models.

  * •

A descriptive analysis of EFRS event occurrence at different time intervals
(i.e., hourly, daily, weekly, monthly, and yearly) at city and neighborhood
levels for use in model selection, explanation, and validation of the
prediction results.

  * •

Predictive models for each EFRS event type with the identified key variables.
These models predict the likelihood of occurrence of EFRS events by
neighborhood and/or fire station. Evaluation of the models using standard
regression metrics and prediction error analysis. These predictions can be
used for emergency management and planning in neighborhoods and/or fire-
station zones.

  * •

We use weekly and monthly prediction values to classify the neighborhoods
according to event risk; this may be useful in decision-making and planning to
reduce/prevent the damage associated with events.

  * •

We assessed the impact of the COVID-19 pandemic on the performance of EFRS
event predictive models using the mean absolute error (MAE) and root mean
square error (RMSE) metrics. We compared models trained with before and after
COVID-19 event data. The analysis of the impact of the COVID-19 pandemic
provides insights on fire and emergency event prediction that can help fire
and rescue service departments in their preparedness planning, response, and
recovery.

The rest of the paper is organized as follows: Section 2 presents related
work. Section 3 discusses data collection and feature extraction. Section 4
analyzes the EFRS events. Section 5 describes feature analysis and selection.
Section 6 presents the predictive model development. Section 7 discusses the
analysis of prediction results by neighborhood, and Section 8 by fire-station
service area. Section 9 discusses the impact of the COVID-19 pandemic on EFRS
events predictions. Section 10 presents insights and key findings. Lastly,
Section 11 provides conclusions and suggests future research directions.

Figure 1: Data categories and their sources

##  2 Related Work

Models to predict and forecast the occurrence of emergency events can have a
significant impact in saving people’s lives and reducing property damage. In
recent years, researchers have made a number and variety of contributions
towards the development of prediction techniques using statistical and
artificial intelligence technologies [1, 10].

Madaio et al. [3] developed a fire risk assessment framework to identify and
prioritize commercial property fire inspections using machine learning models.
Data collected from multiple sources (Fire Rescue Department, City of Atlanta,
Police Department, U. S. Census Bureau) were used to train models. Fire risk
scores were computed for buildings in the city of Atlanta, based on predictive
models for fire events. Several models were considered: Logistic Regression,
Gradient Boosting, Support Vector Machine (SVM), and Random Forest.

In a similar study, Singh Walia et al. [4] proposed two fire risk predictive
models for commercial and residential properties in Pittsburgh. The models
were developed for structural fire risk prediction using historical fire-
related incidents, property inspections and violations, property assessments,
property characteristics and parcel data, tax lien, and American Community
Survey data. The commercial risk model was used to generate a risk score
(i.e., likelihood of fire) for non-residential (commercial, governmental,
industrial, etc.) properties in the city. The commercial risk model was
deployed at the Pittsburgh Bureau of Fire. The residential risk model was used
to predict and generate fire risk scores for residential census blocks in
Pittsburgh. However, both of these studies [3, 4] mainly focused on fire
events only.

Ye et al. [11] proposed a Finite Element (FE)-based machine learning (ML)
framework to predict structural fire response that integrates the advantages
of accuracy from FE methods as well as efficiency from ML techniques.
Numerical data of a steel frame structure with temperature data were used to
train the ML models. Four ML models including Support Vector Machine (SVM),
Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB) were used
in this study. However, the scope of this study is limited to the structural
fire of a building.

Ren et al. [5], and Yuan et al. [6] proposed Convolutional Neural Networks
(CNNs) and Recurrent Neural Networks (RNNs)-based models to predict traffic
accidents, where CNNs and RNNs models are used to capture the spatial and
temporal correlations, respectively. The models predict the occurrence of
traffic accidents at a city level. However, these models lose their accuracy
for short time intervals and small study zones. One of the important causes of
this performance degradation is the zero-inflated problem (i.e., rare
occurrence of accidents), which becomes more dominant as the spatiotemporal
resolution increases [12]. Recently, Zhou et al. [13] developed a promising
set of solutions for tackling the zero-inflated problem with some useful
guidelines for improving the performance of accident predictions in high
temporal resolution. However, the emergency events considered were limited to
traffic accidents.

Data-driven predictive models are widely used in many prior works[14, 15, 16].
Agarwal et al. [14] studied the impact of weather on the damage caused by fire
incidents in the United States. The authors prepared a dataset by collecting
fire incidents and weather data from sources such as the National Fire
Incident Reporting System and the National Oceanic and Atmospheric
Administration, respectively. They then developed predictive models to analyze
the fire risk. A Gradient Boosting Tree (GBT) model was used to predict the
losses due to fire incidents. The paper presents useful insights to fire and
rescue managers and researchers with a detailed framework of big data and
predictive analytics for effective management of fire risk. However, it is
limited to the analysis of weather data and their impact on fire events.

Cortez et al. [15] proposed a Recurrent Neural Networks (RNN)-based prediction
model with a Long Short-Term Memory (LSTM) architecture for the prediction of
emergency events. Spatial clustering, spatial analysis, and LSTM-based
prediction techniques were integrated to develop this model. The model was
compared to autoregressive integrated moving average (ARIMA) models using the
historical emergency event data provided by the national police of Guatemala.
Similarly, Roque et al. [16] developed a neural network-based emergency event
prediction model for the city of Florianópolis, Brazil. Spatial clustering and
Long-Short Term Memory (LSTM) methods were combined to develop their
predictive model. However, the predictions were limited to two types of
observations (pre-hospital care and others) in four regions of the city (4
clusters). Finally, Jin et al. [17] proposed a deep sequence learning-based
fire situation forecasting network to predict the incidents. The network
integrates the structures of Variational auto-encoders and sequence generative
models to obtain the latent representation of the fire situation and to learn
spatiotemporal data from GIS to train a generative model. The study used fire
incident data from the city of San Francisco.

The state-of-the-art research on fire and emergency event prediction has
mainly focused on structural fire and/or medical events at high levels of
spatial granularity (e.g., city level or large regions), and the models that
have been developed targetted a specific consideration, such as commercial
areas or buildings, or a certain event type such as structure fire, medical,
etc. In addition, the models lack the systematic methodology for data
collection and feature extraction, feature analysis and selection, and model
development and evaluation. In this paper, we present a systematic methodology
that describes the life-cycle of statistical analysis and machine learning
development to predict fire and other emergency events not only in large
geographical regions such as cities but also at neighborhood and fire station
service area levels.

We investigate the association of different data sources with fire and other
emergency events and develop a systematic approach to collect the data from a
variety of sources. The predictive models are developed for the prediction of
weekly and monthly event incidents at a small area of a city (fire-station
regions, neighborhood levels) as well.

Figure 2: (a) Neighborhoods, (b) Fire-station locations, and (c) Fire-station
Voronoi regions

##  3 Data Collection and Feature Extraction

The City of Edmonton is the capital of the Alberta province of Canada with a
population of 1,010,899 in 2021 [18]. We collected data for the city from: the
City of Edmonton Open Data Portal [7], and OpenStreetMap [9]. From these two
data sources, we created three categories of data: 1. From the city’s open
data portal we collected neighborhood demographic and socioeconomic data; 2.
We used OpenStreetMap to collect neighborhood Point of Interest and road
related features; and 3. we also used the city’s open data portal to collect
Edmonton Fire Rescue Service Event data. We now discuss the details of these
data.

###  3.1 Data Categories

Data collection and feature extraction is an important part of developing
predictive models. Since various factors can be involved in causing emergency
events, the main step in building an event prediction model is to collect as
much relevant data as possible from available sources [1]. We extracted
features from the following three categories of data.

  * •

City of Edmonton Open Data Portal: We collected neighborhood demographic and
socioeconomic data from the Open Data Portal of the City of Edmonton [7]. The
portal provides diverse demographic information of the neighborhoods such as
building/property type and occupancy [19, 20], length of residency [21],
household income [22], population by education [23], and population by age
[24]. All demographic information by neighborhood is based on the Census 2016.
The descriptions of the selected features are as follows:

    * –

Apartment/Condo (1-4 Stories): number of units occupied in apartments or
condos with 1 to 4 stories.

    * –

Apartment/Condo (5+ Stories): number of units occupied in apartments or condos
with 5 or more stories.

    * –

Duplex/Fourplex: number of units occupied in duplex or fourplex houses.

    * –

Hotel/Motel: number of units occupied in hotels or motels.

    * –

Institution/Collective Residence: number of units occupied in institution and
collective residences.

    * –

Manufactured/Mobile Home: number of units occupied in manufactured or mobile
homes.

    * –

RV/Tent/Other: number of units occupied in RVs, tents, or other houses.

    * –

Row House: number of units occupied in row houses.

    * –

Single Detached House: number of units occupied in single detached houses.

    * –

5 Years or More: number of respondents who have been staying for 5 or more
years.

    * –

3 Years to Less than 5 Years: number of respondents who have been staying 3
years to less than 5 years.

    * –

1 Year to Less than 3 Years: number of respondents who have been staying more
than 1 year but less than 3 years.

    * –

Less than 1 Year: number of respondents who have been staying less than 1
year.

    * –

Low Income: number of people in households with less than $30k$ income.

    * –

Low-medium Income: number of people in households whose income ranges from
$30k$ to $100k$.

    * –

Medium Income: number of people in households with income ranges from $100k$
to $200k$.

    * –

High Income: number of people in households with income greater than $200k$.

    * –

No Certificate, Diploma or Degree: number of people with no certificate,
diploma or degree attained a level of education.

    * –

High School, Trades or Apprenticeship Certificate: number of people for high
school, trades, or apprenticeship certificates attained the level of
education.

    * –

College or University Certificate or Diploma: number of persons with college
or university certificate or diploma attained level of education

    * –

University Bachelor and Medical Degree: number of people with university
bachelor’s or medical degrees attained level of education.

    * –

Master and Doctorate Degree: number of people with a master or doctoral degree
attained level of education.

    * –

Children: number of people of age less than $15$ years.

    * –

Youth: number of people in age group from $15$ to $24$ years.

    * –

Adults: number of people in age group from $25$ to $64$ years.

    * –

Seniors: number of people in age $65$ or above.

    * –

Population: population per square kilometer.

Figure 3: Occurrence of the Different Event Types

  * •

Open Street Map (OSM): We collected Point of Interest (POI) and road-related
features from the OpenStreetMap [9] for amenity, building and highway
categories using the Overpass API [25]. We extracted the number of POI
facilities (e.g., pubs, restaurants, schools/colleges, etc.), and road-related
features for different sub/categories of amenities, buildings, and high-way
types for each neighborhood of the City of Edmonton. We entered a query for
each category to the Overpass API [25]. We grouped POI subcategories into a
single feature based on their similarities. For example, pubs, bars,
restaurants, food courts, etc. are food-related amenities. Nine different
features were collected from the OSM sources which are as follows:

    * –

Food: number of food-related facilities including bars, cafes, fast foods,
food courts, pubs, and restaurants.

    * –

Education: number of educational institutions including college, kindergarten,
library, school, and university.

    * –

Healthcare: number of healthcare facilities such as clinics and hospitals.

    * –

Entertainment: number of entertainment-related facilities including art
centers, cinemas, community centers, events venues, nightclubs, and theatres.

    * –

Public Service: number of public service-related items including the
courthouse, fire stations, police, and town halls.

    * –

Commercial: number of commercial buildings including offices, commercial, and
government.

    * –

Retail: number of retail buildings.

    * –

No. traffic_lights: number of traffic lights.

    * –

No. bus_stops: number of bus stops.

  * •

Edmonton Fire Rescue Service (EFRS) Events: We collected EFRS event data from
the Open Data Portal of the City of Edmonton [7, 8]. This data consists of
information for current and historical EFRS events including event type
(category/subcategory), event dispatch time, location, response code, and
equipment. The EFRS event category/type includes fire events, medical events,
alarm events, citizen assist events, motor vehicle incident events, rescue
events, outside fire events, vehicle fire events, community events,
training/maintenance events, hazardous material events, and other events.

###  3.2 Dataset Preparation

We collected data based on the current 402 city neighborhoods including both
residential and industrial areas in the City of Edmonton. Each neighborhood’s
polygon boundary is shown in Figure 2 (a) where the yellow line indicates the
neighborhood’s boundary. The static features (socioeconomic and demographic
data) for each neighborhood are collected first. Then the neighborhood
features are mapped to the service region for each fire station. There are 30
fire stations in the city and the locations are shown in Figure 2 (b). We
approximate the service region for each fire station by finding the Voronoi
diagram that maps each point in the city to the nearest fire station [26]. In
general, each such service area includes several neighborhoods. Figure 2 (c)
shows the Voronoi region for each fire station. A neighborhood can straddle
multiple fire-station regions. We computed the overlap proportion for the
neighborhood that has straddled multiple fire stations and distributed its
feature values to its associated fire stations. We merged all demographic,
socioeconomic, PoIs, and EFRS events data for different time intervals to
obtain feature vectors for each neighborhood and for each fire station.

##  4 Descriptive Analysis of EFRS Events

In this section, we present a descriptive analysis of event occurrence
properties. We first describe the different EFRS event types and the volume of
associated data samples. Next we present the descriptive statistics for
different event types at the city and neighborhood levels over different time
intervals: hourly, daily, weekly, monthly, and yearly. For this analysis, we
considered event data for the $5$ years from $2016$ to $2021$.

###  4.1 EFRS Event Types

The EFRS event data contains information about event incidents in the City of
Edmonton including: event dispatched time-stamp, event close-time, location,
event category types, etc. for the period 2011-01-01 to 2022-08-30 [7, 8]. The
EFRS event types are as follows:

  * •

Medical (MD)

  * •

Alarms (AL)

  * •

Motor vehicle incident (TA)

  * •

Citizen assist (CA)

  * •

Outside fire (OF)

  * •

Fire (FR)

  * •

Hazardous materials (HZ)

  * •

Training/maintenance (TM)

  * •

Rescue (RC)

  * •

Vehicle fire (VF)

  * •

Community (CM)

  * •

Others (XX)

Figure 3 shows the proportion of the total event occurrences for different
types. Medical events are by far the most frequent events occupying 58.7% of
the event incidents. Alarms (15.4%) and Traffic incidents (7.35%) events are
the second and third most frequent events, respectively. Fire events are
relatively less frequent (6th) occurring at 2.92%.

(a)

(b)

(c)

Figure 4: Plot of daily event occurrence rate by neighborhood

###  4.2 Descriptive Analysis of Events

In this section, we present a descriptive statistical analysis of the six
event types. The analysis helps to identify the event occurrence patterns in
terms of statistical metric such as the mean, standard deviation, and
coefficient of variation of the occurrence of event types in different time
periods. The coefficient of variation (CV) is the standard deviation over the
mean and is a measure of the dispersion of data points about the mean and is
useful for comparing the degree of variability of different event types.

Table 1 presents the descriptive statistics for six event types over the
different periods of time intervals in the city. These statistics are computed
with $5$ years of event data from $2016$ to $2021$. A fire event incident
happens every $7$ hours on average; alarm events happen every $1$ hour;
citizen assist events every $4$ hours; traffic incident events every $2$
hours; rescue events every $20$ hours, and $3.66~{}(\approx 4)$ medical events
happen every hour. The CV of the event occurrence shows that there is a high
degree of variability in the number event occurrences in shorter time periods.
In contrast the CV approaches zero over longer time intervals indicating small
variability about the mean.

Medical events are relatively consistent with CV less than 1 for all time
intervals. Fire events have a significantly larger CV than most other events,
suggesting that the prediction of number of fire events for short time
intervals will be difficult. Rescue events have even more variation than fire
events. We note that the mean number of event occurrences is highest for
medical, with fire next, and then rescue.

Table 1:  Descriptive statistics for the EFRS events at a city level.

Event Type | Time Interval | Mean ($\mu$) |  StdDev ($\sigma$) | CV ($\sigma/\mu$)
---|---|---|---|---
| Hourly | 0.15 | 0.40 | 2.55
| Daily | 3.81 | 2.08 | 0.55
| Weekly | 26.62 | 6.42 | 0.24
| Monthly | 116.26 | 15.34 | 0.13
Fire | Yearly | 1395.20 |  94.89 | 0.07
| Hourly | 3.66 | 2.18 | 0.60
| Daily | 87.90 | 13.28 | 0.15
| Weekly | 612.98 | 63.41 | 0.10
| Monthly | 2676.70 | 214.27 | 0.08
Medical | Yearly | 32120.40 |  2074.58 | 0.06
| Hourly | 0.92 | 1.11 | 1.20
| Daily | 22.14 | 7.41 | 0.33
| Weekly | 154.43 | 35.71 | 0.23
| Monthly | 674.38 | 118.92 | 0.18
Alarms | Yearly | 8092.60 | 1106.22 | 0.14
| Hourly | 0.24 | 0.53 | 2.24
| Daily | 5.76 | 3.42 | 0.59
| Weekly | 40.22 | 15.01 | 0.37
| Monthly | 175.66 | 53.56 | 0.30
Citizen Assist | Yearly | 2108.00 | 126.72 | 0.06
| Hourly | 0.50 | 0.83 | 1.68
| Daily | 11.97 | 5.70 | 0.48
| Weekly | 83.47 | 24.47 | 0.29
| Monthly | 364.50 |  81.18 | 0.22
Motor Vehicle Incident | Yearly | 4374.00 | 773.18 | 0.18
| Hourly | 0.05 | 0.22 | 4.65
| Daily | 1.17 | 1.12 | 0.96
| Weekly | 8.16 | 3.27 | 0.40
| Monthly | 35.66 |  9.09 | 0.26
Rescue | Yearly | 428.00 | 25.96 | 0.06

###  4.3 Events Occurrence by Neighborhood

Table 2 shows the descriptive statistics of the rate of occurrence for six
different event types by neighborhood with different temporal resolutions. As
before the CV decreases as the time intervals increase. What stands out is the
the CV values per neighborhood are much larger than citywide. Fire and rescue
events have significantly large degrees of variation underscoring the
difficulty in predicting the number of events in small geographic areas. Even
medical have significant variability at the neighborhood level. variations at
the neighborhood level. These results show that the prediction of the number
of occurrences of individual event types at the neighborhood level is very
difficult due to the low rate of event occurrences.

Table 2: Descriptive statistics for the EFRS event rate at neighborhood level

Event Type | Time Interval | Mean ($\mu$) |  StdDev ($\sigma$) | CV ($\sigma/\mu$)
---|---|---|---|---
| Hourly | 0.0004 |  0.0204 | 49.2401
| Daily | 0.0099 |  0.1017 | 10.2281
| Weekly | 0.0693 |  0.2897 | 4.1784
| Monthly | 0.3006 |  0.7351 | 2.4456
Fire | Yearly | 3.5665 |  6.0763 | 1.7037
| Hourly | 0.0095 |  0.0997 | 10.4423
| Daily | 0.2289 |  0.6422 | 2.8054
| Weekly | 1.5962 |  3.2154 | 2.0144
| Monthly | 6.9201 |  13.0662 | 1.8882
Medical | Yearly | 82.1074 |  2074.58 | 1.8515
| Hourly | 0.0024 |  0.0497 | 20.6551
| Daily | 0.0577 |  0.2704 | 4.6881
| Weekly | 0.4022 |  0.9922 | 2.4672
| Monthly | 1.7435 |  3.4492 | 1.9783
Alarms | Yearly | 20.6866 |  37.0539 | 1.7912
| Hourly | 0.0006 |  0.0252 | 40.1769
| Daily | 0.015 |  0.1282 | 8.5353
| Weekly | 0.1048 |  0.3932 | 3.7537
| Monthly | 0.4542 |  1.1284 | 2.4846
Citizen Assist | Yearly | 5.3885 |  10.2884 | 1.9093
| Hourly | 0.0013 |  0.0365 | 28.0864
| Daily | 0.0312 |  0.1841 | 5.9065
| Weekly | 0.2174 |  0.537 | 2.4703
| Monthly | 0.9423 |  1.4466 | 1.5351
Motor Vehicle Incident | Yearly | 11.181 |  12.7183 | 1.1375
| Hourly | 0.0001 |  0.0113 | 89.1289
| Daily | 0.0031 |  0.0567 | 18.5966
| Weekly | 0.0213 |  0.1643 | 7.7254
| Monthly | 0.0922 |  0.4335 | 4.7010
Rescue | Yearly | 1.0941 |  3.6425 | 3.3293

Figures 4 (a), (b), and (c) show heat-map plots of the rate of daily event
occurrence of Fire, Medical, Alarms, Citizen Assist, Motor Vehicle (Traffic)
incidents, and Rescue, respectively. (Table 2) shows that a fire event
incident happens every $2500$ hours (around 100 days), and a medical event
every $105$ hours (4 days). However, in the central area neighborhoods, a fire
event incident occurs every $8$ days, and $4$ medical event incidents occur
every day. Therefore, we see that the occurrence rate varies widely by
neighborhood. Fire events are more likely to happen in neighborhoods of the
central city area. In contrast, the traffic incident events are scattered
throughout the city. Most of the medical event incidents occurred in
neighborhoods of the central core city. This suggests that each neighborhood
may have different characteristics that impact the occurrence of different
event types.

Figure 5: Correlation coefficients between feature and event

(a)

(b)

(c)

(d)

(e)

(f)

Figure 6: Results of Feature Importance Analysis with Conditional Random
Forest Model for (a) Fire, (b) Alarms, (c) Medical, (d) Motor Vehicle
Incident, (e) Citizen Assist, and (f) Rescue events

##  5 Feature Analysis & Selection

We now present a correlation analysis of features and event types to help us
determine how a predictor variable (feature-independent variable) is
associated with the event (dependent variable). We expect that a model with
strongly associated predictor variables will make good predictions. We
employed two methods of analysis: Pearson Correlation Coefficient, and
Conditional Random Forest. We analyzed the correlation coefficient of all the
features with the events (dependent variable) using the Pearson correlation
coefficient. To identify the feature set for predicting the events, we used
the Conditional Random Forest (CRF) model.

###  5.1 Correlation Analysis

A correlation coefficient measures the degree of association between two
variables. We analyzed the correlation coefficient between the predictor
variable (i.e., feature) and the response/target variable (i.e., event). Let
$x_{i}$ and $y_{i}$ denote static features and events, respectively. The
Pearson correlation coefficient of features (x) and event (y) denoted by
$\rho$ is defined as follows:

$\rho=\frac{\sum_{i}^{n}(x_{i}-\bar{x})(y_{i}-\bar{y})}{\sqrt{\sum_{i}^{n}(x_{i}-\bar{x})^{2}}\sqrt{\sum_{i}^{n}(y_{i}-\bar{y})^{2}}}$
(1)

A positive correlation indicates that features and events tend to move in the
same direction. A negative correlation indicates features and events tend to
move in opposite directions. Figure 5 shows the correlation coefficients of
features and events (rounded to a single decimal place). We see a strong
association between household income, population, and residential building
types with each of the event types. Food, a point of interest-related feature,
has a significant association with the occurrence of medical, alarms, citizen-
assist, motor vehicle incidents, and rescue events. The number of hotels or
motels is associated with medical, alarms, citizen assist, and rescue events.
The number of traffic lights is only associated with the motor vehicle
incident event. Similarly, the length of term residency feature of less than
one year has a good correlation with fire events.

###  5.2 Feature Importance Analysis

A conditional Random Forest (CRF) [27] analysis was conducted to identify the
important features (i.e., predictors) associated with EFRS event(s) (i.e.,
response/target variable) for the model development. The CRF method involves
developing conditional interface trees (e.g., event trees) by repeatedly and
randomly subsetting data and identifying the features that are most associated
with events in the subsets. The conditional distribution of the statistics is
derived to measure the association between the target variable (i.e., event)
and the predictor variables (e.g., features). A statistical test with
$p$-values is used to determine whether there exist statistically significant
associations between any of the features (predictors) and event (i.e.,
target/response) variables. The most strongly associated feature with the
event (target variable) is selected.

We conducted several experiments to identify the important features (i.e.,
predictor variables) for the predictive model development. The package
“partykit[28]” in R was used for this analysis. This analysis developed 1000
trees (event trees) for each event type; the collective output from those 1000
trees produced the final importance ranking of the features (i.e., predictor
variables) for each event type. In this study, we have considered the six
common EFRS events: Fire, Alarms, Medical, Motor Vehicle Incident, Citizen
Assist, and Rescue events. Figure 6 shows the important features of the six
event categories. The features with the high score from the top are the
important features. Each event type has a different important feature list
because of their different characteristics. Low-income, food and apartments or
condominiums with 5 or more stories are identified as important features of
most of the event types. A list of importance features for each of the event
types with score threshold $\geq 0.05$ is presented in Table 3. The findings
based on both correlation and CRF analysis presented in this section are
summarized as follows:

  * •

There is a strong association between events and low household income and
residential building types. The event incidents increase as the number of the
low-income population, condos/apt, and hotels/motels increase.

  * •

The point of interest (PoI) related feature such as the number of restaurants
and bars/cafes/food courts (food) has a significant association with the
occurrence of events.

  * •

Shorter length of residency term is associated with fire events.

  * •

The commercial building types (commercial or retail) are also associated with
the occurrence of most of the event types.

  * •

Road traffic information including the number of traffic lights is
significantly associated with motor vehicle incident events.

Table 3:  List of importance features with score $\geq 0.05$

.  Event Types Importance Features (Variables) Fire (FR) Low_Income,
Apartment_Condo_1_to_4_stories, Low_medium_Income ,
No_Certificate_Diploma_or_Degree , Less_than_one_Year Medical (MD) Low_Income,
Food, Apartment_Condo_5_or_more_stories , No_Certificate_Diploma_or_Degree,
Apartment_Condo_1_to_4_stories Alarms (AL) Food, Low_Income,
one_Year_to_Less_than_three_Years, commercial, Less_than_one_Year,
Apartment_Condo_5_or_more_stories, Low_medium_Income, No_of_traffic_lights
Citizen Assist (CA) Food, Low_Income, Apartment_Condo_1_to_4_stories,
Apartment_Condo_5_or_more_stories Motor Vehicle Incident (TA)
No_of_traffic_lights, Food, Low_Income, commercial, retail Rescue (RC)
Apartment_Condo_5_or_more_stories, Food, Education, Entertainment, Low_Income,
commercial

##  6 Model Development

We now present the predictive model development for EFRS event prediction and
the associated experimental setup.

###  6.1 Regression Model

We developed a regression model to predict EFRS events using the Negative
Binomial (NB2) model. Negative binomial regression is commonly used to test
for the connections between confounding (events) and predictor variables
(features) on over-dispersed count data. This fits EFRS events because the
event counts are over-dispersed. The NB2 model is based on a Poisson-gamma
mixture distribution where the Poisson heterogeneity is modeled using a gamma
distribution [29, 30]. The NB2 distribution is a two-parameter model with mean
$\mu$ and dispersion $\alpha$. The variance of the NB2 distribution is
estimated using the parameters $\mu$ and $\alpha$.

Let $y$ denotes a vector of EFRS event counts seen for a given time duration
for a given event type. $X$ denotes a matrix of predictor/regressor variables
(e.g., static features), and $y_{i}$ is the number of events in the ith
observation. The mean of $y$, $\mu$ is determined in terms of exposure time
$t$ and a set of $k$ regressor variables (the $x$’s). The mean and variance of
each $y_{i}$ can be defined as follows:

$\mu_{i}=exp(\ln(t_{i})+\beta_{1}x_{1i}+\beta_{2}x_{2i}+\dots\beta_{k}x_{ki})$
(2) $var(y_{i})=\mu_{i}+\alpha\mu_{i}^{2}$ (3)

where, $t_{i}$ represents the exposure time for a particular event (e.g. hour,
day, week, month, year), and $\beta_{1}$, $\beta_{2}$, $\beta_{3}$, …,
$\beta_{k}$ are the (unknown) regression coefficients which are to be
estimated with the training data. The maximum likelihood method is used to
estimate the regression coefficients [30]. Their estimates are denoted by
$b_{1}$, $b_{2}$, …, $b_{k}$. The parameter $\mu$ is a vector of the event
rates which can be estimated using the Poisson model. Each $\mu_{i}$
corresponds to the event count $y_{i}$ in the vector $y$. Each $\mu_{i}$ can
be estimated using the Poisson model. The parameter $\alpha$ is estimated
using ordinary least square regression with $y_{i}$, $i^{th}$ outcome, and
$\mu_{i}$ is $i^{th}$ rate. The probability distribution function of the NB2
model for the $i^{th}$ observation ( i.e., $y_{i}$ event ) is defined using
Eq. 4, where the function $\Gamma(\cdot)$ is the gamma function [30].

${Pr\left({y}_{i}|{\mu}_{i},\alpha\right)=\frac{\Gamma\left({y}_{i}+{\alpha}^{-1}\right)}{\Gamma\left({y}_{i}+1\right)\Gamma\left({\alpha}^{-1}\right)}{\left(\frac{1}{1+\alpha{\mu}_{i}}\right)}^{{\alpha}^{-1}}{\left(\frac{\alpha{\mu}_{i}}{1+\alpha{\mu}_{i}}\right)}^{{y}_{i}}}$
(4)

The performance of the regression models can be evaluated with regression
metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE).
These metrics measure the difference between the actual values and the
predicted values on the regression line. MAE and RMSE evaluation metrics are
given by Eq. 5, and Eq. 6. These equations illustrate the calculation of MAE
and RMSE metrics, where $y_{i}$ is the $i^{th}$ actual value, and
$\hat{y_{i}}$ is the corresponding predicted value. The percentage error (%
Error) for each model is another metric to assess the performance of the
models.

$MAE=\sum_{i=1}^{n}\frac{|y_{i}-\hat{y}_{i}|}{n}\quad$ (5)

$RMSE=\sqrt{\sum_{i=1}^{n}\frac{(y_{i}-\hat{y}_{i})^{2}}{n}}\quad$ (6)

###  6.2 Experimental Setup

We conducted experiments with regression models for neighborhood and fire-
station regions. The prediction of individual event types by neighborhood
proved very difficult because of their low event occurrence rate. Therefore,
we focused on weekly and monthly predictive models for total event incident
counts using the union of all the important predictor variables of event
types. Eq. 7 shows a regression model for the total number of event counts
(Events) with the feature sets (predictors) of neighborhoods, where each
$b_{i}$, $i=0,1,2,\dots$, represents the coefficient of regression variable.

$Y\left(Events\right)=\exp\left[\begin{array}[]{r}b_{0}+b_{1}\times
Food+b_{2}\times Low\\_Income\\\ +b_{3}\times retail+b_{4}\times commercial\\\
+b_{5}\times Education+b_{6}\times Entertainment\\\ +b_{7}\times
one\\_Year\\_to\\_Less\\_than\\_three\\_Years\\\ +b_{8}\times
Apartment\\_Condo\\_5\\_or\\_more\\_stories\\\ +b_{9}\times
Apartment\\_Condo\\_1\\_to\\_4\\_stories\\\ +b_{10}\times
No\\_Certificate\\_Diploma\\_or\\_Degree\\\ +b_{11}\times
Low\\_medium\\_Income\\\ +b_{12}\times No\\_traffic\\_lights\\\ +b_{13}\times
Less\\_than\\_one\\_Year\end{array}\right]$

(7)

For the fire station region prediction, we conducted experiments with
different regression models for each event type. Based on the model developed
in Section 6.1, regression models were formulated for each event type
considering the important predictor variables identified from the feature
importance analysis in Section 5.2, and shown in Table 3. We formulated models
for the event types: fire, medical, alarms, citizen-assist, motor-vehicle
incident, and rescue events. For example, Eq. 8 shows a model expression for
the Fire event. Each $b_{i}$, $i=0,1,2,\dots$, represents the coefficient of
regression variables, which were estimated using model training.

$Y\left(FR\right)=\exp\left[\begin{array}[]{r}b_{0}+b_{1}\times
Low\\_Income\\\ +b_{2}\times Low\\_medium\\_Income\\\ +b_{3}\times
Less\\_than\\_one\\_Year\\\ +b_{4}\times
Apartment\\_Condo\\_1\\_to\\_4\\_stories\\\ +b_{5}\times
No\\_Certificate\\_Diploma\\_or\\_Degree\end{array}\right]$

(8)

Statsmodels [31, 32] APIs were used to build the models and perform the
experiments. We performed experiments for training, prediction, and evaluation
of weekly and monthly durations at different spatial ( fire-station regions
and neighborhoods) resolutions of the city.

##  7 Event Prediction by Neighborhood

In this section, we present the event prediction results by neighborhood
including performance analysis of the models and risk classification of the
neighborhoods. We conducted several experiments to train models and made the
event predictions with neighborhood features for each neighborhood. We build a
generic predictive model with the identified important variables for
forecasting the occurrence of the events in each neighborhood.

(a)

(b)

Figure 7: Weekly Event Predictions by Neighborhood: (a) Predicted Events (b)
Actual Events. The annotation number on the neighborhood polygon map
represents a mean of weekly predicted/actual events. Neighborhoods with dark
(red) color have more event incidents.

###  7.1 Weekly Event Predictions

In this experiment, we trained and evaluated the weekly predictive model with
the neighborhood’s demographic, socioeconomic, and map-related (PoI) features
and weekly event data from 2011-01-03 to 2022-08-01. We randomly split the
data into training and test samples with 70% of data for training and 30% of
data for testing, resulting in 161189 and 68781 samples, respectively. We
forecast the weekly event occurrences in each neighborhood using the
neighborhood’s features. The predictions were validated with out-sample test
data. Figure 7 compares the weekly event count predicted results for each
neighborhood with the actual event observations. Weekly event predictions with
static features show a good performance for forecasting the event occurrence
except for a few special neighborhoods in the downtown core. These Weekly
predictions for each neighborhood can be used in weekly emergency preparedness
and planning.

(a)

(b)

Figure 8: Monthly Event Predictions by Neighborhood: (a) Predicted Events (b)
Actual Events. The annotation number on the neighborhood polygon map
represents a mean of monthly predicted/actual events. Neighborhoods with dark
(red) color have more event incidents.

###  7.2 Monthly Event Predictions

We also conducted experiments to predict monthly event counts with the static
features of the neighborhood. As in the previous section, we trained the
models with monthly event data and then validated the predictions with out-
sample test data. The monthly event data with static features contains 36729
training and 16051 testing samples. Figure 8 compares the mean of the monthly
predictions with the mean of the actual event incidents in each neighborhood.
The monthly predictions by neighborhood are relatively good as compared to the
weekly predictions. However, the prediction for some neighborhoods have a
relatively high error possibly because these neighborhoods have special
characteristics. These monthly predictions can be used by the city for making
prevention and mitigation plans at a neighborhood level.

###  7.3 Model Evaluation & Prediction Error Analysis

We evaluated the model’s performance with regression metrics and residual
distribution analysis. The overall performance of the weekly and monthly
predictive models was evaluated with mean absolute error (MAE) and root mean
square error (RMSE) regression metrics. With weekly predictions, MAE and RMSE
are 0.81 (mean of actual weekly observation is 2.06) and 2.59, respectively.
Similarly, MAE and RMSE with monthly predictions are 4.09 (mean of actual
monthly observation is 8.88), and 15.16, respectively.

We also examined the distribution of the prediction error (residual) for each
neighborhood using an empirical cumulative distribution function (CDF) of the
test sample data. Figure 9 shows the empirical CDF plot with a histogram of
the distribution of the errors of the predictive models. Figure 9 (a) shows a
distribution of the weekly prediction errors with histogram. The results show
that the model performs well except for a few neighborhoods. With this weekly
prediction model, 47% of the neighborhoods (175 out of 377) have 0 residual
error, 42% of the neighborhoods (160 out of 377) have error 1 (1 more or less
event predicted), 5% of the neighborhoods have error 2, and $\approx 90\%$ of
the neighborhoods are with $\leq 1$ error. Similarly, 9 (b) shows a
distribution of monthly prediction errors with histogram. Results show that
this model also performs well except for a few neighborhoods. With this
monthly prediction model, 16% of the neighborhoods (60 out of 377) have 0
residual error, 30% of the neighborhoods (110 out of 377) have 1 error, and
overall, 95% of the neighborhoods are with $\leq 10$ error.

(a)

(b)

Figure 9: Empirical CDF with Histograms of Prediction Errors: (a) Weekly
Prediction Errors (b) Monthly Prediction Errors.

###  7.4 Risk Classification of Neighborhoods

(a)

(b)

Figure 10: Classifying the Neighborhoods by (a) Weekly Prediction; and (b)
Monthly Prediction.

(a)

(b)

Figure 11: Weekly Event Predictions by Fire Stations: (a) Fire Events
Predictions, (b) Predicted and Actual Events for Fire Station 2.

We classified the neighborhoods based on the frequency of the predicted events
in weekly and monthly time intervals. We define risk as the likelihood of
occurrence of an event in a certain time period. We quantitatively expressed
it into four risk classes low, medium, high, and severe based on the
frequencies and applied Jenks’ Natural Breaks [33] method for the
classification. Jenks’ Natural Breaks is a data classification method suitable
for data that have a high variance. This method seems useful to classify and
visualize the event predictions made by our predictive models as we have
predictions with high variations.

We classified neighborhoods based on the forecasting values using the Jenks’
Natural Breaks. We define four risk classes with low, medium, high. Figures 10
(a) and 10 (b) present a risk class for each neighborhood based on the weekly
and monthly predicted values, respectively. Both results show that the
neighborhoods with numbers 1010 and 1090 have severe risk as each of these
neighborhoods has an occurrence rate of 24.6 or more per week and 118.3 or
more events per month. Neighborhoods 1020, 1030, 1140, 1150, 1180, 3240, 4180,
and 5480 have a high risk (weekly 10.6 to 24.6 events predicted), which is
reasonable since these neighborhoods belong to the central city area. This
risk classification can help the city make neighborhood preparedness plans to
help prevent and reduce the impact of event incidents.

##  8 Event Prediction by Fire-station

We conducted several experiments to train models to predict event counts by
Fire-station using the same methodology as for neighborhoods.

Figure 12: Comparative Analysis of Weekly Event Predictions with Actual
Events.

###  8.1  Weekly Event Predictions

We conducted experiments to predict weekly events with the static features of
Fire stations. In these experiments, we trained and evaluated the models with
demographic, map-related (PoI) features, and weekly event data from 2011-01-03
to 2022-08-01. We randomly split the data into training and test samples with
70% of data for training and 30% of data for testing. We made the predictions
for each fire station and validated them with out-sample test event data. The
training and test data consisted of 12721 and 5422 samples, respectively.

Figure 11 shows heap-map plots of weekly prediction and actual results. Figure
11 (a) shows that there is a higher chance for fire events to occur mainly in
two fire stations in the central part of the city. Figure 11 (b) shows the
actual and predicted number of events for different event types in the fire
station 2. The polygon boundaries of each fire station are derived from the
spatially nearest station. Actual zones would be impacted by accessibility and
the further refinement of the station-level polygons could strengthen proper
attribution of emergency event workload predictions.

Figure 12 compares the mean of weekly event predictions for each fire station
with the mean of the weekly actual event counts. The results show that weekly
event predictions with the static features for each fire station exhibit a
good performance for predicting the event occurrences except for a few special
fire stations, stations 1 for medical and 5 for fire events which have a
relatively high error. These two stations seem to have specific features that
the model does not capture fully. There are many encampment and overdose
incident calls to fire station 1 and such calls increase the frequency of
medical events. Similarly, station 5 also has an interesting characteristic,
and specifically for fire events. There are many points of interest (PoIs)
such as stadiums, transit, and recreation centers within the areas of station
5, which would possibly increase the fire event incidents. This model did not
fit well the rescue events because these events occur rarely and randomly. The
weekly predictions made for each fire station with the given features of a
fire station’s region would help for weekly emergency preparedness and
planning.

(a)

(b)

Figure 13: Monthly Event Predictions by Fire Stations: (a) Fire Events
Predictions, (b) Predicted and actual events for a Fire Station 2.

###  8.2 Monthly Event Predictions

We conducted experiments to predict monthly events with the static features of
the fire stations using the same methodology as in the previous section. We
made the predictions for each fire station and then validated the predictions
with out-sample test monthly event data. The monthly event data with static
features contains 2933 training and 1237 testing samples.

Figure 13 shows the heap-map plot of prediction results of the monthly event
prediction models. Similarly, Figure 14 compares the mean of the monthly
predictions with the mean of the actual event incidents for each fire station.
The monthly predictions by the fire station are relatively good in comparison
to the weekly predictions. However, the prediction for stations 1, 5, and 21
have a high error because we developed a single model for all stations, but
these stations have special characteristics. Fire station 21 has many rescue
events because this station is along the river. This station is unique in that
it is the only station without a pumper unit. Instead, it houses just a
Rescue, with staff trained for swift water rescue boat response. The station
location is not associated with event density, but instead river access. This
impacts the call volume as well as call characteristics. As noted, they have a
higher concentration of rescue events due to their proximity to the river,
associated bridges, and surrounding parkland. Similarly, station 5 has
relatively more fire events as there are many PoIs such as stadium, transit
hub, recreation, etc. We can improve the predictions of the model for these
stations by exploring other explanatory factors impacting the occurrence of
these events in these stations. The predictions made for each fire station
with the given demographic and map-related features of the fire station’s
service area help with monthly emergency preparedness and planning.

Figure 14: Comparative Analysis of Monthly Event Predictions with Actual
Events. The X-axis and Y-axis represent fire station number and count of six
different event types, respectively. Figure 15: Fire-stations with weekly
prediction error for different event categories. The annotation vector on the
polygon map represents a fire station number (first element), and the mean of
the weekly predicted events (second element), respectively. Figure 16: Fire
stations with monthly prediction error for different event categories. The
annotation vector on the polygon map represents a fire station number (first
element), and the mean of the monthly predicted events (second element),
respectively.

###  8.3 Model Evaluation & Prediction Error Analysis

We evaluated the performance of the predictive models of different event
categories for each fire station. We analyzed the prediction error of each
fire station for the out-of-sample test data. There are many measures (e,g,
Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute
Percentage Error (MAPE), Akaike Information Criterion (AIC) or Bayesian
Information Criterion (BIC)) that can be used to assess the performance of the
regression models. For our work, we decided to use absolute error or MAE and
RMSE metrics because MAPE is be applicable as there are many event types with
zero actual event incidents. Similarly, AIC and BIC are more appropriate for
comparing models with the same dataset, however, we have different event data
for different event types and durations.

We computed the absolute error (residual) values and used them to determine
how well the model performed. An absolute error of each station is estimated
and compared to the model performance of each station. We also evaluated the
overall performance of the models using common regression metrics such as mean
absolute error (MAE), root mean square error (RMSE), and absolute error. An
absolute error of the model in a fire station is an absolute difference
between the mean predicted value and the mean actual value. Figure 15, and
Figure 16 show the plots of the weekly and monthly prediction errors for each
event type in fire-station service regions. These results show that the
performance of the models varies station by station because each station has
different characteristics.

The error range of the model for weekly prediction of fire events ranges from
0 to 0.5 absolute error. This shows that the fire event prediction model
performed well as we have a very small error range for most stations. However,
the model has more errors with stations 5, 19, 22 and 28. This shows that the
model not performing well for those stations because these stations can have
some special characteristics that influence fire event occurrence.

Similarly, the mean of the absolute error of weekly and monthly prediction
models of medical events by fire station are 0 to 36 and 0 to 170,
respectively. Except for three fire stations 1, 3, and 16, the errors of other
stations are smaller. This model has a very low error of approximately $0$ for
stations 7, 9, 11, 12, 13, 17, and 23, and it shows that it performed very
well for these stations. Station 3 has an interesting error, which shows that
this station can have some different characteristics pertaining to its
location.

Table 4 presents the weekly and monthly predictive model performance by fire
station for six different event types using MAE and RMSE metrics. As noted
above, the prediction error of some of the stations is a relatively high
because each of these stations has certain distinctive characteristics
pertaining to their locations. The medical event prediction model does not
perform well in stations 1 and 5 as these two stations seem to have specific
features. There is a relatively high error with stations 1, 2, and 3 for alarm
events because these stations are likely due to being in the central part of
the city with more population density. Like the medical events, there is a
relatively high error in stations 1, 2, and 5 for citizen-assist events. There
is an interesting and different result with motor vehicle incidents because
this type of event is more likely to happen in areas with more traffic
density, worse road conditions, or bad weather. Fire station 21 has a
significantly large error for rescue events as its location is near the river.
Similarly, almost the same number of rescue events occurred in station 1. This
clearly shows that there should be some association between station 1 and
station 21, particularly for the rescue events. We can improve the prediction
performance for these stations by investigating the factors specific to these
stations.

Table 4: Comparing Model Performance by Fire Station with MAE and RMSE
Metrics.

|  | Event Types
---|---|---
|  | Fire | Medical | Alarms | Citizen-Assist | Motor-Vehicle | Rescue
Station Number | Model | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE
| Weekly | 0.90 | 1.19 | 37.57 | 47.12 | 2.98 | 4.74 | 1.94 | 2.65 | 1.34 | 1.60 | 0.73 | 1.00
1 | Monthly | 1.95 | 2.58 | 177.28 | 222.71 | 11.53 | 16.24 | 7.86 | 10.95 | 3.44 | 4.11 | 1.52 | 2.31
| Weekly | 1.05 | 1.36 | 13.14 | 17.94 | 3.70 | 4.67 | 1.90 | 2.34 | 1.88 | 2.23 | 0.89 | 1.08
2 | Monthly | 3.24 | 3.85 | 42.17 | 51.29 | 8.03 | 10.46 | 4.62 | 5.59 | 5.28 | 6.23 | 2.92 | 3.41
| Weekly | 0.88 | 1.10 | 16.74 | 17.67 | 3.02 | 3.76 | 1.43 | 1.64 | 1.66 | 1.97 | 0.57 | 0.62
3 | Monthly | 1.96 | 2.35 | 74.52 | 78.45 | 10.25 | 11.21 | 4.00 | 4.66 | 5.23 | 6.28 | 1.40 | 1.76
| Weekly | 0.78 | 0.97 | 6.92 | 9.07 | 2.16 | 2.83 | 0.86 | 1.19 | 1.87 | 2.44 | 0.24 | 0.22
4 | Monthly | 1.67 | 2.20 | 32.04 | 42.50 | 6.18 | 7.58 | 2.69 | 3.94 | 5.46 | 6.42 | 0.68 | 0.74
| Weekly | 1.14 | 1.58 | 13.70 | 21.63 | 2.42 | 3.30 | 1.99 | 2.97 | 1.78 | 2.27 | 0.45 | 0.63
5 | Monthly | 2.73 | 4.29 | 54.14 | 79.58 | 6.72 | 9.24 | 5.19 | 6.55 | 5.98 | 7.37 | 1.11 | 1.44
| Weekly | 0.74 | 0.89 | 5.90 | 7.42 | 1.77 | 2.13 | 1.06 | 1.29 | 1.17 | 1.49 | 0.38 | 0.46
6 | Monthly | 1.53 | 1.90 | 26.46 | 31.40 | 4.32 | 5.14 | 2.88 | 3.25 | 3.01 | 3.73 | 0.95 | 1.05
| Weekly | 0.89 | 1.21 | 6.58 | 8.89 | 1.90 | 2.49 | 1.16 | 1.49 | 1.80 | 2.61 | 0.43 | 0.59
7 | Monthly | 1.75 | 2.14 | 25.12 | 28.93 | 4.29 | 5.54 | 3.94 | 5.49 | 4.01 | 5.38 | 0.97 | 1.23
| Weekly | 0.84 | 1.15 | 6.71 | 9.10 | 2.50 | 3.33 | 1.35 | 1.75 | 1.91 | 2.61 | 0.37 | 0.61
8 | Monthly | 2.26 | 2.62 | 23.95 | 30.88 | 5.93 | 8.00 | 3.84 | 4.67 | 6.12 | 8.42 | 0.94 | 1.33
| Weekly | 0.49 | 0.57 | 2.82 | 3.47 | 1.55 | 2.04 | 0.61 | 0.66 | 1.09 | 1.37 | 0.24 | 0.21
9 | Monthly | 0.87 | 1.08 | 7.99 | 10.34 | 3.49 | 4.23 | 1.32 | 1.60 | 2.76 | 3.98 | 0.64 | 0.66
| Weekly | 0.88 | 1.09 | 7.56 | 9.22 | 2.13 | 2.66 | 1.15 | 1.35 | 1.77 | 2.28 | 0.25 | 0.22
10 | Monthly | 2.26 | 2.60 | 31.22 | 38.74 | 6.77 | 7.44 | 2.93 | 3.55 | 6.27 | 7.59 | 0.58 | 0.63
| Weekly | 0.67 | 0.79 | 4.14 | 5.21 | 1.64 | 2.02 | 0.82 | 1.11 | 1.27 | 1.56 | 0.28 | 0.32
11 | Monthly | 1.55 | 2.15 | 16.52 | 19.92 | 4.03 | 4.81 | 2.34 | 2.77 | 3.41 | 4.14 | 0.78 | 0.91
| Weekly | 0.64 | 0.89 | 5.63 | 7.06 | 1.67 | 2.18 | 1.14 | 1.41 | 1.63 | 2.27 | 0.40 | 0.47
12 | Monthly | 1.57 | 1.98 | 19.29 | 24.35 | 4.69 | 5.81 | 3.92 | 5.70 | 4.29 | 6.07 | 1.09 | 1.22
| Weekly | 0.68 | 0.84 | 3.93 | 5.08 | 1.69 | 2.20 | 0.83 | 1.07 | 1.21 | 1.47 | 0.34 | 0.44
13 | Monthly | 1.33 | 1.69 | 13.89 | 17.34 | 4.04 | 4.95 | 1.57 | 1.91 | 2.94 | 3.45 | 1.03 | 1.30
| Weekly | 0.82 | 1.01 | 7.58 | 9.19 | 2.02 | 2.82 | 1.14 | 1.34 | 1.19 | 1.50 | 0.29 | 0.32
14 | Monthly | 1.86 | 2.38 | 30.95 | 35.30 | 7.52 | 11.07 | 3.15 | 3.56 | 3.21 | 4.00 | 0.67 | 0.79
| Weekly | 0.70 | 0.86 | 3.96 | 4.80 | 1.99 | 2.77 | 0.88 | 1.16 | 1.76 | 2.33 | 0.26 | 0.34
15 | Monthly | 1.43 | 1.75 | 11.28 | 14.72 | 5.04 | 6.56 | 2.27 | 2.79 | 6.40 | 7.46 | 0.66 | 0.69
| Weekly | 0.66 | 0.89 | 14.02 | 15.67 | 2.34 | 2.85 | 1.42 | 1.65 | 1.66 | 2.09 | 0.26 | 0.24
16 | Monthly | 1.57 | 1.91 | 59.98 | 65.91 | 9.71 | 10.96 | 3.91 | 4.47 | 4.47 | 5.39 | 0.66 | 0.72
| Weekly | 0.86 | 1.14 | 6.84 | 8.39 | 2.40 | 3.26 | 1.25 | 1.43 | 1.32 | 1.59 | 0.19 | 0.30
17 | Monthly | 2.13 | 2.72 | 24.62 | 28.92 | 6.15 | 8.64 | 3.56 | 4.23 | 4.25 | 5.19 | 0.58 | 0.65
| Weekly | 0.74 | 0.98 | 5.52 | 7.05 | 2.08 | 2.62 | 1.20 | 1.61 | 1.39 | 1.64 | 0.36 | 0.52
18 | Monthly | 1.47 | 1.86 | 21.18 | 27.55 | 5.35 | 6.83 | 3.62 | 4.93 | 4.03 | 4.72 | 0.84 | 1.20
| Weekly | 0.80 | 1.06 | 6.66 | 8.62 | 2.30 | 2.88 | 0.85 | 1.16 | 1.34 | 1.70 | 0.36 | 0.45
19 | Monthly | 1.82 | 2.42 | 25.66 | 33.07 | 7.61 | 9.41 | 2.54 | 3.19 | 3.62 | 4.41 | 0.63 | 0.94
| Weekly | 0.71 | 0.88 | 6.10 | 7.33 | 1.97 | 2.38 | 1.18 | 1.53 | 1.57 | 1.97 | 0.31 | 0.39
20 | Monthly | 1.58 | 1.94 | 24.60 | 27.08 | 4.77 | 6.09 | 3.81 | 5.34 | 3.61 | 4.46 | 0.68 | 0.74
| Weekly | 0.67 | 0.81 | 3.74 | 4.84 | 1.73 | 2.03 | 0.81 | 1.08 | 1.06 | 1.24 | 0.80 | 1.16
21 | Monthly | 1.45 | 1.68 | 9.14 | 11.41 | 5.83 | 6.65 | 2.22 | 2.79 | 3.36 | 4.21 | 2.76 | 3.77
| Weekly | 1.09 | 1.30 | 9.88 | 11.68 | 2.90 | 3.52 | 1.36 | 1.62 | 1.51 | 1.92 | 0.67 | 0.86
22 | Monthly | 2.84 | 3.15 | 45.22 | 51.11 | 8.14 | 9.33 | 4.33 | 4.92 | 3.50 | 4.26 | 1.54 | 1.80
| Weekly | 0.74 | 0.92 | 5.38 | 6.59 | 1.99 | 2.52 | 1.28 | 1.76 | 1.88 | 2.49 | 0.20 | 0.33
23 | Monthly | 1.69 | 2.19 | 17.22 | 22.49 | 5.13 | 6.79 | 3.84 | 5.08 | 5.55 | 6.99 | 0.67 | 0.80
| Weekly | 0.64 | 0.76 | 5.28 | 6.85 | 2.19 | 2.73 | 0.96 | 1.41 | 1.70 | 2.07 | 0.39 | 0.53
24 | Monthly | 1.47 | 1.96 | 19.18 | 25.15 | 8.46 | 10.09 | 2.82 | 3.74 | 4.61 | 5.48 | 0.93 | 1.19
| Weekly | 0.58 | 0.63 | 3.99 | 4.93 | 1.71 | 2.32 | 0.70 | 0.85 | 1.21 | 1.49 | 0.23 | 0.20
25 | Monthly | 1.28 | 1.62 | 16.22 | 18.78 | 6.60 | 8.17 | 2.03 | 2.99 | 3.06 | 3.60 | 0.68 | 0.75
| Weekly | 0.74 | 0.94 | 6.42 | 7.85 | 2.50 | 3.01 | 0.66 | 0.83 | 1.40 | 1.76 | 0.29 | 0.36
26 | Monthly | 1.78 | 2.42 | 27.66 | 32.53 | 8.98 | 11.02 | 2.06 | 3.57 | 4.64 | 5.39 | 0.72 | 0.79
| Weekly | 0.62 | 0.71 | 4.10 | 4.87 | 2.20 | 2.82 | 0.81 | 1.04 | 1.66 | 2.20 | 0.27 | 0.29
27 | Monthly | 1.38 | 1.59 | 12.99 | 16.17 | 7.57 | 9.14 | 2.28 | 3.00 | 4.88 | 5.99 | 0.66 | 0.75
| Weekly | 0.60 | 0.68 | 4.63 | 5.67 | 2.44 | 2.94 | 0.72 | 0.76 | 1.27 | 1.44 | 0.27 | 0.32
28 | Monthly | 1.39 | 1.65 | 18.51 | 21.59 | 8.15 | 9.84 | 2.12 | 2.42 | 4.73 | 5.28 | 0.82 | 1.24
| Weekly | 0.56 | 0.72 | 3.62 | 4.38 | 1.98 | 2.49 | 0.63 | 0.74 | 1.28 | 1.52 | 0.24 | 0.23
29 | Monthly | 0.92 | 1.07 | 12.50 | 15.47 | 6.37 | 7.63 | 1.42 | 1.69 | 3.42 | 4.04 | 0.64 | 0.67
| Weekly | 0.61 | 0.69 | 4.11 | 4.98 | 1.81 | 2.23 | 0.70 | 0.84 | 1.20 | 1.39 | 0.24 | 0.21
30 | Monthly | 1.36 | 1.74 | 14.13 | 17.47 | 6.27 | 7.11 | 1.78 | 2.31 | 3.20 | 3.80 | 0.65 | 0.68

##  9 Impact of COVID-19 Pandemic on Event Predictions

The occurrence patterns of fire and other emergency events changed
significantly during the COVID-19 pandemic. The impact of the pandemic on city
dynamics varied according to conditions such as lockdowns and local
restrictions, population density, and socioeconomic activities, including
business and industrial operations. Many business and commercial activities in
the city were reduced, and some of these activities shifted to residential
areas due to lockdowns and other restrictions. This situation led to the
occurrence of more events in residential areas or suburbs of the city. In
addition, event occurrence patterns may have fluctuated over the time of the
pandemic as local restrictions were implemented and removed. These situations
can impact the performance of the emergency event predictive models. In this
section, we present the results of how the COVID-19 pandemic situation
impacted EFRS event predictions with our model.

We conducted several experiments for each event category with the same
demographic and socioeconomic features of fire station regions. In these
experiments, we trained and evaluated the models before and after the COVID-19
pandemic periods of weekly and monthly events data from 2017-05-01 to
2019-12-30, and 2020-01-01 to 2022-08-01, respectively. As before, we randomly
split the data into train and test samples with 70% of data for training and
30% of data for testing. We trained the models and validated them with out-
sample test event data. The performance of both weekly and monthly predictive
models of each event type was evaluated with MAE and RMSE for before and after
the pandemic periods.

Figure 17 shows the heap-map plot of prediction results with mean absolute
error using before the COVID-19 pandemic weekly event predictive model.
Similarly, Figure 18 depicts the weekly prediction of events by fire station
after the COVID-19 pandemic with mean absolute error (MAE) for the different
event types. The annotation vector on the polygon map represents a fire
station number (first element), and the mean of the weekly predicted events
(second element), respectively. Similarly, Table 5 presents the comparison of
prediction error metric values for assessing the overall performance of the
predictive models before and after the COVID-19 pandemic. Table 6 shows the
model performance comparison by fire station before and after the COVID-19
pandemic using RMSE.

When we trained the models using before COVID-19 and after COVID-19 events
data, we obtained slightly different models (predictors’ coefficients) for
different event types. Thus we have slightly different models for making
predictions before and after the pandemic period. However, the results show
that there is a relatively high error rate with the models using the data
after COVID-19. All of the results tell us that there were significant changes
in the occurrence of events both in terms of locations and volumes during the
COVID-19 pandemic. In addition, there are some stations such as stations 1, 2,
3, 5, 8, and 21 that have relatively high error after-COVID-19 models because
some of these stations have special characteristics in terms of their
locations and their closer proximity to public essential services (e.g.,
hospitals, shelters, and pharmacies) which can lead to the likelihood of
occurrence of more emergency events. This indicates that there was a
significant impact of the COVID-19 pandemic situation on the performance of
the models for the occurrence of fire and other emergency events in the city.

We can improve the predictions of the models by exploring other explanatory
dynamic factors that impact the occurrence of the events during the pandemic.
Some of the potential dynamic factors of the suburban areas can be identified
by keeping track of changes in home occupancy, residential activities,
commercial activities, and people commuting to the urban centers. In addition,
network activity data could help in capturing the people’s mobility in the
city. We can collect these data by counting the mobile phones connected to to
nearby cell towers of the cellular network.

Table 5: Comparison of prediction errors for assessing the overall performance
of the predictive models before and after COVID-19

|  | MAE | RMSE
---|---|---|---
Event Types | Model |  | Before
---
COVID-19
| After
---
COVID-19
| Before
---
COVID-19
| After
---
COVID-19
| Weekly | 0.77 | 0.83 | 0.95 | 1.10
Fire | Monthly | 1.57 | 1.87 | 1.85 | 2.66
| Weekly | 6.01 | 10.23 | 7.07 | 18.20
Medical | Monthly | 21.79 | 37.45 | 24.44 | 70.92
| Weekly | 2.18 | 2.55 | 2.70 | 3.43
Alarms | Monthly | 5.77 | 6.54 | 7.09 | 8.53
| Weekly | 1.02 | 1.23 | 1.29 | 1.71
Citizen Assist | Monthly | 2.77 | 3.62 | 3.18 | 5.03
| Weekly | 1.59 | 1.44 | 2.00 | 1.83
Motor Vehicle | Monthly | 4.33 | 3.66 | 5.19 | 4.66
| Weekly | 0.39 | 0.45 | 0.44 | 0.67
Rescue | Monthly | 0.93 | 1.18 | 1.13 | 1.98

Table 6: Comparing the Prediction Errors with RMSE Metric for Before and After
COVID-19

|  | Event Types
---|---|---
|  | Fire | Medical | Alarms | Citizen-Assist | Motor-Vehicle | Rescue
Station Number | Model | Before | After | Before | After | Before | After | Before | After | Before | After | Before | After
| Weekly | 0.90 | 1.56 | 32.33 | 72.50 | 2.95 | 5.27 | 1.92 | 2.83 | 1.73 | 1.47 | 1.02 | 1.21
1 | Monthly | 1.75 | 3.03 | 135.69 | 307.73 | 12.23 | 6.60 | 5.24 | 12.55 | 2.92 | 4.26 | 2.74 | 6.32
| Weekly | 1.39 | 1.59 | 8.51 | 22.24 | 4.76 | 7.39 | 2.04 | 2.82 | 1.74 | 1.99 | 1.10 | 1.21
2 | Monthly | 2.15 | 4.18 | 20.44 | 79.75 | 13.47 | 19.13 | 6.02 | 8.17 | 4.63 | 4.20 | 3.01 | 2.80
| Weekly | 1.08 | 1.07 | 13.79 | 26.05 | 4.03 | 4.16 | 1.61 | 1.90 | 2.18 | 1.48 | 0.45 | 0.61
3 | Monthly | 1.65 | 3.31 | 63.97 | 97.35 | 13.26 | 18.84 | 4.30 | 5.80 | 7.30 | 5.19 | 1.09 | 1.27
| Weekly | 0.98 | 1.01 | 8.25 | 12.68 | 3.36 | 3.15 | 0.99 | 1.66 | 1.97 | 2.17 | 0.28 | 0.36
4 | Monthly | 2.53 | 2.09 | 36.43 | 31.10 | 9.56 | 8.39 | 2.67 | 4.33 | 8.71 | 6.53 | 0.97 | 0.75
| Weekly | 1.25 | 2.35 | 14.13 | 39.07 | 3.30 | 4.53 | 2.06 | 3.32 | 2.05 | 1.54 | 0.63 | 0.86
5 | Monthly | 2.85 | 8.65 | 36.37 | 132.20 | 6.56 | 8.41 | 6.37 | 7.95 | 5.46 | 5.57 | 1.36 | 1.93
| Weekly | 0.69 | 1.02 | 5.70 | 11.33 | 1.87 | 2.36 | 1.18 | 1.45 | 2.00 | 1.43 | 0.52 | 0.46
6 | Monthly | 0.85 | 2.26 | 25.14 | 31.74 | 5.65 | 6.54 | 3.94 | 2.62 | 4.89 | 3.77 | 1.13 | 0.93
| Weekly | 1.21 | 1.12 | 6.52 | 8.76 | 2.22 | 2.63 | 1.50 | 1.47 | 2.85 | 2.15 | 0.69 | 0.60
7 | Monthly | 1.74 | 2.06 | 18.76 | 24.33 | 5.25 | 4.81 | 3.97 | 8.60 | 7.66 | 4.62 | 0.85 | 1.53
| Weekly | 1.33 | 1.24 | 5.63 | 7.93 | 3.35 | 3.86 | 1.54 | 1.68 | 3.28 | 2.70 | 0.40 | 0.50
8 | Monthly | 1.30 | 2.94 | 5.75 | 26.96 | 9.32 | 7.26 | 3.22 | 5.53 | 8.85 | 7.39 | 1.15 | 0.73
| Weekly | 0.49 | 0.65 | 3.25 | 4.76 | 1.77 | 2.31 | 0.65 | 0.73 | 1.64 | 1.30 | 0.12 | 0.16
9 | Monthly | 1.02 | 1.08 | 9.64 | 17.17 | 3.29 | 5.24 | 1.55 | 1.98 | 4.00 | 3.93 | 0.55 | 0.96
| Weekly | 1.09 | 1.20 | 5.62 | 8.65 | 3.32 | 2.32 | 1.43 | 1.47 | 2.89 | 2.22 | 0.25 | 0.13
10 | Monthly | 1.91 | 2.65 | 13.09 | 25.55 | 6.28 | 4.49 | 2.71 | 4.32 | 8.63 | 5.95 | 0.76 | 0.58
| Weekly | 0.85 | 0.63 | 4.24 | 5.86 | 2.04 | 2.14 | 1.17 | 1.05 | 1.81 | 1.77 | 0.24 | 0.39
11 | Monthly | 1.61 | 1.23 | 14.55 | 6.40 | 5.15 | 5.10 | 2.49 | 2.26 | 4.53 | 4.63 | 0.86 | 1.02
| Weekly | 0.85 | 0.76 | 4.31 | 6.79 | 2.35 | 2.83 | 1.49 | 1.67 | 2.11 | 1.83 | 0.62 | 0.59
12 | Monthly | 2.45 | 1.84 | 17.82 | 18.64 | 6.75 | 5.31 | 3.24 | 4.36 | 5.10 | 7.57 | 0.97 | 1.24
| Weekly | 0.88 | 1.03 | 3.79 | 5.35 | 1.72 | 2.39 | 0.76 | 1.48 | 1.34 | 1.41 | 0.34 | 0.63
13 | Monthly | 1.76 | 1.84 | 7.54 | 15.46 | 3.97 | 5.90 | 1.90 | 2.65 | 3.61 | 4.17 | 1.15 | 1.11
| Weekly | 0.99 | 1.03 | 6.71 | 6.68 | 3.00 | 3.69 | 1.36 | 1.57 | 1.62 | 1.85 | 0.48 | 0.43
14 | Monthly | 1.62 | 3.54 | 14.36 | 26.41 | 5.95 | 13.53 | 3.65 | 3.38 | 3.54 | 1.36 | 0.89 | 0.80
| Weekly | 0.89 | 0.92 | 3.81 | 4.86 | 1.81 | 2.99 | 0.86 | 1.12 | 2.20 | 2.15 | 0.24 | 0.36
15 | Monthly | 1.19 | 1.01 | 11.77 | 24.22 | 4.36 | 4.41 | 2.46 | 2.87 | 8.10 | 3.96 | 0.80 | 0.83
| Weekly | 0.92 | 1.29 | 13.35 | 17.79 | 2.69 | 3.48 | 1.20 | 2.01 | 2.20 | 2.15 | 0.27 | 0.34
16 | Monthly | 2.07 | 1.37 | 60.10 | 72.31 | 8.63 | 7.45 | 3.47 | 5.63 | 5.28 | 4.19 | 0.71 | 0.58
| Weekly | 1.45 | 1.11 | 6.00 | 7.92 | 3.44 | 3.31 | 1.54 | 1.50 | 1.79 | 1.82 | 0.12 | 0.33
17 | Monthly | 1.97 | 1.89 | 13.27 | 24.77 | 7.62 | 5.73 | 4.83 | 3.66 | 5.85 | 4.78 | 0.55 | 1.56
| Weekly | 0.98 | 1.20 | 5.15 | 7.23 | 2.25 | 3.13 | 1.47 | 2.16 | 1.44 | 1.78 | 0.65 | 0.66
18 | Monthly | 1.51 | 1.69 | 16.16 | 26.38 | 6.92 | 4.94 | 3.80 | 4.75 | 3.36 | 3.55 | 1.53 | 1.32
| Weekly | 0.96 | 1.20 | 6.16 | 8.52 | 3.36 | 2.97 | 0.99 | 1.72 | 2.00 | 2.24 | 0.68 | 0.51
19 | Monthly | 2.26 | 2.26 | 21.97 | 27.78 | 5.54 | 7.44 | 2.88 | 3.19 | 3.49 | 4.19 | 0.72 | 1.05
| Weekly | 1.00 | 0.71 | 5.75 | 10.69 | 2.36 | 2.62 | 1.51 | 1.52 | 2.72 | 1.75 | 0.35 | 0.38
20 | Monthly | 1.96 | 1.56 | 21.19 | 39.42 | 5.16 | 7.62 | 4.51 | 3.96 | 6.69 | 3.54 | 0.79 | 0.71
| Weekly | 0.84 | 0.85 | 4.56 | 6.02 | 1.87 | 2.29 | 1.24 | 0.92 | 1.25 | 1.32 | 1.12 | 1.92
21 | Monthly | 1.33 | 2.26 | 14.57 | 12.09 | 5.63 | 10.14 | 2.11 | 2.03 | 3.19 | 3.50 | 3.55 | 5.77
| Weekly | 1.32 | 1.27 | 9.94 | 19.16 | 3.22 | 3.41 | 1.45 | 2.13 | 1.61 | 2.15 | 0.66 | 0.76
22 | Monthly | 3.49 | 2.82 | 39.21 | 72.31 | 14.65 | 10.60 | 4.11 | 6.85 | 4.31 | 4.21 | 2.26 | 2.64
| Weekly | 0.88 | 0.73 | 5.14 | 6.53 | 2.79 | 3.39 | 1.28 | 1.78 | 2.75 | 1.74 | 0.40 | 0.26
23 | Monthly | 1.91 | 1.65 | 12.53 | 16.79 | 5.93 | 5.66 | 2.35 | 3.46 | 7.52 | 3.76 | 0.75 | 1.04
| Weekly | 1.09 | 1.12 | 4.06 | 7.83 | 2.66 | 3.84 | 1.71 | 1.72 | 2.24 | 1.79 | 0.58 | 0.58
24 | Monthly | 2.31 | 1.55 | 22.15 | 21.42 | 9.79 | 9.85 | 2.10 | 3.29 | 3.67 | 4.07 | 0.66 | 0.95
| Weekly | 0.73 | 0.78 | 3.83 | 3.83 | 2.25 | 2.88 | 0.87 | 0.87 | 1.53 | 1.47 | 0.13 | 0.20
25 | Monthly | 1.80 | 1.95 | 11.14 | 12.09 | 5.96 | 4.85 | 1.78 | 2.35 | 1.59 | 3.20 | 0.80 | 0.63
| Weekly | 0.93 | 0.99 | 6.09 | 7.37 | 2.67 | 3.03 | 1.22 | 0.98 | 1.86 | 1.61 | 0.18 | 0.28
26 | Monthly | 2.22 | 1.78 | 18.65 | 29.49 | 5.34 | 7.37 | 2.61 | 5.97 | 4.95 | 5.22 | 0.76 | 0.74
| Weekly | 0.76 | 0.65 | 3.84 | 4.39 | 2.28 | 2.87 | 1.10 | 1.30 | 2.29 | 1.96 | 0.27 | 0.44
27 | Monthly | 1.98 | 1.32 | 9.88 | 16.31 | 4.94 | 8.26 | 2.23 | 2.90 | 8.07 | 5.84 | 0.86 | 0.82
| Weekly | 0.77 | 0.88 | 4.46 | 4.90 | 2.64 | 3.75 | 0.74 | 1.10 | 1.78 | 1.67 | 0.47 | 1.10
28 | Monthly | 1.49 | 1.96 | 17.02 | 10.59 | 6.86 | 5.61 | 1.76 | 2.43 | 3.52 | 3.96 | 0.59 | 1.69
| Weekly | 0.58 | 0.63 | 3.51 | 4.59 | 2.32 | 2.80 | 0.90 | 1.19 | 1.53 | 1.69 | 0.00 | 0.23
29 | Monthly | 1.41 | 1.30 | 8.68 | 9.21 | 4.83 | 5.34 | 1.49 | 1.74 | 2.98 | 3.82 | 0.56 | 0.77
| Weekly | 0.71 | 0.66 | 3.81 | 4.60 | 2.18 | 2.17 | 1.07 | 0.91 | 1.68 | 1.26 | 0.20 | 0.28
30 | Monthly | 1.27 | 1.57 | 8.62 | 17.02 | 3.80 | 6.52 | 1.57 | 2.74 | 3.34 | 3.81 | 0.52 | 0.59

Figure 17: Weekly prediction of events by fire-stations before COVID-19 with
mean absolute error (MAE) for different event types. The annotation vector on
the polygon map represents a fire station number (first element), and the mean
of the weekly predicted events (second element), respectively. Figure 18:
Weekly prediction of events by fire-station after COVID-19 with mean absolute
error (MAE) for the different event types. The annotation vector on the
polygon map represents a fire station number (first element), and the mean of
the weekly predicted events (second element), respectively.

##  10 Key Insights & Findings

In this section, we discuss how our findings answer the research questions Q1
to Q8. We obtained the following insights and findings from this study that
answer to the research questions:

  1. 1.

Potential data sources: We identified the potential data sources for fire and
emergency event prediction, namely, Open Data Portal of the City, Open Street
Map, and Fire Rescue Service Department from where we collected demographic,
socioeconomic, point of interest (PoI) data, and EFRS event incidents data,
respectively.

  2. 2.

Dataset development: We developed a methodology to prepare the datasets at
different spatio-temporal granularities (e.g., neighborhoods and fire-station
regions; and daily, weekly, and monthly). We developed computational methods
and built tools to collect data from different sources, mapped fire-station
service areas, and then converted neighborhood characteristics to fire-
stations.

  3. 3.

Correlation analysis: We identified the following findings from the
correlation analysis of event types with neighborhood features

     * •

Fire events: low-income, apartments/condos with 1-4 stories, and the number of
hotels/motels are the top three features that are correlated with fire events.
This means that the likelihood of fire incidents increases as the number of
low-income populations, condos/apt, and hotels/motels increase.

     * •

Medical events: medical events are highly correlated with the level of income.
The next correlated feature is more crowded places such as food-related
facilities and hotels/motels. This indicates that the increasing low-income
population, and food-related facilities (restaurants, bars, food courts, and
hotels/motels) in a neighborhood can cause the chance of occurring more
medical event incidents.

     * •

Alarm events: apartments/condos with more than 5 stories, food-related
facilities, hotels/motels, and low income are the highly correlated features
with alarm events. This shows that the alarms are likely to happen in
neighborhoods with more apartments or condominiums, food-related facilities,
hotels/motels, and low-income populations (e.g., Downtown).

     * •

Citizen assist events: low-income, food, and condos/apartments are highly
correlated with citizen assist events. This shows the level of income, more
populated places, and food-related facilities of a neighborhood impact the
likelihood of the occurring citizen assist event incidents.

     * •

Motor vehicle events: no. of traffic lights, food, income, and commercial are
the highly correlated features with motor vehicle incidents. These events are
likely to happen in areas with more traffic lights, food facilities,
commercial places, and low-income populations.

     * •

Rescue events: apartments/condos with 5 or more stories, food facilities, and
hotels/motels are three highly correlated features with rescue events. This
shows that rescue events are more likely to happen around more populated
places such as apartments/condos with 5 or more stories, food facilities, and
hotels/motels.

  4. 4.

Predictive model development using neighborhood features: We developed the
linear regression models for weekly and monthly event prediction by
neighborhood and fire station using characteristics such as demographic,
socioeconomic, and point of interest features. These are generic predictive
models built with the identified important feature sets (predictors) for the
events. We developed models for predicting the events in the following two
spatial resolutions:

     * •

At the neighborhood level, two models were developed for predicting the
likelihood of the occurrence of total the weekly or monthly emergency events
with its given features (e.g., income, food-related facilities,
apartments/condos, no. of traffic lights, length of terms of residency, etc.).
These models performed well with an acceptable error rate. However, the models
can not make a good prediction for an individual event type at a neighborhood
level because of their low occurrence rate.

     * •

At a fire station level, we developed different models for different event
types with their corresponding identified importance features (predictors).
For example, a model for a fire event was developed with low and medium-
income, apartments/condos 1 to 4 stories, populations with no certificate or
diploma or degree (no education), and less than one-year term length features
(predictors). The coefficients of these features were estimated during the
training of the models, which were then used for the predictions. Except for
rescue events, our developed models performed accurate predictions at the
fire-station level for all other event types including fire, medical, alarms,
citizen-assist, and motor-vehicle incidents.

  5. 5.

Event predictability analysis: Based on our prediction error analysis, we
summarized the findings on the predictability of events, and event types by
neighborhood and fire station, respectively as follows:

     * •

For most neighborhoods, the events can be predicted with the given features
such as household income, building types and occupancy, length terms
residency, education, food-related facilities, retail, commercial, roads, and
traffic information. A neighborhood with ID 1010 has a very high error and the
models did not make have good prediction performance for it.

     * •

At a fire station level, each event type is predictable with the given
features except for a few special fire stations.

       * –

Fire events are predictable for all fire stations. However, station 5 and
station 22 have a high prediction error rate.

       * –

Medical events are relatively more predictable and the predictions are more
accurate except for station 1. This station has an extremely high number of
actual medical event incidents.

       * –

Alarm events are predictable for most of the fire stations. However, the fire
stations 1, 3, 16, and 22 have high prediction error.

       * –

Citizen-assist events are predictable for all fire stations except station 1.
Also, medical and citizen-assist events have similar characteristics.

       * –

Motor vehicle rescue events are slightly less predictable with these features.
The models could not make good predictions for fire stations 2, 3, 8, 10, 15,
and 28.

       * –

Although there is a small prediction error with rescue events, these events
are relatively less predictable because these events rarely occur and mostly
happen in fire stations 1 and 21 which have a high error rate. It also found
that these two fire stations have some association, particularly for the
rescue events.

  6. 6.

Spatial analysis of event predictability: The predictions with the given
features for the bigger spatial regions such as fire station coverage areas
are more accurate and predictable than smaller neighborhood areas. The
predictions of the likelihood of individual event types occurrence at a
neighborhood level are difficult because the majority of the event types occur
rarely and also there is a very high degree of spatial variation.

  7. 7.

Temporal analysis of event predictability: The predictions of the monthly
events are more accurate and predictable than weekly events. This shows that
the prediction of an individual event type for a shorter time interval (e.g.,
hourly, daily) would be difficult because the majority of the event types
occur rarely and also there is a very high degree of temporal variations.

  8. 8.

Impact of COVID-19 pandemic on event prediction: During COVID-19, there is a
significant impact of the COVID-19 pandemic on event prediction because there
were significant changes in the pattern of occurrence of events both in terms
of locations and frequency. The prediction performance of the models with
after-COVID-19 data is slightly lower than before COVID-19. In particular,
stations 1, 2, 3, 5, 8, and 21 have relatively higher errors with after-
COVID-19 models. There are various factors that impact the occurrences of the
events during the COVID-19 pandemic. There were huge changes in people’s
mobility and other activities which impacted the occurrence of the emergency
events. Identifying these changing patterns, and incorporating them into the
models can help to improve the performance of the predictive models during or
after the pandemic.

##  11 Conclusions & Future Work

In this paper, we develop statistical analysis and machine learning models to
analyze and predict EFRS events for the city of Edmonton, Canada. We prepared
datasets by collecting and joining diverse sources with different temporal
periods at neighborhood and fire-station spatial levels that can be used for
EFRS events prediction. We analyzed the characteristics of EFRS events and
their occurrence pattern at different granularities. We also provided
descriptive statistics and feature associations. We developed and built a
number of predictive models for each EFRS event using their key feature set.
We made predictions with the developed models and validated the results by
comparing the predicted and actual event occurrences. We analyzed the impact
of COVID-19 pandemic situation on EFRS events prediction and assessed the
performance of the predictive models for the pandemic period by comparing its
prediction error metric values with before the COVID-19. The result of our
work can help EFRS in monthly and weekly decision-making, prevention, and
mitigation strategies at city, neighborhood, and fire station levels. The
methodologies developed in our work have wide applicability to other cities as
the data sources and features used are fairly standard. The accuracy of the
models is strongly influenced by the net event occurrence rates as well as
spatial and temporal extent.

In future work, our plan includes (i) investigation of weather, network
activity, and traffic data for building the predictive models; (ii) building
robust predictive models can aid in planning new fire stations in the areas of
the city that have a high incident rate and/or require faster response time;
(iii) development of multivariate time series forecasting model that can help
us to identify the possible number of events happening in the future for a
short interval of time (hour, day). A multivariate time series forecasting
model with weather or any other dynamic features would be helpful in
predicting different events. In addition, it can be used to detect anomalies
using a confidence band for the predicted values and actual data points.

## Acknowledgement

This work was supported by the funding research grants from TELUS and NSERC
Alliance Grant #576812.

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