Related Experiment Video
Updated: Mar 14, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.7K
Theory-Based Cartographic Risk Model Development and Application for Home Fire Safety
Stephen Furmanek1, Carlee Lehna, Carol Hanchette
1*University of Louisville School of Public Health and Information Sciences, Kentucky; †University of Louisville School of Nursing, Kentucky; and ‡Department of Geography and Geosciences, University of Louisville, Kentucky.
Summary
This study developed a predictive cartographic risk model to identify home fire and burn injury hotspots. The model effectively pinpointed high-risk areas, aiding public health interventions.
Area of Science:
- Public Health
- Geographic Information Systems (GIS)
- Epidemiology
Background:
- A significant gap exists in utilizing predictive risk models for identifying home fire and burn injury risks.
- Home fires and burn injuries pose a substantial public health burden, necessitating proactive identification of at-risk populations and areas.
Purpose of the Study:
- To develop, validate, and apply a predictive cartographic risk model for home fires and burn injuries.
- To demonstrate the model's utility using a sample population of parents with newborns in Jefferson County, KY.
- To inform targeted public health interventions and resource allocation for fire prevention.
Main Methods:
- Conducted a literature search to identify key risk factors for home fires and burn injuries.
- Synthesized American Community Survey data at the census tract level to create a predictive cartographic risk model.
- Validated the model using fire incidence data, correlation, regression, and Moran's I analysis.
- Examined model relationship with geocoded participant addresses and proximity to emergency services.
Main Results:
- The predictive model identified significant clustering of high and severe risk for home fires in the northwest section of Jefferson County.
- Modeled risk demonstrated a strong correlation with actual fire rates.
- Key predictors for fire risk included low home value, Black race, and individuals without a high school diploma.
- The intervention sample primarily consisted of participants at lower risk, located near fire departments and hospitals.
Conclusions:
- Predictive cartographic risk models are valuable tools for identifying geographic areas prone to home fires and burn injuries.
- The developed model successfully analyzed participant risk levels and their spatial relationship to emergency services.
- The methodology is generalizable and applicable to other public health challenges, enabling data-driven interventions.
Related Concept Videos
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
347
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
347
01:12Design Example: Flow Through a Fire Extinguisher
509
A fire extinguisher that uses pressurized water relies on fluid dynamics principles to generate a high-velocity stream capable of suppressing flames. The water is stored at a much higher pressure inside the extinguisher than the surrounding atmosphere. This pressure difference forces the water to flow rapidly when the extinguisher is activated, and the behavior of the water as it exits the nozzle can be understood using fundamental equations of fluid dynamics.
The key to understanding how the...
The key to understanding how the...
509
01:29Applications of GIS: Disaster Management and Emergency Response
628
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
628
01:11Hazard Rate
470
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
470
01:12Relative Risk
2.4K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.4K
01:17Design Example: Calculating Safe Diameter for Wind-Exposed Disc
370
Assessing safety in wind-exposed installations is crucial to preventing potential failures. This example explores the calculation and design adjustments needed to mount a circular disc on a building facade, where wind forces are a primary concern. A 4-meter diameter disc was initially designed as an aesthetic feature facing winds at a velocity of 25 meters per second, with an air density of 1.25 kilograms per cubic meter. Given these conditions, the drag force on the disc was determined using...
370