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This project was produced as a part of the University of Pennsylvania’s Master of Urban Spatial Analytics Spring 2019 Practicum (MUSA 801). The course was instructed by Ken Steif, Michael Fichman, and Matthew Harris, for whom we’d like to thank for their guidance. The group would also like to thank the City of Louisville for the opportunity to work on this project. Louisville Data Officer Michael Schnuerle, and to Major Joseph Williams of the Louisville Fire Department were integral partners in the development of this work.

This document is intended to enable others to replicate the methodology of a study of structural fires. Products of this course should be considered works in progress. We first introduce the context of this project, followed by the data, methodology, modeling, and guiding appendices. A table of contents is provided for easy navigation, along with select hyperlinks throughout the document.

1 Introduction

1.1 Abstract Building fires are a relatively rare occurrence, but fire risk, particularly in an older city like Louisville, KY, is omnipresent. This project seeks to predict geospatial fire risk in Louisville, and to use this risk to help inform a smoke detector outreach program. By utilizing a variety of sources, including fire data, property data, and environmental variables, the model assigns a risk score throughout different areas of the city, which can be used to identify the highest risk areas for latent fire risk. While Louisville currently has a smoke detector program, through which citizens can request a smoke detector via 311, the model results illustrate vulnerabilities in the city’s current system. This work can help to direct efforts to mitigate potential fires in at-risk communities, and public locations throughout the city could be utilized for extended smoke detector programming efforts.

1.2 Motivation Fires, as with many environmental risks, are rare enough that they are often disregarded - at least, until directly affected by one. However, there has been an increase of fire events in the city of Louisville in recent years, and more residents are at risk than ever. Over 2,600 fires have occurred in the city since 2013 and have caused more than $29 million in property damage in a three-year period (insert source). Furthermore, as with most cities, a vast majority of structural fires occur on residential properties. This means that each home suffered from an average of $20,000 in damage, which adds an enormous burden to an inherently traumatic situation and is often a pivotal moment in a household’s finances. Additionally, the increase in fires is a strain on the Louisville Fire Department and its emergency response services. Currently, the city mitigates residential fire risk through three main approaches: 21 fire stations service the central part of the city; the city’s inspection system targets a number of residential properties throughout the year; and a focused outreach program provides smoke detectors to residents who call 311 to request installation. We believe there is an opportunity in this final category to establish a more effective system. Relying primarily on inspections can be costly and difficult to arrange for residential properties, but smoke detector outreach can be expounded upon with less manpower, and applied to a broader scope. Last year, there were 700 installations, but we believe that we can help the city not just to increase this quantity, but to get these assets to those who need it most.



To do so, we have built an algorithm that predicts fire risk, with an emphasis on latent risk, rather than depending only on previous occurrences. We have sought to identify where future fires may occur, so that Louisville can mitigate the risk before the fire ever starts.

1.3 Data We compiled the following data for our analysis: environmental data from Louisville’s Open Data website, fire data from the Louisville Fire Department, and property valuation data from the City of Louisville. For further details on these sources, please see Appendix: Data Dictionary. The data was also “wrangled” before being explored in the following section. This process included various transformations of the data in order to optimize predictive ability of each variable. For details on this procedure, please see Feature Engineering and Appendix: Data Wrangling. Through exploratory analysis and the modeling process, the final dataset was narrowed down to a set of best predictors. For results on how the data ultimately performed in the models, please see Model Building.