Testing the Heat Hypothesis: The Relationship between Temperature and Violent Crime Rates
Dublin Core
Title
Testing the Heat Hypothesis: The Relationship between Temperature and Violent Crime Rates
Creator
Georgia Fifer
Date
2015
Description
This paper explored the relationship between temperature and behaviour. In particular the effect heat has on violent crimes. The heat hypothesis states that increased ambient temperatures can cause increased aggressive motives and behaviours. The current study was longitudinal and archival. Data was collated from four different countries: U.S., Japan, Jamaica and Finland over a period of 40 years. Data was collected from reliable online sources for: Temperature in degrees Celsius (℃), rainfall in millimetres (mm), intentional homicide rates, assault rates, rape rates and burglary rates. Rainfall and burglary were control variables. Analyses revealed a significant and positive relationship between temperature and intentional homicide, assault and rape rates. Temperature and burglary were not significantly related. Such results provide support for the heat hypothesis. The relationship between heat and violent crime should be investigated further; as the effects of global warming increase, so may violent crime rates worldwide.
Subject
None
Source
Data
In accord with Anderson et al’s., (1997) methods, data of the following crimes were collected: intentional homicide, assault, rape and burglary within a specified 40 years.
This crime data was sampled from the following countries databases: U.S., Japan, Jamaica and Finland. The inclusion criteria were to include the countries which had the most available data on crime. There were no data exclusions as the current study was retrospective, meaning all data had already been collected.
The 40 years analysed within each country differed depending on data availability. Crime rates were collected in the U.S. between the years 1960 and 2000. Crime rates were collected in Japan between the years 1975 and 2015. Crime rates were collected in Jamaica between the years 1970 and 2010. Crime rates were collected in Finland between the years 1976 and 2016. Therefore there would have been 160 full observations of intentional homicide, assault, rape and robbery. However, due to limited data available there were gaps in the data. For the U.S., 40 full observations of intentional homicide, assault, rape and robbery were obtained. For Japan, 23 full observations were obtained and 17 partial. For Jamaica, 11 full observations were obtained and 29 partial. For Finland, 22 full observations were obtained and 18 partial.
The crime data was police reported and per 100,000 of the population, as smaller figures were easier to manage. Crime data was collected from the following reliable online resources: Bourne et al., (2015), Burns (2013), (Knoema, 2011), (Nation Master, 2003), (Statista, n.d.), (Uniform Crime Reporting, 1930), (United Nations World Surveys, 2006), (UNODC Statistics, 1997). Websites were considered reliable if they were established official government data repositories.
Temperature (℃) and rainfall (mm) data were also collected. This data was obtained from an online climate data portal (Climate Change Knowledge Portal, n.d.). Rainfall was included as a control variable to ensure that any significant effect was a consequence of increased temperatures, rather than reduced rainfall as a consequence of increased temperatures. If rainfall was not controlled for, it would be impossible to decipher whether the observed effect was caused by increased temperature or reduced rainfall.
Apparatus
Microsoft excel and the Statistical Package for the Social Sciences (SPSS) were used for data analyses.
Analytical approach
This study was a longitudinal archival study which analysed existing data. The dependent variable (DV) was crime rates per 100,000 people, collated from reliable online data sources. The independent variables (IV) were: temperature and rainfall. The question asked was whether crime rates can be predicted by temperature and rainfall. The control variables were burglary and rainfall. Burglary was a control dependent variable, as it was expected that temperature would affect violent crime and not non-violent crime such as burglary. Rainfall was a control independent variable, so that rainfall could be controlled for and this made it possible to detect whether temperature alone had an effect on crimes.
The data collected required certain properties: the source had to be reliable, crimes had to be police reported and crime rates needed to be reported per 100,000 of the population. Pre-existing data available online was collected and sorted into an excel spreadsheet. Each variable had a column on the spreadsheet: country, year, intentional homicide, assault, rape, burglary, temperature and rainfall. The country variable was categorical. Countries were coded: 1 for the U.S., 2 for Japan, 3 for Jamaica and 4 for Finland. The remaining variables were continuous. There were 160 observations, 40 years per country. Some observations included data on all four crimes; some were partially completed due to limited data.
Firstly scatter graphs were plotted with crime against temperature for each country. This revealed the general direction of the relationships between the temperature and crimes. The main analysis was a linear mixed-effects model, where temperature and rainfall were fixed effects and country and year were random effects.
This analysis was chosen because of the structure of data. For this study there were multiple samples of crime rate data over 40 different years for each country, and multiple samples of crime rate data for the four different countries for each year. Magezi (2015) described how linear mixed-effects models can include such multiple, nested groups and accommodates for missing data. This was useful because the current study was a longitudinal archival study and consequently had missing data. Analyses were conducted using SPSS. An alpha level of .05 was used for each linear mixed-effects model.
+1 lag model analyses for each crime were also implemented, to account for a possible delay of the effect caused by exposure to temperature. To achieve this, the DV columns were shifted down one row using SPSS. It was necessary to check that all values still aligned with the correct country.
In accord with Anderson et al’s., (1997) methods, data of the following crimes were collected: intentional homicide, assault, rape and burglary within a specified 40 years.
This crime data was sampled from the following countries databases: U.S., Japan, Jamaica and Finland. The inclusion criteria were to include the countries which had the most available data on crime. There were no data exclusions as the current study was retrospective, meaning all data had already been collected.
The 40 years analysed within each country differed depending on data availability. Crime rates were collected in the U.S. between the years 1960 and 2000. Crime rates were collected in Japan between the years 1975 and 2015. Crime rates were collected in Jamaica between the years 1970 and 2010. Crime rates were collected in Finland between the years 1976 and 2016. Therefore there would have been 160 full observations of intentional homicide, assault, rape and robbery. However, due to limited data available there were gaps in the data. For the U.S., 40 full observations of intentional homicide, assault, rape and robbery were obtained. For Japan, 23 full observations were obtained and 17 partial. For Jamaica, 11 full observations were obtained and 29 partial. For Finland, 22 full observations were obtained and 18 partial.
The crime data was police reported and per 100,000 of the population, as smaller figures were easier to manage. Crime data was collected from the following reliable online resources: Bourne et al., (2015), Burns (2013), (Knoema, 2011), (Nation Master, 2003), (Statista, n.d.), (Uniform Crime Reporting, 1930), (United Nations World Surveys, 2006), (UNODC Statistics, 1997). Websites were considered reliable if they were established official government data repositories.
Temperature (℃) and rainfall (mm) data were also collected. This data was obtained from an online climate data portal (Climate Change Knowledge Portal, n.d.). Rainfall was included as a control variable to ensure that any significant effect was a consequence of increased temperatures, rather than reduced rainfall as a consequence of increased temperatures. If rainfall was not controlled for, it would be impossible to decipher whether the observed effect was caused by increased temperature or reduced rainfall.
Apparatus
Microsoft excel and the Statistical Package for the Social Sciences (SPSS) were used for data analyses.
Analytical approach
This study was a longitudinal archival study which analysed existing data. The dependent variable (DV) was crime rates per 100,000 people, collated from reliable online data sources. The independent variables (IV) were: temperature and rainfall. The question asked was whether crime rates can be predicted by temperature and rainfall. The control variables were burglary and rainfall. Burglary was a control dependent variable, as it was expected that temperature would affect violent crime and not non-violent crime such as burglary. Rainfall was a control independent variable, so that rainfall could be controlled for and this made it possible to detect whether temperature alone had an effect on crimes.
The data collected required certain properties: the source had to be reliable, crimes had to be police reported and crime rates needed to be reported per 100,000 of the population. Pre-existing data available online was collected and sorted into an excel spreadsheet. Each variable had a column on the spreadsheet: country, year, intentional homicide, assault, rape, burglary, temperature and rainfall. The country variable was categorical. Countries were coded: 1 for the U.S., 2 for Japan, 3 for Jamaica and 4 for Finland. The remaining variables were continuous. There were 160 observations, 40 years per country. Some observations included data on all four crimes; some were partially completed due to limited data.
Firstly scatter graphs were plotted with crime against temperature for each country. This revealed the general direction of the relationships between the temperature and crimes. The main analysis was a linear mixed-effects model, where temperature and rainfall were fixed effects and country and year were random effects.
This analysis was chosen because of the structure of data. For this study there were multiple samples of crime rate data over 40 different years for each country, and multiple samples of crime rate data for the four different countries for each year. Magezi (2015) described how linear mixed-effects models can include such multiple, nested groups and accommodates for missing data. This was useful because the current study was a longitudinal archival study and consequently had missing data. Analyses were conducted using SPSS. An alpha level of .05 was used for each linear mixed-effects model.
+1 lag model analyses for each crime were also implemented, to account for a possible delay of the effect caused by exposure to temperature. To achieve this, the DV columns were shifted down one row using SPSS. It was necessary to check that all values still aligned with the correct country.
Publisher
Lancaster University
Format
Data/Excel.xslx
Identifier
Fifer2015
Contributor
Rebecca James
Rights
Open
Relation
None
Language
English
Type
Data
Coverage
LA1 4YF
LUSTRE
Supervisor
Dermot Lynott
Project Level
MSc
Topic
Social
Sample Size
4 countries
Statistical Analysis Type
Linear mixed effects modelling, longitudinal, archival, heat hypothesis
Files
Collection
Citation
Georgia Fifer
, “Testing the Heat Hypothesis: The Relationship between Temperature and Violent Crime Rates,” LUSTRE, accessed April 25, 2024, https://www.johnntowse.com/LUSTRE/items/show/91.