The daily rhythms of the city, the ebb and flow of people undertaking routines activities, inform the spatial and temporal patterning of crime. Being able to capture citizen mobility and delineate a crime specific population denominator is a vital prerequisite of the endeavour to both explain and address crime. This paper introduces the concept of an exposed population-at-risk, defined as the mix of residents and non-residents who may play an active role as an offender, victim or guardian in a specific crime type, present in a spatial unit at a given time. This definition is deployed to determine the exposed population-at-risk for violent crime, associated with the night-time-economy, in public spaces. Through integrating census data with m...
In recent years, it has increasingly been recognized that due to the uncertain geographic context pr...
Interpreting the spatio-temporal patterning of crime, it is vital to consider the interplay of trave...
In this paper, we present a novel approach to predict crime in a geographic space from multiple data...
The daily rhythms of the city, the ebb and flow of people undertaking routines activities, inform th...
The daily rhythms of the city, the ebb and flow of people undertaking routines activities, inform th...
People ebb and flow across the city. The spatial and temporal patterning of crime is, in part, refle...
People ebb and flow across the city. The spatial and temporal patterning of crime is, in part, refle...
The patterning of crime varies with the daily rhythms of the city. The ebb and flow of urban populat...
AbstractPurposeCrime analysts need accurate population-at-risk measures to quantify crime rates. Thi...
Purpose Crime analysts need accurate population-at-risk measures to quantify crime rates. This resea...
Mapping mayhem in the city? Exploring the space time dynamics of criminal damage and alcohol with G...
This Special Issue is a collection of seven papers that seek to better our understanding of how urba...
Traditional estimates of the population focus on residential populations and capture a single point ...
People ebb and flow across the city. The spatial and temporal patterning of crime is, in part, refle...
It is well known that, due to that inherent differences in their underlying causal mechanisms, diffe...
In recent years, it has increasingly been recognized that due to the uncertain geographic context pr...
Interpreting the spatio-temporal patterning of crime, it is vital to consider the interplay of trave...
In this paper, we present a novel approach to predict crime in a geographic space from multiple data...
The daily rhythms of the city, the ebb and flow of people undertaking routines activities, inform th...
The daily rhythms of the city, the ebb and flow of people undertaking routines activities, inform th...
People ebb and flow across the city. The spatial and temporal patterning of crime is, in part, refle...
People ebb and flow across the city. The spatial and temporal patterning of crime is, in part, refle...
The patterning of crime varies with the daily rhythms of the city. The ebb and flow of urban populat...
AbstractPurposeCrime analysts need accurate population-at-risk measures to quantify crime rates. Thi...
Purpose Crime analysts need accurate population-at-risk measures to quantify crime rates. This resea...
Mapping mayhem in the city? Exploring the space time dynamics of criminal damage and alcohol with G...
This Special Issue is a collection of seven papers that seek to better our understanding of how urba...
Traditional estimates of the population focus on residential populations and capture a single point ...
People ebb and flow across the city. The spatial and temporal patterning of crime is, in part, refle...
It is well known that, due to that inherent differences in their underlying causal mechanisms, diffe...
In recent years, it has increasingly been recognized that due to the uncertain geographic context pr...
Interpreting the spatio-temporal patterning of crime, it is vital to consider the interplay of trave...
In this paper, we present a novel approach to predict crime in a geographic space from multiple data...