The when and where of an emerging crime type: the example of metal theft from the railway network of Great Britain

Metal theft has become an increasingly common crime in recent years, but lack of data has limited research into it. The present study used police-recorded crime data to study the spatial and temporal concentration of metal theft from the railway network of Great Britain. Metal theft was found to exhibit only weak seasonality, to be concentrated at night and to cluster in a few locations close to – but not in – major cities. Repeat-victimisation risk continued for longer than has been found for other crime types. These and other features appear to point to metal theft being a planned, rather than opportunistic, offence and to the role of scrap-metal dealers as facilitators

Spatial clustering of mental disorders and associated characteristics of the neighbourhood context in Malmö, Sweden, in 2001

Study objective: Previous research provides preliminary evidence of spatial variations of mental disorders and associations between neighbourhood social context and mental health. This study expands past literature by (1) using spatial techniques, rather than multilevel models, to compare the spatial distributions of two groups of mental disorders (that is, disorders due to psychoactive substance use, and neurotic, stress related, and somatoform disorders); and (2) investigating the independent impact of contextual deprivation and neighbourhood social disorganisation on mental health, while assessing both the magnitude and the spatial scale of these effects. Design: Using different spatial techniques, the study investigated mental disorders due to psychoactive substance use, and neurotic disorders. Participants: All 89 285 persons aged 40–69 years residing in Malmö, Sweden, in 2001, geolocated to their place of residence. Main results: The spatial scan statistic identified a large cluster of increased prevalence in a similar location for the two mental disorders in the northern part of Malmö. However, hierarchical geostatistical models showed that the two groups of disorders exhibited a different spatial distribution, in terms of both magnitude and spatial scale. Mental disorders due to substance consumption showed larger neighbourhood variations, and varied in space on a larger scale, than neurotic disorders. After adjustment for individual factors, the risk of substance related disorders increased with neighbourhood deprivation and neighbourhood social disorganisation. The risk of neurotic disorders only increased with contextual deprivation. Measuring contextual factors across continuous space, it was found that these associations operated on a local scale. Conclusions: Taking space into account in the analyses permitted deeper insight into the contextual determinants of mental disorders

Weekly Crime Concentration

Objectives: Examine and visualise the temporal concentration of different crime types and detect if their intensity varies through distinct moments of the week. Methods: The “heartbeat of the crime signal” is constructed by overlapping the weekly time they were suffered. This study is based on more than 220,000 crimes reported to the Mexico City Police Department between January 2016 and March 2020 to capture the day and time of crimes and detect moments of the week in which the intensity exceeds the average frequency. A new metric for the temporal concentration of crime is constructed for different types of crime and regions of the city based on the corresponding heartbeats. Results: The temporal concentration of crime is a stable signature of different types of crime. The intensity of robberies and theft is more homogeneous from Monday to Sunday, but robberies of a bank user are highly concentrated in a week, meaning that few hours of the week capture most of the burning moments. The concentration is not homogeneously distributed in the city, with some regions experiencing a much higher temporal concentration of crime. Conclusions: Crime is highly concentrated when observed in its weekly patterns, but different types of crime and regions exhibit substantially distinct concentration levels. The temporal trace indicates specific moments for the burning times of different types of crime, which is a critical element of a policing strategy

New trends in South-South migration: The economic impact of COVID-19 and immigration enforcement

This paper evaluates the impact of the pandemic and enforcement at the US and Mexican borders on the emigration of Guatemalans during 2017-2020. During this period, the number of crossings from Guatemala fell by 10%, according to the Survey of Migration to the Southern Border of Mexico. Yet, there was a rise of nearly 30% in the number of emigration crossings of male adults travelling with their children. This new trend was partly driven by the recent reduction in the number of children deported from the US. For a one-point reduction in the number of children deported from the US to Guatemalan municipalities, there was an increase of nearly 14 in the number of crossings made by adult males leaving from Guatemala for Mexico; and nearly 0.5 additional crossings made by male adults travelling with their children. However, the surge of emigrants travelling with their children was also driven by the acute economic shock that Guatemala experienced during the pandemic. During this period, air pollution in the analysed Guatemalan municipalities fell by 4%, night light per capita fell by 15%, and homicide rates fell by 40%. Unlike in previous years, emigrants are fleeing poverty rather than violence. Our findings suggest that a reduction in violence alone will not be sufficient to reduce emigration flows from Central America, but that economic recovery is needed

Night light pollution: how global religious customs shape its patterns

In the last decades, dark skies colored with an orange glow have grown to be the main norm during night in urban areas all around the world. Artificial light at night has grown a rough average of 6% every year globally, and its implications in detriment of the environment and the biodiversity are vast and diverse. The only way to mitigate its effects is based on restrictive olicymaking both publicly and privately; to do so, we need to understand better its ehavior. Local and regional patterns regarding light pollution have usually been demonstrated with socioeconomic data. However, with the normalization of the access to satellite remote imagery, we can extract essential information from everything that is visible (and/or measurable) on Earth. With these innovative tools, we wanted to test the hypothesis of worldwide religious events orfestivities shaping the annual light pollution peaks or highest frequency periods. Thanks to satellite imagery taken by the VIIRS sensor on board of the Suomi NPP weather satellite, and the Google Earth Engine platform, we were able to extract the monthly Light Frequency Time Series for the period 2016-2019, for 136 countries. Along with Circular Statistics techniques to study the cyclical time patterns of light pollution – and data visualization, we succeeded to relate the global religious events with a cyclical light pollution pattern. Above all, Christmas, Ramadan, and Diwali festivities, showed to have a significant effect on the annual timing of light pollution peaks in countries of Europe, Asia, and North America, except for Africa in the case of the Muslim festivity, Ramadan. Overall, however, Christmas turned out to be the most light-pollutant festivity in the world, due to the export of Christmas from christian countries to Asian countries such as China. For Islam, only countries from Middle East & North Africa –Islam’s cradle – showed a clear pattern during Ramadan months; in other Muslim countries, the yearly light pollution peak occurred during Christmas. Our evidence highlighted, once again, the gregarious behavior of humans regarding their light pemission patterns, and how predictable are the annual periods of highest artificial light pollution at night. We believe this can be useful regarding new policies for the mitigation of the effects of night light pollution during the celebration of global religious events such as Christmas, to have a responsible and sustainable relation with our environment

An evaluation of small-area statistical methods for detecting excess risk: with applications in breast and colon cancer mortality in Scotland 1986-1995

The need to report data at small-area level is constantly increasingly. In a society which is both health-conscious and environmentally aware, statistics at small-area level have a high degree of political significance. This type of data is required to plan and implement regional policies and apportion health care in accordance to the differing needs of the population. Recent advances in computer power has brought many advances to this area of study. For all the advances in technology and methodology, the problem of small numbers consistently appears. Is there an excess risk or is it down to chance? This is a question which is paramount in small-area statistics and will be addressed in this thesis. An overview of the thesis is provided below: Chapter 1 introduces the concept of small-area statistics and some of the social and political issues connected with this topic. There is a discussion of the analysis of small-area health data and the principal ideas that need to be considered in a statistical, political and social sense in this area of work. The aims of ISD Scotland are introduced and how they can be linked to this field of study. Chapter 2 describes an overview of the methods used in small-area statistics. The chapter begins by firstly considering the Standardised Incidence Ratio (SIR) which is the technique mainly used in the basic analysis done by ISD Scotland. Other techniques are then considered, however not all of these techniques are directly comparable to each other. The strengths and weaknesses of these techniques in previous research are discussed to give an idea of how the techniques perform in different scenarios. Chapter 3 is a simulation study of three of the techniques discussed in Chapter 2, these being the SIR, Circular Spatial Scan and Flexibly-Shaped Spatial Scan. The reason for this simulation study is to evaluate these techniques on simulated data arising from real scenarios. The strengths and weaknesses of these techniques are then highlighted which will prove helpful when analysing the data in Chapter 4. Chapter 4 provides an analysis of the mortality of breast and colon cancer in Scotland in the ten-year time period from 1986 to 1995. Using data provided by ISD Scotland, the analysis is carried out to identity any potential mortality clusters in both diseases. Chapter 5 provides a conclusion to this research by providing a summary of findings of the thesis and gives recommendations based upon these findings. A discussion is also given for potential further study in this field that could provide some value to ISD Scotland as they look to other ways of analysing their small-area data

The heartbeat of the city

Human activity is organised around daily and weekly cycles, which should, in turn, dominate all types of social interactions, such as transactions, communications, gatherings and so on. Yet, despite their strategic importance for policing and security, cyclical weekly patterns in crime and road incidents have been unexplored at the city and neighbourhood level. Here we construct a novel method to capture the weekly trace, or "heartbeat" of events and use geotagged data capturing the time and location of more than 200,000 violent crimes and nearly one million crashes in Mexico City. On aggregate, our findings show that the heartbeats of crime and crashes follow a similar pattern. We observe valleys during the night and peaks in the evening, where the intensity during a peak is 7.5 times the intensity of valleys in terms of crime and 12.3 times in terms of road accidents. Although distinct types of events, crimes and crashes reach their respective intensity peak on Friday night and valley on Tuesday morning, the result of a hyper-synchronised society. Next, heartbeats are computed for city neighbourhood 'tiles', a division of space within the city based on the distance to Metro and other public transport stations. We find that heartbeats are spatially heterogeneous with some diffusion, so that nearby tiles have similar heartbeats. Tiles are then clustered based on the shape of their heartbeat, e.g., tiles within groups suffer peaks and valleys of crime or crashes at similar times during the week. The clusters found are similar to those based on economic activities. This enables us to anticipate temporal traces of crime and crashes based on local amenities

Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space-Time 3D Bins of Geocoded Daily Cases

ABSTRACT: The space-time behaviour of COVID-19 needs to be analysed frommicrodata to understand the spread of the virus. Hence, 3D space-time bins and analysis of associated emerging hotspots are useful methods for revealing the areas most at risk from the pandemic. To implement these methods, we have developed the SITAR Fast Action Territorial Information System using ESRI technologies. We first modelled emerging hotspots of COVID-19 geocoded cases for the region of Cantabria (Spain), then tested the predictive potential of the method with the accumulated cases for two months ahead. The results reveal the difference in risk associated with areas with COVID-19 cases. The study not only distinguishes whether a bin is statistically significant, but also identifies temporal trends: a reiterative pattern is detected in 58.31% of statistically significant bins (most with oscillating behaviour over the period). In the testing method phase, with positive cases for two months ahead, we found that only 7.37% of cases were located outside the initial 3D bins. Furthermore, 83.02% of new cases were in statistically significant previous emerging hotspots. To our knowledge, this is the first study to show the usefulness of the 3D bins and GIS emerging hotspots model of COVID-19 microdata in revealing strategic patterns of the pandemic for geoprevention plans

CircStat: A MATLAB Toolbox for Circular Statistics

Directional data is ubiquitious in science. Due to its circular nature such data cannot be analyzed with commonly used statistical techniques. Despite the rapid development of specialized methods for directional statistics over the last fifty years, there is only little software available that makes such methods easy to use for practioners. Most importantly, one of the most commonly used programming languages in biosciences, MATLAB, is currently not supporting directional statistics. To remedy this situation, we have implemented the CircStat toolbox for MATLAB which provides methods for the descriptive and inferential statistical analysis of directional data. We cover the statistical background of the available methods and describe how to apply them to data. Finally, we analyze a dataset from neurophysiology to demonstrate the capabilities of the CircStat toolbox.