Exploring the practices of steal-to-order burglars: A different brand of offender?

This research helps shed light on the largely overlooked practices amongst steal-to-order offenders, with a view to identifying ways in which steal-to-order offences may be disrupted through targeted intervention. Interviews were conducted with a sample of incarcerated burglars who have previously engaged in steal-to-order offences. In addition to highlighting a number of parallels between steal-to-order and non-steal-to-order offences, this paper illustrates the nature of professionalism exhibited by offenders during steal-to-order offences. Moreover, this paper reveals a behavioural continuum amongst offenders engaging in steal-to-order offences: those who steal-to-offer, those who steal-to-order more general items, and those who steal-to-order more specialist goods. The paper also highlights the potential lack of flexibility experienced by steal-to-order offenders, and the implications of this in challenging criminological theory of offender decision making. The paper concludes by discussing how steps at both a residential and organisational level may be taken to effectively disrupt the practices of offenders during steal-to-order offences

Investigating the Behaviour of Twitter Users to Construct an Individual-Level Model of Metropolitan Dynamics.

In this paper, consideration is given to the use of new forms of social network data as a means to enrich our understanding of complex structures and activity patterns in urban areas. Specifically, a sample of Twitter messages (‘tweets’) in the city of Leeds is assembled from publicly available sources, and spatial and temporal patterns in these data are demonstrated, with special reference to the geodemographic profiles of service users. It is argued that classical space-time models of individual behaviour provide one possible framework for the interpretation of patterns, and the process of attempting to classify activities is begun with reference to the geographical distribution, timing and, importantly, the content of messages. Some initial analysis is undertaken to examine emerging networks of interconnection between users and individual users’ spatio-temporal behaviour. In the discussion, it is suggested that the integration of this form of social data analysis with existing microscale representations and multi-agent models of city structure and dynamics will provide fertile ground for future research

Integrating Blue: How do we make Nationally Determined Contributions work for both blue carbon and local coastal communities?

Blue Carbon Ecosystems (BCEs) help mitigate and adapt to climate change but their integration into policy, such as Nationally Determined Contributions (NDCs), remains underdeveloped. Most BCE conservation requires community engagement, hence community-scale projects must be nested within the implementation of NDCs without compromising livelihoods or social justice. Thirty-three experts, drawn from academia, project development and policy, each developed ten key questions for consideration on how to achieve this. These questions were distilled into ten themes, ranked in order of importance, giving three broad categories of people, policy & finance, and science & technology. Critical considerations for success include the need for genuine participation by communities, inclusive project governance, integration of local work into national policies and practices, sustaining livelihoods and income (for example through the voluntary carbon market and/or national Payment for Ecosystem Services and other types of financial compensation schemes) and simplification of carbon accounting and verification methodologies to lower barriers to entry

Estimating Individual Behaviour for Massive Social Data

This chapter presents the most recent developments in an on-going programme of work towards a realistic agent-based model of urban dynamics. The focus of the chapter is on the development of a framework for calibrating an agent-based model of urban dynamics using novel data from the Twitter social media service. In particular, we discuss initial attempts to elucidate information about peoples' daily spatio-temporal behaviour and how such insight can be used for the bene�t of agent-based models. The ultimate aim of the modelling work is to better understand the spatio- temporal movement patterns within the city

Exploring Population Dynamics with Crowd-Sourced Data

Increasingly large volumes of geo-located data from social messaging are available in the public domain. These data contain valuable clues about daily activity patterns. For some of the more prolific users it is possible to trace sequences of activity with considerable spatial and temporal detail. In this paper, we use a substantial sample of twitter data from the city of Leeds to investigate space-time activity patterns. A framework of activity types will be presented, and we will evaluate different methods for the classification of behaviour based on message content. These methods range from manual calibration by an intelligent observer through text recognition to machine learning. Relevant metrics will be proposed and tested for the automated procedures which will be necessary in order to process data of this type in useful volumes. We will also present substantive results which illustrate variations in activity patterns across the city at different times in the day, and consider the potential for examination of individual sequences and movement patterns. We will offer some thoughts on both the ethical robustness of this approach, and its potential for generalisation beyond a self-selecting sample of twitter users. The potential for model-based approaches built on the foundations of this analysis will be considered

Burglars as Optimal Foragers: Exploring Modern-Day Tricks of the Trade

Based on semi-structured interviews with 23 incarcerated burglars, this paper details findings from a qualitative examination into how the principles of Optimal Forager Theory (to minimise time and effort, minimise risk of detection, and maximise reward) apply to the behavioural methods utilised by offenders. Findings included the use of ‘serial targets’ (to minimise time and effort), as well as offenders’ ability to ‘blend in’ to their surroundings (to minimise risk of detection). To maximise reward, offenders used brands of consumables (evident from packaging found in residents’ rubbish) as a proxy for wealth, as well as personal details gathered through residents’ discarded mail to establish their ethnicity (for the targeting of Asian gold). The findings support the notion of ‘dysfunctional expertise’, and demonstrate how efforts to maximise time and effort, minimise reward, and maximise risk of detection for offenders can be used to develop crime prevention policy to reduce future burglaries