The politics of judicial independence in the UK's changing constitution

Judicial independence is generally understood as requiring that judges must be insulated from political life. The central claim of this work is that far from standing apart from the political realm, judicial independence is a product of it. It is defined and protected through interactions between judges and politicians. In short, judicial independence is a political achievement. This is the main conclusion of a three-year research project on the major changes introduced by the Constitutional Reform Act 2005, and the consequences for judicial independence and accountability. The authors interviewed over 150 judges, politicians, civil servants and practitioners to understand the day-to-day processes of negotiation and interaction between politicians and judges. They conclude that the greatest threat to judicial independence in future may lie not from politicians actively seeking to undermine the courts, but rather from their increasing disengagement from the justice system and the judiciary

An Agent-Based Model of the 2020 International Policy Diffusion in Response to the COVID-19 Pandemic with Particle Filter

Global problems, such as pandemics and climate change, require rapid international coordination and diffusion of policy. These phenomena are rare however, with one notable example being the international policy response to the COVID-19 pandemic in early 2020. Here we build an agent-based model of this rapid policy diffusion, where countries constitute the agents and with the principal mechanism for diffusion being peer mimicry. Since it is challenging to predict accurately the policy diffusion curve, we utilize data assimilation, that is an “on-line” feed of data to constrain the model against observations. The specific data assimilation algorithm we apply is a particle filter because of its convenient implementation, its ability to handle categorical variables and because the model is not overly computationally expensive, hence a more efficient algorithm is not required. We find that the model alone is able to predict the policy diffusion relatively well with an ensemble of at least 100 simulation runs. The particle filter however improves the fit to the data, reliably so from 500 runs upwards, and increasing filtering frequency results in improved prediction

New insights into seasonal foraging ranges and migrations of minke whales from the Salish Sea and coastal British Columbia.

In the Salish Sea and coastal waters of British Columbia, minke whales are known to establish small home ranges during the feeding season. Beyond the feeding season little is known of their movements or distribution. To determine movement patterns of minke whales in these waters we used photo-identification data that were collected opportunistically from 2005-2012. These data were from four non-overlapping areas between 48ºN and 53ºN. Despite year-round search effort, minke whales were only encountered between April and October. Most of the 44 unique minke whales identified in 405 encounters displayed fidelity to areas both within and among feeding seasons. Five of these individuals also made relatively large-scale intra-annual movements between areas on six occasions. They were documented to move up to at least 424km in a northerly direction early in the season and up to at least 398km in a southerly direction late in the season. We believe that the seasonal patterns of these movements provide new insight into the foraging ranges and migrations of individuals. Ecological markers provide further evidence that the minke whales we photographed undertake annual long distance migrations. Scars believed to be from cookiecutter shark bites were observed on 43 individuals and the majority of minke whales documented with good quality images each year had acquired new scars since the previous feeding season. Furthermore, the commensal barnacle Xenobalanus globicipitis was observed on three individuals. Since these sharks and barnacles are from warm waters, it can be inferred that they interacted with the minke whales at lower latitudes. These findings may have important implications for our understanding of minke whale populations in the Salish Sea and the management of this species in the North Pacific

Coupling an Agent-Based Model and Ensemble Kalman Filter for Real-Time Crowd Modelling

Agent-based modelling has emerged as a powerful tool for modelling systems that are driven by discrete, heterogeneous individuals and has proven particularly popular in the realm of pedestrian simulation. However, real-time agent-based simulations face the challenge that they will diverge from the real system over time. This paper addresses this challenge by integrating the Ensemble Kalman Filter (EnKF) with an agent-based crowd model to enhance its accuracy in real-time. Using the example of Grand Central Station in New York, we demonstrate how our approach can update the state of an agent-based model in real-time, aligning it with the evolution of the actual system. The findings reveal that the EnKF can substantially improve the accuracy of agent-based pedestrian simulations by assimilating data as they evolve. This approach not only offers efficiency advantages over existing methods but also presents a more realistic representation of a complex environment than most previous attempts. The potential applications of this method span the management of public spaces under ‘normality’ to exceptional circumstances such as disaster response, marking a significant advancement for real-time agent-based modelling applications

Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter

Agent-based modelling is a valuable approach for modelling systems whose behaviour is driven by the interactions between distinct entities, such as crowds of people. However, it faces a fundamental difficulty: there are no established mechanisms for dynamically incorporating real-time data into models. This limits simulations that are inherently dynamic, such as those of pedestrian movements, to scenario testing on historic patterns rather than real-time simulation of the present. This paper demonstrates how a particle filter could be used to incorporate data into an agent-based model of pedestrian movements at run time. The experiments show that although it is possible to use a particle filter to perform online (real time) model optimisation, the number of individual particles required (and hence the computational complexity) increases exponentially with the number of agents. Furthermore, the paper assumes a one-to-one mapping between observations and individual agents, which would not be the case in reality. Therefore this paper lays some of the fundamental groundwork and highlights the key challenges that need to be addressed for the real-time simulation of crowd movements to become a reality. Such success could have implications for the management of complex environments both nationally and internationally such as transportation hubs, hospitals, shopping centres, etc

Twitter Watch: Leveraging Social Media to Monitor and Predict Collective-Efficacy of Neighborhoods

Sociologists associate the spatial variation of crime within an urban setting, with the concept of collective efficacy. The collective efficacy of a neighborhood is defined as social cohesion among neighbors combined with their willingness to intervene on behalf of the common good. Sociologists measure collective efficacy by conducting survey studies designed to measure individuals' perception of their community. In this work, we employ the curated data from a survey study (ground truth) and examine the effectiveness of substituting costly survey questionnaires with proxies derived from social media. We enrich a corpus of tweets mentioning a local venue with several linguistic and topological features. We then propose a pairwise learning to rank model with the goal of identifying a ranking of neighborhoods that is similar to the ranking obtained from the ground truth collective efficacy values. In our experiments, we find that our generated ranking of neighborhoods achieves 0.77 Kendall tau-x ranking agreement with the ground truth ranking. Overall, our results are up to 37% better than traditional baselines.Comment: 10 pages, 7 figure

Chronic arthritis in children and adolescents in two Indian health service user populations

BACKGROUND: High prevalence rates for rheumatoid arthritis, spondyloarthopathies, and systemic lupus erythematosus have been described in American Indian and Alaskan Native adults. The impact of these diseases on American Indian children has not been investigated. METHODS: We used International Classification of Diseases-9 (ICD-9) codes to search two Indian Health Service (IHS) patient registration databases over the years 1998–2000, searching for individuals 19 years of age or younger with specific ICD-9-specified diagnoses. Crude estimates for disease prevalence were made based on the number of individuals identified with these diagnoses within the database. RESULTS: Rheumatoid arthritis (RA) / juvenile rheumatoid arthritis (JRA) was the most frequent diagnosis given. The prevalence rate for JRA in the Oklahoma City Area was estimated as 53 per 100,000 individuals at risk, while in the Billings Area, the estimated prevalence was nearly twice that, at 115 per 100,000. These rates are considerably higher than those reported in the most recent European studies. CONCLUSION: Chronic arthritis in childhood represents an important, though unrecognized, chronic health challenge within the American Indian population living in the United States

Evaluating the potential of agent-based modelling to capture consumer grocery retail store choice behaviours

Evolving consumer behaviours with regards to store and channel choice, shopping frequency, shopping mission and spending heighten the need for robust spatial modelling tools for use within retail analytics. In this paper, we report on collaboration with a major UK grocery retailer to assess the feasibility of modelling consumer store choice behaviours at the level of the individual consumer. We benefit from very rare access to our collaborating retailers’ customer data which we use to develop a proof-of-concept agent-based model (ABM). Utilising our collaborating retailers’ loyalty card database, we extract key consumer behaviours in relation to shopping frequency, mission, store choice and spending. We build these observed behaviours into our ABM, based on a simplified urban environment, calibrated and validated against observed consumer data. Our ABM is able to capture key spatiotemporal drivers of consumer store choice behaviour at the individual level. Our findings could afford new opportunities for spatial modelling within the retail sector, enabling the complexity of consumer behaviours to be captured and simulated within a novel modelling framework. We reflect on further model development required for use in a commercial context for location-based decision-making

The ‘Exposed’ Population, Violent Crime in Public Space and the Night-time Economy in Manchester, United Kingdom

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 mobile phone data and utilising fine-grained temporal and spatial violent crime data, the paper demonstrates the value of deploying an exposed (over an ambient) population-at-risk denominator to determine violent crime in public space hotspots on Saturday nights in Greater Manchester (UK). In doing so, the paper illuminates that as violent crime in public space rises, over the course of a Saturday evening, the exposed population-at-risk falls, implying a shifting propensity of the exposed population-at-risk to perform active roles as offenders, victims and/or guardians. The paper concludes with a discussion of the theoretical and policy relevance of these findings