The Impacts of the Deregulation Act (2015) on Taxi-Related Incidents and Crimes in Leeds

New ride hailing apps, such as Uber and Lift, are disrupting the traditional means by which consumers interact with private hire services. The 2015 Deregulation Act has helped to fuel these changes by effectively allowing drivers to operate in licensing authorities other than those that they have been licensed in. • This report investigates the changes in ‘taxi-related’ incidents and crime events, using data collected by West Yorkshire Police for the Leeds district between 1 April 2013 and 31 March 2017. It seeks to highlight any changes to recorded crime levels that might be attributed to the Licensing Act and/or the activities of new ride hailing services. • The main findings include: o After approximately December 2015, not long after the introduction of the Deregulation Act, the volumes of calls for service for taxi-related crimes began to decrease, whereas all calls (i.e. non-taxi-related) began to increase. o Examining taxi-related Nuisance and Civil Dispute incidents in particular, these diverged considerably from all other (non-taxi) incidents around the time of the introduction of the Act. This could be a due to fewer cash-based payments (these are a common cause of incidents). o As with incidents, the volume of taxi-related crime events also began to diverge (and decrease) from all other comparable crimes around the time of the introduction of the Act. o There appears to have been a large (38%) increase in new private hire driver license applications in Leeds after the introduction of the Deregulation Act. Much of this increase can be attributed to Uber applications (up by 1316% across the study period), but some other firms such as Amber Cars saw increases as well. • The report recommendations that licensing authorities (continue to) offer de-escalation training to reduce the number of Civil Disputes, and that they should collect more information about the drivers who are working in their area. • The report provides compelling evidence that taxi-related crime has declined since the introduction of the Licencing Act, but is not yet in a position to state, categorically, that these changes are as a result of the Act.

Estimating Spatio-Temporal Risks from Volcanic Eruptions using an Agent-Based Model

Managing disasters caused by natural events, especially volcanic crises, requires a range of approaches, including risk modelling and analysis. Risk modelling is commonly conducted at the community/regional scale using GIS. However, people and objects move in response to a crisis, so static approaches cannot capture the dynamics of the risk properly, as they do not accommodate objects’ movements within time and space. The emergence of Agent-Based Modelling makes it possible to model the risk at an individual level as it evolves over space and time. We propose a new approach of Spatio-Temporal Dynamics Model of Risk (STDMR) by integrating multi-criteria evaluation (MCE) within a georeferenced agent-based model, using Mt. Merapi, Indonesia, as a case study. The model makes it possible to simulate the spatio-temporal dynamics of those at risk during a volcanic crisis. Importantly, individual vulnerability is heterogeneous and depends on the characteristics of the individuals concerned. The risk for the individuals is dynamic and changes along with the hazard and their location. The model is able to highlight a small number of high-risk spatio-temporal positions where, due to the behaviour of individuals who are evacuating the volcano and the dynamics of the hazard itself, the overall risk in those times and places is extremely high. These outcomes are extremely relevant for the stakeholders, and the work of coupling an ABM, MCE, and dynamic volcanic hazard is both novel and contextually relevant

Modelling Individual Evacuation Decisions during Natural Disasters: A Case Study of Volcanic Crisis in Merapi, Indonesia

As the size of human populations increases, so does the severity of the impacts of natural disasters. This is partly because more people are now occupying areas which are susceptible to hazardous natural events, hence, evacuation is needed when such events occur. Evacuation can be the most important action to minimise the impact of any disaster, but in many cases there are always people who are reluctant to leave. This paper describes an agent-based model (ABM) of evacuation decisions, focusing on the emergence of reluctant people in times of crisis and using Merapi, Indonesia as a case study. The individual evacuation decision model is influenced by several factors formulated from a literature review and survey. We categorised the factors influencing evacuation decisions into two opposing forces, namely, the driving factors to leave (evacuate) versus those to stay, to formulate the model. The evacuation decision (to stay/leave) of an agent is based on an evaluation of the strength of these driving factors using threshold-based rules. This ABM was utilised with a synthetic population from census microdata, in which everyone is characterised by the decision rule. Three scenarios with varying parameters are examined to calibrate the model. Validations were conducted using a retrodictive approach by performing spatial and temporal comparisons between the outputs of simulation and the real data. We present the results of the simulations and discuss the outcomes to conclude with the most plausible scenario

Estimates of the Ambient Population: Assessing the Utility of Conventional and Novel Data Sources

This paper will critically assess the utility of conventional and novel data sources for building fine-scale spatio-temporal estimates of the ambient population. It begins with a review of data sources employed in existing studies of the ambient population, followed by preliminary analysis to further explore the utility of each dataset. The identification and critiquing of data sources which may be useful for building estimates of the ambient population are novel contributions to the literature. This paper will provide a framework of reference for researchers within urban analytics and other areas where an accurate measurement of the ambient population is required. This work has implications for national and international applications where accurate small area estimates of the ambient population are crucial in the planning and management of urban areas, the development of realistic models and informing policy. This research highlights workday population estimates, in conjunction with footfall camera and Wi-Fi sensors data as potentially valuable for building estimates of the ambient populatio

Calibrating Agent-Based Models Using Uncertainty Quantification Methods

Agent-based models (ABMs) can be found across a number of diverse application areas ranging from simulating consumer behaviour to infectious disease modelling. Part of their popularity is due to their ability to simulate individual behaviours and decisions over space and time. However, whilst there are plentiful examples within the academic literature, these models are only beginning to make an impact within policy areas. Whilst frameworks such as NetLogo make the creation of ABMs relatively easy, a number of key methodological issues, including the quantification of uncertainty, remain. In this paper we draw on state-of-the-art approaches from the fields of uncertainty quantification and model optimisation to describe a novel framework for the calibration of ABMs using History Matching and Approximate Bayesian Computation. The utility of the framework is demonstrated on three example models of increasing complexity: (i) Sugarscape to illustrate the approach on a toy example; (ii) a model of the movement of birds to explore the efficacy of our framework and compare it to alternative calibration approaches and; (iii) the RISC model of farmer decision making to demonstrate its value in a real application. The results highlight the efficiency and accuracy with which this approach can be used to calibrate ABMs. This method can readily be applied to local or national-scale ABMs, such as those linked to the creation or tailoring of key policy decisions

28 Months Later: Pandemic Crime in England and Wales to July 2022

By July 2022, violence and sexual offences, and theft from the person, remained statistically significantly lower than expected levels, with burglary, car crime, robbery and shoplifting far below expected levels (but falling within the rapidly expanding 95% confidence intervals). The other six crime categories plus anti social behaviour were at or trending towards expected levels. Recorded crime rates per 10,000 population are in orange, expected rates are dashed, with grey-shaded 95% CIs

Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities

Despite reaching a point of acceptance as a research tool across the geographical and social sciences, there remain significant methodological challenges for agent‐based models. These include recognizing and simulating emergent phenomena, agent representation, construction of behavioral rules, and calibration and validation. While advances in individual‐level data and computing power have opened up new research avenues, they have also brought with them a new set of challenges. This article reviews some of the challenges that the field has faced, the opportunities available to advance the state‐of‐the‐art, and the outlook for the field over the next decade. We argue that although agent‐based models continue to have enormous promise as a means of developing dynamic spatial simulations, the field needs to fully embrace the potential offered by approaches from machine learning to allow us to fully broaden and deepen our understanding of geographical systems

Commentary – ordering lab tests for suspected rheumatic disease

One of the least-appreciated advances in pediatric rheumatology over the past 25 years has been the delineation of the many ways in which children with rheumatic disease differ from adults with the same illnesses. Furthermore, we are now learning that paradigms that are useful in evaluating adults with musculoskeletal complaints have limited utility in children. Nowhere is that more true than in the use of commonly used laboratory tests, particularly antinuclear antibody (ANA) and rheumatoid factor (RF) assays. This short review will provide the practitioner with the evidence base that supports a more limited use of ANA and RF testing in children

Spatio-temporal crime hotspots and the ambient population

It is well known that, due to that inherent differences in their underlying causal mechanisms, different types of crime will have variable impacts on different groups of people. Furthermore, the locations of vulnerable groups of people are highly temporally dynamic. Hence an accurate estimate of the true population at risk in a given place and time is vital for reliable crime rate calculation and hotspot generation. However, the choice of denominator is fraught with difficulty because data describing popular movements, rather than simply residential location, are limited. This research will make use of new ‘crowd-sourced’ data in an attempt to create more accurate estimates of the population at risk for mobile crimes such as street robbery. Importantly, these data are both spatially and temporally referenced and can therefore be used to estimate crime rate significance in both space and time. Spatio-temporal cluster hunting techniques will be used to identify crime hotspots that are significant given the size of the ambient population in the area at the time