The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns

Crime rate is a statistic used to summarize the risk of criminal events. However, research has shown that choosing the appropriate denominator is non-trivial. Different crime types exhibit different spatial opportunities and so does the population at risk. The residential population is the most commonly used population at risk, but is unlikely to be suitable for crimes that involve mobile populations. In this article, we use "crowd-sourced" data in Leeds, England, to measure the population at risk, considering violent crime. These new data sources have the potential to represent mobile populations at higher spatial and temporal resolutions than other available data. Through the use of two local spatial statistics (Getis-Ord GI* and the Geographical Analysis Machine) and visualization, we show that when the volume of social media messages, as opposed to the residential population, is used as a proxy for the population at risk, criminal event hot spots shift spatially. Specifically, the results indicate a significant shift in the city center, eliminating its hot spot. Consequently, if crime reduction/prevention efforts are based on resident population based crime rates, such efforts may not only be ineffective in reducing criminal event risk, but be a waste of public resources

Building cities from slime mould, agents and quantum field theory

Managing the unprecedented growth of cities whilst ensuring that they are sustainable, healthy and equitable places to live, presents significant challenges. Our current thinking conceptualise cities as being driven by processes from the bottom-up, with an emphasis on the role that individual decisions and behaviour play. Multiagent systems, and agent-based modelling in particular, are ideal frameworks for the analysis of such systems. However, identifying the important drivers within an urban system, translating key behaviours from data into rules, quantifying uncertainty and running models in real time all present significant challenges. We discuss how innovations in a diverse range of fields are influencing empirical agent-based models, and how models designed for the simplest biological systems might transform the ways that we understand and manage real cities

Towards the development of societal twins

A digital twin is a virtual data-driven replica of a real-world system. Recently, digital twins have become popular in engineering and infrastructure planning where they provide insights into complex physical systems or processes. Yet, to date, considerably less research has explored how digital replicas of social systems - representing the decisions, behaviors and interactions of individual people, and, in turn, their emergent outcomes - might be developed and integrated with those of physical systems. In this position paper we discuss the need for such societal twins, what they might look like, and set out key challenges that will need to be overcome if these benefits are to be realised

Can social media data be useful in spatial modelling? A case study of ‘museum Tweets’ and visitor flows

This paper explores the potential of volunteered geographical information from social media to inform geographical models of behavior. Based on a case study of museums in Yorkshire, we created a spatial interaction model of visitors to 15 museums from 179 administrative zones to test this potential. Instead of relying on limited official data on the magnitude of flows from different attractions we used volunteered geographic information’ (VGI) to calibrate the model. The method represents the potential of VGI for applications beyond descriptive statistics and visuals and highlights potential uses of georeferenced social media data for geographic models. The main input dataset comprised geo-tagged messages harvested using the Twitter Streaming Application Programming Interface (API). We successfully calibrated the distance decay parameter of the model and conclude that social media data have great potential for aiding models of spatial behavior. However, we also caution that there are dangers associated with the use of social media data. Researchers should weigh up the wider costs and benefits of harnessing such ‘big data’ before blindly harnessing this low quality, high volume resource. Our case study also serves as the basis for discussion of the ethics surrounding the use of privately harvested VGI by publicly funded academics

Place-Based Simulation Modeling: Agent-Based Modeling and Virtual Environments

Since the earliest geographical explorations of criminal phenomena, scientists have come to the realization that crime occurrences can often be best explained by analysis at local scales. For example, the works of Guerry and Quetelet—which are often credited as being the first spatial studies of crime—analyzed data that had been aggregated to regions approximately similar to US states. The next major seminal work on spatial crime patterns was from the Chicago School in the 20th century and increased the spatial resolution of analysis to the census tract (an American administrative area that is designed to contain approximately 4,000 individual inhabitants). With the availability of higher-quality spatial data, as well as improvements in the computing infrastructure (particularly with respect to spatial analysis and mapping), more recent empirical spatial criminology work can operate at even higher resolutions; the “crime at places” literature regularly highlights the importance of analyzing crime at the street segment or at even finer scales. These empirical realizations—that crime patterns vary substantially at micro places—are well grounded in the core environmental criminology theories of routine activity theory, the geometric theory of crime, and the rational choice perspective. Each theory focuses on the individual-level nature of crime, the behavior and motivations of individual people, and the importance of the immediate surroundings. For example, routine activities theory stipulates that a crime is possible when an offender and a potential victim meet at the same time and place in the absence of a capable guardian. The geometric theory of crime suggests that individuals build up an awareness of their surroundings as they undertake their routine activities, and it is where these areas overlap with crime opportunities that crimes are most likely to occur. Finally, the rational choice perspective suggests that the decision to commit a crime is partially a cost-benefit analysis of the risks and rewards. To properly understand or model these three decisions it is important to capture the motivations, awareness, rationality, immediate surroundings, etc., of the individual and include a highly disaggregate representation of space (i.e. “micro-places”). Unfortunately one of the most common methods for modeling crime, regression, is somewhat poorly suited capturing these dynamics. As with most traditional modeling approaches, regression models represent the underlying system through mathematical aggregations. The resulting models are therefore well suited to systems that behave in a linear fashion (e.g., where a change in model input leads to a predictable change in the model output) and where low-level heterogeneity is not important (i.e., we can assume that everyone in a particular group of people will behave in the same way). However, as alluded to earlier, the crime system does not necessarily meet these assumptions. To really understand the dynamics of crime patterns, and to be able to properly represent the underlying theories, it is necessary to represent the behavior of the individual system components (i.e. people) directly. For this reason, many scientists from a variety of different disciplines are turning to individual-level modeling techniques such as agent-based modeling

Dealing with uncertainty in agent-based models for short-term predictions

Agent-based models (ABMs) are gaining traction as one of the most powerful modelling tools within the social sciences. They are particularly suited to simulating complex systems. Despite many methodological advances within ABM, one of the major drawbacks is their inability to incorporate real-time data to make accurate short-term predictions. This paper presents an approach that allows ABMs to be dynamically optimized. Through a combination of parameter calibration and data assimilation (DA), the accuracy of model-based predictions using ABM in real time is increased. We use the exemplar of a bus route system to explore these methods. The bus route ABMs developed in this research are examples of ABMs that can be dynamically optimized by a combination of parameter calibration and DA. The proposed model and framework is a novel and transferable approach that can be used in any passenger information system, or in an intelligent transport systems to provide forecasts of bus locations and arrival times

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.

A stochastic schedule-following simulation model of bus routes

Microsimulation models of bus routes allow transit operators to both better understand the dynamics of bus routes and facilitate better policy making. Several simulation models of bus routes have been proposed in the literature, including cellular-automata, bus-following and traffic-following models. The majority of these approaches aim to simulate the interactions of a bus with other buses (the bus-following model), with passengers or the surrounding traffic (the traffic-following model), but they all fail to consider the important interactions between buses and their schedules. In a conventional schedule-based public transport system, bus drivers aim to arrive at each stop on time. This means that they will either speed up or slow down if their vehicles are not meeting the schedule. The research within this paper is a novel contribution to the literature of bus route simulation. We introduce the first schedule-following model where buses try to adhere to their schedule in a conventional schedule-based public transport system. A simulated numerical analysis shows the characteristics of the proposed schedule-following model and compares it to existing models. Finally, the model is calibrated using Automatic Vehicle Location and Smart Card data from Brisbane, Australia. The results show good model performance against the observed data. The model is relatively simple, yet the fundamental mechanisms that drive the model are novel and it has the potential to be applied in any city with well-defined bus schedules

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

Quantifying the Ambient Population using Hourly Population Footfall Data and an Agent-Based Model of Daily Mobility

The ambient population, i.e. the demographics and volume of people in a particular location throughout the day, has been studied less than the night-time residential population. Although the spatio-temporal behaviour of some groups, such as commuters, are captured in sources such as population censuses, much less is known about groups such as retired people who have less documented behaviour patterns. This paper uses agent-based modelling to disaggregate some ambient population data to estimate the size and demographics of the constituent populations during the day. This is accomplished by first building a model of commuters to model typical 9–5 workday patterns. The differences between the model outputs and real footfall data (the error) can be an indication of the contributions that other groups make to the overall footfall. The research then iteratively simulates a wider range of demographic groups, maximising the correspondence between the model and data at each stage. An application of this methodology to the town centre of Otley, West Yorkshire, UK, is presented. Ultimately this approach could lead to a better understanding about how town- and city-centres are used by residents and visitors, contributing useful information in a situation where raw data on the populations do not exist