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GWR is a popular approach for investigating the spatial variation in relationships between response and predictor variables, and critically for investigating and understanding process spatial heterogeneity. The geographically weighted (GW) framework is increasingly used to accommodate different types of models and analyses, reflecting a wider desire to explore spatial variation in model parameters and outputs. However, the growth in the use of GWR and different GW models has only been partially supported by package development in both R and Python, the major coding environments for spatial analysis. The result is that refinements have been inconsistently included within GWR and GW functions in any given package. This paper outlines the structure of a new gwverse package, that may over time replace GW model, that takes advantage of recent developments in the composition of complex, integrated packages. It conceptualizes gwverse as having a modular structure, that separates core GW functionality and applications such as GWR. It adopts a function factory approach, in which bespoke functions are created and returned to the user based on user-defined parameters. The paper introduces two demonstrator modules that can be used to undertake GWR and identifies a number of key considerations and next steps. Volume54, Issue3

The Modifiable Areal Unit Problem or MAUP is frequently alluded to but rarely addressed directly. The MAUP posits that statistical distributions, relationships and trends can exhibit very different properties when the same data are aggregated or combined over different reporting units or scales. This paper explores a number of approaches for determining appropriate scales of spatial aggregation. It examines a travel survey, undertaken in Ha Noi, Vietnam, that captures attitudes towards a potential ban of motorised transport in the city centre. The data are rich, capturing travel destinations, purposes, modes and frequencies, as well as respondent demographics (age, occupation, housing etc) including home locations. The dataset is highly dimensional, with a large n (26339 records) and a large m (142 fields). When the raw individual level data are used to analyse the factors associated with travel ban attitudes, the resultant models are weak and inconclusive - the data are too noisy. Aggregating the data can overcome this, but this raises the question of appropriate aggregation scales. This paper demonstrates how aggregation scales can be evaluated using a range of different metrics related to spatial and non-spatial variances. In so doing it demonstrates how the MAUP can be directly addressed in analyses of spatial data.

Agent-based modelling (ABM) is a facet of wider Multi-Agent Systems (MAS) research that explores the collective behaviour of individual ‘agents’, and the implications that their behaviour and interactions have for wider systemic behaviour. The method has been shown to hold considerable value in exploring and understanding human societies, but is still largely confined to use in academia. This is particularly evident in the field of Urban Analytics; one that is characterised by the use of new forms of data in combination with computational approaches to gain insight into urban processes. In Urban Analytics, ABM is gaining popularity as a valuable method for understanding the low-level interactions that ultimately drive cities, but as yet is rarely used by stakeholders (planners, governments, etc.) to address real policy problems. This paper presents the state-of-the-art in the application of ABM at the interface of MAS and Urban Analytics by a group of ABM researchers who are affiliated with the Urban Analytics programme of the Alan Turing Institute in London (UK). It addresses issues around modelling behaviour, the use of new forms of data, the calibration of models under high uncertainty, real-time modelling, the use of AI techniques, large-scale models, and the implications for modelling policy. The discussion also contextualises current research in wider debates around Data Science, Artificial Intelligence, and MAS more broadly.

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.

As a crime rate denominator, the ambient population has seen very limited use in a multivariate context. The current study employs a new measure of this population, constructed using cell tower location data from OpenCellID, to compare residential and ambient population-based crime rates. The chosen study area is Vancouver, BC, but the conclusions generalize to other administrations and the OpenCellID data have global coverage so the implications are applicable elsewhere. Five disaggregated property crime types are examined at the dissemination area level. Findings demonstrate striking differences in the spatial patterns of crime rates constructed using these two different measures of the population at risk. Multivariate results from spatial error models also highlight the substantial impact that the use of a theoretically informed crime rate denominator can have on pseudo R2 values, variable retention, and trends in significant relationships. Implications for theory testing and policy are discussed. In particular, the results suggest that policies designed around residential-based crime rates risk having no effect, or even of increasing crime.

The analysis of geographically referenced data, specifically point data, is predicated on the accurate geocoding of those data. Geocoding refers to the process in which geographically referenced data (addresses, for example) are placed on a map. This process may lead to issues with positional accuracy or the inability to geocode an address. In this paper, we conduct an international investigation into the impact of the (in)ability to geocode an address on the resulting spatial pattern. We use a variety of point data sets of crime events (varying numbers of events and types of crime), a variety of areal units of analysis (varying the number and size of areal units), from a variety of countries (varying underlying administrative systems), and a locally-based spatial point pattern test to find the levels of geocoding match rates to maintain the spatial patterns of the original data when addresses are missing at random. We find that the level of geocoding success depends on the number of points and the number of areal units under analysis, but generally show that the necessary levels of geocoding success are lower than found in previous research. This finding is consistent across different national contexts.

Temporary cities provide unique challenges in terms of urban planning and management. In particular, there are problems in terms of providing adequate service provision and ensuring an equitable access to those services. In addition, when temporary cities also involve a mass gathering for an event, there is a need for a strategic plan for managing the movement of this crowd over time. The Hajj is an annual religious event which hosts about 3 million pilgrims each year. It is the second largest annual gathering of Muslims in the world. The pilgrims reside for a number of days in the “tent city” of Mina. The organisers of the Hajj must plan the location of services to support this huge temporary population and also provide a plan to facilitate the performing of the religious rituals in an easy and safe manner (in particular to avoid overcrowding). In this paper, we estimate and map the distribution of populations across Mina during the Hajj and explore the location of existing services to investigate how well served the population is within various parts of the City. For illustrative purposes, we explore access to health centres and civil defence (mainly fire services). After analysing the current provision of these services across the City (through building suitable accessibility measures) we use a location allocation model to compare the current provision with a set of optimal, model-based locations. The model is also used to operationalise a series of what-if scenarios including reducing the number of facilities in line with increased Government concerns over escalating costs and changing demand in line with pilgrim movements throughout the day. The results of the location-allocation modelling could also help revamp staffing rotas—not only can the model provide optimal locations they can estimate the workloads associated with each facility location (based on the volume of local demand), meaning the thousands of health workers could be also located more optimally.

Commonly-used data assimilation methods are being adapted for use with agent-based models with the aim of allowing optimisation in response to new data in real-time. However, existing methods face difficulties working with categorical parameters, which are common in agent-based models. This paper presents a new method, the RJUKF, that combines the Unscented Kalman Filter (UKF) data assimilation algorithm with elements of the Reversible Jump (RJ) Markov chain Monte Carlo method. The proposed method is able to conduct data assimilation on both continuous and categorical parameters simultaneously. Compared to similar techniques for mixed state estimation, the RJUKF has the advantage of being efficient enough for online (i.e. real-time) application. The new method is demonstrated on the simulation of a crowd of people traversing a train station and is able to estimate both their current position (a continuous, Gaussian variable) and their chosen destination (a categorical parameter). This method makes a valuable contribution towards the use of agent-based models as tools for the management of crowds in busy places such as public transport hubs, shopping centres, or high streets.

OBJECTIVESThis scoping review aims to answer the following question: To what extent have the mechanisms behind crime generators and attractors been studied? METHODSThis work follows the procedure laid out by the Joanna Briggs Institute for scoping reviews (Tricco, 2017). RESULTSWithin the studies included in this review, a great deal of inconsistency in the definitions of crime generators and attractors was found. Moreover, of the 48 papers included, only 11 examined the mechanisms behind crime generators and attractors. CONCLUSIONSThis scoping review finds a dearth of research into the mechanisms behind crime generators and attractors. This review suggests three potential reasons for this: the lack of consistent definitions for these types of spaces; challenges identifying suitable datasets and methodologies to study these processes empirically; and the fact that crime generators and attractors are not mutually exclusive.

This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models.

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

The assessment of grassland grazing strategies and institutional arrangements is essential for ensuring the sustainable development of grassland grazing systems. By employing per-pixel grazing information derived from remote sensing data, this paper presents an agent-based model of grassland grazing (ABMGG) for Zeku, China that was designed as a framework for assessing the effects of different combinations of grazing strategies and institutional arrangements on grassland status. By calibrating the parameter values of the ABMGG to the system status values under a policy that has already been implemented, the ABMGG can help us to understand grassland degradation in response to management interventions for each patch of land. In the Zeku implementation, it was found that although different grazing policy scenarios could not significantly improve or decrease the overall grassland leaf area index, a rotational group grazing scenario with a land market tenure system did produce a smaller number of severely degraded grass patches than other policy scenarios (except regional continuous grazing). This provides a new perspective on the consequences of grassland management practices where past research has concentrated more on overall grassland productivity. The ABMGG can extend the ability of policy assessment tools to a high resolution level with pixel-specific real-time remote sensing data, making the assessment results more accurate and representative.

Having access to and visiting urban green space (UGS) improves liveability and provides considerable benefits to residents. However, traditional methods of investigating UGS visitation, such as questionnaires and social surveys, are usually time- and resource-intensive, and frequently provide less transferable, site-specific outcomes. This study uses social media data (Twitter) to examine spatio-temporal changes in UGS use in London associated with COVID-19 related lockdowns. It compares georeferenced Tweets posted in a 3 month period from 23 March to 23 June for 3 years covering the first lockdown in the UK in 2020, with Tweets for the same period in 2019 and 2021. The results show that (1) the land-use type of Public Park and Garden was the most frequently visited type of UGS, which may be correlated with these UGS areas remaining opening during the lockdown period; (2) the usage of UGS decreased in central London and increased in other areas during lockdown, which may correlated with working from home restrictions; (3) activities were positively associated with Physical activities maybe as a result of allowing people to take a single daily exercise, and (4) people spent more time in UGS areas on weekdays than weekends compared to pre-lockdown. This is the first study to examine social media data over consistent time period before, during and after the lockdown in relation to UGS. The results show that the findings and method can inform policy makers in their management and planning of UGS, especially in a period of social crisis like the COVID-19 pandemic.

Diversity within a population has been linked to levels of both social cohesion and crime. Neighborhood crimes are the result of a complex set of factors, one of which is weak community cohesion. This article seeks to explore the impacts of diversity on burglary crime in a range of neighborhoods, using Leeds, UK as a case study. We propose a new approach to quantifying the correlates of burglary in urban areas through the use of diversity metrics. This approach is useful in unveiling the relationship between burglary and diversity in urban communities. Specifically, we employ stepwise multiple regression models to quantify the relationships between a number of neighborhood diversity variables and burglary crime rates. The results of the analyses show that the variables that represent diversity were more significant when regressed against burglary crime rates than standard socio‐demographic data traditionally used in crime studies, which do not generally use diversity variables. The findings of this study highlight the importance of neighborhood cohesion in the crime system, and the key place for diversity statistics in quantifying the relationships between neighborhood diversities and burglary. The study highlights the importance of policy planning aimed at encouraging community building in promoting neighborhood safety.

Agent-based modelling methodologies offer a number of advantages when it comes to socio-ecological systems research. In particular, they enable experiments to be conducted that are not practical or feasible to conduct in real world settings; they can capture heterogeneity in agent circumstances, knowledge, behaviour, and experiences; and they facilitate a multi-scale, causal understanding of system dynamics. However, developing detailed, empirically informed agent-based models is typically a time and resource intensive activity. Here, we describe a detail-rich, ethnographically informed agent-based model of a Nepalese smallholder village that was created for the purpose of studying the impact of multiple stressors on mountain communities. In doing so, we aim to make the model accessible to other researchers interested in simulating such communities and to provide inspiration for other socio-ecological system modellers.•The model is described using the ODD protocol.•The number of replicate runs required for experiments is discussed, and the model validation and sensitivity analysis processes that have been conducted are explained.•Suggestions are made for how the model can practically be used and for how model outputs can be analysed., Graphical abstract Image, graphical abstract

Funder: Aerospace Technology Institute, Funder: UK Research and Innovation, Funder: The Alan Turing Institute, A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations.

Spatial crime simulations contribute to our understanding of the mechanisms that drive crime and can support decision-makers in developing effective crime reduction strategies. Agent-based models that integrate geographical environments to generate crime patterns have emerged in recent years, although data-driven crime simulations are scarce. This article (1) identifies numerous important drivers of crime patterns, (2) collects relevant, openly available data sources to build a GIS-layer with static and dynamic geographical , as well as temporal features relevant to crime, (3) builds a virtual urban environment with these layers, in which individual offender agents navigate, (4) proposes a data-driven decision-making process using machine-learning for the agents to decide whether to engage in criminal activity based on their perception of the environment and, finally, (5) generates fine-grained crime patterns in a simulated urban environment. The novelty of this work lies in the various large-scale data layers, the integration of machine learning at individual agent level to process the data layers, and the high resolution of the resulting predictions. The results show that the spatial, temporal, and interaction layers are all required to predict the top street segments with the highest number of crimes. In addition, the spatial layer is the most informative, which means that spatial data contributes most to predictive performance. Thus, these findings highlight the importance of various open data sources and the potential of theory-informed, data-driven simulations for the purpose of crime prediction. The resulting model is applicable as a predictive tool and as a test platform to support crime reduction., Computers, Environment and Urban Systems, 89, ISSN:0198-9715, ISSN:1873-7587

Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.

Every day, weather forecasting centres around the world make use of noisy, incomplete observations of the atmosphere to update their weather forecasts. This process is known as data assimilation, data fusion or state estimation and is best expressed as Bayesian inference: given a set of observations, some prior beliefs and a model of the target system, what is the probability distribution of some set of unobserved quantities or latent variables at some time, possibly in the future? While data assimilation has developed rapidly in some areas, relatively little progress has been made in performing data assimilation with agent-based models. This has hampered the use of agent-based models to make quantitative claims about real-world systems. Here we present an algorithm that uses Markov-Chain-Monte-Carlo methods to generate samples of the parameters and trajectories of an agent-based model over a window of time given a set of possibly noisy, aggregated and incomplete observations of the system. This can be used as-is, or as part of a data assimilation cycle or sequential-MCMC algorithm. Our algorithm is applicable to time-stepping, agent-based models whose agents have a finite set of states and a finite number of ways of acting on the world. As presented the algorithm is only practical for agents with a few bytes of internal state although we discuss ways of removing this restriction. We demonstrate the algorithm by performing data assimilation with an agent-based, spatial predator-prey model., Comment: 22 pages, 4 figures

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.

Monitoring grazing activities on grassland is crucial for ensuring sustainable grassland development and for protecting it from grazing-led degradation. The Leaf Area Index (LAI), which measures leaf coverage over a surface area, is commonly used as a proxy for grassland condition. However, current studies focus on the year-round or seasonal aggregated LAI change rather than the change that can be attributed explicitly to grazing, which is the important indicator for quantifying grassland grazing. This paper presents a new exponential growth function under grazing with an estimation algorithm, the purpose of which is to extract grazing-led LAI changes for every 8 days' satellite observations. All the analyses are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2H products. An improved MODIS LAI and an expected LAI are produced separately, considering both current and previous grazing-led LAI changes. The differences between expected LAI and improved LAI are then converted to the equivalent carbon mass of grazed material. This grazed carbon mass is then aggregated within the growing season, and compared with the expected carbon mass consumed by livestock (calculated from statistics yearbooks). In addition, Net Primary Productivity (NPP) is produced using the improved LAI, simulated by a Light Use Efficiency with Vegetation Photosynthesis Model (LUE-VPM). This is compared with the NPP produced by LUE-VPM based on original MODIS LAI, MODIS NPP products (MOD17A2H) and grassland monitoring stations' in situ measured data. Results show that the NPP calculated from the improved LAI is statistically the same as in situ converted NPP with a p-value equalling 0.998 (the RMSE between the two is 97.77 gC/m2). Conversely, the p-value between converted in situ measured carbon mass and the MODIS NPP product is 0.011 (the RMSE between the two is 133.98 gC/m2), indicating they are statistically different. The results detailed in this paper provide precise and almost real-time grassland grazing monitoring information for policy makers managing grassland.

BackgroundA key issue in the analysis of many spatial processes is the choice of an appropriate scale for the analysis. Smaller geographical units are generally preferable for the study of human phenomena because they are less likely to cause heterogeneous groups to be conflated. However, it can be harder to obtain data for small units and small-number problems can frustrate quantitative analysis. This research presents a new approach that can be used to estimate the most appropriate scale at which to aggregate point data to areas.Data and methodsThe proposed method works by creating a number of regular grids with iteratively smaller cell sizes (increasing grid resolution) and estimating the similarity between two realisations of the point pattern at each resolution. The method is applied first to simulated point patterns and then to real publicly available crime data from the city of Vancouver, Canada. The crime types tested are residential burglary, commercial burglary, theft from vehicle and theft of bike.FindingsThe results provide evidence for the size of spatial unit that is the most appropriate for the different types of crime studied. Importantly, the results are dependent on both the number of events in the data and the degree of spatial clustering, so a single 'appropriate' scale is not identified. The method is nevertheless useful as a means of better estimating what spatial scale might be appropriate for a particular piece of analysis.

Purpose Crime analysts need accurate population-at-risk measures to quantify crime rates. This research evaluates five measures to find the most suitable ambient population-at-risk estimate for ‘theft from the person’ crimes. Method 1. Collect ‘ambient’ datasets: the 2011 Census, aggregate mobile telephone locations, and social media. 2. Correlate the population measures against crime volumes to identify the strongest predictor. 3. Use the Gi* statistic to identify statistically significant clusters of crime under alternative denominators. 4. Explore the locations of clusters, comparing those that are significant under ambient and residential population estimates. Results and Discussion The research identifies the Census workday population as the most appropriate population-at-risk measure. It also highlights areas that exhibit statistically significant rates using both the ambient and residential denominators. This hints at an environmental backcloth that is indicative of both crime generators and attractors – i.e. places that attract large numbers of people for non-crime purposes (generators) as well as places that are used specifically for criminal activity (attractors). Regions that are largely residential and yet only exhibit hotspots under the ambient population might be places with a higher proportion of crime attractors to stimulate crime, but fewer generators to attract volumes of people.

Investigate the spatial concentrations and spatial stability of criminal event data at the micro-spatial unit of analysis in Vancouver, British Columbia. Geo-referenced crime data, 2003–2013, representing four property crime types (commercial burglary, mischief, theft from vehicle, theft of vehicle) are analyzed considering crime concentrations at the street segment and street intersection level as well as through the use of a nonparametric spatial point pattern test that identifies the stability in spatial point patterns in pairwise and longitudinal contexts. Property crime in Vancouver is highly concentrated in a small percentage of street segments and intersections, as few as 5 % of street segments and intersections in 2013 depending on the crime type. The spatial point pattern test shows that spatial stability is almost always present when considering all street segments and intersections. However, when only considering the street segments and intersections that have crime, spatial stability is only present in recent years for pairwise comparisons and moderately stable in the longitudinal tests. Despite the crime drop that has occurred in Vancouver, there is still spatial stability present over time at levels suitable for theoretical development. However, caution must be taken when developing initiatives for situational crime prevention.

The work presented here demonstrates the potential of new generation data arising from innovative policies (based on persuasive technologies) in the transport sector. Improved understanding of the spatial distribution of health impacts arising from the introduction of new travel initiatives will support more targeted and efficient policy development across both the transport and health sectors. Typical health impacts include those arising from changes in levels of personal activity with alternative mode choices. With a sectoral approach to policy development, positive impacts for one sector (i.e. improved transport services) may be negated by dis-benefits in another (e.g. low levels of active travel choice and increased obesity related disease burden). The horizontal notion of equity (Thomopoulos, Grant-Muller and Tight, 2009) is applied using a range of transport-related health outcomes including cancer, heart disease and depression. The research methodology interfaces new generation ‘Track and Trace’ information on individuals location and mode choices (detected as mobile phone app-based sensor data) with a new integrated transport and health model (IHITM), finally calculating an equity indicator based on distributional impacts.

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.

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. Electronic supplementary material The online version of this article (10.1007/s10707-019-00346-1) contains supplementary material, which is available to authorized users.

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.

Agent-based modeling (ABM) is a technique that allows us to explore how the interactions of heterogeneous individuals impact on the wider behavior of social/spatial systems. In this article, we introduce ABM and its utility for studying geographical systems. We discuss how agent-based models have evolved over the last 20 years and situate the discipline within the broader arena of geographical modeling. The main properties of ABM are introduced and we discuss how models are capable of capturing and incorporating human behavior. We then discuss the steps taken in building an agent-based model and the issues of verification and validation of such models. As the focus of the article is on ABM of geographical systems, we then discuss the need for integrating geographical information into models and techniques and toolkits that allow for such integration. Once the core concepts and techniques of creating agent-based models have been introduced, we then discuss a wide range of applications of agent-based models for exploring various aspects of geographical systems. We conclude the article by outlining challenges and opportunities of ABM in understanding geographical systems and human behavior.

Big data offers researchers the scope to simulate population behavior through vastly more powerful agent-based models (ABMs), presenting exciting opportunities in the design and appraisal of policies and plans. Agent-based simulations capture system richness by representing micro-level agent choices and their dynamic interactions. They aid analysis of the processes that drive emergent population level phenomena, their change in the future and their response to interventions. The potential of ABMs has led to a major increase in the number of applications, yet such models are limited in that the individual-level data required for robust, reliable calibration are often only available in aggregate form. New (‘big’) sources of data offer a wealth of information about the behavior (e.g., movements, actions, decisions) of individuals. By building ABMs with BD, it is possible to simulate society across many application areas, providing insight into the behavior, interactions and wider social processes that drive urban systems. This chapter will discuss, in context of urban simulation, how big data can unlock the potential of ABMs and how ABMs can leverage real value from big data. In particular, we will focus on how big data can improve an agent’s abstract behavioral representation and suggest how combining these approaches can both reveal new insights into urban simulation and also address some of the most pressing issues in ABM; particularly those of calibration and validation.

Improved understanding of the interfaces between travel choices and the consequential energy, health and security impacts would support the development of policies that deliver positive benefits across the sectors. This would represent a step change from the common sectoral based approach, whereby positive impacts for one sector may be negated by dis-benefits in another. This concept paper presents a precis of on-going research to harness the potential of new generation data ('Big Data') arising from innovative policies (based on persuasive technologies) in the transport sector to model, then analyse, cross sectoral impacts. The key issues for exploration include 1) the scope and veracity of both new generation data and established data, and 2) the developments needed to existing model interfaces and internal algorithms (transport and cross-sectoral) to accommodate new generation data. The research is based on data generated as the result of a large scale implementation of positive incentives to encourage changes to travel choices using mobile phone apps in Newcastle (UK). The broader research program involves ten other cities across Europe.

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.