Assisted Agent-Based Simulations: Fusing non-player character movement with Space Syntax

Agent-based simulation is one of the core tools of spatial analysis utilised to provide an understanding of space when complex parameters come into play, such as how the visible space changes while traversing a building, or what happens when there is a destination to be reached. This type of simulation has a lot in common with techniques used in video games to create movement trajectories for non-player characters. Although these techniques have been developed over the years to provide more realistic and more “human-like” behaviour, they are rarely woven back into analytical and simulation tools. As a first step to remedy that, we developed a new methodology that fuses non-player character movement from computer games with simulation techniques traditionally used for agent-based analysis in Space Syntax. This first attempt utilises a different type of underlying representation of space, known as a navigation mesh. We first examine in detail two traditional techniques utilised in depthmapX agent-based analysis and highlight their strengths and limitations. We then describe how this technique differs from the classic space syntax methods, as well as how it can be combined to create hybrid analytical models of movement. The hybrid model developed in this case is that of a classic space syntax agent assisted by the aforementioned technique. We then tested and evaluated the traditional and new models for their capacity to explore two gallery spaces. The results extracted from the new hybrid simulation model depict agents with more capacity to explore, a significant addition to the traditional space syntax agent based methods

Space Syntax: Understanding human movement, co-presence and encounters in relation to the spatial structure of workplaces

The theory of space syntax provides a way to formalise the connection between spatial configuration and human behaviour. While originally developed for urban space it has been harnessed for the study of workplaces, providing evidence on how the configuration of office space shapes the activities of people. Within the domain of space syntax, configuration means connecting elements of space into a network using a multitude of representational techniques to enable the measurement of spatial network properties. Paired with methods to capture human activity such as observations, questionnaires and staff social networks, the theory of space syntax allows for hypothesising and proofing how spatial configuration affects human behaviour, and how design might be informed to consider this evidence. The theory and methods are becoming more and more relevant today, as evidence-based design is increasingly sought after by practitioners. This chapter gives an overview of this body of work and summarises insights but also critiques current shortcomings, mainly regarding scattered applications and contradictory evidence in small samples, making generalisation hard. The chapter closes by sketching a vision for how space syntax can be advanced as a fruitful workplace theory for future work on office spaces

Differential perceptions of teamwork, focused work and perceived productivity as an effect of desk characteristics within a workplace layout

The impact of the physical workplace on behaviors and attitudes at work is a much-studied topic. Major research streams over the last decades investigated either satisfaction with offices in relation to physical comfort, or how layout decisions influenced interaction and collaboration in the workplace with a focus on open-plan offices. Rather little is known on the effect a workplace layout (such as its openness) has on perceptions of staff regarding teamwork, focused work and perceived productivity. We aim to close this gap by taking a differential approach which appreciates detailed variations within open-plan offices. Not every corner of an office is the same, so the question arises whether satisfaction with workspace differs depending on where someone is sitting. Bringing results of a staff survey in the UK headquarters of a global technology company together with a detailed analysis of spatial qualities at desks based on isovist and visual field analysis, we find that staff are less likely to rate their workplace environment favorably when they have higher numbers of desks within their own field of vision; and when they are facing away from the room with a relatively larger area behind their back compared to the area surrounding them. Aspects of teamwork that are negatively affected include sharing information with others, as well as team identity and cohesion. Focused work (concentration) and working productively are impacted even more so with the largest effect sizes throughout. These findings highlight the relevance of investigating detailed spatial qualities of micro-locations in workplace layouts. Our results also raise important questions regarding the current popular practice in workplace design of providing large open-plan offices for technology companies

Travel Concentration: The effects of attractor-bound movement on workplace activity

PURPOSE: The purpose of this paper is to explore the effects of office attractors on workplace activity. First, it aims to describe how movement towards different attractors such as canteens and entrances can be approximated in a 2D spatial model, and second, to show how those simulated effects relate to actual observations of movement and interaction. THEORY: Human activity in physical workspace is typically examined from the perspective of the purely geometric properties of the space (i.e. in the field of space syntax), or by other properties of workspaces, such as barriers and distance between workers. Movement in offices however is an activity that is driven by both geometric and non-geometric properties. The non-geometric properties relate to the functional configuration of space (where seats/canteens/meeting rooms are) but the activity itself happens in the real space and it is thus bound by spatial configuration.Furthermore, while the driver for movement is the need to travel to specific attractors, it is the actual space that allows for secondary effects such as serendipitous interactions to emerge. Thus, it can be expected that a successful approximation of workplace movement will also contribute to understanding interaction, especially that which happens away from spaces programmed for it such as meeting rooms. This paper examines the two activities of movement and interaction under the hypothesis that a spatial model that properly simulates attractor-bound movement can successfully identify the locations where movement happens, but also provide relevant hints for serendipitous interaction. DESIGN/METHODOLOGY/APPROACH: To study this hypothesis, we constructed paths from each seat to a set of three types of attractors, specifically the building entrance, the closest canteen or kitchen and the closest WC. These paths were then transformed to zones of visibility to take into account the surrounding space as well as to allow for interaction to be examined as that activity is unlikely to happen directly on the path. The final result is a metric of travel concentration that measures how likely is it that a space will be seen from those generated paths. The metric is validated against actual observations of movement and interaction in a linear model, tested initially against a large sample of different workplaces (216 floors), but also against two sets of floors, one with high and one with low seat density. FINDINGS: The new metric fares well against both movement and interaction on the whole sample, but on the two sets of floors the effects are less robust. In high-density floors the main driver of attractor movement is the one generated from outside the floor and to a lesser extent the one that comes from within the floor. In low density floors only interaction is somewhat predictable albeit with a weak effect and only in relation to travel from within the floor. Travel concentration was found to be less effective than the existing Visual Mean Depth metric, however combinations of the two were found, in some cases to yield the best results. ORIGINALITY/VALUE: The new metric presented here is a useful simulation of movement in office spaces which can be applied to the analysis of existing spaces, but also provide a way for designers to test against floor plans of new buildings

Exploring the role of spatial configuration and behavior on the spread of the epidemic: A study of factors that affect Covid-19 spreading in the city

This research explores how exterior public space - defined through the configuration of the city - and human behavior affect the spread of disease. In order to understand the virus spreading mechanism and influencing factors of the epidemic which accompany residents' movement, this study attempts to reproduce the process of virus spreading in city areas through computer simulation. The simulation can be divided into residents' movement simulation and the virus spreading simulation. First, the Agent-based model (ABM) can effectively simulate the behavior of the individual and crowd;real location data - uploaded by residents via mobile phone applications - is used as a behavioral driving force for the agent's movement. Second, a mathematical model of infectious diseases is constructed based on SIR (SEIR) Compartmental models in epidemiology. Finally, by analyzing the simulation results of the agent's movement in the city and the virus spreading under different conditions, the influence of multiple factors of city configuration and human behavior on the virus spreading process is explored, and the effectiveness of countermeasures such as social distancing and lockdown are further demonstrated

Acquaintances or Familiar Strangers?:How Similarity and Spatial Proximity Shape Neighbour Relations within Residential Buildings

While scholars have long established that city dwellers choose with whom to develop relationships on the basis of social proximity, spatial proximity remains the basis for neighbour relations involving greetings, social conversation, and the exchange of services. Few studies have systematically compared the respective roles of spatial and social proximity in neighbour relations. In this paper, we investigate these two factors through statistical analysis of four social network datasets representing relationships within four rented apartment buildings in Geneva, Switzerland. Using a measure of distance that takes into account how the layout and materiality of buildings shape relationships through accessibility, visibility and audibility, we compare the effects of spatial proximity with the effects of individual determinants and similarity. Our study also breaks new ground by comparing weak ties–between people who interact regularly–and “invisible ties”, or ties to familiar strangers. Our study confirms that spatial proximity increases the likelihood of weak ties and questions the underlying mechanisms. It also shows that in addition to sociability, familiarity and anonymity are constitutive dimensions of neighbouring, even at the scale of buildings

Big Data and Workplace Micro-Behaviours: A closer inspection of the social behaviour of eating and interacting

Evidence-based design aims to understand human behaviour so that strategic decisions are well-informed when creating a new space. Workplace research to date has provided interesting insights, but has mostly done so on a case-by-case basis. This approach does not yield generalisable patterns, making results problematic to use in an evidence-based design context. This paper builds upon previous large-scale analysis done by the authors and focuses on two aspects of workplace behaviour – eating and interacting. We aim to understand the nuances of these behaviours, thus we explore them as independent phenomena, separate them into subcategories and set out to understand the reasons behind these observations. The examined dataset includes 23 organisations in the UK, with a wide variety of sizes, numbers of floors and buildings. It consists of human activity data collected through direct observation, Visibility Graph Analysis and organisational parameters such as industry and flexibility of desk occupancy. The first behaviour we focus on – interaction – has already been explored in previous research and has been found to happen primarily in workspace and meeting rooms. In this instance we initially classify interactions according to the activity of the members and the type of space they occur in. The analysis of the second behaviour – eating – revolves around the activities and locations of people at lunchtime. We aim to discover where people choose to eat and how this is affected by the characteristics and availability of eating spaces. For the two behaviours studied, we examine how each activity relates to the space it is happening in, taking into account a set of spatial and organisational factors. In the first case we test each interaction against proximity to circulation and local visibility of the space, while in the second we examine the popularity of different types of spaces, for example canteens and breakout spaces, against their proximity to workspace and what possibilities of inter-visibility they offer. This paper provides detailed insights into the phenomena of interacting and eating, and reflects on limitations of traditional statistical analysis. It will also highlight further opportunities for handling these types of big datasets using different techniques such as Principal Component Analysis and machine learning

Partitioning indoor space using visibility graphs: investigating user behavior in office spaces

An abstract representation of interior space is the foundation for any spatial analysis of human activity in such environments. It must capture high level concepts such as rooms, areas and corridors, but also allow for the discrete appearance of human behaviour (for example two people will not walk through the same corridor in the same way). Within the field of Space Syntax three such representations have been proposed, axial lines, convex spaces and visibility graphs. However none of these representations are both unambiguous and allow for aggregating results. Axial lines are reductions of the space into longest lines of sight and convex spaces are ”the largest and fattest convex spaces” possible. While both are meaningful abstractions, they are ambiguous and depend on the person creating them. Visibility Graphs on the other hand provide a uniform unit of analysis by dividing the space using a lattice grid into cells of equal size and connecting the cells if they are intervisible. This representation however does not allow for a meaningful aggregation of spatial human behaviour data, given its very precise nature. We propose a new representation, one which clusters adjacent cells of the visibility graph based on different metrics and thus provides both aggregatable areas and a robust method of creation. We explore how these various metrics and properties of the visibility graph create different types of clusters and specifically examine connectivity and Visual Mean Depth on various types of spaces, from simple shapes, to complex multi-floor buildings. Finally, we demonstrate how this aids the analysis of human activity in indoor spaces by focusing on a large sample of observed activity in office spaces. We argue that this new representation provides a robust but also meaningful foundation for the analysis of indoor space

Physical Distancing Potential Inside Buildings: What we know (and don't know) about movement and interaction patterns

The global Covid-19 pandemic has forced us to reconsider the design of the built environment. This review was produced at speed in April 2020 as a working paper to reflect on the state-of-the-art knowledge on movement and interaction inside buildings

Spatial databases: Generating new insights on office design and human behaviours in the workplace

Space Syntax research has shown how human behaviours in the workplace are shaped by spatial configuration; in turn, evidence-based design practices have highlighted ways in which this data can be used to inform tailor-made solutions in office design. Yet, existing research focuses on either single case studies or comparisons of a few cases on a small scale. Also, each study uses its own methods and metrics which makes it difficult to establish wider patterns beyond single datasets. This paper presents a larger than usual data set on workplaces, which has been collected by Spacelab, a design and consultancy practice based in London. This dataset includes spatial and space usage information such as syntactic analysis and desk occupancy on client companies. It resides in a spatial relational database, allowing for systematic combination of the collected data, useful for doing either deeper analysis, or generating benchmarks and baselines. These insights are not only highly relevant to clients but also give rise to opportunities to generate new insights on office design and human behaviours in the workplace from a research perspective Two main research questions relating to the size of samples are discussed: Firstly, whether large samples are necessary to fully understand phenomena, and secondly, whether behavioural patterns vary across cases. Observation data and syntactic analysis are combined to understand in which areas of an office different activities take place. Observation data is also brought together with the functional allocations of space in order to ask whether activities follow the programme introduced by functions such as meeting rooms, kitchens, workspaces, etc. It is shown that observation data only becomes robust and reliable with longer periods of observations than previously recommended. Three to four full days seems to produce reasonably stable results for desk occupancy, while five full days seemed required for percentages of people walking and interacting. Some surprising findings were revealed regarding the distribution of activities in space, for instance dispelling the myth that interactions happen in corridors and highlighting that interactions tend to occur in rather segregated spaces. While it is argued that predictive power of the analysis varies, first steps towards establishing generic patterns have clearly been taken