Space syntax and spatial cognition: or why the axial line?

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Example-Based Urban Modeling

The manual modeling of virtual cities or suburban regions is an extremely time-consuming task, which expects expert knowledge of different fields. Existing modeling tool-sets have a steep learning curve and may need special education skills to work with them productively. Existing automatic methods rely on rule sets and grammars to generate urban structures; however, their expressiveness is limited by the rule-sets. Expert skills are necessary to typeset rule sets successfully and, in many cases, new rule-sets need to be defined for every new building style or street network style. To enable non-expert users, the possibility to construct urban structures for individual experiments, this work proposes a portfolio of novel example-based synthesis algorithms and applications for the controlled generation of virtual urban environments. The notion example-based denotes here that new virtual urban environments are created by computer programs that re-use existing digitized real-world data serving as templates. The data, i.e., street networks, topography, layouts of building footprints, or even 3D building models, necessary to realize the envisioned task is already publicly available via online services. To enable the reuse of existing urban datasets, novel algorithms need to be developed by encapsulating expert knowledge and thus allow the controlled generation of virtual urban structures from sparse user input. The focus of this work is the automatic generation of three fundamental structures that are common in urban environments: road networks, city block, and individual buildings. In order to achieve this goal, the thesis proposes a portfolio of algorithms that are briefly summarized next. In a theoretical chapter, we propose a general optimization technique that allows formulating example-based synthesis as a general resource-constrained k-shortest path (RCKSP) problem. From an abstract problem specification and a database of exemplars carrying resource attributes, we construct an intermediate graph and employ a path-search optimization technique. This allows determining either the best or the k-best solutions. The resulting algorithm has a reduced complexity for the single constraint case when compared to other graph search-based techniques. For the generation of road networks, two different techniques are proposed. The first algorithm synthesizes a novel road network from user input, i.e., a desired arterial street skeleton, topography map, and a collection of hierarchical fragments extracted from real-world road networks. The algorithm recursively constructs a novel road network reusing these fragments. Candidate fragments are inserted into the current state of the road network, while shape differences will be compensated by warping. The second algorithm synthesizes road networks using generative adversarial networks (GANs), a recently introduced deep learning technique. A pre- and postprocessing pipeline allows using GANs for the generation of road networks. An in-depth evaluation shows that GANs faithfully learn the road structure present in the example network and that graph measures such as area, aspect ratio, and compactness, are maintained within the virtual road networks. To fill empty city blocks in road networks we propose two novel techniques. The first algorithm re-uses real-world city blocks and synthesizes building footprint layouts into empty city blocks by retrieving viable candidate blocks from a database. We evaluate the algorithm and synthesize a multitude of city block layouts reusing real-world building footprint arrangements from European and US-cities. In addition, we increase the realism of the synthesized layouts by performing example-based placement of 3D building models. This technique is evaluated by placing buildings onto challenging footprint layouts using different example building databases. The second algorithm computes a city block layout, resembling the style of a real-world city block. The original footprint layout is deformed to construct a textit{guidance map}, i.e., the original layout is transferred to a target city block using warping. This guidance map and the original footprints are used by an optimization technique that computes a novel footprint layout along the city block edges. We perform a detailed evaluation and show that using the guidance map allows transferring of the original layout, locally as well as globally, even when the source and target shapes drastically differ. To synthesize individual buildings, we use the general optimization technique described first and formulate the building generation process as a resource-constrained optimization problem. From an input database of annotated building parts, an abstract description of the building shape, and the specification of resource constraints such as length, area, or a number of architectural elements, a novel building is synthesized. We evaluate the technique by synthesizing a multitude of challenging buildings fulfilling several global and local resource constraints. Finally, we show how this technique can even be used to synthesize buildings having the shape of city blocks and might also be used to fill empty city blocks in virtual street networks. All algorithms presented in this work were developed to work with a small amount of user input. In most cases, simple sketches and the definition of constraints are enough to produce plausible results. Manual work is necessary to set up the building part databases and to download example data from mapping services available on the Internet

Stable Infrastructure-based Routing for Intelligent Transportation Systems

Intelligent Transportation Systems (ITSs) have been instrumental in reshaping transportation towards safer roads, seamless logistics, and digital business-oriented services under the umbrella of smart city platforms. Undoubtedly, ITS applications will demand stable routing protocols that not only focus on Inter-Vehicle Communications but also on providing a fast, reliable and secure interface to the infrastructure. In this paper, we propose a novel stable infrastructure- based routing protocol for urban VANETs. It enables vehicles proactively to maintain fresh routes towards Road-Side Units (RSUs) while reactively discovering routes to nearby vehicles. It builds routes from highly stable connected intersections using a selection policy which uses a new intersection stability metric. Simulation experiments performed with accurate mobility and propagation models have confirmed the efficiency of the new protocol and its adaptability to continuously changing network status in the urban environment

Future exposure modelling for risk-informed decision making in urban planning

Population increases and related urban expansion are projected to occur in various parts of the world over the coming decades. These future changes to the urban fabric could fundamentally alter the exposure to natural hazards and the associated vulnerability of people and the built environment with which they interact. Thus, modelling, quantifying, and reducing future urban disaster risk require forward-looking insights that capture the dynamic form of cities. This paper specifically focuses on the exposure component of dynamic natural-hazard disaster risk, by considering urban planning as the centre of future exposure characterisation in a given region. We use the information provided by urban plans and propose an integrated data structure for capturing future exposure to hazards. The proposed data structure provides the necessary detailing for both future physical and socio-demographic exposure in disaster risk modelling. More specifically, it enables users to develop a comprehensive multi-level, multi-scale exposure dataset, characterising attributes of land use, buildings, households and individuals. We showcase the proposed data schema using the virtual urban testbed Tomorrowville. In this case study, we also demonstrate how simplified algorithmic procedures and disaggregation methods can be used to populate the required data. This implementation demonstrates how the proposed exposure data structure can effectively support the development of forward-looking urban visioning scenarios to support decision-making for risk-sensitive and pro-poor urban planning and design in tomorrow's cities

Procedural modeling of cities with semantic information for crowd simulation

En aquesta tesi de màster es presenta un sistema per a la generació procedural de ciutats poblades. Avui en dia poblar entorns virtuals grans tendeix a ser una tasca que requereix molt d’esforç i temps, i típicament la feina d’artistes o programadors experts. Amb aquest sistema es vol proporcionar una eina que permeti als usuaris generar entorns poblats d’una manera més fàcil i ràpida, mitjançat l’ús de tècniques procedurals. Les contribucions principals inclouen: la generació d’una ciutat virtual augmentada semànticament utilitzant modelat procedural basat en gramàtiques de regles, la generació dels seus habitants virtuals utilitzant dades estadístiques reals, i la generació d’agendes per a cada individu utilitzant també un mètode procedural basat en regles, el qual combina la informació semàntica de la ciutat amb les característiques i necessitats dels agents autònoms. Aquestes agendes individuals són usades per a conduir la simulació dels habitants, i poden incloure regles com a tasques d’alt nivell, l’avaluació de les quals es realitza al moment de començar-les. Això permet simular accions que depenguin del context, i interaccions amb altres agents.En esta tesis de máster se presenta un sistema para la generación procedural de ciudades pobladas. Hoy en día poblar entornos virtuales grandes tiende a ser una tarea que requiere de mucho tiempo y esfuerzo, y típicamente el trabajo de artistas o programadores expertos. Con este sistema se pretende proporcionar una herramienta que permita a los usuarios generar entornos poblados de un modo más fácil y rápido, mediante el uso de técnicas procedurales. Las contribuciones principales incluyen: la generación de una ciudad virtual aumentada semánticamente utilizando modelado procedural basado en gramáticas de reglas, la generación de sus habitantes virtuales utilizando datos estadísticos reales, y la generación de agendas para cada individuo utilizando también un método procedural basado en reglas, el cual combina la información semántica de la ciudad con las características y necesidades de los agentes autónomos. Estas agendas individuales son usadas para conducir la simulación de los habitantes, y pueden incluir reglas como tareas de alto nivel, la evaluación de las cuales se realiza cuando empiezan. Esto permite simular acciones que dependan del contexto, e interacciones con otros agentes.In this master thesis a framework for procedural generation of populated cities is presented. Nowadays, the population of large virtual environments tends to be a time-consuming task, usually requiring the work of expert artists or programmers. With this system we aim at providing a tool that can allow users to generate populated environments in an easier and faster way, by relying on the usage of procedural techniques. Our main contributions include: a generation of semantically augmented virtual cities using procedural modelling based on rule grammars, a generation of a virtual population using real-world data, and a generation of agendas for each individual inhabitant by using a procedural rule-based approach, which combines the city semantics with the autonomous agents characteristics and needs. The individual agendas are then used to drive a crowd simulation in the environment, and may include high-level rule tasks whose evaluation is delayed until they get triggered. This feature allows us to simulate context-dependant actions and interactions with other agents

The Aha! Experience of Spatial Reorientation

The experience of spatial re-orientation is investigated as an instance of the wellknown phenomenon of the Aha! moment. The research question is: What are the visuospatial conditions that are most likely to trigger the spatial Aha! experience? The literature suggests that spatial re-orientation relies mainly on the geometry of the environment and a visibility graph analysis is used to quantify the visuospatial information. Theories from environmental psychology point towards two hypotheses. The Aha! experience may be triggered by a change in the amount of visual information, described by the isovist properties of area and revelation, or by a change in the complexity of the visual information associated with the isovist properties of clustering coefficient and visual control. Data from participants’ exploratory behaviour and EEG recordings are collected during wayfinding in virtual reality urban environments. Two types of events are of interest here: (a) sudden changes of the visuospatial information preceding subjects' response to investigate changes in EEG power; and (b) participants brain dynamics (Aha! effect) just before the response to examine differences in isovist values at this location. Research on insights, time-frequency analysis of the P3 component and findings from navigation and orientation studies suggest that the spatial Aha! experience may be reflected by: a parietal alpha power decrease associated with the switch of the representation and a frontocentral theta increase indexing spatial processing during decision-making. Single-trial time-frequency analysis is used to classify trials into two conditions based on the alpha/theta power differences between a 3s time-period before participants’ response and a time-period of equal duration before that. Behavioural results show that participants are more likely to respond at locations with low values of clustering coefficient and high values of visual control. The EEG analysis suggests that the alpha decrease/theta increase condition occurs at locations with significantly lower values of clustering coefficient and higher values of visual control. Small and large decreases in clustering coefficient, just before the response, are associated with significant differences in delta/theta power. The values of area and revelation do not show significant differences. Both behavioural and EEG results suggest that the Aha! experience of re-orientation is more likely to be triggered by a change in the complexity of the visual-spatial environment rather than a change in the amount, as measured by the relevant isovist properties

Cultural heritage visits supported on visitors' preferences and mobile devices

Monuments, museums and cities are great places to feel and experience neat and interesting things. But cultural heritage is experienced differently by different visitors. The more erudite may know beforehand what they intend to explore, while the least literate usually know and are capable of expressing some of their preferences but do not exactly realize what to see and explore. This paper proposes the use of a mobile application to set an itinerary where you can move at your own pace and, at the same time, have all the complementary information you need about each of the points of interest. The application is designed in face of an adaptive user interface where the routing and augmented reality are connected to acknowledge the needs of different user categories, such as elders, kids, experts or general usersPortuguese Foundation for Science and Technology (FCT)Portuguese Foundation for Science and Technology [UID/EEA/50009/2013, UID/SOC/04020/2013]CRESC ALGARVE 2020, PORTUGAL 2020 [3322]FEDER European Commissioninfo:eu-repo/semantics/publishedVersio

Neural basis of route-planning and goal-coding during flexible navigation

Animals and humans are remarkable in their ability to flexibly adapt to changes in their surroundings. Navigational flexibility may take many forms and in this thesis we investigate its neural and behavioral underpinnings using a variety of methods and tasks tailored to each specific research aim. These methods include functional resonance magnetic imaging (fMRI), freely moving virtual reality, desktop virtual reality, large-scale online testing, and computational modelling. First, we reanalysed previously collected rodent data in the lab to better under- stand behavioural bias that may occur during goal-directed navigation tasks. Based on finding some biases we designed a new approach of simulating results on maze configurations prior to data collection to select the ideal mazes for our task. In a parallel line of methods development, we designed a freely moving navigation task using large-scale wireless virtual reality in a 10x10 space. We compared human behaviour to that of a select number of reinforcement learning agents to investigate the feasibility of computational modelling approaches to freely moving behaviour. Second, we further developed our new approach of simulating results on maze configuration to design a novel spatial navigation task used in a parallel experiment in both rats and humans. We report the human findings using desktop virtual reality and fMRI. We identified a network of regions including hippocampal, caudate nu- cleus, and lateral orbitofrontal cortex involvement in learning hidden goal locations. We also identified a positive correlation between Euclidean goal distance and brain activity in the caudate nucleus during ongoing navigation. Third, we developed a large online testing paradigm to investigate the role of home environment on wayfinding ability. We extended previous reports that street network complexity is beneficial in improving wayfinding ability as measured using a previously reported virtual navigation game, Sea Hero Quest, as well as in a novel virtual navigation game, City Hero Quest. We also report results of a navigational strategies questionnaire that highlights differences of growing up inside and outside cities in the United States and how this relates to wayfinding ability. Fourth, we investigate route planning in a group of expert navigators, licensed London taxi drivers. We designed a novel mental route planning task, probing 120 different routes throughout the extensive street network of London. We find hip- pocampal and retrosplenial involvement in route planning. We also identify the frontopolar cortex as one of several brain regions parametrically modulated by plan- ning demand. Lastly, I summarize the findings from these studies and how they all come to provide different insights into our remarkable ability to flexibly adapt to naviga- tional challenges in our environment