Descripción de los esquemas maladaptativos

Standing in Trafalgar Square (London) it is possible to see Nelson‟s column, The National Gallery, and many statues and fountains. The space is largely defined by the visual field; which icons can be seen, their relative locations and how clearly they are in view. These landmarks are prominent features because of their visual, semantic or architectural attraction (Raubal and Winter 2002), and form useful reference points (Millonig and Schechtner 2007) which assist in building a mental model of the region useful for navigation and exploration (Fisher-Gewirtzman and Wagner 2003). Currently Location Based Services (LBS) under-use landmark visibility modelling (May et al. 2005), and rely on more simplistic proximity filters. However by introducing visibility modelling a LBS would be able to more closely match the user‟s experience and determine the impact of landmarks within the visual field. This chapter presents a novel method for quantifying landmark visibility with the eventual aim of providing the foundation for natural interaction with the user that aligns with their mental models, and matches their visual experiences.

The advent of high resolution Light Detection and Ranging (LiDAR) sourced terrain models, which capture building form and topography, has extended the scope of visibility modelling applications. LBS is one area able to benefit, these applications determine relevant information by integrating the user‟s location with other datasets (Spiekermann 2004). This definition includes mobile phone applications able to assist the user in locating nearby entities (e.g. banks, restaurants, friends), in-car navigational devices, historical city guides, location based games,

35 virtual fitness training partners, and mobile commerce (e.g. customised local advertising or targeted discount coupons).

Typically filtering is performed using syntax matching and feature proximity as search inputs, however more relevant results may be found when the user‟s context is also considered, such as the time of day, or current weather conditions (Yu et al. 2005). The incorporation of visibility analysis allows the LBS application to model the user‟s environment more closely and determine which surrounding features are in view, and therefore supply more meaningful assistance. For example landmarks have been recognised as useful navigational points (Millonig and Schechtner 2007), and by modelling their visibility an LBS may announce them as they come into view (Elias and Brenner 2004), leading to more natural navigation narratives.

While most Geographic Information Systems (GIS) provide facilities to calculate the extent of visible space from a given point (De Smith et al. 2007), they do not provide the functionality to model the visibility of a landmark across space. This is addressed in the concept of visual exposure presented by Llobera (2003) by modelling the field of view occupied by a landmark from any surrounding location. This concept was further developed through the introduction of a number of additional metrics to quantify the visibility of landmark buildings within the urban environment (Bartie et al. 2008). This chapter continues that work through synthetic and real world trials, resulting in improvements to five of the visibility metrics, each described and demonstrated in the urban context. These metrics are designed with the intent that they may be used in future LBSs, such that consideration is given to contextual information regarding the user‟s situation, their viewpoint and the relevance of surrounding landmarks. In so doing announcements may be made in order of landmark visual prominence, or by referring to particular visual attributes (e.g. „the large tower on the skyline‟). The metrics may be used to

establish, from standard GIS datasets, how much of a feature is visible given in terms of the surface area exposed, or how large an object appears when considering the viewing distance. Such metrics would permit a LBS to establish the visual dominance of a feature, so for example features receive attention appropriate to their perceived size. Calculations of landmark clarity are also possible by considering the location of visual blockades, enabling the use of terminology such as “very clear” or “mostly hidden” to be introduced into narratives. Also it is possible to find the highest and lowest visible points on a feature, and to calculate how much appears on the skyline.

Incorporating visibility analysis means LBSs are able to more closely model the surrounding urban form and user‟s context so that results may be sorted according to their visual relevance and features which are out of sight may be disregarded. To do this, visibility modelling at feature level (i.e. landmark buildings) needs to be developed further. This chapter presents a modified LOS algorithm (Section 3.4) able to establish a wide range of visibility metrics for nominated features (Section 3.5), with the ability to map which parts of a feature are visible. The algorithm

36 was tested with a number of synthetic trials (Section 3.6) before exploring its use in the real world (Section 3.7).

3.3

Background

Previous research on visibility has been divided into urban and rural disciplines. Studies in the urban landscape have tended to be based on isovists (Tandy 1967), using in particular Benedikt‟s (1979) interpretation and definitions. Essentially 2D isovists describe the space which is visible from a vantage point as a closed polygon. Consideration is given to the form of the built environment through the use of architectural plans which denote the building footprint and position, however building height is ignored. The topography of the land surface is disregarded, as is the continuation of the lines of sight beyond the first intersection with a building footprint. Therefore isovists depict lines which when traversed from the vantage point offer a continuous view of the target, and disregard more distant features. More recently there have been developments in 3D Isovist theory (Morello and Ratti 2009), which access a Digital Elevation Model (DEM) to consider building form and topography. These may be used to describe the space around an observer, using metrics such as openness, and maximum viewing distance.

In the rural context, viewsheds have been used to show regions which are visible from a vantage point (Tandy 1967, Lynch 1976). These are created by calculating which cells of a DEM are visible from an observation point, using a line of sight (LOS) algorithm. They are also used in military and tourist applications for finding the most hidden route, or those with the best views (Lee and Stucky 1998). More complex forms include complete intervisibility databases (Caldwell et al. 2003) able to report how often a region is visible from the surrounding space, and visibility graphs (O'Sullivan and Turner 2001, Turner et al. 2001) which describe the pattern of mutually visible regions in a landscape.

However to model a feature from its surroundings, as required by an LBS, an alternative model based on calculating „visual exposure‟ (Llobera 2003) is used. This estimates how much of a feature can be viewed from the surrounding space, enabling the creation of surfaces to show in which direction an observer would need to move to view the target more, or less, clearly. In Llobera‟s work visual exposure is a measure of the horizontal and vertical fields of view occupied by an object from the surrounding locations. This technique can be used to find visual corridors, or visual ridges, and forms a useful basis for considering feature visibility in the context of LBS.

The model presented in this chapter builds on the visual exposure model, establishing a range of visual metrics able to describe the visibility of a nominated Feature of Interest (FOI). This differs from the 3D Isovist approach which quantifies the space around the observer, as here the attention is on how much of a target feature is visible. The visual exposure model is developed

37 through extending the basic LOS algorithm to provide more information on how much, and which parts, of a feature are visible. Groups of cells within a defined boundary are treated as single objects, enabling the visible exposure of entire features to be modelled. The calculations are based on a Digital Surface Model (DSM) generated from a LiDAR dataset, able to capture high resolution height values including topography, vegetation and building profiles. These have been shown to be suitable for such applications in the urban environment in previous visibility studies (Palmer and Shan 2002, Bartie and Mackaness 2006). LiDAR models were also used to good effect in a navigation application by Elias and Brenner (2004) to assess the visibility of landmarks. The approach they adopted was to render perspective views of scenes, with each landmark drawn in a unique single colour so that simple image processing techniques could be used to quantify the visibility of each building. The technique benefits from being able to use the graphics card‟s processing power, but is limited by the range of metrics available and integration with existing spatial algorithms. Also the results are generated at a feature level and it is not possible to drill down into the raw data to determine which individual parts of a feature are visible, for example if the entrance to a building is visible. It was for these reasons a new model was developed able to report the visual exposure for FOIs.