Abstract When people explore new environments they often use landmarks as reference points to help navigate and orientate themselves. This research paper examines how spatial datasets can be used to build a system for use in an urban environment which functions as a city guide, announcing Features of Interest (FoI) as they become visible to the user (not just proximal), as the user moves freely around the city. Visibility calculations for the FoIs were pre‐calculated based on a digital surface model derived from LIDAR (Light Detection and Ranging) data. The results were stored in a text‐based relational database management system (RDBMS) for rapid retrieval. All interaction between the user and the system was via a speech‐based interface, allowing the user to record and request further information on any of the announced FoI. A prototype system, called Edinburgh Augmented Reality System (EARS) , was designed, implemented and field tested in order to assess the effectiveness of these ideas. The application proved to be an innovative, ‘non‐invasive’ approach to augmenting the user's reality.
The COVID-19 pandemic led to widespread repercussions, affecting all aspects of society, from global economics to everyday social interactions. Due to the significant uncertainty caused by the pandemic, many individuals sought solace from nature. Freshwater environments, or inland blue spaces, are one type of natural environment that may have acted as a vital public health resource for communities during the pandemic. This research used semi-structured interviews combined with narrative analysis to capture detailed insight into the impact of, and nuanced benefits and challenges associated with, accessing inland blue spaces over the course of the COVID-19 pandemic. Participants from a range of backgrounds across Scotland were involved to determine the influence of their health and 'shielding' status on inland blue space experiences. In the initial stages of the pandemic, those who were taking shielding precautions described experiencing a heightened awareness of, and anxiety towards, other users of inland blue spaces. However, across the sample, individuals emphasised the overall beneficial impact of accessing freshwater areas for maintaining mental and physical wellbeing levels during the pandemic. Positive health outcomes were achieved through participating in a wide range of leisure and recreational opportunities at inland blue spaces. The research further justifies the value of accessing inland blue spaces and demonstrates the benefits of integrating access and exposure to natural environments into future pandemic response strategies. The qualitative insight also highlights the need for context-specific landscape management strategies to promote blue space access across user groups and address existing environmental inequalities.
Visual exposure modelling establishes the extent to which a nominated feature may be seen from a specified location. The advent of high-resolution light detection and ranging (LiDAR)-sourced elevation models has enabled visual exposure modelling to be applied in urban regions, for example, to calculate the field of view occupied by a landmark building when observed from a nearby street. Currently, visual exposure models access a single surface elevation model to establish the lines of sight (LoSs) between the observer and the landmark feature. This is a cause for concern in vegetated areas where trees are represented as solid protrusions in the surface model totally blocking the LoSs. Additionally, the observer's elevation, as read from the surface model, would be incorrectly set to the tree top height in those regions. The research presented here overcomes these issues by introducing a new visual exposure model, which accesses a bare earth terrain model, to establish the observer's true elevation even when passing through vegetated regions, a surface model for the city profile and an additional vegetation map. Where there is a difference between terrain and surface elevations, the vegetation map is consulted. In vegetated areas the LoS is permitted to continue its journey, either passing under the canopy with clear views or partially through it depending on foliage density, otherwise the LoS is terminated. This approach enables landmark visual exposure to be modelled more realistically, with consideration given to urban trees. The model's improvements are demonstrated through a number of real-world trials and compared to current visual exposure methods.
Effective management of diffuse microbial water pollution from agriculture requires a fundamental understanding of how spatial patterns of microbial pollutants, e.g. E. coli, vary over time at the landscape scale. The aim of this study was to apply the Visualising Pathogen & Environmental Risk (ViPER) model, developed to predict E. coli burden on agricultural land, in a spatially distributed manner to two contrasting catchments in order to map and understand changes in E. coli burden contributed to land from grazing livestock. The model was applied to the River Ayr and Lunan Water catchments, with significant correlations observed between area of improved grassland and the maximum total E. coli per 1 km2 grid cell (Ayr: r = 0.57; p < 0.001, Lunan: r = 0.32; p < 0.001). There was a significant difference in the predicted maximum E. coli burden between seasons in both catchments, with summer and autumn predicted to accrue higher E. coli contributions relative to spring and winter (P < 0.001), driven largely by livestock presence. The ViPER model thus describes, at the landscape scale, spatial nuances in the vulnerability of E. coli loading to land as driven by stocking density and livestock grazing regimes. Resulting risk maps therefore provide the underpinning evidence to inform spatially-targeted decision-making with respect to managing sources of E. coli in agricultural environments.
Inland blue spaces, or freshwater environments, have been shown to provide people with positive mental health and wellbeing outcomes. Most inland blue space research focusing on wellbeing outcomes has so far been cross-sectional, utilising questionnaires and interviews. Therefore, there is significant uncertainty regarding the potential for inland waterways to benefit human populations over longer-term time scales. Across a sixteen-month data collection period, this study recruited four distinct sample groups to complete diaries for periods of three-months, focusing on inland blue space experiences in Scotland. The aim of the study was to use solicited diary methods to establish whether restorative exposure outcomes gained from visiting blue spaces may vary across time. Results from the diary data show that visiting freshwater areas consistently led to positive restorative outcomes, with minimal variation in restorative outcomes observed across time. Participants recorded three principal categories of blue space experiences; routine visits, getting fresh air; and day trips, with each visit type providing a range of restorative benefits. The findings highlight the potential for inland blue spaces to act as versatile public health resources and the need to consider long-term strategies to ensure these environments benefit populations across time.
Visibility modelling calculates what an observer could theoretically see in the surrounding region based on a digital model of the landscape. In some cases, it is not necessary, nor desirable, to compute the visibility of an entire region (i.e. a viewshed), but instead it is sufficient and more efficient to calculate the visibility from point to point, or from a point to a small set of points, such as computing the intervisibility of predators and prey in an agent-based simulation. This paper explores how different line-of-sight (LoS) sample ordering strategies increase the number of early target rejections, where the target is considered to be obscured from view, thereby improving the computational efficiency of the LoS algorithm. This is of particular importance in dynamic environments where the locations of the observers, targets and other surface objects are being frequently updated. Trials were conducted in three UK cities, demonstrating a robust fivefold increase in performance for two strategies (hop, divide and conquer). The paper concludes that sample ordering methods do impact overall efficiency, and that approaches which disperse samples along the LoS perform better in urban regions than incremental scan methods. The divide and conquer method minimises elevation interception queries, making it suitable when elevation models are held on disk rather than in memory, while the hopping strategy was equally fast, algorithmically simpler, with minimal overhead for visible target cases.
Fracking has proven to be a contentious issue in Great Britain, receiving wide press coverage from the initial sale of exploration and development licences, to the current moratorium. This research tracks the public activity online related to this 'fracking' journey by analysing over 317 million geolocated tweets from 2015 to 2020, mapping their location to compare the spatial distribution against the shale gas exploration sites. To spatially normalise the results for population density a χ-squared expectation surface was generated revealing higher than expected levels of interest near the previously active fracking site of Preston New Road and licenced extraction blocks in Lancashire. The data granularity allows for peaks of activity to be identified and topics analysed at higher temporal and spatial resolution than previously possible with more traditional surveys. The paper demonstrates the use of χ-squared expectation surfaces for normalising geotweets and the value of social media spatial-temporal analysis for monitoring local involvement in environmental issues, and for monitoring the changing level of interest across different regions in reaction to political decisions.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(72 0 0)'/%3e%3cuse xlink:href='%23a' transform='rotate(-72 0 0)'/%3e%3cuse xlink:href='%23a' transform='scale(-1 1) rotate(72)'/%3e%3c/g%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h1200v600H0z'/%3e%3cpath stroke='%23FFF' stroke-width='60' d='m0 0 600 300M0 300 600 0' clip-path='url(%23b)'/%3e%3cpath stroke='%23C8102E' stroke-width='40' d='m0 0 600 300M0 300 600 0' clip-path='url(%23c)'/%3e%3cpath stroke='%23FFF' stroke-width='100' d='M300 0v300M0 150h600' clip-path='url(%23b)'/%3e%3cpath stroke='%23C8102E' stroke-width='60' d='M300 0v300M0 150h600' clip-path='url(%23b)'/%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='matrix(45.4 0 0 45.4 900 120)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='matrix(30 0 0 30 900 120)'/%3e%3cg transform='rotate(82 900 240)'%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='rotate(-82 519.022 -457.666) scale(40.4)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='rotate(-82 519.022 -457.666) scale(25)'/%3e%3c/g%3e%3cg transform='rotate(82 900 240)'%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='rotate(-82 668.57 -327.666) scale(45.4)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='rotate(-82 668.57 -327.666) scale(30)'/%3e%3c/g%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='matrix(50.4 0 0 50.4 900 480)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='matrix(35 0 0 35 900 480)'/%3e%3c/svg%3e)