Search

Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as "flickering" in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy. [ABSTRACT FROM AUTHOR]

Topography and the computational mesh grid are fundamental inputs to all two‐dimensional (2D) hydrodynamic models, however their resolutions are often arbitrarily selected based on data availability. With the increasing use of drone technology, the end user can collect topographic data down to centimeter‐scale resolution. With this advancement comes the responsibility of choosing a resolution. In this study, we investigated how the choice of mesh grid and digital elevation model (DEM) resolutions affect 2D hydrodynamic modeling results, specifically water depths, velocities, and inundation extent. We made pairwise comparisons between simulations from a 2D HEC‐RAS model with varying mesh grid resolutions (1 and 2 m) and drone‐based lidar DEM resolutions (0.1, 0.25, 0.5, 1, and 2 m) over a 1.5 km reach of Stroubles Creek in Blacksburg, Virginia. The model was rerun for up to ±4% change in floodplain roughness to determine how the DEM and mesh grid changes relate to an equivalent change in roughness. We found that the modeled differences from resolution change were equivalent to altering floodplain roughness by up to 12% for depths and 44% for velocities. The largest differences in velocity were concentrated at the channel‐floodplain interface, whereas differences in depth occurred laterally throughout the floodplain and were not correlated with lidar ground point density. We also found that the inundation boundary is dependent on the DEM resolution. Our results suggest that modelers should carefully consider what resolution best represents the terrain while also resolving important riparian topographic features. Key Points: Two‐dimensional hydrodynamic model results are affected by digital elevation model and computational mesh grid resolutionsResolution changes are equivalent to altering floodplain roughness by up to 12 percent for depths and 44 percent for velocitiesModeled inundation boundary is dependent on digital elevation model resolution [ABSTRACT FROM AUTHOR]

Game-based Web sites and applications are changing the ways in which students learn the world map. In this study, a Web map-based digital learning tool was used as a study aid for a university-level geography course in order to examine the way in which global scale cognitive maps are constructed. A network analysis revealed that clicks were negatively correlated with familiarity, but were positively correlated with area. More significantly, well-known countries did not act as central nodes within network-based mental models, but served a more complex mediating role in structuring the space between subregions.

This study presents a method to assess the contributions of 21st-century sea-level rise and groundwater extraction to sea water intrusion in coastal aquifers. Sea water intrusion is represented by the landward advance of the 10,000 mg/L iso-salinity line, a concentration of dissolved salts that renders groundwater unsuitable for human use. A mathematical formulation of the resolution of sea water intrusion among its causes was quantified via numerical simulation under scenarios of change in groundwater extraction and sea-level rise in the 21st century. The developed method is illustrated with simulations of sea water intrusion in the Seaside Area sub-basin near the City of Monterey, California (USA), where predictions of mean sea-level rise through the early 21st century range from 0.10 to 0.90 m due to increasing global mean surface temperature. The modeling simulation was carried out with a state-of-the-art numerical model that accounts for the effects of salinity on groundwater density and can approximate hydrostratigraphic geometry closely. Simulations of sea water intrusion corresponding to various combinations of groundwater extraction and sea-level rise established that groundwater extraction is the predominant driver of sea water intrusion in the study aquifer. The method presented in this work is applicable to coastal aquifers under a variety of other scenarios of change not considered in this work. For example, one could resolve what changes in groundwater extraction and/or sea level would cause specified levels of groundwater salinization at strategic locations and times.

The California Department of Water Resources (2006) estimated a rise in mean sea level along California’s coastline ranging from 10 to 90 cm over the 21st century due to rising global mean surface temperature. This range of sea-level rise is consistent with the Intergovernmental Panel for Climate Change (2007) estimates. The rise in sea level threatens coastal aquifers by exacerbating the risk of saline intrusion. This study simulated the effect of sea-level rise on the Seaside Area sub-basin near the City of Monterey, California. The simulation was carried out with a state-of-art, finite-element, variable-density, numerical model that accounts for the effects of salinity on groundwater density and viscosity. Seawater intrusion was simulated for various scenarios of sea-level rise, varying from 0 m to 1 m assuming a linear increase of sea level through the 21st century. Each scenario contemplated the same level of predicted groundwater extraction through the 21st century in the study aquifers. The numerical simulations of seawater intrusion indicate that one meter of sea-level rise would contribute an additional 10 to 15 meters of inland spread of the 1,000 mg/L saline front and 20 to 30 meters of the 10,000 mg/L saline front. The effect of sea-level rise on seawater intrusion in the Seaside Area sub-basin, therefore, appears minor when compared with historical measurements of seawater intrusion caused primarily by groundwater pumping since the early 1900s. Other aquifers with less topographical relief and more complex hydrostratigraphy could be more vulnerable to sea-level rise, however. One such possibility is posed by the Oxnard Plain groundwater sub-basin, in Ventura County, California. This study compiled a hydrogeologic database and structured the basic elements of a sea-water intrusion numerical simulation model for the Oxnard Plain aquifer. The Oxnard Plain sub-basin is a complex, multi-formation, aquifer that has undergone several decades of groundwater extraction, and, which is known to experience seawater intrusion in several of its coastal areas. The Oxnard Plain sub-basin features an important offshore hydrogeologic section that encompasses its boundary under seawater. Time limitations prevented calibration and validation of the numerical simulation model for seawater intrusion in the Oxnard Plain sub-basin. Nevertheless, the elements needed to complete the Oxnard Plain sub-basin’s seawater-intrusion simulation model in the near future (with additional funding) have been assembled and are presented in this report. The approach presented in this report for the relatively assessment of groundwater extraction and sea-level rise effects on seawater intrusion into coastal aquifers holds potential for wide-ranging applicability in a variety of hydrogeologic settings. In particular, the finite-element spatial grid provides distinct capabilities to represent accurately the geographical layout of an aquifer.

Slope exerts a powerful influence on the route selection processes of humans. Attempts to model human movement in hilly and mountainous terrain which have largely focused on least-time route transformations can be improved by incorporating research suggesting that humans systematically overestimate slopes. Such research suggests that cost functions derived from slope should be more expensive than time derivations alone would indicate. This paper presents a method for empirically estimating cost functions for slopes. We use the method to predict routes and paths that are more likely to be selected by humans based on their perceptions of slope. An evaluation of the method found that it successfully predicts road, track and trail locations over a variety of conditions and distances. KEYWORDS: Least-cost path, friction surface, route choice, cognitive slope, hiking function, Introduction Route selection is one of the most basic and ubiquitous problems faced by humans, yet one of the primary influences on long-distance travel--overcoming slope--is not well considered. When people [...]

We present the results of an experimental comparison of existing and new methods to estimate tree height from LiDAR data and to subsequently estimate tree diameter allometrically from species and height. Ground truth of tree height was established using a LaserAce Rangefinder to directly measure the height and diameter of a sample of 952 parkway trees. Based on this sample, it was possible to directly calculate the accuracy and error budget for the LiDAR and allometry-derived metrics. The results can inform the optimal estimation techniques and expectations of accuracy for biogeographers studying urban forests. [ABSTRACT FROM AUTHOR]

Indoor spaces are essential to most humans' lives. Furthermore, in many cases, buildings are shared indoor environments that contain diverse people and resources. Spatial patterns of use are important but under‐examined aspects of human‐building interactions. This study leverages perspectives from human‐environment geography and mechanical engineering to examine spatial patterns of use within a network of shared indoor spaces in an academic building at a research university in the United States. Here we ask: (1) What spaces and resources do building users value? and (2) How are values associated with observed measures of use? We hypothesise that spatial patterns of use follow an ideal free distribution (IFD), a common ecological model of resource use. To test this, we define measures of value and use derived from mixed qualitative (n = 50) and survey‐based social data (n = 196) and data from a building‐based system of accelerometers. Our analyses provide some support for the IFD hypothesis. We discuss the implications of this finding and potential new avenues for geographic research in shared indoor environments. [ABSTRACT FROM AUTHOR]