GPS driving: a digital biomarker for preclinical Alzheimer disease

Background Alzheimer disease (AD) is the most common cause of dementia. Preclinical AD is the period during which early AD brain changes are present but cognitive symptoms have not yet manifest. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. However, the use of these methods is limited by cost, acceptability, and availability. The preclinical stage of AD may have a subtle functional signature, which can impact complex behaviours such as driving. The objective of the present study was to evaluate the ability of in-vehicle GPS data loggers to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD using machine learning methods. Methods We followed naturalistic driving in cognitively normal older drivers for 1 year with a commercial in-vehicle GPS data logger. The cohort included n = 64 individuals with and n = 75 without preclinical AD, as determined by cerebrospinal fluid biomarkers. Four Random Forest (RF) models were trained to detect preclinical AD. RF Gini index was used to identify the strongest predictors of preclinical AD. Results The F1 score of the RF models for identifying preclinical AD was 0.85 using APOE ε4 status and age only, 0.82 using GPS-based driving indicators only, 0.88 using age and driving indicators, and 0.91 using age, APOE ε4 status, and driving. The area under the receiver operating curve for the final model was 0.96. Conclusion The findings suggest that GPS driving may serve as an effective and accurate digital biomarker for identifying preclinical AD among older adults.

The Utility of GIS for Identifying Areas of Recreation Conflict for Targeted Interpretive Messaging

Past research has extensively studied interpretive messaging and visitor conflict within parks and protected areas. However, comprehensive understanding of how to identify trailside interpretive sign locations is lacking. The purpose of this study was to introduce an approach using geographic information systems (GIS) that supplements decision-making regarding sign placement. The study site was Grand Canyon National Park’s Rim to Rim (R2R) corridor. To identify sign placement locations, two analytical phases were conducted. First, GPS data loggers were distributed to visitors and their travel patterns were analyzed for spatial behaviors and spatial interactions that are known to influence the likelihood of recreation conflict. Specifically, locations with a high variance of visitor travel speeds and locations with concentrated visitor use were identified. Second, geographic data were analyzed to identify locations for a combination of features that together influence the likelihood of recreation conflict. Specifically, popular bidirectional trail segments with significant elevation change were identified. We reported these locations and areas using GPS coordinates for evaluation by future research. This research was a necessary step towards comprehensively understanding how to identify locations for interpretive signs.

Visual analytics of sensor movement data for cheetah behaviour analysis

Current tracking technology such as GPS data loggers allows biologists to remotely collect large amounts of movement data for a large variety of species. Extending, and often replacing interpretation based on observation, the analysis of the collected data supports research on animal behaviour, on impact factors such as climate change and human intervention on the globe, as well as on conservation programs. However, this analysis is difficult, due to the nature of the research questions and the complexity of the data sets. It requires both automated analysis, for example, for the detection of behavioural patterns, and human inspection, for example, for interpretation, inclusion of previous knowledge, and for conclusions on future actions and decision making. For this analysis and inspection, the movement data needs to be put into the context of environmental data, which helps to interpret the behaviour. Thus, a major challenge is to design and develop methods and intuitive interfaces that integrate the data for analysis by biologists. We present a concept and implementation for the visual analysis of cheetah movement data in a web-based fashion that allows usage both in the field and in office environments. Graphic abstract

Black kites of different age and sex show similar avoidance responses to wind turbines during migration

Populations of soaring birds are often impacted by wind-power generation. Sex and age bias in turbine collisions can exacerbate these impacts through demographic changes that can lead to population decline or collapse. While several studies have reported sex and age differences in the number of soaring birds killed by turbines, it remains unclear if they result from different abundances or group-specific turbine avoidance behaviours, the latter having severer consequences. We investigated sex and age effects on turbine avoidance behaviour of black kites ( Milvus migrans ) during migration near the Strait of Gibraltar. We tracked the movements of 135 individuals with GPS data loggers in an area with high density of turbines and then modelled the effect of proximity of turbines on bird utilization distribution (UD). Both sexes and age classes showed similar patterns of displacement, with reduced UD values in the proximity of turbines and a clear peak at 700–850 m away, probably marking the distance at which most birds turn direction to avoid approaching the turbines further. The consistency of these patterns indicates that displacement range can be used as an accurate proxy for collision risk and habitat loss, and should be incorporated in environmental impact assessment studies.

Habitat use by wandering pet cats (Felis catus) in a patchy urban environment

Pet cats (Felis catus) often have negative effects on wildlife. This is of growing concern in urban areas as these are increasingly becoming hotspots of native wildlife activity, and as the human population increases, so too does the pet cat population. To maintain biodiversity in urban areas, further knowledge on pet cat behaviour and impacts is required so that management strategies for pet cats are well informed and have public and government support. Here, we offer insights into the wandering activity of pet cats in a patchy urban—heavily vegetated landscape on the east coast of Australia. Our estimated pet cat movement ranges were generally larger than those previously observed in similar landscapes, as well as in more urbanized and rural habitats. Using GPS data loggers, we found that pet cats did not utilize vegetated spaces more than urban areas, nor did they prefer them relative to their availability. Half of our study cats selected urban habitats, whilst the other half displayed no selection or a slight preference for vegetated spaces; these cats had fewer barriers to overcome to reach them. We did not observe any large differences in movements or habitat use between day and night, but displacement distances and preference for vegetated space habitat were marginally lower at night. All pet cats monitored spent most of their time outside their houses. As both urban and vegetated spaces in patchy urban landscapes provide habitat for native wildlife, pet cat activity across both habitat types requires management action.

Description of Behavioral Patterns Displayed by a Recently Weaned Cohort of Healthy Dairy Calves

Animals display movement patterns that can be used as health indicators. The movement of dairy cattle can be characterized into three distinct cluster types. These are cluster type 1 (resting), cluster type 2 (traveling), and cluster type 3 (searching). This study aimed to analyze the movement patterns of healthy calves and assess the relationship between the variables that constitute the three cluster types. Eleven Holstein calves were fitted with GPS data loggers, which recorded their movement over a two week period during spring. The GPS data loggers captured longitude and latitude coordinates, distance, time and speed. It was found that the calves were most active during the afternoon and at night. Slight inconsistencies from previous studies were found in the cluster movements. Cluster type 2 (traveling) reported the fastest rate of movement, whereas cluster type 1 (resting) reported the slowest. These diverse movement patterns could be used to enhance the assessment of dairy animal health and welfare on farms.

An analysis of movement patterns in mass casualty incident simulations

Background Mass casualty incidents (MCI) such as train or bus crashes, explosions, collapses of buildings, or terrorist attacks result in rescue teams facing many victims and in huge challenges for hospitals. Simulations are performed to optimize preparedness for MCI. To maximize the benefits of MCI simulations, it is important to collect large amounts of information. However, a clear concept and standardization of a data-driven post-exercise evaluation and debriefing are currently lacking. Methods GPS data loggers were used to track the trajectories of patients, medics, and paramedics in two simulated MCI scenarios using real human actors. The distribution of patients over the treatment area and their time of arrival at the hospital were estimated to provide information on the quality of triage and for debriefing purposes. Results The results show the order in which patients have been treated and the time for the individual arrivals as an indicator for the triage performance. The distribution of patients at the accident area suggested initial confusion and unclear orders for the placement of patients with different grades of injury that can be used for post-exercise debriefing. The dynamics of movement directions allowed to detect group behavior during different phases of the MCI. Conclusions Results indicate that GPS data loggers can be used to collect precise information about the trajectories of patients and rescue teams at an MCI simulation without interfering with the realism of the simulation. The exact sequence of the deliverance of patients of different triage categories to their appropriate destinations can be used to evaluate team performance for post-exercise debriefing. Future MCI simulations are planned to validate the use of GPS loggers by providing “hot-debrief” immediately after the MCI simulation and to explore ways in which group detection can provide relevant information for post-exercise evaluations Trial registration Not applicable.

“Seeing” or “Being Seen”: Research on the Sight Line Design in the Lion Grove Based on Visitor Temporal–Spatial Distribution and Space Syntax

Research on the sight line design of the Classical Chinese Garden (CCG) is an important issue of CCGs’ sustainable development. Taking the Lion Grove as a case, GPS data loggers and questionnaires were employed to collect visitor temporal–spatial data and visiting motivations. We then calculated the “Revisiting Proportion” and “Average Speed” values. Furthermore, we selected the “Visual Control” values analyzed by Depthmap as an indicator of visibility. The statistical analysis of the relationship among “Revisiting Proportion”, “Average Speed”, and “Visual Control” values of each space showed that the spatial visual characteristic affected the visitor temporal–spatial distribution. Scenery spots in and around the large water pool, within one-step visual depth of each other, occupying the visual advantage of both “seeing” and “being seen”, can facilitate the transformation of sight lines and form the visual effect of “one step, one scene”. This research also proved that the sight line design of the Lion Grove was more intentional than random.