GPS Location Data Research Articles

Behavioral adaptations of a large carnivore to human activity in an extremely arid landscape

AbstractDriven by the availability of food subsidies and landscape transformation, large carnivore populations are increasingly inhabiting the vicinity of humans. To persist in human proximity, while avoiding conflict and mortality, they must make adjustments in their spatial behavior. Understanding such behavioral adaptations can be crucial to formulate conservation strategies. We used GPS location data to study the movement and space use of protected Arabian wolves in the extremely arid Negev Desert (Israel), where natural prey densities are reduced. We examined wolf preference for the proximity of human infrastructure, while testing the hypothesis that wolves would use areas where ungulates were more likely to occur. Wolves showed a strong spatial association with humans, spending a large proportion of their time in proximity of human infrastructure and moving primarily along roads. Wolves also increased their movement during the night. In addition, wolves that had higher availability of human infrastructure in their home ranges showed increased selection for such features, suggesting a positive functional response. Finally, wolves did not show preference for areas of high ungulate occurrence at either spatial scale. These findings, combined with recent diet analyses, indicate that wolves rely on human subsidies, proportioned by the availability of garbage and agricultural produce. Our results demonstrate that wolves fine‐tune their space use to persist in the vicinity of humans. Additional conditions that enable such persistence include effective legal protection and positive local attitudes toward wolves. We conclude that such circumstances can promote spatial coexistence of humans and large carnivores. The strong observed spatial association and evident dietary dependence of wolves on subsidies from a low‐density human population may also reflect the conditions that led to dog domestication.

Utilization analysis methodology for fleet telematics of heavy earthwork equipment

Abstract Fleet telematics aims to comprehensively control equipment groups through location tracking, maintenance control, and productivity monitoring of individual equipment based on real-time location and status transmitted to fleet control center from the equipment. Logistics industry, automobile industry, as well as transportation (land, sea, and air) industry are actively applying fleet telematics in order to efficiently and safely operate multiple groups of equipment. Earthwork, a basic operation for any type of construction, depends heavily on equipment. The productivity and safety of earthwork equipment are decided by effective management of the equipment. Therefore, fleet telematics can also improve the earthwork operation as well. The goal of this research is to develop a GPS only-based fleet telematics system for heavy earthwork equipment which can analyze time log information of each equipment using GPS location data without utilizing any other on-board sensors and CAN bus data. The earthwork site (loading and dumping area) and moving route, hazardous areas of the project are all visualized on a map via geo-fencing technology considering the characteristics of earthwork operation. Lastly, algorithms to analyze the utilization time of earthwork equipment using GPS location information are designed and their reliability are verified through field tests.

Hunger mediates apex predator's risk avoidance response in wildland-urban interface.

Conflicts between large mammalian predators and humans present a challenge to conservation efforts, as these events drive human attitudes and policies concerning predator species. Unfortunately, generalities portrayed in many empirical carnivore landscape selection studies do not provide an explanation for a predator's occasional use of residential development preceding a carnivore-human conflict event. In some cases, predators may perceive residential development as a risk-reward trade-off. We examine whether state-dependent mortality risk-sensitive foraging can explain an apex carnivore's (Puma concolor) occasional utilization of residential areas. We assess whether puma balance the risk and rewards in a system characterized by a gradient of housing densities ranging from wildland to suburban. Puma GPS location data, characterized as hunting and feeding locations, were used to assess landscape variables governing hunting success and hunting site selection. Hunting site selection behaviour was then analysed conditional on indicators of hunger state. Residential development provided a high energetic reward to puma based on increases in prey availability and hunting success rates associated with increased housing density. Despite a higher energetic reward, hunting site selection analysis indicated that pumas generally avoided residential development, a landscape type attributed with higher puma mortality risk. However, when a puma experienced periods of extended hunger, risk avoidance behaviour towards housing waned. This study demonstrates that an apex carnivore faces a trade-off between acquiring energetic rewards and avoiding risks associated with human housing. Periods of hunger can help explain an apex predator's occasional use of developed landscapes and thus the rare conflicts in the wildland-urban interface. Apex carnivore movement behaviours in relation to human conflicts are best understood as a three-player community-level interaction incorporating wild prey distribution.

Drifting space use of common cranes—Is there a mismatch between daytime behaviour and management?

Many populations of large grazing birds (cranes, geese, swans) have recovered following protection. During migration, these birds often aggregate in large numbers at staging sites and feed on agricultural crops. Because staging sites often coincide with protected wetlands, extensive crop damages may avert both bird and wetland conservation. There is a need to integrate damage mitigation and conservation of large grazing birds staging in agricultural landscapes, based on knowledge of large grazing birds’ spacing behavior. Their space use forms the basis for assessment of damage risk and for the scale at which measures should be implemented. We used high-resolution GPS location data to assess space use of common cranes (Grus grus) at an important staging site in south-central Sweden. We focus on daytime behaviour because this is the time when foraging cranes may cause crop damage and when preventive measures such as scaring and culling are conducted. We found that the daily activity area (mean 4.4km2) did not vary within staging periods. Cranes exhibited high site fidelity during staging, as their activity area over the staging period (mean 15.6km2) was considerably smaller than the entire staging site (>200km2). However, on a daily scale cranes gradually shifted activity areas, forming a space use pattern analogous to overlapping rings. This pattern is presumably explained by heterogeneous and unpredictable food availability caused by continuous changes in agricultural practices, weather conditions and competition. Considering the size of crane activity areas over the staging period and the scale of current damage preventive measures (e.g., hunting permissions given for a few fields [mean size 0.049km2] at a time), we suggest that current preventive measures might be implemented on a too small scale compared to that of crane space use. Our findings highlight the necessity of adapting crop damage preventive measures to the scale of bird space use to facilitate both bird conservation and agricultural practices at wetlands staging sites along the flyways.

Seasonal and year-round use of the Kushiro Wetland, Hokkaido, Japan by sika deer (Cervus nippon yesoensis).

The sika deer (Cervus nippon yesoensis) population in the Ramsar-listed Kushiro Wetland has increased in recent years, and the Ministry of the Environment of Japan has decided to take measures to reduce the impact of deer on the ecosystem. However, seasonal movement patterns of the deer (i.e., when and where the deer inhabit the wetland) remain unclear. We examined the seasonal movement patterns of sika deer in the Kushiro Wetland from 2013 to 2015 by analyzing GPS location data for 28 hinds captured at three sites in the wetland. Seasonal movement patterns were quantitatively classified as seasonal migration, mixed, dispersal, nomadic, resident, or atypical, and the degree of wetland utilization for each individual was estimated. The area of overlap for each individual among intra-capture sites and inter-capture sites was calculated for the entire year and for each season. Our results showed that the movement patterns of these deer were classified not only as resident but also as seasonal migration, dispersal, and atypical. Approximately one-third of the individuals moved into and out of the wetland during the year as either seasonal migrants or individuals with atypical movement. Some of the individuals migrated to farmland areas outside the wetland (the farthest being 69.9 km away). Half of the individuals inhabited the wetland all or most of the year, i.e., 81–100% of their annual home range was within the wetland area. Even among individuals captured at the same site, different seasonal movement patterns were identified. The overlap areas of the home ranges of individuals from the same capture sites were larger than those for individuals from different capture sites (e.g., mean of annual home range overlap with intra-capture sites: 47.7% vs. inter-sites: 1.3%). To achieve more effective ecosystem management including deer management in the wetland, management plans should cover inside and outside of the wetland and separate the population into multiple management units to address the different movement patterns and wetland utilization of the population.

Residential development alters behavior, movement, and energetics in an apex predator, the puma.

Human development strongly influences large carnivore survival and persistence globally. Behavior changes are often the first measureable responses to human disturbances, and can have ramifications on animal populations and ecological communities. We investigated how a large carnivore responds to anthropogenic disturbances by measuring activity, movement behavior, and energetics in pumas along a housing density gradient. We used log-linear analyses to examine how habitat, time of day, and proximity to housing influenced the activity patterns of both male and female pumas in the Santa Cruz Mountains. We used spatial GPS location data in combination with Overall Dynamic Body Acceleration measurements recorded by onboard accelerometers to quantify how development density affected the average distances traveled and energy expended by pumas. Pumas responded to development differently depending on the time of day; at night, they were generally more active and moved further when they were in developed areas, but these relationships were not consistent during the day. Higher nighttime activity in developed areas increased daily caloric expenditure by 10.1% for females and 11.6% for males, resulting in increases of 3.4 and 4.0 deer prey required annually by females and males respectively. Our results support that pumas have higher energetic costs and resource requirements in human-dominated habitats due to human-induced behavioral change. Increased energetic costs for pumas are likely to have ramifications on prey species and exacerbate human-wildlife conflict, especially as exurban growth continues. Future conservation work should consider the consequences of behavioral shifts on animal energetics, individual fitness, and population viability.

How Well Can a User’s Location Privacy Preferences be Determined Without Using GPS Location Data?

The recent proliferation of GPS-enabled mobile phones has allowed people to share their current locations with others. Because disclosing one’s location can be valuable but risky, many services and studies employ a user’s GPS coordinates to determine automatically whether or not those coordinates can be disclosed by comparing them with handcrafted rules or privacy models trained using the user’s actual preferences. However, these approaches that employ GPS coordinates constitute a drain on a phone’s battery when the services assume continuous location sharing. In addition, recent positioning methods (assisted GPS and a WiFi-based positioning) rely on external location providers. That is, when a user’s current location preference is determined using her coordinate point, her location information is disclosed to external providers even if this is not her wish. In this paper, we explore a way of learning a user’s location privacy preference using sensors that are energy saving and that do not rely on external providers. This enables us to save energy and protect a user’s privacy when she is unwilling to disclose her location. Note that the machine learning based approach cannot deal well with a user’s private situations that are not included in its training data. So, this paper proposes a new model that can determine a user’s privacy preferences and handle such outlying situations.

Implications of fidelity and philopatry for the population structure of female black-tailed deer

Lay SummaryReturning to previously used areas (site fidelity) or place of birth (philopatry) are important behavioral adaptations in many species, with known fitness benefits. The combined population level consequences of site fidelity and philopatry, however, have received little attention. Based on GPS location data and mitochondrial DNA we demonstrate how high levels of fidelity and philopatric behavior in the matrilineage of black-tailed deer can lead to fine-scale population structure and discuss possible implications for population dynamics.

Space–time visualization analysis of bus passenger big data in Beijing

It is possible to quantify individual motion trajectories with the rapid development of sensor applications such as mobile positioning and wireless communication, and the characteristics like a large number, long time series and fine spatiotemporal granularity of GPS location data, bus IC data, and mobile phone data provide a hopeful premise for the study of human behavior. Based on a large amount of mobile information equipment, the effective mining of these data and the use of reasonable processing methods can make the processing results closer to reflect the actual human behavior patterns, and better serve the real traffic life. In this paper, by discussing the results of previous studies on human mobility, spatial interpolation method is used to discrete bus passenger flow obtained from big data of Beijing bus IC card into the continues area distribution, and we analyze the changed trend of passenger flow in Beijing of the whole day by utilizing the Spatial-temporal method. To a certain extent, the analysis of urban bus passenger distribution studied from Beijing bus IC data can understand the rules of human behavior and provide reliable data guidance for reasonable decision-making on Beijing passenger traffic planning, such like solving problem effectively that the number of bus passenger and the number of bus station does not match.

Covariate selection from telematics car driving data

Car insurance companies have started to collect high-frequency GPS location data of their car drivers. This data provides detailed information about the driving habits and driving styles of individual car drivers. We illustrate how this data can be analyzed using techniques from pattern recognition and machine learning. In particular, we describe how driving styles can be categorized so that they can be used for a regression analysis in car insurance pricing.

Canada lynx use of burned areas: Conservation implications of changing fire regimes.

A fundamental problem in ecology is forecasting how species will react to major disturbances. As the climate warms, large, frequent, and severe fires are restructuring forested landscapes at large spatial scales, with unknown impacts on imperilled predators. We use the United States federally Threatened Canada lynx as a case study to examine how predators navigate recent large burns, with particular focus on habitat features and the spatial configuration (e.g., distance to edge) that enabled lynx use of these transformed landscapes. We coupled GPS location data of lynx in Washington in an area with several recent large fires and a number of GIS layers of habitat data to develop models of lynx habitat selection in recent burns. Random Forest habitat models showed lynx‐selected islands of forest skipped by large fires, residual vegetation, and areas where some trees survived to use newly burned areas. Lynx used burned areas as early as 1 year postfire, which is much earlier than the 2–4 decades postfire previously thought for this predator. These findings are encouraging for predator persistence in the face of fires, but increasingly severe fires or management that reduces postfire residual trees or slow regeneration will likely jeopardize lynx and other predators. Fire management should change to ensure heterogeneity is retained within the footprint of large fires to enable viable predator populations as fire regimes worsen with climate change.

Soil color sensor data collection using a GPS-enabled smartphone application

Application of accurate and low-cost sensor technology to collect soil color data provides an opportunity to increase the density, quality and quantity of soil data to monitor our changing soil resources. The objective of this study was to develop a mobile application that would enable users to create their own soils database consisting of GPS location and soil color data gathered using the application and a mobile sensor. A mobile application was created utilizing the Nix™ Pro color sensor that produces multiple color results, including Munsell color notation. The application also allows users to toggle between “in-field” sampling as well as dry or moist soil samples. Users can choose to record GPS location and a photo of the soil sample to upload into an online database for storage. The application was tested for functionality in the field and for its ability to match Munsell notation values determined using a Munsell Soil Color Chart (MSCC). Field data were synchronized to a cloud database and subsequently retrieved and used to produce a Geographic Information Systems (GIS) layout showing sample point locations and soil color attributes. The Soil Scanner application allows for rapid analysis and collection of soils data that can be stored for further study and reference using various color systems and location data.

Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students.

BackgroundResearch in psychology demonstrates a strong link between state affect (moment-to-moment experiences of positive or negative emotionality) and trait affect (eg, relatively enduring depression and social anxiety symptoms), and a tendency to withdraw (eg, spending time at home). However, existing work is based almost exclusively on static, self-reported descriptions of emotions and behavior that limit generalizability. Despite adoption of increasingly sophisticated research designs and technology (eg, mobile sensing using a global positioning system [GPS]), little research has integrated these seemingly disparate forms of data to improve understanding of how emotional experiences in everyday life are associated with time spent at home, and whether this is influenced by depression or social anxiety symptoms.ObjectiveWe hypothesized that more time spent at home would be associated with more negative and less positive affect.MethodsWe recruited 72 undergraduate participants from a southeast university in the United States. We assessed depression and social anxiety symptoms using self-report instruments at baseline. An app (Sensus) installed on participants’ personal mobile phones repeatedly collected in situ self-reported state affect and GPS location data for up to 2 weeks. Time spent at home was a proxy for social isolation.ResultsWe tested separate models examining the relations between state affect and time spent at home, with levels of depression and social anxiety as moderators. Models differed only in the temporal links examined. One model focused on associations between changes in affect and time spent at home within short, 4-hour time windows. The other 3 models focused on associations between mean-level affect within a day and time spent at home (1) the same day, (2) the following day, and (3) the previous day. Overall, we obtained many of the expected main effects (although there were some null effects), in which higher social anxiety was associated with more time or greater likelihood of spending time at home, and more negative or less positive affect was linked to longer homestay. Interactions indicated that, among individuals higher in social anxiety, higher negative affect and lower positive affect within a day was associated with greater likelihood of spending time at home the following day.ConclusionsResults demonstrate the feasibility and utility of modeling the relationship between affect and homestay using fine-grained GPS data. Although these findings must be replicated in a larger study and with clinical samples, they suggest that integrating repeated state affect assessments in situ with continuous GPS data can increase understanding of how actual homestay is related to affect in everyday life and to symptoms of anxiety and depression.

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications.

The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution.

Home Ranges and Habitat Selection by Black Bears in a Newly Colonized Population in Florida

Understanding how animals use space and resources in newly colonized, anthropogenically altered habitats is important for species management because animals in fragmented habitats may use the landscape differently than conspecifics in contiguous habitats. We collected GPS-location data for 16 individuals (6 females, ages 1–9 y; 10 males, ages 2–8 y) from the summer of 2011 to the summer of 2013 to study space and habitat use by a recently established population of Ursus americanus floridanus (Florida Black Bear) in a fragmented landscape of north-central Florida. Average (± 1 SE) female and male homerange sizes estimated using the kernel density method were 31.16 ± 8.23 km2 and 220.93 ± 28.48 km2, respectively. Average 95% minimum convex polygon estimates were 34.49 ± 12.76 km2 for females and 226.04 ± 45.32 km2 for males. Home ranges in our study area were generally larger than those reported for Black Bears inhabiting the nearby contiguous forested habitat of Ocala National Forest, indicating that fragmentation may influence home-range size. Compositional analysis and generalized linear mixed models revealed that Black Bears selected most strongly for riparian forests; urban areas were generally avoided. These results suggest that large carnivores that inhabit fragmented landscapes may require more space than conspecifics in habitats with better connectivity, and highlight the importance of riparian forests for Black Bears.

Identifying behaviour objective from traffic behaviour log data by using facility ontology

Traffic behaviour surveys by hand require both a lot of money and human resources. Recently, traffic behaviour surveys using information technology have been carried out. In this study, we propose a method to extract staying points from GPS-based positional data, to identify the types of staying facilities by using Google Places API, a facility ontology, the regularity which is analysed from trip chains about traffic behaviour and to determine the behaviour objectives based on the rules between the behaviour objectives and the types of staying facilities. This method could identify 67.9% behaviour objectives in the evaluation using GPS location data from the traffic behaviour survey in Nagasaki.

Estimation of Origin and Destination Information from Bluetooth and Wi-Fi Sensing for Transit

Public urban transit offers a convenient, affordable, and sustainable mode of transportation for many. However, limited subsidies and revenue collected from bus fares place restrictions on the number of bus lines that can operate; these restrictions in turn limit the number of individuals who can benefit from public transit. To make well-informed operational decisions for transit planning and operations, understanding the origin and destination patterns of riders is crucial. However, traditional methods of transit data collection are labor-intensive and costly. Although some transit agencies use data from smart card transactions to obtain trip origin information readily, the trip destination information cannot be directly inferred. To aid in transit data acquisition efforts, this study presents a new technique that uses the Bluetooth and wireless fidelity (Wi-Fi) sensing technologies to estimate the origin and destination information for transit lines. Sensors were installed on four buses to collect Bluetooth, Wi-Fi, and GPS location data for a 4-week period. New methods for data processing and reduction were introduced to exclude invalid detections. On the basis of valid samples, the origin and destination information at different bus stops was estimated for a university operated transit line. The developed methods have the potential to be applied for large-scale transit networks.

Covariate Selection from Telematics Car Driving Data

Car insurance companies have started to collect high-frequency GPS location data of their car drivers. This data provides detailed information about the driving habits and driving styles of individual car drivers. We illustrate how this data can be analyzed using techniques from pattern recognition and machine learning. In particular, we describe how driving styles can be categorized so that they can be used for a regression analysis in car insurance pricing.

WildSense: Monitoring Interactions among Wild Deer in Harsh Outdoor Environments Using a Delay-Tolerant WSN

Biologists and ecologists often monitor the spread of disease among deer in the wild by using tracking systems that record their movement patterns, locations, and interaction behavior. The existing commercial systems for monitoring wild deer utilize collars with GPS sensors, deployed on captured and rereleased deer. The GPS sensors record location data every few hours, enabling researchers to approximate the interaction behavior of tracked deer with their GPS locations. However, the coarse granularity of periodically recorded GPS location data provides only limited precision for determining deer interaction behavior. We have designed a novel system to monitor wild deer interaction behavior more precisely in harsh wilderness environments. Our system combines the functionalities of both GPS and RF-radio sensors with low-cost and minimal-resource motes. We designed and built our system to be able to operate robustly for a period of up to several months for continual tracking and monitoring of the locations and interaction behaviors of wild deer in harsh environments. We successfully deployed six deer collars on six wild deer that were captured and rereleased in the Soapstone Prairie Natural Area of northern Colorado over a one-month period. In this paper, we describe how we designed and built this system and evaluate its successful operation in a wilderness area.

복수의 이용자를 지원하는 스마트 안심귀가 서비스의 구현

When user wants to receive a smart safe return service, user sends a service request using a smart device to support GPS location data. After the smart safe return system receives the location of user, it selects the neighboring CCTV cameras based on user`s location information, and controls the selected CCTV cameras to take a picture of the user, and to chase the user automatically. This service can be useful when there is a crime-ridden district on the path to return home late at night. Previous systems can`t provide the smart safe return service to multiple user simultaneously. In this paper, we propose the smart safe return system which can provide the service to multiple users simultaneously.