Detecting Stops from GPS Trajectories: A Comparison of Different GPS Indicators for Raster Sampling Methods

With the increasing prevalence of GPS tracking capabilities on smartphones, GPS trajectories have proven to be useful for an extensive range of research topics. Stop detection, which estimates activity locations, is fundamental for organizing GPS trajectories into semantically meaningful journeys. With previous methods overwhelmingly dependent on thresholds, contextual information or a pre-understanding of the GPS records, this paper addresses the challenge by contributing a ‘top-down’ raster sampling method which samples pre-calculated GPS indicators and clusters the raster cells with significantly different values as stops. We report a comparison of a set of precalculated GPS indicators with two baseline methods. By referencing a ground truth travel dairy, the raster sampling method demonstrates good and reliable capabilities on producing high accuracy, low redundancy and close proximity to the ground truth in three distinct travel use cases. This further indicates a good generic stop detection method

Seamless Interactions Between Humans and Mobility Systems

As mobility systems, including vehicles and roadside infrastructure, enter a period of rapid and profound change, it is important to enhance interactions between people and mobility systems. Seamless human—mobility system interactions can promote widespread deployment of engaging applications, which are crucial for driving safety and efficiency. The ever-increasing penetration rate of ubiquitous computing devices, such as smartphones and wearable devices, can facilitate realization of this goal. Although researchers and developers have attempted to adapt ubiquitous sensors for mobility applications (e.g., navigation apps), these solutions often suffer from limited usability and can be risk-prone. The root causes of these limitations include the low sensing modality and limited computational power available in ubiquitous computing devices. We address these challenges by developing and demonstrating that novel sensing techniques and machine learning can be applied to extract essential, safety-critical information from drivers natural driving behavior, even actions as subtle as steering maneuvers (e.g., left-/righthand turns and lane changes). We first show how ubiquitous sensors can be used to detect steering maneuvers regardless of disturbances to sensing devices. Next, by focusing on turning maneuvers, we characterize drivers driving patterns using a quantifiable metric. Then, we demonstrate how microscopic analyses of crowdsourced ubiquitous sensory data can be used to infer critical macroscopic contextual information, such as risks present at road intersections. Finally, we use ubiquitous sensors to profile a driver’s behavioral patterns on a large scale; such sensors are found to be essential to the analysis and improvement of drivers driving behavior.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163127/1/chendy_1.pd

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

The social implications of Submarine Groundwater Discharge from an Ecosystem Services perspective: A systematic review

Altres ajuts: Acord transformatiu CRUE-CSICUnidad de excelencia María de Maeztu CEX2019-000940-MSubmarine Groundwater Discharge (SGD) is recognized as a fundamental hydrological process that supports many coastal biogeochemical cycles and social-ecological systems. However, very little has been investigated about how SGD affects society and, specifically, human well-being. This study systematically examines the published scientific literature on the social implications of SGD by using an Ecosystem Service (ES) perspective. Coastal services provided by ecosystems dependent on SGD are analyzed and clustered in the four main categories of Ecosystem Services (i.e., Provisioning, Supporting, Regulating and Cultural), which are in turn divided into subcategories defined as outcomes. This allows identifying and discussing both benefits and threats to coastal societies resulting from SGD outcomes. From the 1532 articles initially reviewed, the most frequently mentioned category was the supporting services (835) due to the mainstream trend in scientific literature to focus on the role of SGD as a process influencing coastal biogeochemical cycles. Conversely, cultural ES were mentioned in only 49 cases, which should not necessarily be interpreted as a lack of research or interest in this topic, but that this type of references are often not found in the scientific literature but in the grey literature. A detailed publication review was additionally conducted, identifying 114 case studies from 96 different locations worldwide that reported cases in which SGD had social implications on the well-being. Our review also shows how the different types of Ecosystem Services can have multiple synergies and trade-offs between them, resulting in unequal impacts among stakeholder groups. Overall, this study identifies research gaps related to Ecosystem Services provided by SGD as well as opportunities for further studies, while developing an analytical framework that relies on the Ecosystem Services approach to guide future research on the social implications of SGD