Environmental actions to reduce household ecological footprints

This paper presents the ideas of UK householders on how to reduce their Ecological Footprint (EF) after applying a tool called 'EcoCal', which assesses the environmental impacts of households by measuring footprints arising from Transport, Energy, Shopping, House and Garden, Water and Waste. Analysis of the EcoCal results of nearly 700 adult Open University (OU) student households showed that transport and energy impacts are the biggest contributors to the household footprint. Reducing these impacts poses the greatest challenge to achieving a globally sustainable household EF of approximately 0.5 hectares per person. Analysis of the ideas that the OU students were prepared to consider to reduce their household EF revealed a variety of technical and behavioural changes. However, in the context of their own household, the OU students identified several constraints on the implementation of their environmental action plans. Despite such constraints, many of the students instigated changes that helped to make their households more sustainable

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

Improving obesogenic environmental assessments with advanced geospatial methods

This thesis explores the intricate connections between the envir- onment and obesity. It develops and applies advanced geospatial methods to enhance the assessment of obesogenic environments and obesity risks. Its primary objective is to evaluate obesogenic environments and explore potential associations between environ- mental factors and obesity, crucial for effective obesity prevention. The thesis is structured around four key objectives. The first sub-objective involves an investigation into the current literature on the measurement of the built environment. Street View Imagery (SVI) and advanced urban visual intelligence technologies have transformed Built Environment Auditing (BEA) substantially, enabling large-scale auditing at a detailed geographical level. A me- ticulous review of 96 articles published before September 15, 2023, reveals key areas for improvement in SVI-based BEA. Recommend- ations include standardized datasets for more accurate audits, the integration of multi-source SVI for comprehensive assessments, and the design of auditing tools tailored to developing countries. Ad- dressing these areas enhances the potential of SVI in environmental auditing, as they contribute to a better understanding of the built environment’s health impact and facilitate informed decision-making in urban planning and public health initiatives. The second sub-objective focuses on analyzing exposure to in- creasing PM2.5 pollution, associated with rising morbidity and mor- tality. An ensemble machine learning model, integrating multi-source geospatial data, is presented to map hourly street-level PM2.5 concen- trations in the city of Nanjing, China, at a 100 m spatial resolution. The study concludes that mapping these concentrations reveals spati- otemporal trends, supporting the establishment of exposome studies. The third sub-objective addresses the development of a framework to evaluate Physical Activity (PA) opportunities (bikeability) in urban environments, aiming to enhance sustainable urban transportation planning. A framework is proposed that comprises safety, comfort, accessibility, and vitality sub-indices. It uses open-source data, ad- vanced deep neural networks, and GIS spatial analysis, to eliminate subjective evaluations and enhance efficiency. Experimental results in the city of Xiamen, China, demonstrate the framework’s effectiveness in identifying areas for improvement and enhancing cycling mobility. The fourth sub-objective investigates the associations between PA opportunities, specifically walkability, and obesity. Using a cross- sectional cohort from Nanjing, China. A Logistic regression model with a double robust estimator estimates the effects of walkability on obesity risks. A newly developed walkability index shows a sig- nificant negative association with obesity, particularly when using a data-based-buffer derived from web-mapping navigation that better represents individual activity spaces. These findings provide evidence for developing explicit strategies for obesity prevention. In summary, this thesis contributes to addressing the knowledge gap in health geography between obesogenic environments and obesity risks, employing advanced geospatial methods. The integration of multisource geospatial data, machine learning methods like deep learning in a GIS environment, and spatial statistics presents a major step forward

Non-L\'evy mobility patterns of Mexican Me'Phaa peasants searching for fuelwood

We measured mobility patterns that describe walking trajectories of individual Me'Phaa peasants searching and collecting fuelwood in the forests of "La Monta\~na de Guerrero" in Mexico. These one-day excursions typically follow a mixed pattern of nearly-constant steps when individuals displace from their homes towards potential collecting sites and a mixed pattern of steps of different lengths when actually searching for fallen wood in the forest. Displacements in the searching phase seem not to be compatible with L\'evy flights described by power-laws with optimal scaling exponents. These findings however can be interpreted in the light of deterministic searching on heavily degraded landscapes where the interaction of the individuals with their scarce environment produces alternative searching strategies than the expected L\'evy flights. These results have important implications for future management and restoration of degraded forests and the improvement of the ecological services they may provide to their inhabitants.Comment: 15 pages, 4 figures. First version submitted to Human Ecology. The final publication will be available at http://www.springerlink.co