Developing a Citizen Social Science approach to understand urban stress and promote wellbeing in urban communities

This paper sets out the future potential and challenges for developing an interdisciplinary, mixed-method Citizen Social Science approach to researching urban emotions. It focuses on urban stress, which is increasingly noted as a global mental health challenge facing both urbanised and rapidly urbanising societies. The paper reviews the existing use of mobile psychophysiological or biosensing within urban environments—as means of ‘capturing’ the urban geographies of emotions. Methodological reflections are included on primary research using biosensing in a study of workplace and commuter stress for university employees in Birmingham (UK) and Salzburg (Austria) for illustrative purposes. In comparing perspectives on the conceptualisation and measurement of urban stress from psychology, neuroscience and urban planning, the difficulties of defining scientific constructs within Citizen Science are discussed to set out the groundwork for fostering interdisciplinary dialogue. The novel methods, geo-located sensor technologies and data-driven approaches to researching urban stress now available to researchers pose a number of ethical, political and conceptual challenges around defining and measuring emotions, stress, human behaviour and urban space. They also raise issues of rigour, participation and social scientific interpretation. Introducing methods informed by more critical Citizen Social Science perspectives can temper overly individualised forms of data collection to establish more effective ways of addressing urban stress and promoting wellbeing in urban communities

Quality assessment of OpenStreetMap data using trajectory mining

OpenStreetMap (OSM) data are widely used but their reliability is still variable. Many contributors to OSM have not been trained in geography or surveying and consequently their contributions, including geometry and attribute data inserts, deletions, and updates, can be inaccurate, incomplete, inconsistent, or vague. There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data. Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs. This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users. The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature. Using such rules, some sets of potential bugs and errors can be identified and stored for further investigation

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Laws of Geography

This article explores and unpacks the entanglements between law and geography that enable and advance the exclusion of non-citizens from entry into sovereign territory. We suggest that states manipulate jurisdiction within and beyond sovereign territory to extend enforcement. This jurisdiction applies primarily to the bodies of migrants themselves as opposed to fixed spaces. Like Elden’s (2009, 2013) imperio, or imperial power, this extension is spatially boundless, limitless in internal checks and administrative. Imperio places migrant bodies into new legal regimes with subjectivities that overlap and override existing protections, such as international refugee law. We develop our argument by considering enforcement practices in three areas: the waiting zone at Paris Charles de Gaulle airport, search and rescue areas on the Central Mediterranean Sea and Australian excision zones

Teaching Agent-Based Modelling and Machine Learning in an integrated way

The integration of Agent-Based Modelling (ABM) and Machine Learning (ML) provides many promising opportunities, yet this research field is underdeveloped. Different reasons are given for this lack of integration, including a shortage of behavioural data and technical implementation difficulties. However, we think that one crucial problem is being overlooked. In our educational system, we teach topics one by one and do not explicitly focus on the integration of various modelling paradigms. This is a missed opportunity that should be addressed, to prepare our students for a world where models are increasingly complex and where data and model integration becomes inevitable. In this paper, we share our experiences in a course in Geoinformatics, where integrated ABM and ML modelling is central. In our class, we use the Living Textbook to work on interlinked concept maps, and we have an overarching case study assignment. Preliminary outcomes show that students’ learning and project work could benefit from simplifying the case study assignment and introducing the parallel teaching of ABM and ML. In general, different teaching methods and setups still need to be explored, to ensure that our future model designers are well equipped for their task

Digital Earth Ethics

Digital Earth scholars have recently argued for a code of ethics to protect individuals’ location privacy and human dignity. In this chapter, we contribute to the debate in two ways. First, we focus on (geo)privacy because information about an individual’s location is substantially different from other personal information. The compound word (geo)privacy suggests that location can be inferred from people’s interests, activities, and sociodemographics, not only from traditional geographic coordinates. (Geo)privacy is a claim of individuals to determine for themselves when,how, and to what extent location information about them is communicated to others.Second, we take an interdisciplinary perspective. We draw from (geo)computing to describe the transformation of volunteered, observed, and inferred information and suggest privacy-preserving measures. We also draw from organization studies to dis-sect privacy into ideal types of social relationships and privacy-preserving strategies.We take the point of view of Alice, an individual ‘data subject’ encountered in data protection legislation, and suggest ways to account for privacy as a sociocultural phenomenon in the future. Although most of the discussion refers to the EU and the US, we provide a brief overview of data protection legislation on the African continent and in China as well as various global and regional ethics guidelines that are of very recent vintage

Journal of Empirical Research on Human Research Ethics / A Geoprivacy by Design Guideline for Research Campaigns That Use Participatory Sensing Data

Participatory sensing applications collect personal data of monitored subjects along with their spatial or spatiotemporal stamps. The attributes of a monitored subject can be private, sensitive, or confidential information. Also, the spatial or spatiotemporal attributes are prone to inferential disclosure of private information. Although there is extensive problem-oriented literature on geoinformation disclosure, our work provides a clear guideline with practical relevance, containing the steps that a research campaign should follow to preserve the participants privacy. We first examine the technical aspects of geoprivacy in the context of participatory sensing data. Then, we propose privacy-preserving steps in four categories, namely, ensuring secure and safe settings, actions prior to the start of a research survey, processing and analysis of collected data, and safe disclosure of datasets and research deliverables.(VLID)262474