Recent advances in citizen weather station (CWS) networks, with data accessible via crowd-sourcing, provide relevant climatic information to urban scientists and decision makers. In particular, CWS can provide long-term measurements of urban heat and valuable information on spatio-temporal heterogeneity related to horizontal heat advection. In this study, we make the first compilation of a quasi-climatologic dataset covering six years (2015-2020) of hourly near-surface air temperature measurements obtained via 1560 suitable CWS in a domain covering south-east England and Greater London. We investigated the spatio-temporal distribution of urban heat and the influences of local environments on climate, captured by CWS through the scope of Local Climate Zones (LCZ)-a land-use land-cover classification specifically designed for urban climate studies. We further calculate, for the first time, the amount of advected heat captured by CWS located in Greater London and the wider south east England region. We find that London is on average warmer by about 1.0 ∘C-1.5 ∘C than the rest of south-east England. Characteristics of the southern coastal climate are also captured in the analysis. We find that on average, urban heat advection (UHA) contributes to 0.22 ± 0.96 ∘C of the total urban heat in Greater London. Certain areas, mostly in the centre of London are deprived of urban heat through advection since heat is transferred more to downwind suburban areas. UHA can positively contribute to urban heat by up to 1.57 ∘C, on average and negatively by down to -1.21 ∘C. Our results also show an important degree of inter- and intra-LCZ variability in UHA, calling for more research in the future. Nevertheless, we already find that UHA can impact green areas and reduce their cooling benefit. Such outcomes show the added value of CWS when considering future urban design.

BACKGROUND: It has been hypothesized that low access to healthy and nutritious food increases health disparities. Low-accessibility areas, called food deserts, are particularly commonplace in lower-income neighborhoods. The metrics for measuring the food environment\'s health, called food desert indices, are primarily based on decadal census data, limiting their frequency and geographical resolution to that of the census. We aimed to create a food desert index with finer geographic resolution than census data and better responsiveness to environmental changes.
METHODS: We augmented decadal census data with real-time data from platforms such as Yelp and Google Maps and crowd-sourced answers to questionnaires by the Amazon Mechanical Turks to create a real-time, context-aware, and geographically refined food desert index. Finally, we used this refined index in a concept application that suggests alternative routes with similar ETAs between a source and destination in the Atlanta metropolitan area as an intervention to expose a traveler to better food environments.
RESULTS: We made 139,000 pull requests to Yelp, analyzing 15,000 unique food retailers in the metro Atlanta area. In addition, we performed 248,000 walking and driving route analyses on these retailers using Google Maps\' API. As a result, we discovered that the metro Atlanta food environment creates a strong bias towards eating out rather than preparing a meal at home when access to vehicles is limited. Contrary to the food desert index that we started with, which changed values only at neighborhood boundaries, the food desert index that we built on top of it captured the changing exposure of a subject as they walked or drove through the city. This model was also sensitive to the changes in the environment that occurred after the census data was collected.
CONCLUSIONS: Research on the environmental components of health disparities is flourishing. New machine learning models have the potential to augment various information sources and create fine-tuned models of the environment. This opens the way to better understanding the environment and its effects on health and suggesting better interventions.

Anonymous web-based experiments are increasingly used in many domains of behavioral research. However, online studies of auditory perception, especially of psychoacoustic phenomena pertaining to low-level sensory processing, are challenging because of limited available control of the acoustics, and the inability to perform audiometry to confirm normal-hearing status of participants. Here, we outline our approach to mitigate these challenges and validate our procedures by comparing web-based measurements to lab-based data on a range of classic psychoacoustic tasks. Individual tasks were created using jsPsych, an open-source JavaScript front-end library. Dynamic sequences of psychoacoustic tasks were implemented using Django, an open-source library for web applications, and combined with consent pages, questionnaires, and debriefing pages. Subjects were recruited via Prolific, a subject recruitment platform for web-based studies. Guided by a meta-analysis of lab-based data, we developed and validated a screening procedure to select participants for (putative) normal-hearing status based on their responses in a suprathreshold task and a survey. Headphone use was standardized by supplementing procedures from prior literature with a binaural hearing task. Individuals meeting all criteria were re-invited to complete a range of classic psychoacoustic tasks. For the re-invited participants, absolute thresholds were in excellent agreement with lab-based data for fundamental frequency discrimination, gap detection, and sensitivity to interaural time delay and level difference. Furthermore, word identification scores, consonant confusion patterns, and co-modulation masking release effect also matched lab-based studies. Our results suggest that web-based psychoacoustics is a viable complement to lab-based research. Source code for our infrastructure is provided.

Point-of-Interests (POIs) represent geographic location by different categories (e.g., touristic places, amenities, or shops) and play a prominent role in several location-based applications. However, the majority of POIs category labels are crowd-sourced by the community, thus often of low quality. In this paper, we introduce the first annotated dataset for the POIs categorical classification task in Vietnamese. A total of 750,000 POIs are collected from WeMap, a Vietnamese digital map. Large-scale hand-labeling is inherently time-consuming and labor-intensive, thus we have proposed a new approach using weak labeling. As a result, our dataset covers 15 categories with 275,000 weak-labeled POIs for training, and 30,000 gold-standard POIs for testing, making it the largest compared to the existing Vietnamese POIs dataset. We empirically conduct POI categorical classification experiments using a strong baseline (BERT-based fine-tuning) on our dataset and find that our approach shows high efficiency and is applicable on a large scale. The proposed baseline gives an F1 score of 90% on the test dataset, and significantly improves the accuracy of WeMap POI data by a margin of 37% (from 56 to 93%).

One of the most fundamental yet challenging tasks for aquatic ecologists is to precisely delineate the range of species, particularly those that are broadly distributed, require specialized sampling methods, and may be simultaneously declining and increasing in different portions of their range. An exemplar is the Pacific lamprey Entosphenus tridentatus, a jawless anadromous fish of conservation concern that is actively managed in many coastal basins in western North America. To efficiently determine its distribution across the accessible 56,168 km of the upper Snake River basin in the north-western United States, we first delimited potential habitat by using predictions from a species distribution model based on conventionally collected historical data and from the distribution of a potential surrogate, Chinook salmon Oncorhynchus tshawytscha, which yielded a potential habitat network of 10,615 km. Within this area, we conducted a two-stage environmental DNA survey involving 394 new samples and 187 archived samples collected by professional biologists and citizen scientists using a single, standardized method from 2015 to 2021. We estimated that Pacific lamprey occupied 1875 km of lotic habitat in this basin, of which 1444 km may have been influenced by recent translocation efforts. Pacific lamprey DNA was consistently present throughout most river main stems, although detections became weaker or less frequent in the largest and warmest downstream channels and near their headwater extent. Pacific lamprey were detected in nearly all stocked tributaries, but there was no evidence of indigenous populations in such habitats. There was evidence of post-stocking movement because detections were 1.8-36.0 km upstream from release sites. By crafting a model-driven spatial sampling template and executing an eDNA-based sampling campaign led by professionals and volunteers, supplemented by previously collected samples, we established a benchmark for understanding the current range of Pacific lamprey across a large portion of its range in the interior Columbia River basin. This approach could be tailored to refine range estimates for other wide-ranging aquatic species of conservation concern.

In this article, we consider the impacts of the COVID-19 pandemic on Indigenous Peoples (IPs) by reporting on information-gathering work across two non-governmental and Indigenous organisations to compensate where federal systems failed. Strategies IPs have employed to understand and respond to the pandemic, and described here, include: collaborative efforts across communities intra- and inter-nationally; open-source data platforms; and small-scale epidemiological research. Our review exposes the informational politics faced by Indigenous organisations and communities, and their struggle to pursue needed resources or protections while avoiding the critiques of \'post-neoliberal\' and \'science denialism\'. We conclude by suggesting ways that Indigenous communities improve our understanding of their needs during public health crises, and maintain both informational and medical self-governance.

This paper describes CliniCrowd, a patient-designed, entrepreneurial, crowd-sourced citizen-science approach to evaluating mannitol-essentially, an orphan drug-as a Parkinson\'s disease treatment. As such, CliniCrowd addresses \'undone science\', and our paper contributes to the sociological literature thereon. Based on 38 qualitative interviews, fieldwork, and content analyses (2017-2020), we trace CliniCrowd\'s background and rationale. We: discuss undone science and its wider contexts; present earlier iterations of citizen-science and treatment activism; examine CliniCrowd\'s application of crowd-sourced citizen-science to address undone science around \'orphan drug\' treatment for Parkinson\'s disease; explore how CliniCrowd has evolved, and re-framed its work, since its founding; ponder its future; and consider whether their approach can guide future citizen-science treatment research. Our paper contributes to the existing literature in four ways. First, we focus on medical treatment issues, an under-studied area of undone science. Second, we highlight orphan drugs as both major source of, and fruitful area for research on, undone science. Third, we describe CliniCrowd\'s pragmatic, entrepreneurial-rather than the more common activist-citizen-science approach to addressing undone treatment science. Finally, from our data on CliniCrowd we distil a preliminary model for future treatment activism around undone science.

Noise is a major source of pollution with a strong impact on health. Noise assessment is therefore a very important issue to reduce its impact on humans. To overcome the limitations of the classical method of noise assessment (such as simulation tools or noise observatories), alternative approaches have been developed, among which is collaborative noise measurement via a smartphone. Following this approach, the NoiseCapture application was proposed, in an open science framework, providing free access to a considerable amount of information and offering interesting perspectives of spatial and temporal noise analysis for the scientific community. After more than 3 years of operation, the amount of collected data is considerable. Its exploitation for a sound environment analysis, however, requires one to consider the intrinsic limits of each collected information, defined, for example, by the very nature of the data, the measurement protocol, the technical performance of the smartphone, the absence of calibration, the presence of anomalies in the collected data, etc. The purpose of this article is thus to provide enough information, in terms of quality, consistency, and completeness of the data, so that everyone can exploit the database, in full control.

A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote.

We trained a computer vision algorithm to identify 45 species of snakes from photos and compared its performance to that of humans. Both human and algorithm performance is substantially better than randomly guessing (null probability of guessing correctly given 45 classes = 2.2%). Some species (e.g., Boa constrictor) are routinely identified with ease by both algorithm and humans, whereas other groups of species (e.g., uniform green snakes, blotched brown snakes) are routinely confused. A species complex with largely molecular species delimitation (North American ratsnakes) was the most challenging for computer vision. Humans had an edge at identifying images of poor quality or with visual artifacts. With future improvement, computer vision could play a larger role in snakebite epidemiology, particularly when combined with information about geographic location and input from human experts.