Language inclusion in ecological systematic reviews and maps: Barriers and perspectives.

Systematic reviews and maps are considered a reliable form of research evidence, but often neglect non-English-language literature, which can be a source of important evidence. To understand the barriers that might limit authors' ability or intent to find and include non-English-language literature, we assessed factors that may predict the inclusion of non-English-language literature in ecological systematic reviews and maps, as well as the review authors' perspectives. We assessed systematic reviews and maps published in Environmental Evidence (n = 72). We also surveyed authors from each paper (n = 32 responses), gathering information on the barriers to the inclusion of non-English language literature. 44% of the reviewed papers (32/72) excluded non-English literature from their searches and inclusions. Commonly cited reasons included constraints related to resources and time. Regression analysis revealed that reviews with larger author teams, authors from diverse countries, especially those with non-English primary languages, and teams with multilingual capabilities searched in a significantly greater number of non-English languages. Our survey exposed limited language diversity within the review teams and inadequate funding as the principal barriers to incorporating non-English language literature. To improve language inclusion and reduce bias in systematic reviews and maps, our study suggests increasing language diversity within review teams. Combining machine translation with language skills can alleviate the financial and resource burdens of translation. Funding applications could also include translation costs. Additionally, establishing language exchange systems would enable access to information in more languages. Further studies investigating language inclusion in other journals would strengthen these conclusions.

Best practices for spatial language data harmonization, sharing and map creation-A case study of Uralic.

Despite remarkable progress in digital linguistics, extensive databases of geographical language distributions are missing. This hampers both studies on language spatiality and public outreach of language diversity. We present best practices for creating and sharing digital spatial language data by collecting and harmonizing Uralic language distributions as case study. Language distribution studies have utilized various methodologies, and the results are often available as printed maps or written descriptions. In order to analyze language spatiality, the information must be digitized into geospatial data, which contains location, time and other parameters. When compiled and harmonized, this data can be used to study changes in languages' distribution, and combined with, for example, population and environmental data. We also utilized the knowledge of language experts to adjust previous and new information of language distributions into state-of-the-art maps. The extensive database, including the distribution datasets and detailed map visualizations of the Uralic languages are introduced alongside this article, and they are freely available.

A street-view-based method to detect urban growth and decline: A case study of Midtown in Detroit, Michigan, USA.

Urban growth and decline occur every year and show changes in urban areas. Although various approaches to detect urban changes have been developed, they mainly use large-scale satellite imagery and socioeconomic factors in urban areas, which provides an overview of urban changes. However, since people explore places and notice changes daily at the street level, it would be useful to develop a method to identify urban changes at the street level and demonstrate whether urban growth or decline occurs there. Thus, this study seeks to use street-level panoramic images from Google Street View to identify urban changes and to develop a new way to evaluate the growth and decline of an urban area. After collecting Google Street View images year by year, we trained and developed a deep-learning model of an object detection process using the open-source software TensorFlow. By scoring objects and changes detected on a street from year to year, a map of urban growth and decline was generated for Midtown in Detroit, Michigan, USA. By comparing socioeconomic changes and the situations of objects and changes in Midtown, the proposed method is shown to be helpful for analyzing urban growth and decline by using year-by-year street view images.

Applying height differentiation of tactile symbols to reduce the minimum horizontal distances between them on tactile maps.

In this paper, we wanted to verify the hypothesis that extruding cartographic symbols on tactile maps to different heights might allow reducing the minimum (suggested in the literature) horizontal distances between them, without impacting the overall map's legibility. This approach might allow preparing tactile maps in smaller scales and thus, reducing production cost, or putting additional spatial information on the same map sheet that would not fit otherwise. To verify the hypothesis we have prepared 6 different stimuli variants with or without height differentiation applied and different horizontal distances between tactile symbols adopted (1 mm, 2 mm and 3 mm). In the controlled study sessions with 30 participants with visual impairments we have measured the times required for solving 3 different spatial tasks on 3D printed tactile stimuli. We have also performed qualitative analysis to learn participants' opinions about the proposed design and materials used. It turns out that applying height differentiation not only results in shorter times required for solving spatial tasks but is also considered by blind individuals as a convenient improvement in terms of use comfort and allows reduction of recommended minimum horizontal distances between symbols on tactile maps.

A 3-D groundwater isoscape of the contiguous USA for forensic and water resource science.

A wide range of hydrological, ecological, environmental, and forensic science applications rely on predictive "isoscape" maps to provide estimates of the hydrogen or oxygen isotopic compositions of environmental water sources. Many water isoscapes have been developed, but few studies have produced isoscapes specifically representing groundwaters. None of these have represented distinct subsurface layers and isotopic variations across them. Here we compiled >6 million well completion records and >27,000 groundwater isotope datapoints to develop a space- and depth-explicit water isoscape for the contiguous United States. This 3-dimensional model shows that vertical isotopic heterogeneity in the subsurface is substantial in some parts of the country and that groundwater isotope delta values often differ from those of coincident precipitation or surface water resources; many of these patterns can be explained by established hydrological and hydrogeological mechanisms. We validate the groundwater isoscape against an independent data set of tap water values and show that the model accurately predicts tap water values in communities known to use groundwater resources. This new approach represents a foundation for further developments and the resulting isoscape should provide improved predictions of water isotope values in systems where groundwater is a known or potential water source.

Micotoxinas en el análisis de los alimentos: Un estudio con indicadores bibliométricos y mapas de visualización / Mycotoxins in food analysis: A study with bibliometric indicators and visualization maps

El presente estudio tuvo como objetivo identificar el desarrollo de la producción científica según año y revistas; determinar la estructura temática e identificar la red de coautoría de la literatura sobre micotoxinas en el análisis de los alimentos. Se realizó un estudio descriptivo con indicadores bibliométricos y mapas de visualización, en el que se usó la base de datos Scopus de los documentos incluidos desde el año 2011 al 2020. Se consideraron 453 artículos que cumplieron con los criterios de inclusión, 382 artículos de investigación y 71 revisiones. El promedio de citas por artículo varió anualmente entre 6 (2019) y 40,5 (2012). La revista más productiva fue Mycotoxin Research, de la editorial Springer Nature. En el mapa de coocurrencias se obtuvieron 5 clústeres de descriptores. En el mapa de coautoría se hallaron 21 clústeres, 14 de ellos correspondieron a autores que investigaron en coautoría. El desarrollo científico tuvo una producción anual no uniforme, con crecimiento lento. La estructura temática según la red de coocurrencia indica que el término más frecuente es micotoxinas, correspondiente al enfoque micotoxinas que influyen en la salud pública del primer clúster. La red de coautoría indica que el 98,2 por ciento de los artículos fueron elaborados en coautoría y el autor más productivo fue Krska Rudolf, de la Queen's University Belfast, con 24 artículos(AU)

The purpose of the study was to identify the development of scientific production by year and journal, determine the thematic structure and identify the co-authorship network of the literature about mycotoxins in food analysis. A descriptive study was conducted of documents published in the database Scopus from 2011 to 2020, using bibliometric indicators and visualization maps. A total 453 articles were included which met the inclusion criteria. Of these, 382 were research articles and 71 were reviews. The average annual number of citations per article ranged from 6 in 2019 to 40.5 in 2012. The most productive journal was Mycotoxin Research from Springer Nature publishers. The co-occurrence map displayed five descriptor clusters, whereas the co-authorship map displayed 21 clusters, 14 of which corresponded to authors researching in co-authorship. Scientific development had a non-uniform, slowly growing annual output. The thematic structure according to the co-occurrence network shows that the most frequent term is mycotoxins, corresponding to mycotoxins influencing public health from the first cluster. The co-authorship network shows that 98.2percent of the articles were co-authored, and the most productive author was Krska Rudolf, from Queen's University, Belfast, with 24 articles(AU)

Mapping conflict of interests: scoping review.

OBJECTIVE: To identify all known ties between the medical product industry and the healthcare ecosystem. DESIGN: Scoping review. METHODS: From initial literature searches and expert input, a map was created to show the network of medical product industry ties across parties and activities in the healthcare ecosystem. Through a scoping review, the ties were then verified, cataloged, and characterized, with data abstracted on types of industry ties (financial, non-financial), applicable policies for conflict of interests, and publicly available data sources. MAIN OUTCOME MEASURES: Presence and types of medical product industry ties to activities and parties, presence of policies for conflict of interests, and publicly available data. RESULTS: A map derived through synthesis of 538 articles from 37 countries shows an extensive network of medical product industry ties to activities and parties in the healthcare ecosystem. Key activities include research, healthcare education, guideline development, formulary selection, and clinical care. Parties include non-profit entities, the healthcare profession, the market supply chain, and government. The medical product industry has direct ties to all parties and some activities through multiple pathways; direct ties extend through interrelationships among parties and activities. The most frequently identified parties were within the healthcare profession, with individual professionals described in 422 (78%) of the included studies. More than half (303, 56%) of the publications documented medical product industry ties to research, with clinical care (156, 29%), health professional education (145, 27%), guideline development (33, 6%), and formulary selection (8, 1%) appearing less often. Policies for conflict of interests exist for some financial and a few non-financial ties; publicly available data sources seldom describe or quantify these ties. CONCLUSIONS: An extensive network of medical product industry ties to activities and parties exists in the healthcare ecosystem. Policies for conflict of interests and publicly available data are lacking, suggesting that enhanced oversight and transparency are needed to protect patient care from commercial influence and to ensure public trust.

Toponym-assisted map georeferencing: Evaluating the use of toponyms for the digitization of map collections.

A great deal of information is contained within archival maps-ranging from historic political boundaries, to mineral resources, to the locations of cultural landmarks. There are many ongoing efforts to preserve and digitize historic maps so that the information contained within them can be stored and analyzed efficiently. A major barrier to such map digitizing efforts is that the geographic location of each map is typically unknown and must be determined through an often slow and manual process known as georeferencing. To mitigate the time costs associated with the georeferencing process, this paper introduces a fully automated method based on map toponym (place name) labels. It is the first study to demonstrate these methods across a wide range of both simulated and real-world maps. We find that toponym-based georeferencing is sufficiently accurate to be used for data extraction purposes in nearly half of all cases. We make our implementation available to the wider research community through fully open-source replication code, as well as an online georeferencing tool, and highlight areas of improvement for future research. It is hoped that the practical implications of this research will allow for larger and more efficient processing and digitizing of map information for researchers, institutions, and the general public.

Measuring the accuracy of gridded human population density surfaces: A case study in Bioko Island, Equatorial Guinea.

BACKGROUND: Geospatial datasets of population are becoming more common in models used for health policy. Publicly-available maps of human population make a consistent picture from inconsistent census data, and the techniques they use to impute data makes each population map unique. Each mapping model explains its methods, but it can be difficult to know which map is appropriate for which policy work. High quality census datasets, where available, are a unique opportunity to characterize maps by comparing them with truth. METHODS: We use census data from a bed-net mass-distribution campaign on Bioko Island, Equatorial Guinea, conducted by the Bioko Island Malaria Elimination Program as a gold standard to evaluate LandScan (LS), WorldPop Constrained (WP-C) and WorldPop Unconstrained (WP-U), Gridded Population of the World (GPW), and the High-Resolution Settlement Layer (HRSL). Each layer is compared to the gold-standard using statistical measures to evaluate distribution, error, and bias. We investigated how map choice affects burden estimates from a malaria prevalence model. RESULTS: Specific population layers were able to match the gold-standard distribution at different population densities. LandScan was able to most accurately capture highly urban distribution, HRSL and WP-C matched best at all other lower population densities. GPW and WP-U performed poorly everywhere. Correctly capturing empty pixels is key, and smaller pixel sizes (100 m vs 1 km) improve this. Normalizing areas based on known district populations increased performance. The use of differing population layers in a malaria model showed a disparity in results around transition points between endemicity levels. DISCUSSION: The metrics in this paper, some of them novel in this context, characterize how these population maps differ from the gold standard census and from each other. We show that the metrics help understand the performance of a population map within a malaria model. The closest match to the census data would combine LandScan within urban areas and the HRSL for rural areas. Researchers should prefer particular maps if health calculations have a strong dependency on knowing where people are not, or if it is important to categorize variation in density within a city.

The design of the arrangement of evacuation routes on a passenger ship using the method of genetic algorithms.

The article concerns the problem of evacuation from passenger ships. It is important because it has not yet been possible to eliminate all the hazards associated with sea travel. In this paper, a concept of a method allowing to determine the arrangement of evacuation routes, for which evacuation time would be minimal, was presented. The genetic algorithm method was used in the calculations, and an original method of coding the considered problem was proposed. Sample calculations were performed to verify the correctness of the proposed algorithm. The results of applying the developed method to calculate the evacuation time on a real passenger ship are presented.

Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot.

Spatial datasets of building footprint polygons are becoming more widely available and accessible for many areas in the world. These datasets are important inputs for a range of different analyses, such as understanding the development of cities, identifying areas at risk of disasters, and mapping the distribution of populations. The growth of high spatial resolution imagery and computing power is enabling automated procedures to extract and map building footprints for whole countries. These advances are enabling coverage of building footprint datasets for low and middle income countries which might lack other data on urban land uses. While spatially detailed, many building footprints lack information on structure type, local zoning, or land use, limiting their application. However, morphology metrics can be used to describe characteristics of size, shape, spacing, orientation and patterns of the structures and extract additional information which can be correlated with different structure and settlement types or neighbourhoods. We introduce the foot package, a new set of open-source tools in a flexible R package for calculating morphology metrics for building footprints and summarising them in different spatial scales and spatial representations. In particular our tools can create gridded (or raster) representations of morphology summary metrics which have not been widely supported previously. We demonstrate the tools by creating gridded morphology metrics from all building footprints in England, Scotland and Wales, and then use those layers in an unsupervised cluster analysis to derive a pattern-based settlement typology. We compare our mapped settlement types with two existing settlement classifications. The results suggest that building patterns can help distinguish different urban and rural types. However, intra-urban differences were not well-predicted by building morphology alone. More broadly, though, this case study demonstrates the potential of mapping settlement patterns in the absence of a housing census or other urban planning data.

Interrater Reliability of Historical Virtual Audits Using Archived Google Street View Imagery.

Impaired mobility occurs in up to half of community-dwelling older adults and is associated with poor health outcomes and high health care costs. Although the built environment impacts mobility, most studies of older adults lack information about environmental-level factors. In-person observational audits can be utilized but cannot assess the historical environment. We applied a 78-item checklist to archived Google Street View imagery to assess historical residence access and neighborhood characteristics. Interrater reliability between two raters was tested on 50 addresses using prevalence-adjusted and bias-adjusted kappa (PABAK). The mean PABAK for all items was .75, with 81% of the items having substantial (PABAK ≥ .61) or almost perfect (PABAK ≥ .81) agreement. Environmental assessment using archived virtual imagery has excellent reliability for factors related to residence access and many neighborhood characteristics. Archived imagery can assess past neighborhood characteristics, facilitating the use of historical environment data within existing cohorts.

A morphing approach for continuous generalization of linear map features.

With the development of web maps, people are no longer satisfied with fixed and limited scale map services but want to obtain personalized and arbitrary scale map data. Continuous map generalization technology can be used to generate arbitrary scale map data. This paper proposes a morphing method for continuously generalizing linear map features using shape context matching and hierarchical interpolation (SCM-HI). More specifically, shape characteristics are quantitatively described by shape context on which shape similarity is measured based on a chi-square method; then, two levels of interpolation, skeleton and detail interpolations, are employed to generate the geometry of intermediate curves. The main contributions of our approach include (1) exploiting both the geometry and spatial structure of a vector curve in shape matching by using shape context, and (2) preserving both the main shape structure as-rigid-as-possible and local geometric details as gradual and smooth as possible for intermediate curves by hierarchical interpolation. Experiments show that our method generates plausible morphing effects and can thus serve as a robust approach for continuous generalization of linear map features.

Building a sanitary vulnerability map from open source data in Argentina (2010-2018).

BACKGROUND: Designing public health policies to target the needs of specific places requires highly granular data. When geographic health statistics from official sources are absent or lacking in spatial detail, Sanitary Vulnerability metrics derived from Census and other georeferenced public data can be used to identify areas in particular need of attention. With that aim, a Vulnerability Map was developed, identifying areas with a substantial deficit in its population health coverage. As a result a novel methodology for measuring Sanitary Vulnerability is presented, that can potentially be applied to different time periods or geographies. METHODS: Census, official listings of public health facilities and crowdsourced georeferenced data are used. The Vulnerability Index is built using dimensionality reduction techniques such as Autoencoders and Non-parametric PCA. MAIN RESULTS: The high resolution map shows the geographical distribution of a Sanitary Vulnerability Index, produced using official and crowdsourced open data sources, overcoming the lack of official sources on health indicators at the local level. CONCLUSIONS: The Sanitary Vulnerability Map's value as a tool for place specific policymaking was validated by using it to predict local health related metrics such as health coverage. Further lines of work contemplate using the Map to study the interaction between Sanitary Vulnerability and the prevalence of different diseases, and also applying its methodology in the context of other public services such as education, security, housing, etc.

An elimination method for isolated meshes in a road network considering stroke edge feature.

The road network is the skeletal element of topographic maps at different scales. In general, urban roads are connected by road segments, thus forming a series of road meshes. Mesh elimination is a key step in evaluating the importance of roads during the road network data management and a prerequisite to the implementation of continuous multiscale spatial representation of road networks. The existing mesh-based method is an advanced road elimination method whereby meshes with the largest density are sequentially selected and road segments with the least importance in each mesh are eliminated. However, the road connectivity and integrity may be destroyed in specific areas by this method because some eliminated road segments could be located in the middle of road strokes. Therefore, this paper proposed an elimination method for isolated meshes in a road network considering stroke edge feature. First, small meshes were identified by using mesh density thresholds, which can be obtained by the sample data statistical algorithm. Thereafter, the small meshes related to the edge segments of road strokes were taken out and defined as stroke edge meshes, and the remaining small meshes were defined as stroke non-edge meshes. Second, by computing the mesh density of all stroke edge meshes, the mesh with the largest density was selected as the starting mesh, and the least important edge segment in the mesh was eliminated. The difference between the existing mesh-based method and the proposed method is that the starting mesh is a stroke edge mesh, not any given small mesh, and the eliminated segment is just only one of edge segments of strokes not chosen from among all segments. Third, mesh elimination was implemented by iteratively processing the stroke edge meshes with the largest mesh density until all of them were eliminated and their mesh density exceeded the threshold. The stroke non-edge meshes were directly preserved. Finally, a 1:10,000 topographic road map of an area in Jiangsu Province of China was used for validation. The experimental results show that for all stroke non-edge meshes and 23% of the stroke edge meshes, compared to the mesh-based method, the road stroke connectivity and integrity of road strokes were better preserved by the proposed method, and the remaining 77% of the elimination results for the stroke edge meshes were the same under the two methods.

Exploring the neighbourhood-level correlates of Covid-19 deaths in London using a difference across spatial boundaries method.

This paper explores neighbourhood-level correlates of the Covid-19 deaths in London during the initial rise and peak of the pandemic within the UK - the period March 1 to April 17, 2020. It asks whether the person-level predictors of Covid-19 that are identified in reports by Public Health England and by the Office of National Statistics also hold at a neighbourhood scale, remaining evident in the differences between neighbours. In examining this, the paper focuses on localised differences in the number of deaths, putting forward an innovative method of analysis that looks at the differences between places that share a border. Specifically, a difference across spatial boundaries method is employed to consider whether a higher number of deaths in one neighbourhood, when compared to its neighbours, is related to other differences between those contiguous locations. It is also used to map localised 'hot spots' and to look for spatial variation in the regression coefficients. The results are compared to those for a later period, April 18 - May 31. The findings show that despite some spatial diffusion of the disease, a greater number of deaths continues to be associated with Asian and Black ethnic groups, socio-economic disadvantage, very large households (likely indicative of residential overcrowding), and fewer from younger age groups. The analysis adds to the evidence showing that age, wealth/deprivation, and ethnicity are key risk factors associated with higher mortality rates from Covid-19.

Familiarity with teammate's attitudes improves team performance in virtual reality.

Virtual reality (VR) is a potentially challenging social environment for effective communication and collaboration. Thus, we conducted a VR study to determine whether increased familiarity with a teammate would improve performance on a joint decision making task. Specifically, because attitude familiarity, or knowledge of another person's attitudes, has been correlated with better relationship functioning in the past, we anticipated that team performance would improve when teammates were first asked to discuss their task-relevant attitudes with one another. We also hypothesized that increased familiarity would be particularly useful in immersive VR, where typical social and other nonverbal cues were lacking. Twenty pairs recruited from a workplace environment were randomly assigned to either the Familiar or Control condition before completing a joint decision making task both in VR and on desktop monitors. The manipulation of attitude familiarity was successful: pairs in the Familiar condition were significantly more aware of their partners' unique task-relevant attitudes. Results found that in VR, Familiar pairs were more accurate at determining patterns to events. Additionally, for teams less experienced in VR, Familiar pairs were also more accurate at predicting future events. However, there was no meaningful statistical difference in pairs' ability to identify information. Familiar teams also took more time to answer questions, and we found no difference in self-reported communication quality. Overall, this was the first successful manipulation of attitude familiarity and results indicate that such an intervention may prove useful in a collaborative work environment, as Familiar teams demonstrated greater accuracy, especially in VR.

Community venue exposure risk estimator for the COVID-19 pandemic.

Complexities of virus genotypes and the stochastic contacts in human society create a big challenge for estimating the potential risks of exposure to a widely spreading virus such as COVID-19. To increase public awareness of exposure risks in daily activities, we propose a birthday-paradox-based probability model to implement in a web-based system, named COSRE (community social risk estimator) and make in-time community exposure risk estimation during the ongoing COVID-19 pandemic. We define exposure risk to mean the probability of people meeting potential cases in public places such as grocery stores, gyms, libraries, restaurants, coffee shops, offices, etc. Our model has three inputs: the real-time number of active and asymptomatic cases, the population in local communities, and the customer counts in the room. With COSRE, possible impacts of the pandemic can be explored through spatiotemporal analysis, e.g., a variable number of people may be projected into public places through time to assess changes of risk as the pandemic unfolds. The system has potential to advance understanding of the true exposure risks in various communities. It introduces an objective element to plan, prepare and respond during a pandemic. Spatial analysis tools are used to draw county-level exposure risks of the United States from April 1 to July 15, 2020. The correlation experiment with the new cases in the next two weeks shows that the risk estimation model offers promise in assisting people to be more precise about their personal safety and control of daily routine and social interaction. It can inform business and municipal COVID-19 policy to accelerate recovery.

OpenStreetMap mapathons support critical data and visual literacy instruction.

OpenStreetMap (OSM) mapathons can offer a learner-centered means for critical data literacy and visual literacy instruction. Mapathons are coordinated humanitarian mapping events in which participants use geographic information system (GIS) data and satellite imagery to create open-source maps for humanitarian support. Visual mapping is an effective learning activity because it encourages students to use big data to create a deliverable-an open-source map-that allows instructors to engage learners in data literacy and visual literacy at the highest cognitive level.