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•Mixed-effect model predictions showed hyperlocal variation of air pollution in urban settings.•First time that UFP map has been created with measurements on all street ...segments.•Hotspot and ratio analyses revealed differences in spatial variation between BC, NO2 and UFP.•May helps policymakers by zooming into the areas of interest and adapt urban topography.•May helps epidemiologists to differentiate between the health effects of pollutants.
Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO2), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 – March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables.
We then explored the concentration ratio between the different normalised pollutants to understand possible contributing sources to the observed hyperlocal variations. The maps developed in this work reflect, (i) expected elevated pollution concentrations along busy roads, and (ii) similar concentration patterns on specific road types, e.g., motorways, for both cities. In the ratio maps, we observed a clear pattern of elevated concentrations of UFP near the airport in both cities, compared to BC and NO2.
This is the first study to produce hyperlocal maps for BC and UFP using high-quality mobile measurements. These maps are important for policymakers and health-effect studies, trying to disentangle individual effects of key air pollutants of interest (e.g., UFP).
Background
Hashimoto thyroiditis (HT) is an autoimmune thyroid disease and the leading cause of hypothyroidism in areas with sufficient iodine intake. The quality-of-life impact and financial burden ...of hypothyroidism and HT highlight the need for additional research investigating the disease etiology with the aim of revealing potential modifiable risk factors.
Objective
Implementation of measures against such risk factors, once identified, has the potential to lessen the financial burden while also improving the quality of life of many individuals. Therefore, we aimed to examine the potential seasonality of HT in Europe using the Google Trends data to explore whether there is a seasonal characteristic of Google searches regarding HT, examine the potential impact of the countries’ geographic location on the potential seasonality, and identify potential modifiable risk factors for HT, thereby inspiring future research on the topic.
Methods
Monthly Google Trends data on the search topic “Hashimoto thyroiditis” were retrieved in a 17-year time frame from January 2004 to December 2020 for 36 European countries. A cosinor model analysis was conducted to evaluate potential seasonality. Simple linear regression was used to estimate the potential effect of latitude and longitude on seasonal amplitude and phase of the model outputs.
Results
Of 36 included European countries, significant seasonality was observed in 30 (83%) countries. Most phase peaks occurred in spring (14/30, 46.7%) and winter (8/30, 26.7%). A statistically significant effect was observed regarding the effect of geographical latitude on cosinor model amplitude (y = –3.23 + 0.13 x; R2=0.29; P=.002). Seasonal increases in HT search volume may therefore be a consequence of an increased incidence or higher disease activity. It is particularly interesting that in most countries, a seasonal peak occurred in spring and winter months; when viewed in the context of the statistically significant impact of geographical latitude on seasonality amplitude, this may indicate the potential role of vitamin D levels in the seasonality of HT.
Conclusions
Significant seasonality of HT Google Trends search volume was observed in our study, with seasonal peaks in most countries occurring in spring and winter and with a significant impact of latitude on seasonality amplitude. Further studies on the topic of seasonality in HT and factors impacting it are required.
Scene classification has been studied to allow us to semantically interpret high spatial resolution (HSR) remote sensing imagery. The bag-of-visual-words (BOVW) model is an effective method for HSR ...image scene classification. However, the traditional BOVW model only captures the local patterns of images by utilizing local features. In this letter, a local-global feature bag-of-visual-words scene classifier (LGFBOVW) is proposed for HSR imagery. In LGFBOVW, the shape-based invariant texture index is designed as the global texture feature, the mean and standard deviation values are employed as the local spectral feature, and the dense scale-invariant feature transform (SIFT) feature is employed as the structural feature. The LGFBOVW can effectively combine the local and global features by an appropriate feature fusion strategy at histogram level. Experimental results on UC Merced and Google data sets of SIRI-WHU demonstrate that the proposed method outperforms the state-of-the-art scene classification methods for HSR imagery.
This study explores the main direct and underlying causes of deforestation in Brazil's Legal Amazon region by considering spatial differences. The computation of localized parameters is based on ...geographically weighted regression (GWR). The novelty of this paper lies in its incorporation of economic, rather than Euclidean, distances into the GWR. Economic distances are measured by travel time, sourced from Google Inc. A global approach revealed several important factors that affect deforestation, including: rural population, GDP (suggesting a U-shaped environmental Kuznets curve), forest stock, cattle ranching, timber value, and road networks (both official and unofficial). Local analysis uncovered patterns not seen under global models, especially in the state of Pará. Most notably, crop cultivation was found to accelerate deforestation in southeastern Pará and northeastern Mato Grosso, while in some regions (especially in the northeastern corner of Pará), the area covered by crop plantations was negatively associated with deforestation. For Pará, rural credit constraints, larger territories designated as sustainable use areas and indigenous lands, and higher levels of precipitation inhibit deforestation. Further, rural population has a very heterogeneous impact on deforestation across Legal Amazon: it is not a significant factor of deforestation in northern Pará and Amapá, but it has a relatively strong effect in the western parts of Mato Grosso and Rondônia. Also, official and illegal roads create significantly more pressure on forests in remote regions compared to developed areas. Finally, the use of economic distances, as opposed to Euclidean distances, leads to notably different GWR results.
•Cattle ranching causes most deforestation in Pará.•Generally, crop cultivation is weakly associated with deforestation.•Rural credit scheme inhibits deforestation in Pará.•Official and unofficial roads lead to most deforestation in remote areas.•Travel time as compared to Euclidean distances changes results for some areas.
•World Antimicrobial Awareness Week did not improve public awareness of antimicrobial resistance (AMR).•The COVID-19 pandemic may have reduced public interest in AMR.•Google Trends data were used as ...a surrogate for public awareness.
To evaluate the impact of the World Antimicrobial Awareness Week (WAAW) on public awareness of antimicrobial resistance using Google Trends analysis.
The impact of WAAW on public awareness of ‘antimicrobial resistance’ (AMR), ‘antibacterial’, and ‘antibiotics’ in Japan, the UK, the United States, and worldwide from 2015 to 2020 was analyzed, using the relative search volume (RSV) of Google Trends as a surrogate. A joinpoint regression analysis was performed to identify a statistically significant time point of a change in trend.
No joinpoints around WAAW were identified in Japan, the United Kingdom, or the United States from 2015 to 2020 with RSVs of ‘AMR’, whereas increasing RSVs were noted worldwide in 2017 and 2020. Further, there were decreasing RSVs of ‘antibiotics’ in the first half of 2020, which could be due to the COVID-19 pandemic. The study results suggest that WAAW did little to improve public awareness of AMR in the selected countries despite its contribution worldwide.
This study implies that we need to develop a more effective method to improve public awareness to fight against AMR.
Presents corrections to author names in the paper, “Jutge.org: Characteristics and experiences,” IEEE Trans. Learn. Technol., vol. 11, no. 3, pp. 321–333, Jul.–Sep. 2018.
This article investigates whether Microsoft Academic can use its web search component to identify early citations to recently published articles to help solve the problem of delays in research ...evaluations caused by the need to wait for citation counts to accrue. The results for 44,398 articles in Nature, Science and seven library and information science journals 1996–2017 show that Microsoft Academic and Scopus citation counts are similar for all years, with no early citation advantage for either. In contrast, Mendeley reader counts are substantially higher for more recent articles. Thus, Microsoft Academic appears to be broadly like Scopus for citation count data, and is apparently not more able to take advantage of online preprints to find early citations.
The aim of this study is to examine the attention and acceptance of popular information systems (IS) theory. Both Google Scholar (GS) and Google Trends (GT) data related to popular IS theories from ...2006 to 2020 are collected. The study provides an overview of popular IS theories and tests the pair data to show changes in GS’s total publications and GT’s public interest. Correlation analyses are conducted to investigate the relationships between GS and GT data, as well as between GS and GT data. Trend analysis reveals that there are certain IS theories that researchers and practitioners should pay attention to. In addition, this study provides an overview of recent and future directions in this field. Despite the significant increase in research using Google data, the use of big data to explore the interests and applications of IS theories has been rarely explored.
Reliable fire severity mapping is a vital resource for fire scientists and land management agencies globally. Satellite derived pre- and post-fire differenced severity indices (∆FSI), such as the ...differenced Normalised Burn Ratio (∆NBR), are widely used to map the severity of large wildfires. Fire severity classification is commonly undertaken through the identification of severity class thresholds in ∆FSI. However, several shortcomings have been identified with severity classifications using ∆FSI, including poor delineation of low fire severity classes, and unsatisfactory performance when ∆FSI classification thresholds are applied to new landscapes. Our study assesses the performance of the Random Forest classifier (RF) for improving the accuracy of satellite based wildfire severity mapping across heterogeneous landscapes using Landsat imagery. We collected point based fire severity training data (n = 10,855) from sixteen large wildfires occurring across south-eastern Australia between 2006 and 2016. The predictive accuracy of fire severity classification using ∆NBR and the RF incorporating numerous spectral indices, was assessed using bootstrapping and cross validation. Image acquisition and index calculation for each fire was undertaken in Google Earth Engine (GEE). Results of the bootstrapping validation show that the RF classifier had very high classification accuracy (>95%) for unburnt (UB), crown scorch (CS) and crown consumption (CC) severity classes, and high classification accuracy (>74%) for low severity classes (crown unburnt, CU; partial crown scorch, PCS). The RF classification outperformed the ∆NBR classification for all severity classes, increasing classification accuracy by between 6%–21%. Cross validation using independent fires produced similar median classification accuracy as the bootstrapping validation, though the RF classification of CU was substantially reduced to ~55%. ∆NBR, ∆NDWI and ∆NDVI were the three most important variables in the RF model. The Landsat satellite platform used to derive the indices had little effect on classification accuracy. Maps produced using the RF classifier in GEE had similar spatial patterns in fire severity classes as maps produced using time-consuming hand digitisation of aerial images. GEE was found to be a highly efficient platform for image acquisition, processing and production of severity maps. Our study shows that fire severity mapping using RF classifiers provides a robust method for broad scale mapping of fire severity across heterogeneous landscapes. Furthermore, the GEE-based classification framework supports the operational application of this approach in a land management agency context for the rapid production of fire severity maps.
•We tested the utility of Random Forest (RF) for fire severity classification.•RF had high classification accuracy (>90%) of fire severity using Landsat data.•RF outperformed commonly used methods of fire severity mapping (e.g. ∆NBR).•RF can potentially improve severity mapping undertaken by management agencies.
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