Hasil untuk "Geography"

Laura J. Bowman

Abstract Statista is a business data platform that gathers existing data from over 22,500 sources and conducts its own research to prepare analyses across industries and geographies.

Yongze Song, Jinfeng Wang, Y. Ge et al.

ABSTRACT Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.

E. Klein, T. V. Van Boeckel, E. Martínez et al.

Significance Antibiotic resistance, driven by antibiotic consumption, is a growing global health threat. Our report on antibiotic use in 76 countries over 16 years provides an up-to-date comprehensive assessment of global trends in antibiotic consumption. We find that the antibiotic consumption rate in low- and middle-income countries (LMICs) has been converging to (and in some countries surpassing) levels typically observed in high-income countries. However, inequities in drug access persist, as many LMICs continue to be burdened with high rates of infectious disease-related mortality and low rates of antibiotic consumption. Our findings emphasize the need for global surveillance of antibiotic consumption to support policies to reduce antibiotic consumption and resistance while providing access to these lifesaving drugs. Tracking antibiotic consumption patterns over time and across countries could inform policies to optimize antibiotic prescribing and minimize antibiotic resistance, such as setting and enforcing per capita consumption targets or aiding investments in alternatives to antibiotics. In this study, we analyzed the trends and drivers of antibiotic consumption from 2000 to 2015 in 76 countries and projected total global antibiotic consumption through 2030. Between 2000 and 2015, antibiotic consumption, expressed in defined daily doses (DDD), increased 65% (21.1–34.8 billion DDDs), and the antibiotic consumption rate increased 39% (11.3–15.7 DDDs per 1,000 inhabitants per day). The increase was driven by low- and middle-income countries (LMICs), where rising consumption was correlated with gross domestic product per capita (GDPPC) growth (P = 0.004). In high-income countries (HICs), although overall consumption increased modestly, DDDs per 1,000 inhabitants per day fell 4%, and there was no correlation with GDPPC. Of particular concern was the rapid increase in the use of last-resort compounds, both in HICs and LMICs, such as glycylcyclines, oxazolidinones, carbapenems, and polymyxins. Projections of global antibiotic consumption in 2030, assuming no policy changes, were up to 200% higher than the 42 billion DDDs estimated in 2015. Although antibiotic consumption rates in most LMICs remain lower than in HICs despite higher bacterial disease burden, consumption in LMICs is rapidly converging to rates similar to HICs. Reducing global consumption is critical for reducing the threat of antibiotic resistance, but reduction efforts must balance access limitations in LMICs and take account of local and global resistance patterns.

J. Tobias, C. Sheard, A. Pigot et al.

Functional traits offer a rich quantitative framework for developing and testing theories in evolutionary biology, ecology and ecosystem science. However, the potential of functional traits to drive theoretical advances and refine models of global change can only be fully realised when species-level information is complete. Here we present the AVONET dataset containing comprehensive functional trait data for all birds, including six ecological variables, 11 continuous morphological traits, and information on range size and location. Raw morphological measurements are presented from 90,020 individuals of 11,009 extant bird species sampled from 181 countries. These data are also summarised as species averages in three taxonomic formats, allowing integration with a global phylogeny, geographical range maps, IUCN Red List data and the eBird citizen science database. The AVONET dataset provides the most detailed picture of continuous trait variation for any major radiation of organisms, offering a global template for testing hypotheses and exploring the evolutionary origins, structure and functioning of biodiversity.

A. Fotheringham, Wenbai Yang, Wei Kang

B. Ma, Hai-zhen Wang, M. Dsouza et al.

Soil microbiota play a critical role in soil biogeochemical processes and have a profound effect on soil functions. Recent studies have revealed microbial co-occurrence patterns in soil microbial communities, yet the geographic pattern of topological features in soil microbial co-occurrence networks at the continental scale are largely unknown. Here, we investigated the shifts of topological features in co-occurrence networks inferred from soil microbiota along a continental scale in eastern China. Integrating archaeal, bacterial and fungal community datasets, we inferred a meta-community co-occurrence network and analyzed node-level and network-level topological shifts associated with five climatic regions. Both node-level and network-level topological features revealed geographic patterns wherein microorganisms in the northern regions had closer relationships but had a lower interaction influence than those in the southern regions. We further identified topological differences associated with taxonomic groups and demonstrated that co-occurrence patterns were random for archaea and non-random for bacteria and fungi. Given that microbial interactions may contribute to soil functions more than species diversity, this geographic shift of topological features provides new insight into studying microbial biogeographic patterns, their organization and impacts on soil-associated function.

Moritz U. G. Kraemer, Marianne E. Sinka, Kirsten A. Duda et al.

Dengue and chikungunya are increasing global public health concerns due to their rapid geographical spread and increasing disease burden. Knowledge of the contemporary distribution of their shared vectors, Aedes aegypti and Aedes albopictus remains incomplete and is complicated by an ongoing range expansion fuelled by increased global trade and travel. Mapping the global distribution of these vectors and the geographical determinants of their ranges is essential for public health planning. Here we compile the largest contemporary database for both species and pair it with relevant environmental variables predicting their global distribution. We show Aedes distributions to be the widest ever recorded; now extensive in all continents, including North America and Europe. These maps will help define the spatial limits of current autochthonous transmission of dengue and chikungunya viruses. It is only with this kind of rigorous entomological baseline that we can hope to project future health impacts of these viruses. DOI: http://dx.doi.org/10.7554/eLife.08347.001

R. Bivand, Danlin Yu

D. Gubler, E. Ooi, S. Vasudevan et al.

T. Pringsheim, N. Jetté, A. Frolkis et al.

T. Blaschke, G. Hay, M. Kelly et al.

The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.

R. Hijmans

H. Mackinder

D. Mercatelli, F. Giorgi

The novel respiratory disease COVID-19 has reached the status of worldwide pandemic and large efforts are currently being undertaken in molecularly characterizing the virus causing it, SARS-CoV-2. The genomic variability of SARS-CoV-2 specimens scattered across the globe can underly geographically specific etiological effects. In the present study, we gather the 48,635 SARS-CoV-2 complete genomes currently available thanks to the collection endeavor of the GISAID consortium and thousands of contributing laboratories. We analyzed and annotated all SARS-CoV-2 mutations compared with the reference Wuhan genome NC_045512.2, observing an average of 7.23 mutations per sample. Our analysis shows the prevalence of single nucleotide transitions as the major mutational type across the world. There exist at least three clades characterized by geographic and genomic specificity. In particular, clade G, prevalent in Europe, carries a D614G mutation in the Spike protein, which is responsible for the initial interaction of the virus with the host human cell. Our analysis may facilitate custom-designed antiviral strategies based on the molecular specificities of SARS-CoV-2 in different patients and geographical locations.

J. Etten

The R package gdistance provides classes and functions to calculate various distance measures and routes in heterogeneous geographic spaces represented as grids. Least-cost distances as well as more complex distances based on (constrained) random walks can be calculated. Also the corresponding routes or probabilities of passing each cell can be determined. The package implements classes to store the data about the probability or cost of transitioning from one cell to another on a grid in a memory-efficient sparse format. These classes make it possible to manipulate the values of cell-to-cell movement directly, which offers flexibility and the possibility to use asymmetric values. The novel distances implemented in the package are used in geographical genetics (applying circuit theory), but also have applications in other fields of geospatial analysis.

P. Choudhury, Cirrus Foroughi, B. Larson

ABSTRACT An emerging form of remote work allows employees to work-from-anywhere, so that the worker can choose to live in a preferred geographic location. While traditional work-from-home (WFH) programs offer the worker temporal flexibility, work-from-anywhere (WFA) programs offer both temporal and geographic flexibility. WFA should be viewed as a nonpecuniary benefit likely to be preferred by workers who would derive greater utility by moving from their current geographic location to their preferred location. We study the effects of WFA on productivity at the United States Patent and Trademark Office (USPTO) and exploit a natural experiment in which the implementation of WFA was driven by negotiations between managers and the patent examiners’ union, leading to exogeneity in the timing of individual examiners’ transition from a work-from-home to a work-from-anywhere program. This transition resulted in a 4.4 percent increase in output without affecting the incidence of rework. We also report results related to a plausible mechanism: an increase in observable effort as the worker transitions from a WFH to a WFA program. We employ illustrative field interviews, micro- data on locations, and machine learning analysis to shed further light on geographic flexibility, and summarize worker, firm, and economy-wide implications of provisioning WFA. Keywords geographic flexibility, work-from-anywhere, remote work, telecommuting, worker mobility ABOUT THE AUTHOR/S Prithwiraj Choudhury Harvard Business School pchoudhury@hbs.edu Prithwiraj (Raj) Choudhury is the Lumry Family Associate Professor at the Harvard Business School. He was an Assistant Professor at Wharton prior to joining Harvard. His research is focused on studying the Future of Work, especially the changing Geography of Work. In particular, he studies the productivity effects of geographic mobility of workers, causes of geographic immobility and productivity effects of remote work practices such as ‘Work from anywhere’ and ‘All-remote’. Cirrus Foroughi Harvard Business School cforoughi@hbs.edu Barbara Larson Northeastern University b.larson@northeastern.edu New Future of Work 2020, August 3–5, 2020 © 2020 Copyright held by the owner/author(s)

L. Pulido

R. Udo

Kim Rossmo

Geographic profiling is a criminal investigative methodology for determining the most probable area of offender residence through a geospatial analysis of the locations in a crime series. The technique, based on the theories and concepts of environmental criminology, helps detectives prioritize suspects during a criminal investigation. Recently, geographic profiling has been used in a number of innovative applications outside of policing. Biologists in England have studied the foraging patterns of pipistrelle bat colonies in Scotland and the search routes of bumblebees using the geoprofiling algorithm. The technique has been applied by zoologists to identify the hunting epicenter of white sharks in False Bay, South Africa, while ecologists have identified the origins of invasive plant species in Great Britain and the base of a Mediterranean algal invasion. Finally, epidemiologists have used geoprofiling to prioritize sources for such diseases as cholera, malaria, and anthrax. Geographic profiling is a rare example of a method from the social sciences traveling across disciplines to the natural sciences. This presentation will explain the theory and mathematics of the technique and discuss its various applications in the biological sciences.

Nicolas Page, Alireza Gholami, Qian Zhang

In response to the global challenge of climate change, financial institutions are increasingly called upon to assess and disclose their carbon emissions. Various global carbon quantification and reporting standards were developed, such as the Greenhouse Gas (GHG) Protocol, Task Force on Climate-related Financial Disclosures (TCFD), Partnership for Carbon Accounting Financials (PCAF) and others. Unfortunately, the now diverse landscape of standards increases the complexity for institutions seeking to develop voluntary carbon quantification and reporting. This study addresses the complexity issue by developing a criteria-based tool that summarizes the various components and requirements of the carbon standards. We propose eight criteria that summarize the standards’ key elements, requirements and relevance to the financial industry. We analyze seven major carbon quantification and reporting standards, systematically evaluating them against our tool. By doing so, we provide financial institutions with valuable insights in selecting appropriate standards to inform their emissions quantification and reporting decisions.