scholarly journals Comparing spatial regression to random forests for large environmental data sets

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0229509
Author(s):  
Eric W. Fox ◽  
Jay M. Ver Hoef ◽  
Anthony R. Olsen
Keyword(s):  
Random Forests ◽  
Spatial Regression ◽  
Environmental Data ◽  
Data Sets

scholarly journals An Environmental GIS-based Variable Analysis of SARS-CoV-2 in the City of Recife, Brazil

2020 ◽  
Author(s):  
Arthur Souza ◽  
Caroline Mota ◽  
Amanda Rosa ◽  
Ciro Figueiredo ◽  
Ana Lucia Candeias
Keyword(s):  
Low Income ◽  
Public Services ◽  
Spatial Regression ◽  
Case Fatality ◽  
Global Scale ◽  
Environmental Data ◽  
Data Sets ◽  
Spatial Regression Analysis ◽  
Effective Drugs ◽  
The City

Abstract Background: Given the increasing rates at which cases of people infected by Covid-19 have been evolving to case-fatality rates on a global scale and the context of there being a world-wide socio-economic crisis, decision-making must be undertaken based on prioritizing effective measures to control and combat the disease since there is a lack of effective drugs and as yet no vaccine. Method: This paper explores the determinant factors of the COVID-19 pandemic and its impacts on Recife, Pernambuco-Brazil by performing both local and global spatial regression analysis on two types of environmental data-sets. Data were obtained from ten specific days between late April and early July 2020, comprehending the ascending, peak and descending behaviours of the curve of infections.Results: This study highlights the importance of identifying and mapping clusters of the most affected neighbourhoods and their determinant effects. We have identified that it is increasingly common for there to be a phase in which hotspots of confirmed cases appear in a well-developed and heavily densely-populated neighbourhood of the city of Recife. From there, the disease is carried to areas characterised by having a precarious provision of public services and a low-income population and this quickly creates hotspots of case-fatality rates. The results also help to understand the influence of the age, income, level of education of the population and, additionally, of the extent to which they can access public services, on the behaviour of the virus across neighbourhoods.Conclusion: This study supports government measures against the spread of Covid-19 in heterogeneous cities, evidencing social inequality as a driver for a high incidence of fatal cases of the disease. Understanding the variables which influence the local dynamics of the virus spread becomes vital for identifying the most vulnerable regions for which prevention actions need to be developed.

scholarly journals An Environmental GIS-based Variable Analysis on SARS-CoV-2 in the City of Recife, Brazil

2020 ◽  
Author(s):  
Arthur Souza ◽  
Caroline Maria de Miranda Mota ◽  
Amanda Rosa ◽  
Ciro Figueiredo ◽  
Ana Lucia Candeias
Keyword(s):  
Low Income ◽  
Public Services ◽  
Spatial Regression ◽  
Case Fatality ◽  
Global Scale ◽  
Environmental Data ◽  
Data Sets ◽  
Spatial Regression Analysis ◽  
Effective Drugs ◽  
The City

Abstract Background: Given the increasing rates at which people have been infected by Covid-19 evolving to case-fatality rates on a global scale and the context of there being a world-wide socio-economic crisis, decision-making must be undertaken based on prioritizing effective measures to control and combat the disease since there is a lack of effective drugs. Method: This paper explores the determinant factors of the COVID-19 pandemic and its impacts on Recife, Pernambuco-Brazil by performing both local and global spatial regression analysis on two types of environmental data-sets. Data were obtained from ten specific days between late April and early July 2020, comprehending the ascending, peaking and descending behaviours of the curve of infections.Results: This study highlights clusters of the most affected neighbourhoods and their determinant effects. We have observed the increasing phase with hotspots of confirmed cases in a well-developed and heavily densely-populated neighbourhood of Recife city, then evolving for hotspots of case-fatality rates into areas characterised by having a precarious provision of public services and low-income population. The results also help to understand the influence of the age, income, level of education of the population and, additionally, the people’s access to public services, on the behaviour of the virus across neighbourhoods.Conclusion: This study supports government measures against the spread of Covid-19 in heterogeneous cities, evidencing social inequality as a driver for a high incidence of fatal cases of the disease. Understanding the variables which influence the local dynamics of the virus spread becomes vital for identifying the most vulnerable regions for which prevention actions need to be developed.

Governance and Regional Variation of Homicide Rates

2016 ◽  
Vol 61 (1) ◽  
pp. 25-45 ◽  
Author(s):  
Liqun Cao ◽  
Yan Zhang
Keyword(s):  
Spatial Regression ◽  
Sub Saharan Africa ◽  
Development Economic ◽  
Data Sets ◽  
Asian Countries ◽  
Ceteris Paribus ◽  
Region Data ◽  
Sub Saharan ◽  
Criminological Theories ◽  
Cross National

Criminological theories of cross-national studies of homicide have underestimated the effects of quality governance of liberal democracy and region. Data sets from several sources are combined and a comprehensive model of homicide is proposed. Results of the spatial regression model, which controls for the effect of spatial autocorrelation, show that quality governance, human development, economic inequality, and ethnic heterogeneity are statistically significant in predicting homicide. In addition, regions of Latin America and non-Muslim Sub-Saharan Africa have significantly higher rates of homicides ceteris paribus while the effects of East Asian countries and Islamic societies are not statistically significant. These findings are consistent with the expectation of the new modernization and regional theories.

Recent Progress in Applying Advanced Computation Methods to Radar-Based Wave Measurements

2021 ◽  
Author(s):  
Morten Loell Vinther ◽  
Torbjørn Eide ◽  
Aurelia Paraschiv ◽  
Dickon Bonvik-Stone
Keyword(s):  
Doppler Radar ◽  
Risk Mitigation ◽  
Weather Conditions ◽  
Data Access ◽  
Environmental Data ◽  
Data Sets ◽  
Wave Measurements ◽  
Current Profiler ◽  
Pulse Doppler ◽  
Calm Weather

Abstract High quality environmental data are critical for any offshore activity relying on data insights to form appropriate planning and risk mitigation routines under challenging weather conditions. Such data are the most significant driver of future footprint reduction in offshore industries, in terms of costs savings, as well as operational safety and efficiency, enabled through ease of data access for all relevant stakeholders. This paper describes recent advancements in methods used by a dual-footprint Pulse-Doppler radar to provide accurate and reliable ocean wave height measurements. Achieved improvements during low wind weather conditions are presented and compared to data collected from other sources such as buoys and acoustic doppler wave and current profiler (ADCP) or legacy. The study is based on comparisons of recently developed algorithms applied to different data sets recorded at various sites, mostly covering calm weather conditions.

Mining Environmental Data in the ADMIRE Project Using New Advanced Methods and Tools

2012 ◽  
pp. 296-308
Author(s):  
Ondrej Habala ◽  
Martin Šeleng ◽  
Viet Tran ◽  
Branislav Šimo ◽  
Ladislav Hluchý
Keyword(s):  
Data Mining ◽  
Environmental Data ◽  
Environmental Applications ◽  
Data Sets ◽  
Distributed Data ◽  
New Methods ◽  
Prospective Application ◽  
Using Data ◽  
Computer Power

The project Advanced Data Mining and Integration Research for Europe (ADMIRE) is designing new methods and tools for comfortable mining and integration of large, distributed data sets. One of the prospective application domains for such methods and tools is the environmental applications domain, which often uses various data sets from different vendors where data mining is becoming increasingly popular and more computer power becomes available. The authors present a set of experimental environmental scenarios, and the application of ADMIRE technology in these scenarios. The scenarios try to predict meteorological and hydrological phenomena which currently cannot or are not predicted by using data mining of distributed data sets from several providers in Slovakia. The scenarios have been designed by environmental experts and apart from being used as the testing grounds for the ADMIRE technology; results are of particular interest to experts who have designed them.

Watering Down Environmental Regulation in China*

2020 ◽  
Vol 135 (4) ◽  
pp. 2135-2185 ◽  
Author(s):  
Guojun He ◽  
Shaoda Wang ◽  
Bing Zhang
Keyword(s):  
Water Quality ◽  
Environmental Regulation ◽  
Spatial Regression ◽  
Oxygen Demand ◽  
Economic Cost ◽  
Water Quality Monitoring ◽  
Data Sets ◽  
Firm Level ◽  
Emission Estimate ◽  
The Government

Abstract This article estimates the effect of environmental regulation on firm productivity using a spatial regression discontinuity design implicit in China's water quality monitoring system. Because water quality readings are important for political evaluations and the monitoring stations only capture emissions from their upstream regions, local government officials are incentivized to enforce tighter environmental standards on firms immediately upstream of a monitoring station, rather than those immediately downstream. Exploiting this discontinuity in regulation stringency with novel firm-level geocoded emission and production data sets, we find that immediate upstream polluters face a more than 24% reduction in total factor productivity (TFP), and a more than 57% reduction in chemical oxygen demand emissions, as compared with their immediate downstream counterparts. We find that the discontinuity in TFP does not exist in nonpolluting industries, only emerged after the government explicitly linked political promotion to water quality readings, and was predominantly driven by prefectural cities with career-driven leaders. Linking the TFP estimate with the emission estimate, a back-of-the-envelope calculation indicates that China's water regulation efforts between 2000 and 2007 were associated with an economic cost of more than 800 billion Chinese yuan.

Contexts of Cultural Change in Insular California

1997 ◽  
Vol 62 (2) ◽  
pp. 300-318 ◽  
Author(s):  
Jeanne E. Arnold ◽  
Roger H. Colten ◽  
Scott Pletka
Keyword(s):  
Cultural Change ◽  
Terrestrial Ecosystems ◽  
Environmental Data ◽  
Data Sets ◽  
American Antiquity ◽  
New Information ◽  
The Status ◽  
Status Differentiation ◽  
Animal Resources ◽  
Climatic Disturbances

Archaeological and ethnohistorical researchers in California are reaping the rewards from a wealth of new information about precontact and early historical cultural diversity, technologies, and marine and terrestrial ecosystems. Our recent investigations into the later prehistory of the island groups of southern California have centered on processes of sociopolitical evolution, including the emergence of status differentiation, evidence for intensification of craft production, and associated changes in human uses of animal resources as societies became more complex. We have linked some specific changes in diet, labor organization, and exchange to documented climatic disturbances, suggesting that opportunities created by such disruptions may have accounted in part for the timing of changes, but were not their cause in any mechanistic or simplistic sense. A recent American Antiquity report overlooks the primary results of this research and isolates the environmental data from a broad multidimensional model of cultural change in coastal California. We provide an update on the status of Channel Islands archaeology and identify the fundamental problems with approaches that extract and decontextualize environmental processes from cultural processes by assessing limited faunal data sets.

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