Statistical Analysis of Population Density Data of Soil Animals, with Particular Reference to Enchytraeidae (Oligochaeta)

Oikos ◽  
1970 ◽  
Vol 21 (2) ◽  
pp. 276 ◽  
Author(s):  
Gunnar Abrahamsen ◽  
Lars Strand
Keyword(s):  
Statistical Analysis ◽  
Population Density ◽  
Soil Animals ◽  
Density Data

Nursing Homes in Spain and Their High Number of Deaths by COVID-19 as an Alarm in the Study of Indoor Air Quality

2021 ◽  
pp. 118-145
Author(s):  
Gastón Sanglier Contreras ◽  
Jose Amílcar Rizzo Sierra ◽  
Eduardo José López Fernández ◽  
Roberto Alonso González Lezcano
Keyword(s):  
Energy Efficiency ◽  
Statistical Analysis ◽  
Air Quality ◽  
Nursing Homes ◽  
Population Density ◽  
Indoor Air Quality ◽  
Autonomous Communities ◽  
La Mancha ◽  
Number Of Factors ◽  
Determining Factor

Nursing homes have been one of the most prominent targets of the COVID-19 coronavirus in Spain. The factors that have determined that this is the case are very diverse. In this study, physical agents and chemical pollutants, population density, and different capacities of the residences are analyzed to see their influence on the number of elderly people who have died in the geriatric centres in the different autonomous communities (AACC) of Spain. A statistical analysis has been carried out on the variables observed. The conclusions show that in many places where this overflow of deaths has occurred, the residences were private, with some exceptions. The influence of physical agents and pollutants has been shown to be a determining factor, especially for the communities of Extremadura and Castilla-La Mancha, although it is true that the large number of factors makes the study complicated. The dilemma between air quality and energy efficiency is of great importance, especially when human health is at stake.

scholarly journals Significant locations in auxiliary data as seeds for typical use cases of point clustering

2018 ◽  
Vol 1 ◽  
pp. 1-6
Author(s):  
Johannes Kröger
Keyword(s):  
Population Density ◽  
Data Clustering ◽  
Use Cases ◽  
Auxiliary Data ◽  
Density Data ◽  
Local Maxima ◽  
Point Data ◽  
Seed Points ◽  
Apparent Distribution ◽  
Grid Based

Random greedy clustering and grid-based clustering are highly susceptible by their initial parameters. When used for point data clustering in maps they often change the apparent distribution of the underlying data. We propose a process that uses precomputed weighted seed points for the initialization of clusters, for example from local maxima in population density data. Exemplary results from the clustering of a dataset of petrol stations are presented.

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