Spatial Population Analysis.

1979 ◽  
Vol 33 (2) ◽  
pp. 384
Author(s):  
Michael Batty ◽  
P. H. Rees ◽  
A. G. Wilson
Keyword(s):  
Population Analysis ◽  
Spatial Population

Spatial Population Analysis.

1978 ◽  
Vol 7 (3) ◽  
pp. 298
Author(s):  
Nathan Keyfitz ◽  
P. H. Rees ◽  
A. G. Wilson
Keyword(s):  
Population Analysis ◽  
Spatial Population

Heterogeneity, Spatial Population Dynamics, and the Migration Rate

1992 ◽  
Vol 24 (6) ◽  
pp. 775-791 ◽  
Author(s):  
A Rogers
Keyword(s):  
Return Migration ◽  
Migration Rate ◽  
Evolutionary Dynamics ◽  
Population Analysis ◽  
Model Fitting ◽  
Initial Model ◽  
Migration Rates ◽  
Net Migration ◽  
Spatial Population ◽  
True Occurrence

The literature on the impacts of heterogeneity and selection in population analysis has been limited largely to the conventional unistate perspective in which only decrements are considered; and their temporal (selectivity) impacts on independent subpopulations examined. In this paper, the focus is on the evolutionary dynamics of (multistate) multiregional populations whose interdependent subpopulations can experience increments as well as decrements. It is shown that in such instances migration rates that are not true occurrence-exposure rates are ambiguous, because they depend on the relative weightings existing during the initial model-fitting period. Net migration rates, lifetime migration rates, and return migration proportions all are imperfect measures of migration propensities, and their use as input measures to an analysis should be avoided whenever possible.

Automated 3-D cell population analysis in thick tissue sections using laser-scanning confocal-microscopy data

1993 ◽  
Vol 51 ◽  
pp. 562-563
Author(s):  
Hakan Ancin
Keyword(s):  
Laser Scanning ◽  
Population Analysis ◽  
Three Dimensional ◽  
Large Data ◽  
Laser Scanning Confocal Microscopy ◽  
Tissue Slices ◽  
Scanning Confocal Microscopy ◽  
Tissue Sections ◽  
Spatially Adaptive ◽  
Microscopy Data

This paper presents methods for performing detailed quantitative automated three dimensional (3-D) analysis of cell populations in thick tissue sections while preserving the relative 3-D locations of cells. Specifically, the method disambiguates overlapping clusters of cells, and accurately measures the volume, 3-D location, and shape parameters for each cell. Finally, the entire population of cells is analyzed to detect patterns and groupings with respect to various combinations of cell properties. All of the above is accomplished with zero subjective bias.In this method, a laser-scanning confocal light microscope (LSCM) is used to collect optical sections through the entire thickness (100 - 500μm) of fluorescently-labelled tissue slices. The acquired stack of optical slices is first subjected to axial deblurring using the expectation maximization (EM) algorithm. The resulting isotropic 3-D image is segmented using a spatially-adaptive Poisson based image segmentation algorithm with region-dependent smoothing parameters. Extracting the voxels that were labelled as "foreground" into an active voxel data structure results in a large data reduction.

Population Analysis of a Commercial Beef Cattle Herd

1985 ◽  
Vol 61 (1) ◽  
pp. 44-54 ◽  
Author(s):  
D. Schons ◽  
W. D. Hohenboken ◽  
J. D. Hall
Keyword(s):  
Beef Cattle ◽  
Population Analysis ◽  
Cattle Herd

Mobile Phone Craving And Problem Use In The Spanish Population: Analysis Of An Internet-Based Survey (Preprint)

2020 ◽  
Author(s):  
Jose De-Sola ◽  
Gabriel Rubio ◽  
Hernan Talledo ◽  
Carmen Jaudenes ◽  
Andrea DÁgostino ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Cell Phone ◽  
Basic Education ◽  
Population Analysis ◽  
Spanish Population ◽  
Behavioral Disorder ◽  
Urban Centers ◽  
Autonomous Communities ◽  
Cell Phone Use ◽  
Device Use

BACKGROUND Problem phone use is a growing concern in most societies, being associated to social and health problems in a similar way to other behavioral addictions. However, both, its prevalence and its nature as a behavioral disorder are not well described nor understood. More over it is not universally recognized yet as a clinical entity that deserves health-oriented interventions. OBJECTIVE In this study, both, an evaluation of cell phone craving and problematic cell phone use among the Spanish population was carried out. Our initial hypothesis is that both problematic cell phone use and cravings have increased in the last 5 years. METHODS Methods We used our adaptation of the original Mobile Phone Problematic Use Scale (MPPUS) as well as the Mobile Phone Addiction Craving Scale (MPACS) A total of 1,612 online interviews were conducted in 17 autonomous communities in the Spanish territory, with a global and specific analysis by gender, age, profession, education level and population center size. RESULTS The results indicate that the prevalence of problematic cell phone use in Spain is currently 4.8%, with 14.8% of users who abuse and are at risk of dependence and lack of control. In total, 19.6% of the population currently has mobile device use issues. These results do not differ significantly from those obtained in our previous investigation; in contrast, the level of craving increased significant. Both in the prevalence of problematic cell phone use and in craving, the highest incidence is observed for people between 16 and 35 years old, students, and users without education or basic education and in urban centers. There are no differences between genders. CONCLUSIONS Therefore, the present study confirms that prevalence of problem phone use remained high in the Spanish Population, despite the rising concerns around its unhealthy consequences. In addition, we confirmed an increase in cell phone craving scores, indicating that technological dependence and need is increasing every day. CLINICALTRIAL The present study is not an intervention study so no Trial Registration was requested

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