Spatial factors affecting statistical power in testing marine fauna displacement

2011 ◽  
Vol 21 (7) ◽  
pp. 2756-2769 ◽  
Author(s):  
B. Pérez Lapeña ◽  
K. M. Wijnberg ◽  
A. Stein ◽  
S. J. M. H. Hulscher
Keyword(s):  
Statistical Power ◽  
Marine Fauna ◽  
Factors Affecting ◽  
Spatial Factors

scholarly journals Using Hotspots Algebra to analyze crime events and their urban context

2017 ◽  
Vol 18 (1) ◽  
pp. 9-26
Author(s):  
Lenin Heredia G. ◽  
Germán E. Bravo C.
Keyword(s):  
Temporal Analysis ◽  
The Other ◽  
Crime Analysis ◽  
Temporal Context ◽  
Urban Context ◽  
Factors Affecting ◽  
Spatial Factors ◽  
Spatial Influence ◽  
Police Agencies ◽  
The Subject

Hotspots analysis is essential in the criminology field and quite important in decisions making for police agencies because it permits the enhancement of allocation of police resources for timely and adequate actions. There exist different techniques for analysis and generation of hotspots, limited by its difficulty to consider other urban and demographic factors that could be the cause of the emergence of these hotspots or their influence over other factors. On the other hand, it is also difficult and unusual to analyze hotspots in a temporal context. This paper seeks, through spatial-temporal operations based in hotspots, to go beyond of classical crime analysis with hotspots, by looking for the spatial influence of other spatial factors over and analyzing also their relationship in a temporal context. The paper initially presents and analyze the performance of various techniques for hotspot generation and determines that STAC technique from CrimeStat is the more suitable for the proposed objective. Then, it defines a hotspot algebra allowing the combined study of crime and spatial factors affecting it and/or been affected by crime events. Temporal analysis includes the hotspots generation for days of the week and/or months in a year. In this way it is possible to study causality relationships and/or correlations among the studied phenomena and the spatial-temporal environment where crime occurs. Therefore, it is possible to define and apply informed actions, primarily concerning the allocation of police resources. Finally, it shows some application examples, thematic as well as temporal analysis, of hotspot algebra using crime data from Bogota for the years 2011 to 2013; finally, some future works in the subject are proposed.

Factors Affecting Aerial Surveys of Marine Fauna, and Their Relationship to a Census of Dugongs in the Coastal Waters of the Northern-Territory

1986 ◽  
Vol 13 (1) ◽  
pp. 27 ◽  
Author(s):  
P Bayliss
Keyword(s):  
Coastal Waters ◽  
Total Population ◽  
Surface Condition ◽  
High Tide ◽  
Northern Territory ◽  
Probability Of Detection ◽  
Correction Factors ◽  
Marine Fauna ◽  
Factors Affecting ◽  
Similar Range

Some factors that may affect the aerial counts of dugongs, dolphins and turtles were examined experimentally. Tidal influence did not affect the counts of dugongs or dolphins, but those of turtles increased around high tide. A combined doubling of survey altitude and transect width reduced observed density of all three classes of animals by 50%. The counts of four observers did not differ significantly, but further data counsel caution. Overcast weather depressed counts of dugongs and turtles. Dolphin counts were affected by water surface condition, counts being lowest in choppy seas. A mark-recapture model was used on tandem observations to derive correction factors for groups of animals missed on the surface of a transect. Observers missed between 33% and 75% of dugong groups on the surface, the probability of detection decreasing with increased survey altitude and transect width. A similar range and pattern of probabilities was found for dolphins and turtles. Dugongs were censused in the coastal waters of the Northern Territory between the Daly River and Millingimbi in December 1983, an area of 28 746 km*2. Sampling intensity was 7.6%. A minimum population of 2953 � 530 (standard error) was estimated, an overall relative density of 0.11 � 0.02 km-2. A theoretical correction for submerged dugongs not seen yielded a total population estimate of 38 000, an overall density of 1.46 km-2. The distribution of dugongs in the survey area was patchy, the highest densities being associated with shallow coastal waters, sheltered bays, and large islands.

scholarly journals Analysis of Spatial Factors Affecting Rental House Prices: A Case Study of Nyeri Town Constituency, Kenya

2021 ◽  
Vol 9 (3) ◽  
pp. 110-123
Author(s):  
Evanson Ndung’u Kimani ◽  
Bartholomew Thiong’o Kuria ◽  
Moses Murimi Ngigi
Keyword(s):  
House Prices ◽  
Factors Affecting ◽  
Spatial Factors

scholarly journals Factors affecting employee engagement: using sem model for study case of social insurance in Ba Ria - Vung Tau province

2021 ◽  
Vol 5 (3) ◽  
pp. first
Author(s):  
Trần Quang Cảnh ◽  
Vũ Trực Phức ◽  
Hồ Ngọc Minh
Keyword(s):  
Factor Analysis ◽  
Organizational Behavior ◽  
Employee Engagement ◽  
Leadership Style ◽  
Statistical Power ◽  
Social Insurance ◽  
Degrees Of Freedom ◽  
Structural Model ◽  
Empirical Formulas ◽  
Factors Affecting

The employee engagement is an approach in the study of organizational behavior. There have been many studies done to find out the factors affecting the employee engagement to the organization. Limitation of previous studies is that, when choosing the number of factors to be retained, authors based only on the Eigienvalues (eigenvalue-one criterion). They did not take into account the cumulative percentage, screening test, percentage. The variance is calculated for each factor and the interpretability of each factor (The Interpretability Criterion). When doing confirmatory factor analysis (CFA), previous studies also did not test the statistical power of the studies. Samples of those studies, have often been taken according to empirical formulas that did not take into account the required statistical power and degrees of freedom of the study. This study was conducted at the social insurance agency of Ba Ria - Vung Tau province, from January 2019 to May 2019, with the aims to finding out and identify the factors that affect to employee engagement, with the analysis has supplementing and overcoming the shortcomings as mentioned above. In this paper, We use statistical software SAS to perform steps key component analysis (CPA), assess the reliability of the scale by Cronbach's Alpha index, exploratory factor analysis (EFA), Confirmation factor analysis (CFA) and Linear structural model analysis (SEM). The analysis results show that employee engagement with the organization is positively affected by 5 factors. The order of impact level are: Salary, bonus and welfare; Training and development opportunities; Organizational culture; Relationships with colleagues and Organizational Leadership Style.

Sign in / Sign up

Export Citation Format

Share Document