population density

Suicide rates tend to be high in urban areas of the UK 5,6 , but high rates are also found in rural areas in some countries, particularly among young adults 7-10 . The Scottish strategy lists rural areas as one of the areas of intervention. Previous work in Scotland found higher male rates in the Highlands 11 . People sometimes travel to scenic areas to kill themselves. These deaths contribute to higher rural rates, but the rate in Highland remained high even when the deaths of visitors were excluded from the figures 12 . Further research found that some other rural parts of Scotland had higher than average male suicide rates among people resident in the area 4 . Levin and Leyland 3 reported that small remote rural communities have higher standardised mortality ratios than urban areas, but no information has been available on the relationship between population density and suicide in Scotland.

Background: Delivering health services to dense populations is more practical than to dispersed populations, other factors constant. This engenders the hypothesis that population density positively affects coverage rates of health services. This hypothesis has been tested indirectly for some services at a local level, but not at a national level. Methods: We use cross-sectional data to conduct cross-country, OLS regressions at the national level to estimate the relationship between population density and maternal health coverage. We separately estimate the effect of two measures of density on three population-level coverage rates (6 tests in total). Our coverage indicators are the fraction of the maternal population completing four antenatal care visits and the utilization rates of both skilled birth attendants and in-facility delivery. The first density metric we use is the percentage of a population living in an urban area. The second metric, which we denote as a density score, is a relative ranking of countries by population density. The score ’ s calculation discounts a nation ’ s uninhabited territory under the assumption those areas are irrelevant to service delivery.

We present a method for solving population density equations (PDEs)—a mean-field technique describing homogeneous populations of uncoupled neurons—where the populations can be subject to non-Markov noise for arbitrary distributions of jump sizes. The method combines recent developments in two different disciplines that traditionally have had limited interaction: computational neuroscience and the theory of random networks. The method uses a geometric binning scheme, based on the method of characteristics, to capture the deterministic neurodynamics of the population, separating the deterministic and stochastic process cleanly. We can independently vary the choice of the deterministic model and the model for the stochastic process, leading to a highly modular numerical solution strategy. We demonstrate this by replacing the master equation implicit in many formulations of the PDE formalism by a generalization called the generalized Montroll-Weiss equation—a recent result from random network theory—describing a random walker subject to transitions realized by a non-Markovian process. We demonstrate the method for leaky- and quadratic-integrate and fire neurons subject to spike trains with Poisson and gamma-distributed interspike intervals. We are able to model jump responses for both models accurately to both excitatory and inhibitory input under the assumption that all inputs are generated by one renewal process.

In this paper we made an attempt to consider these effects from the viewpoint of the “energy-time” approach. For this purpose,we assumed that members of a population have to spend a certain time for interactions with other individuals, this time being a cost of such intra-population interactions. I also could not avoid paying attention to the joint influence of food availability and population density upon energy and time budgets. To evaluate the importance of the factors under study, we assumed that daily production rate (i.e. difference between energy assimilation and its expenditures) is to be maximised. Although daily production itself is not a direct index of fitness, it is obviously related to such cha- racteristics as the total mass of newborn animals and/or individual growth rate. The former trait is related to fecun- dity and, consequently, to intrinsic population growth rate, while the latter determines size at maturity and/or longevity of maturation period. Therefore, I regarded production rate

he three basic concepts which are fundamental to the framework of this study are the 3Ds –Density, Distance and Division. These Variables was introduced by World Development Report 2009.Population Density refers to the Population mass per unit of land area, or the geographic compactness of population. Distance refers to the ease or difficulty for goods, services, labor, capital, information, and ideas to traverse space. Distance, in this sense, is an economic concept, not just a physical one. Division is the most important dimension internationally. Religion, ethnicity, and language are among the main attributes that lead to divisions between places. Thus, the main aim of this paper is analysis of the effect of population density, economic distance and division on regional economic growth. For this aim, this study was proposed a simple theoretical framework to study the impact of population density, economic distance and division on regional economic growth. The framework has presented in a unified way the main insights of NEG models with endogenous growth and free capital mobility.

The result of the dry weight of 100 seeds weight revealed that seeds weight among treatments were not significantly different each other but the maximum 100 seeds weight (104 g) was obtained from 105 cm spacing treatment. Biomass yield was also not significantly different in statistical analysis but the maximum biomass dry weight (171.5 g) was obtained from 105 cm spacing treatment. Simmod and Willian (1989) reported that the wider spacing lost more water from soil through evaporation and the seasonal transpiration was also strongly influenced by population density.

When studying an ecosystem, ecologists — scientists that study natural communities — first try to survey what populations of organisms naturally live there. They then also measure how many of each creature lives there. This is referred to as the population density of that species. Ecologists measure population density by counting the number of each species in a sample area, called a quadrat. If they count the population size in a number of quadrats chosen at random around the ecosystem, scientists can estimate how many of each species live in the whole ecosystem. The population size of each creature that the environment can support is called the carrying capacity of that community. The carrying capacity is how many of a certain species that can survive in an area given the resources (food, water, and nesting sites) available.

Over the past two decades, sudden virus outbreaks that could have led to widespread human pandemics, including H1N1 [37], SARS [38], and H5N1 [39] prompted a series of studies on non-pharmaceutical interventions [40-48], including measures to increase social distance, such as the creation of spatial barriers through quarantine [49,50], relocating populations to ‘safe’ areas [51,52], or imposing travel restrictions [53]. Evacuations, a last resort among social distancing measures, are still used; in the aftermath of the Haiti hurricane of November, 2009, and the subsequent earthquake of January 12, 2010, the government ordered the evacuation of the capital, Port-au-Prince, to prevent the spread of epidemic cholera [54]. In sum, it is widely believed that public health interventions, in- cluding social distancing measures and the controlled movement of people to either sequester those infected or as a means to lower population density below some critical threshold can significantly decrease the likeli- hood of a contagious disease spreading. Yet, to date, there is little if any guidance as to what such a popula- tion density threshold might be for any disease. Using the influenza pandemic of 1918 as a case, this paper presents an approach to identifying such a threshold value as a guideline for public health policy.

i) Inadequate agricultural and livestock waste technology. Data and technical obstacles on the development of cattle population in Indonesia as explained above occurred because the current approaches done did not consider the real condition of environmental supporting capacity. This could be seen from the food availability from which Indonesia could only produce 35-40% of the total population, whereas only 27.84-45.57% of waste from agriculture and plantation was utilized in feeding, dan functional shift of green areas (paddy, field, and yard) could only reach the average of 23.41%/year (Indraningsih et al.,2011; Wiyatna et al.,2012; Sudarwati and Susilawati, 2013), while the population growth was limited by the equilibrium density around the supporting capacity of the environment, which was the maximum population size supported by its surrounding (Wirakusuma, 2003; Leksono, 2007). Based on the explanation, it was necessary to conduct a thorough evaluation on the population density and supporting capacity of cattle in Indonesia to discover its development pattern in accordance to the region’s potential. The advantage of this study was as recommendation and reference in developing cattle in Indonesia.

The identification of the population density of a logistic equation backwards in time associated with nonlocal dif- fusion and nonlinear reaction, motivated by biology and ecology fields, is investigated. The diffusion depends on an integral average of the population density whilst the reaction term is a global or local Lipschitz function of the population density. After discussing the ill-posedness of the problem, we apply the quasi-reversibility method to construct stable approximation problems. It is shown that the regularized solutions stemming from such method not only depend continuously on the final data, but also strongly converge to the exact solution in L 2 -norm. New error estimates together with stability results are obtained. Furthermore, numerical examples are provided to illustrate the theoretical results.

production. The body electrolytes sodium and potassium helps in maintaining new health. The arum hydroxide (gold particles) have been proved to be present in list cow urine by Junagadh Agricultural University Scientists. It acts as antimicrobial anti dose and immunostimulant agent. Recently the practice of using cow urine as therapeutics is formed as cowpathy. Though it has lot of disputes, the clinical properties of cow urine is scientifical validated by various authors (29). The application of cow urine in aquaculture as fresh cow urine (30-31) Cow urine Distillate (32) and also herbal cow urine extracts (33) have been explored in different fish models like Cirrhinus mirgala, Labeo rohita and Oreochromis mossambicus. The optimal concentration of CUD for promoting growth and biochemical composition is well documented Padmapriya and venaktalskhmi, 2014 for fishes. However the optimal concentration of cow urine distillate has not yet been established for zooplankton. Hence the present study has been attempted to find the optimal concentration of CUD to be treated for copepod culture. From the results obtained, it is clear that 0.05% of CUD is the optimal concentration for plankton growth. This is in concentration with the results obtained for fish studies which showed 0.1% of CUD as the optimal concentration. This might be due to the difference in the body mass. Due to the higher body mass and complexity of the physiological systems, the fishes require 0.1% concentration of CUD to show maximum response. Whereas the copepods, which are micro crustaceans with less evolutionary development of physiological systems need only lesser quantity of CUD for exhibiting maximum response. The CUD shows a dose dependent effect on the population density of copepods. The lower concentration has

Population density data was sourced from the Socioeconomic Data and Applications Centre (SEDAC) at Columbia University. Three Year 2000 Gridded Population of the World version 3 (GPW v3) datasets with a spatial resolution of 2.5 arc-minute grid cells (~5 km² at the equator) were downloaded: Population Count Grid [3], Land and Geographic Unit Area Grids [4] and Population Density Grid [5].

Hence, the cost of providing public good will reduce at initial levels when population density increases and after a certain point, here the saturation point of the road, the cost of pro[r]

This paper demonstrates an evaluation of welfare policies and regional allocation of public investment using Data Envelopment Analysis (DEA). Specifically, the efficiency of the welfare policies of the Greek prefectures for the census years of 1980, 1990 and 2000 are compared and analyzed. The paper using bootstrap techniques on unconditional and conditional full frontier applications determines whether the government investments have been used efficiently by the local authorities in order to stimulate regional welfare among the Greek prefectures. Our empirical results indicate that there are major welfare inefficiencies among the prefectures over the three census years. The analysis reveals that the population density among the Greek prefectures hasn’t been taken into account in regional welfare planning over the years. In addition, the paper demonstrates empirically how the new advances in DEA analysis can be incorporated into different stages of regional planning investment and evaluation. In addition, the impact of external factors can be directly measured and evaluated accordingly.

From 1936 to 1966, there were four redrawn wards comprising 742 acres North City, Rotunda, Royal Exchange and Mansion House, and since 1971 there are seven wards covering the same area..[r]

This programme has faced numerous challenges, among them insecticide resistance, non-compliant human behaviour, changes in biting habits of the vec- tor, changes in species composition, and vector density. It has been shown that Anopheles gambiae has devel- oped resistance to pyrethroids in western Kenya [6]. Bit- ing behaviour has seen a small but significant increase in early biting of malaria vectors in the western Kenya lowlands [7]. The proportion of Anopheles arabienis has progressively increased in the western Kenya highlands [8]. Although high ownership of ITNs has been reported in western Kenya, the usage has not been as high [9, 10]. Consequently, this has led to high transmission of the malaria parasite in the population with low usage.

under current conditions on Savannah River Site, the range-wide USFWS target of >45 pines >35.6 cm dbh/ha had slightly negative effects on resource selection by foraging RCWs. In contrast, selection of habitat satisfying the lower piecewise regression breakpoint of >22 pines >35.6 cm dbh suggests reducing the minimum requirement for large pines would provide a more appropriate target to maintain open canopy structure and moderate stocking densities that are associated with increased RCW productivity (e.g., James et al. 1997, 2001, Walters et al. 2002). In Florida, Hardesty et al. (1997) reported inverse relationships between RCW group reproduction and BA of pines >30.5 cm dbh and density of all pines >25.4 cm dbh/ha within group home ranges, suggesting canopy closure due to increased pine densities, including large pines, can decrease habitat quality and reproduction. Natural pruning could occur at greater rates in dense pine stands, which can limit prevalence of large dead branches that support high arthropod biomass in RCW foraging habitat (Smith 1955, Hooper 1996). Additionally, high stand densities could decrease levels of calcium and nitrogen in the soil, which in turn may indirectly limit nutritive value of RCW arthropod prey (Taylor 1986, Graveland and Van Gijzen 1994, James et al. 1997, Palik et al. 1997). Recent studies reported a higher threshold for pines >35.6 cm dbh/ha could be adopted on many other sites but would require site-specific adjustments (McKellar et al. 2014). Based on our results, Savannah River Site would require site-specific adjustments that lower the threshold requirement for density of pines >35.6 cm dbh from >45 to >22 pines/ha.

With this model as a guide, experimental data were collected from laboratory populations of Hippelates pusio Loew within the range of 0.15 to 25 flies per cm3 (sex ratio 1: 1), which rev[r]

On both the theoretical and empirical sides of this argument, we also need to explore the link between entropy, information and dimension (Takayasu, 1989); for in doing so, we are likely[r]

Cue rate (number of cues per unit time) is a special multiplier required for any cue-based method of density estimation. Cues might be as diverse as dive starts, clicks, calls, songs, or any other event produced by the animals that might be detected acoustically. Naturally, cue rates varying over time or space will have impact in comparisons across time or space (or both). This variation might be due to say behavioural state or sex-ratio differences over space and time, and must be accounted for. Cue rates are not well known for most species, and we anticipate future research focused on estimating and modelling these as a function of additional covariates such as time of day, season, sex, behaviour, etc. The cue rate also depends on variables that are usually not observed during the survey period. As an example, animals might produce cues at very different rates depending on behavioural state, hence biasing results if cue rates are estimated under a behavioural state other than that observed during the survey itself. This adds complexity, as it necessitates prediction of the cue rate for the animals in the study area while the survey was underway. Tags deployed on animals, like the A-Tag (Akamatsu et al., 2005), the Acousonde (Burgess, 2009), and the DTAG (Johnson & Tyack, 2003), can be extremely valuable for estimation of cue rates. Further developments in this area are essential, given that reliable and precise estimates of cue rate are fundamental to obtaining density estimates (see Marques et al., 2011, for an example). If animals produce different types of sounds, or if cue rate varies over time, there are clear benefits in focusing inference on cues or time periods for which the variability across animals is low. Note however that as we are interested in a mean cue rate, provided the sample size is large enough we can always obtain it with high precision. On the other hand, if the survey period itself is short, then whether the long-term average is reasonable or not becomes an issue. Therefore, given that the variability in cue rate is directly reflected in the variance of the density estimate, careful consideration of what and when to survey might lead to significant increases in efficiency. For