Questions related to Demography
I am doing my master thesis regarding waste management behaviours. The variables are as follows:
DV = waste management behaviours, Ordinal
IV = People's perception on waste (there are 4 IVs in total), Ordinal
Moderating variable= socio demographic factors (5 MVs in total), Nominal
I did regression analysis for DV and 4 IVs. I would like to know which process should I do in stata to test whether demographic factors can moderate the relationship of DV and IV. Thanks in advance. Have a nice day.
I would like to conduct a social research like "Reducing the marital dissatisfaction through assertiveness techniques". In my research, is it essential to control the demographic factors and how?
Or was it the other way around?
Is the diffusion property of idea outliers from agents simply a numbers game and causes cultural movements due to critical mass properties?
I am looking for a publicly available database that contains data on maternal demographics as well as their children's health outcomes, cancer incidences, etc.
We are analysing the geometric morphometrics of different populations of an orchid, including populations distributed along its geographic range.
We aim to test the central-periphery hypothesis, which expects lower morphological diversity at the distribution edges. We were able to use MorphoJ at several steps of morphometric analysis, from organizing the landmarks to running PCA, Anova tests etc.
However, we are struggling to find out how to measure the morphological diversity of each population. I believe it is something related to a measure of dispersion of the morphological variability of the individuals from one population, comparing to the centroid of the same population. But there are other methods in the literature.
Thus, I would like to ask you if MorphoJ could be used to provide a morphological diversity estimate for each population we are analysing.
Hi, everyone. :)
Language maintenance and language shifting is an interesting topic. Talking about Indonesia, our linguists note that until 2022 Indonesia has 718 languages. Indonesia really cares about the existing languages.
One thing that is interesting, language maintenance and language shift are also influenced by geographical conditions.
To accommodate 718 different languages, Indonesia has a geographical condition of islands. If we move from island to island in Indonesia, the use of the language is very contrasting, there is contact of different languages between us.
Some literature states that language maintenance and language shift are strongly influenced by the concentration of speakers in an area.
So, in the developments related to the topic of language maintenance and language shift regarding geographical conditions, to what extent have linguists made new breakthroughs in this issue?
I think that the study of language maintenance and language shifts related to regions is the same as the study of food availability or state territory which makes the area the main factor for this defense.
I throw this question at all linguists, do you have a new point of view in the keywords language, maintenance, and geographical.
Kind regards :)
Assuming that a researcher does not know the nature of population distribution (the parameters or the type e.g. normal, exponential, etc.), is it possible that the sampling distribution can indicate the nature of the population distribution.
According to the central limit theorem, the sampling distribution is likely to be normal. So, the exact population distribution can not be known. The shape of the distribution for a large sample size is enough? or It has to be inferred logically based on different factors?
Am I missing some points? Any lead or literature will help.
I am a very curious person. During Covid-19 in 2020, I through coded data and taking only the last name, noticed in my country that people with certain surnames were more likely to die than others (and this pattern has remained unchanged over time). Through mathematical ratio and proportion, inconsistencies were found by performing a "conversion" so that all surnames had the same weighting. The rest, simple exercise of probability and statistics revealed this controversial fact.
Of course, what I did was a shallow study, just a data mining exercise, but it has been something that caught my attention, even more so when talking to an Indian researcher who found similar patterns within his country about another disease.
In the context of pandemics (for the end of these and others that may come)
I think it would be interesting to have a line of research involving different professionals such as data scientists; statisticians/mathematicians; sociology and demographics; human sciences; biological sciences to compose a more refined study on this premise.
Some questions still remain:
What if we could have such answers? How should Research Ethics be handled? Could we warn people about care? How would people with certain last names considered at risk react? And the other way around? From a sociological point of view, could such a recommendation divide society into "superior" or "inferior" genes?
What do you think about it?
Note: Due to important personal matters I have taken a break and returned with my activities today, February 13, 2023. I am too happy to come across many interesting feedbacks.
I hope you are doing well. I am trying to get the sampling distribution for a normally distributed population in R and plot a boxplot of that sampling distribution.
However, I am not so sure how to go about it. I appreciate any tips
For planning and decision making, we need population distribution data. I want to know how to integrate population of an area and remote sensing to monitor SDGs relating to Coastal hazards and access to resources.
I looking for a lab manual that details the equipment used and lists the procedures for estimating nematode population distributions in a given soil sample. I have an idea of a process one can follow but am wondering about efficient methods. Is there documented standard process?
I have an experimental data set in which two variables coming from normally distributed populations are correlated. The correlation, from theoretical models, follows a power law, i.e. y=a*x^b.
With a NLS method, I found an R-squared of 0.89. When log-transforming the data and using Pearson correlation, the Pearson coefficient is -0.74. It is clear that data are correlated, but which method is the most correct to present the results?
The timing of de-domestication events could be inferred using genomic data. Weedy rice is one of the few reported de-domesticates that has been thoroughly investigated using molecular data. Both the coalescence method and demographic analyses suggested that weedy rice has evolved from cultivated rice multiple times since 1000 years ago, which is much more recent than the previous estimate of 5600 years based on a demographic scenario analysis of weedy rice from Asian high latitudes. Some recent de-domestication events have been identified based on a comparison of genomic information from different rice lineages. According to phylogeny analysis that included the parental pedigrees, some de-domesticates were directly descended from modern cultivars only decades ago.
[Extracted from Wu, D., Lao, S. and Fan, L., 2021. De-domestication: an extension of crop evolution. Trends in Plant Science.]
Therefore, I am interested in evaluating the divergence time of different Oryza types in the "Domesticated-Wild-Weedy complex (DWWC)" in the Sri Lankan rice ecosystem.
Discussion is open for possible analysis particulars to evaluate the timing of de-domestication events in the complex rice ecosystem (DWWC).
Say a researcher was interested in determining the number of adults vs. juveniles of species X trapped during a small mammal survey. Does there exist a relatively reliable way of doing this based on standard field measurements?
Let’s say a total of 200 individuals of species X were sampled, and the following data recorded: sex, total length, tail length, hind foot length, ear length, and weight. For the sake of this question imagine no additional data is available (e.g. additional observations recorded in the field, access to collected specimen material, etc.).
- Is there a way to ascertain a point or “threshold” from a range of data based on the distribution of values to distinguish between juvenile and adult individuals with a meaningful degree of accuracy? For example, male species X with weight > 142 g = adults; < 142 g = juveniles.
- If yes, which of these measurements would be most indicative? Or perhaps a combination/ratio of more than one (e.g. ratio of hind foot length to ear height > 1 = adult, etc.)?
Thanks, and looking forward to the feedback.
I applied EFA and save anderson-rubin score of each factor.
then I applied ANOVA to check the effect of demographic factor on these factors.
But which mean value will be shown in descriptive table of factor across different demographic factor (like age).?
if i applied ANOVA on Anderson-rubin Score then the mean value comes in +ve and -ve value.
Is it correct way to show the mean in descriptive table in research paper?
your response will be highly appreciated
A population decline (or depopulation) in humans is a reduction in a human population caused by events such as long-term demographic trends, as in sub-replacement fertility, urban decay due to violence, disease, or other catastrophes. According to a controversial theory: shrink and prosper, the accompanying benefits of depopulation could be identified after the post-Civil War Gilded Age, post-World War I economic boom, and the post-World War II economic boom.
I am working on longitudinal data (from a panel survey) that were analyzed using mixed effects modelling to estimate risk differences, the outcome is contact with any health provider, and predictors being both fixed (like socio demographic factors) and random (like having symptoms). I am finding it difficult to interprete the results of the adjusted models, certainly because I don't fully understand the concept of this modelling technique. Could you advise on any reference for (non statistician) clinicians? Thank you
Demography basically, has to do with the human population in terms of sizes, density, changes which occurs overtime, and so on. Demography can have both positive and also, adverse effects on the society.
What are the effects of demography on social inequality in different regions across the globe?
Demography basically, has to do with the human population in terms of sizes, density, changes which occurs overtime and so on. Demography can have both positive and adverse effects on the society.
What are the effects of demography on social inequality in different regions across the globe?
Hello to everyone and, first of all, I hope you are doing well in the difficult circumstances we are all in..
I am a PhD student and investigate female labor supply decisions and how several demographic factors affect labor supply . I use data from the British Household Panel Survey in the UK. The epicentre of my research is how health-related variables affect women’s work decisions. My models so far are dynamic discrete choice random effects probit models where health is considered as exogenous.
I am currently investigating the dual causality between health and employment statuses. I want to try to examine the endogeneity issue of the health-related variables in a framework of a joint estimation model, i.e. in a two-equation system and not in separate equations (for example, Alessie, R., Hochguertel, S. and van Soest, A. 2004, ‘Ownership of Stocks and Mutual Funds: A Panel Data Analysis’, The Review of Economics and Statistics, vol. 86, no. 3, pp. 783-796).
I searched a lot but cannot find out how I could execute such a dynamic approach with the use of a statistical package. Could anyone provide me with some technical help , are there any commands that I could use in Stata, Gretl or R?
I have been struggling with deciding the best analysis for my data. I have an online survey consisting of two questionnaires: one on academic stress and the other on premenstrual symptoms. The premenstrual symptom questionnaire consists of three sub-scales; the academic stress scale consists of two. Both use likert type scales. I'm also going to look at how social demographic factors (ethnicity, age and level of study (undergraduate vs postgraduate)). I was thinking that a multiple regression would that be the best analysis of data to do?
I'm looking for research studies / articles (dated 2015 to 2020) conducted on differences in employees' PsyCap levels based on demographic factors, such as gender, age, educational level, non-managerial positions, managerial positions, and departments (sales, call-centers, amongst others).
Any ideas which articles to refer to?
my question is related to post hoc when dealing with huge size effect, e.g. Cohen's d> 2.
No one asked me to perform this kind of analysis, it is just a matter of personal curiosity. It is claimed that in post hoc power analyses, p-value is in a 1-to-1 relation with the observed power, and this is clearly true. However, for huge size effects, I used a Monte Carlo simulation to understand if this holds anyway. Surprisingly, the curve shows a quite big area instead of a line (you can see it attached, N=1000 experiments from normally distributed populations).
Am I wrong to say that for huge size effects, PHP analysis gives reasonable results?
Stochastic Mortality Models (SMM) have often be used by insurance coy to model the risk of mortality in general, but I want to apply and restrict it to childhood mortality. How do I go about it?
I am doing research related to efficient fund utilization of Swachh Bharat Mission by different states of India. I have collected data related to institution, demography, ease of doing business, etc. I have calculated relative efficiencies of Indian states towards utilizing the funds for Swach Bharat Mission and delivering tangible outcomes. I have used the technique of Deta Envelopment Analysis for the same. Kindly help me with the issue. I have tried bootstrapping, but it did not work well.
I am using EpiInfo to analysis survey data.
However there some issues that I still do not know how to figure out. What is the formula used to compute the Population survey. I found one n = [DEFF*Np(1-p)]/ [(d2/Z21-α/2*(N-1)+p*(1-p)]. But I do not know how to estimate the "Np". What makes NP different from N ?
Supposing that I have
Trying this I have a sample size of 484 from EpiInfo.
Please could you help me.
I want to estimate the unknown parameter of normal distribution by MLE of my data set of weight of new born babies of Nepal Demographic Health Survey. I want to construct confidence interval of parameter.
Interest rates have plummeted in many western economies. Japan and Germany are, among others, countries, in which interest rates are particularly low, as seen by negative returns on government bonds. Both countries exhibit an advanced process of demographic aging.
Some people argue that low interest rates reflect the efforts of the pre-retirement cohorts to save for pension schemes. The large supply of money and the demand for investments drives house, bond and share prices and weighs on the interest level.
Once the numerous cohorts ("the baby boomers") reach retirement, they start to de-invest on a large scale, thus initiating a price decay of houses, shares and bonds ("asset melt down"). Interest rates were then to recover.
My question is: Do you think that such a scenario is likely? Are we living in a asset price bubble driven by the excess money of cohorts in the pre-retirement age? Or which alternative approach can account for the present low interest rates?
how can we model the distribution of the population from a facility to other near facilities (for example a school or a hospital) when it is decided to be destroyed
ANOVA is a time consuming method (calculating SSC, SSE, and so on). How can we manually test a hypothesis that there is no difference in means of three (or four) samples?
Assuming sample data are taken from normally distributed populations.
Discussions about how to deal with demographic ageing and shrinking are taking place in increasing numbers of countries, especially in East Asia and Europe. Japan is my focus. So, is large scale immigration a solution to the perceived problems of ageing and depopulation in developed countries, but particularly in Japan and East Asia?
Is there any software where I can make custom population models?
I want to have mathematical model for some cell population distribution.
What I am looking for is something that will allow me to:
- define total area,
- define variable population density at various points in that area (fig 1 and 2)
- find out total population in any custom sub-area. (fig 3)
I have added some diagrams for example
In the study, l selected 12 clusters based on some specific criteria and have obtained the estimated population of each cluster. The clusters have very unequal population distribution estimates and are heterogeneous. The challenge l have is calculating the sample size for the clusters so that the sample size reflects the population distribution of the clusters.
To assess the role of television in social change I am going to develop a questionnaire which will be simply a portion of demographic profiles of respondents, can anyone help regarding what variables of demography should i affix in my questionnaire??
example prolonged second stage of labor and and demographics factors with fetal and maternal outcome.
I intend to measure the individual and interactive impact of demographic factors as moderators, namely the age, gender, education, income and native(rural/urban), on five latent endogenous constructs. I humbly request to refer me to some study material to learn the methodology for the same in SEM using AMOS.
My objective is to know about the population mean of a population of which the form of probability distribution is unknown. It is possible to obtain estimate of the population mean from a random sample drawn from the population. It is also possible to obtain confidence interval estimate of the population mean with desired degree of confidence (which is less than 100%). By obtaining confidence interval estimates with the confidence 99% or 99.73% or 99.9% etc., it is possible to obtain almost certain interval estimate of the population mean.
My thrust is to obtain the certain interval value of the population mean with the help of one random sample or more random samples drawn from the population.. However, it is beyond my knowledge on how to obtain this. Thus, the question is
" How can we obtain certain interval value of the mean of a population if the form of probability distribution of population is unknown ?.
There are many definitions of model-based sciences, which have a different philosophical meaning. This is due to the fact that the signification of the term ‘model’ is ambiguous: some of these theories may be based only on one kind of models and are unable to integrate in their field the other ones. I will give here two examples of model-based theories, but you can find many other ones.
The semantic theory of models, more recently called a ‘model-based’ view of science attacked the empirical explanatory models, which dominated the philosophy of science before the 1960’s, and promoted formal explanatory models during the following decade. Even if various versions of this approach differ (Patrick Suppes, Frederick Suppe, Bas van Fraassen, etc.), it continues to be developed nowadays. In this approach models, as abstract representations of some portion of the world which are different from empirical laws, are the central element of scientific knowledge. For 21st century researcher, computer modelling will permit the statement, manipulation, and evaluation of more and more complex theoretical models, as Thomas Burch (2017) said. But how in this case identify the relations between the theoretical model and the empirical observations, and test the fit of a simulation model? There is a real danger to construct theoretical models without any relationship with observed data and no way to verify this relationship.
The mechanistic view, which had been mainly developed for biological sciences during the 1990’s, is also considered as a model-based science. Again various versions of this approach differ (William Bechtel, Carl Carver, Stuart Glennan, etc.), but its development nowadays is increasing not only for biological sciences but for social sciences. A more recent version of this view is given by Robert Franck (2002) as the functional-mechanistic approach. As the semantic view the mechanistic theory of models rejects the empirical explanatory approach, and may appear as similar. But, while for the semantic approach a theory is a formal system empty of any empirical content, the mechanistic one infers, from the sustained observation of some property of nature, its functional structure –in classical terms the axiom, form, principle or law- which rules the process generating this property, and without which this property could not come about as it does. By focusing on the mechanism, generating a social property, the functional structure is treated independently of the causal structure, and may be generalized. We used this approach, with other researchers, in a recent paper on model-based demography (2017).
Under this question, I would like to discuss here the different model-based theories, their main aims, and the use of the term model.
Burch T. (2017). Model-based demography. Springer.
Courgeau D., Bijak J., Franck T., Silverman E. (2017). Model-based demography: towards a research agenda. In Agent-based modelling in Population Studies, Grow A., van Bavel J. eds., Springer.
Franck R. ed. (2001). The explanatory power of models. Kluwer Academic Publishers.
Mäki U. (2001). Models: philosophical aspects. In International Encyclopedia of the Social & Behavioral Sciences, Smelser N.J., Baltes P.B. eds., Elsevier Ltd.
In socio-environmental impact assessment studies, the first step is to know the context where the scheme is going to be implemented. For that aim, it would be very useful to set an indicator system to define the different dimensions of the context: demography, phisycal geography, social aspects, economic structure, political characteristics... Lot of literature touch those dimensions, but separately one of each other. Adding those cited system, the final result is a quite long and complex indicator system. Suggestions about integrated context indicator system will be very wellcome. Thanks.
Hello, I have a question for demographers. I am working on population change in municipalities in Italy. Are there in demography single measures or indexes that combine and can be used to express population aging AND population contraction (growth) over time? In other words a single index that integrates how population get older (or younger) and shrinks (or expands)?
Thank you in advance!
I am looking for help finding clear and relatively certain material about the when and where of the migrations of haplogroup h, including any details about the cultures, the climates, and the geography. Thank you.
See details here:
Marie Durand was 123y old in 1883 and had been a widow for 96 years! Many details are given in a brief summary in Med Times Gaz 1883;2:282, so it should be easy enough for someone in France to check her out. Did she meet the current record holder, Jeanne Calment?
I want to calculate population density for Bolivia's municipalities. I have census data, but it doesn't have area (superficie) and I'm having trouble finding it. Help? I want to run some simple multivariate models to see what factors affect the 2015 municipal elections, but I want to control for population density. Thanks.
Ten years ago, I wrote with Atam Vetta a paper on 'Demographic behavior and behavior genetics', which showed clearly that Fisher's assumptions and heritability analysis are based on false assumptions (see the joint attachment). However behavior genetics had been increasingly used by a number of social scientists in psychology, in demography, in gerontology, in medicine, in biometry, etc. More recently, after sequencing the human genome, a number of scientists believed it would be possible to draw up a list of behavioral traits linked to each gene. Such a belief again was not verified., but this did not prevent behavior geneticists to argue that the links between genes and behavioral phenotypes will permit new advances in the understanding of human behavior.
There are in fact two questions to be answered: (1) Is human behavior influenced by genes? (2) If, yes, what is the 'magnitude' of this influence? The answer to the first question is evidently 'yes', and it can be said pointless. For the second question we can refer to Gilbert Gottlieb in a paper on 'Genetics and development' (2001): 'It is now known that both genes and environment are involved in all traits and that it is not possible to specify their respective weighting or quantitative influence on any trait'. He adds: 'this had been a hard-won scientific insight that had not yet percolated to the mass of humanity'.
This Nature-Nurture question is always under debate, and I think it may be of interest for many ResearchGate members to discuss it.
Looking for statistical information
- Immigration London (last 20 years)
- City government policies
- Socio political analysis
What are the parameters we have to include to get complete demography of certain population? Is there is any standard for conducting the same?
I have Bolivian census data for 2001 and 2012. There are noted discrepancies between the two, particularly the significant drop (as % of total) for "indigenous" population between the two. However, the data for 2012 seems to be on the basis of the total, but the 2001 data seems to be only on the basis of those who answered the question (in 2001 "non-indigenous" was an option, in 2012 it was not). Taken together, this suggests that the 2001 data significantly *overcounted* the share of indigenous population. However, I'm not a demographer. Any advice on how to proceed with using this data?
The discrepancy is large. If only looking at those who answered the question, then in 2001 the indigenous population was about 62% and dropped to about 40% in 2012 (which was calculated using the "didn't answer" figure). But if we use the "no answer" data from 2001, then the indigenous population in Bolivia dropped to 38% (a number closer to the 2012 figure). But does this mean that everyone has been overestimating the share of indigenous population for Bolivia for the past decade?
I want to collect (all) melioidosis cases between years 1995 till 2014 to study patient's data variables such as demography, type of melioidosis, predisposing factors and clinical outcomes. I'll do association analysis using regression and so on.
1. so for this project: the study design is Retrospective?
2. Shall I calculate sample size (remember I'm willing to take all cases within defined year range
3. Or some how, shall I use power sample size?
I am comparing longevity using as a proxy simulations of maximum conditional lifespan based on published matrix models. Most of the transitions that I’m using are based on annual population changes (from 1 year to the next year), however, some other transition matrices are based on variable time intervals (months, 2-year, 5-year, etc… How should I standardize these values to make them comparable? An easy way I have in mind would be just to divide the estimated lifespan by 12 in the case of monthly matrices or multiply by 2 when dealing with lifespan calculated based on biannual matrices, but I am not sure if that would be appropriated. Any suggestion?
Are long-lived species more resistant to extreme disturbances (i. e. heat waves, fires, hurricanes, etc) than shorter-lived species? If I’m right, longevity is driven by survival rates. Therefore, I expect long-lived species to be more resistant to external disturbances (at least disturbances that were frequent in the past), unfortunately I haven't found any empirical evidence of this pattern. I’d welcome any reference related to this topic. Thanks.
A survey give us just the distribution of deaths by age. to extract a complete life table, we need the population structure for the same period, and we need also a Crude Death Rate, and we can not une CDR calculated on the global population for this issue. I remind that the objectif of this process is to compare the survey life table and the official life table. My idea was to suppose any CDR (near to the official CDR) and to compare the form of the two mortality curves (Survey Vs Official statistics) .
Is this process doable and does it lead to the attended results?
I am trying to figure out what archaeologists and others mean when they say "demographic exhaustion". It is hard to find any specifics on *exactly* what processes are being invoked.
A small lutheran community coming from Germany exists in Lyon from the 16 century. This group owned a church, settled in Geneva from 1707.It was mostly composed of traders who went to Geneva four times a year for the holy communion. But, from 1770 onward, when the Calvinists from Lyons got their priest, the Lutherans went more and more to that church, letting down Geneva. For about 75 years, the Lutherans disappeared from Lyons. At the turn of the eighteen and nineteen centuries, the community spent her life in the shade of the Calvinist church. Between 1800 and 1850, the immigration movement of swiss, germans and Alsatians was quickening. In 1851, after multiples fruitless tries during the last fifty years, the Lutheran reverend Georges Mayer create an evangelic german church which is quickly linked with the Augsburg Confession. The german community managed the church for nearly 30 years until the arrival of the first French vicar in Lyons .For another 30 years, the relations were stormies between the two communities. The first world war marked the death of the german parish. The French church survived with difficulties during the twenties and thirties. The “renaissance” was due to two extraordinary personalities: André Desbaumes and Henry Bruston The Lutheran church became an inescapable part of the Lyons’s oecumenism and opened itself to the world.2007 marked the beginning of the merger between the Calvinist and Lutheran churches.
Most of the existing habitat suitability index (HSI) modeling are related to wildlife, very rarely used in case of plants. It is a simple mathematical expressions for calculating one or more environmental variables, not related to demography and survival of parental and their offsprings. The calculated HSI values typically mapped and analyzed potential distribution and high value indicated more suitable habitat, whereas low value is unsuitable habitat.
It is usually assumed that the demographic transition (major intrinsic changes in fertility / mortality and life expectancy) is a purely human population phenomenon. There are several explanatory theories that suggest the next causes: (1) social development [Condorcet, 1794], (2) economical/technological changes [Galor,O. 2011], (3) evolutionary change [Clark, G. 2007].
However, these factors (in some degrees) operate within non-human populations. Moreover, some of the eco-evolutionary models indicate that the demographic transition may be a common consequence of co-selection adaptation of individual to each others within group) in hierarchically structured populations.
There are several (not all) cases in which demographic transition can be suspected:
- Regular extinction/ reemerging of local populations without apparent external reasons.
- Pronounced long-term demographic changes, which cannot be explained by environment variations or inbreeding depression.
- Sudden appearance of unusually old or unusually big individuals in populations.
- Sadden epidemic outbreaks of previously limited infections.
- Visible absence of equilibrium size (carrying capacity); variations of population size weakly correlated with environment factors.
- Fusion/split of local populations.
So, is a demographic transition in the non-human populations? I would be very grateful if you share your thoughts or maybe data that will help answer this question.
I am attempting to edit a custom life table in DemProj - it seems to be constructed by using splicing method to create model life tables based on Brass. Does anybody have experience doing that?
The common or garden, or simple assumption about the relationship between population size and resource consumption states that there is a direct relationship between the two. Clearly this assumption misses the point as to who is doing the consuming, when comparing those in developed and less developed regions. But I am wondering if the reverse assumption also holds, that population losses would lead to consumption falls, and whether this has been researched anywhere.
I am trying to frame the results recently obtained in my work, about Rattus rattus' (amongst other invasive species) density on islands. I can't seem to find any extensive review about density ranges of this species. Any help would be more than welcome.
Currently, there is a large number of scientific papers on simulation of the flooding of coastal areas for different scenarios of sea level rise. In 2013, there was published a comprehensive report by IPCC on this issue (Climate Change 2013: The Physical Science Basis). When testing the most relevant term “sea level rise” with Google Scholar engine (advance search, with the exact phrase), we have received 148,000 (anywhere in the article) and 5,840 (in the article title) matches.
But a much more challenging task is to model (forecast) human migration from flooded coastal areas. Testing through Google Scholar the terms “sea level rise” and “migration” in combination with each other, we have noticed that there is a lot of qualitative research, considering the local migration in the wake of small plots of land being flooded (eg., in estuaries). At the same time, we have not found any papers on modeling migration flows over large territories. For carrying out such studies one should use, in our opinion, detailed maps of the population density for these territories and should be able to establish linkage between the area of the gradually flooded territories and the number of people living there, taking into account the demographic forecast.
We must also understand where to send emerging migration flows so that it does not affect the neighboring territories. In coastal areas to be flooded in the first place, it is vital now to have plans for the relocation of people. This problem is of great geopolitical importance for global sustainability. Meanwhile, on the Internet we can only read that according to the worst-case scenarios of sea level rise, millions of people will have to migrate. As I see it, this problem can be solved effectively only by creating a network consortium within FP8. What do you think about it, dear colleagues?
Do all time series regressions need stationarity tests, or is assumption of stationarity explanatory variables enough?
We already tried with Robert Franck to find an answer to this question. But if it is clearly possible to give different paradigms for these sciences, a real axiomatic approach is not yet attained in our view.
In social sciences many kinds of rates are estimated but they rarely permit a true international comparison due to the effect of time, space, etc. For example, for internal migration only instantaneous rates of changes of residence are comparable. It will be useful to see in other domains which are the rates that permit an international comparison.