Poster session on Spatial demography

The Moscow Region is a part of the largest capital region in the Russian Federation with diversified social and economic potential and specific demographic characteristics.
Moscow has always transfered part of capital functions to suburbs. At the same time Moscow Region played a role of the "buffer" constraining a centripetal flow of migrants regulated by methods of a planned economy. As a result by the beginning of market transformations of the 1990th spatial placement of the population in Moscow Region wasn't responsible to market economy characteristics.
During the period between population census 1989 and 2010 there is a tendency of population movement from remote (Kolomna, Orekhovo-Zuevo etc.) to closer to Moscow districts (Balashikha, Khimki etc).
Moscow changes nature of placement of settlements in the Moscow region, it introduces city way of life through dachas. Dachas begin as a curtsey to rural life, but over time become more comfortable and lose the initial functional purpose.
Modern "country" possession of Muscovites – cottages are used not only as seasonal but also as the second constant housing and number of such settlements increases every year.
The way of suburbanization in Moscow Region depends on final decisions about development of the territories included in borders of «Big Moscow».

Among western developed countries (WDCs), the ‘Anglo Settler’ Countries (Australasia and North America) are demographically rather deviant cases. They seem to have sustainable positive growth: TFRs that are close to, or even above, replacement; large inflows of migrants effecting migration replacement; and, relatively speaking, more youthful age structures with extreme structural ageing and natural decline arguably a more distant prospect. It seems, therefore, almost an oxymoron to apply the term ‘depopulation’ to their demographic patterns and trends. Yet, if one scratches beneath the surface, demographic sustainability appears less assured; in fact their population dynamics are a variant of the more general model found across the WDCs. Indeed empirical evidence identifies that the shift to zero growth/permanent depopulation has been unfolding across subnational New Zealand and Australia for some time; one region after another passing thresholds beyond which a resumption of natural – then absolute – growth is unlikely. This paper demonstrates the use of a set of such indicators which can be thought of as early warning signals. The sequential passing of these thresholds foreshadows the end of growth for affected regions, and may assist in the development of policies which proactively rather than reactively manage the process.

The modern dynamics of the Belarusian urban population differs from the total population dynam-ics in the country. The total population size of Belarus decreased from 10.1 to 9.5 million or 6 %, the ur-ban population increased from 6.2 to 6.7 million, or 8.2% in the period from 1989 to 2009 years. The level of the natural population loss remains high in the rural area (13.6 ‰), while a stable natural population in-crease is observed in urban settlements (0.5‰, 2010).
Indicators of natural movement of the urban population of Belarus have significant differentiation between regions. On the basis of factors which generate demographic situation in Belarusian cities spatial regularities of dynamics of urban demographic development have been established. Currently two region types can be distinguished in the Belarusian urbanised space: 1) central-south-western type with population increase; 2) north-western-southern type with population decrease. The first type includes the cities of Brest, Grodno, Minsk regions and the Minsk city with the highest indexes in the Brest region (2.4 ‰). The second type includes the cities of Vitebsk, Gomel and Mogilev regions with the highest natural population loss characteristics in the Vitebsk region (2.7 ‰).

In the four past decades, demographic researchers noticed the emergence of a type of cities defined as medium-sized cities, playing an important role in the regional imbalance in the Brazilian urban universe. Using the data taken from IBGE (2010), this study finds that the country counts 245 medium-sized cities with populations ranking from 100.000 to 500.000 inhabitants, which are experiencing a remarkable demographic performance by showing larger population growth rates than the country as a whole. In fact, this kind of demographic size entitles such cities to fill up the position of regional poles, functioning as a leading base for balanced economic growth and territorial organization of activities, providing good accessibility to services of general economic interest, as an integrated system connected to stronger metropolitan areas. This paper aims at a twofold objective. First, it analyses the demographic growth and social dynamics in medium-sized cities, presenting a brief review on the literature which explores the features of the urban poverty in the Brazilian context. Second, it approaches the subject of inequalities and spatial poverty distribution, using a demographic methodology and data from the National Population Census of 2010, in an attempt to better understanding both processes.

The measurement of spatial segregation phenomena has been mainly studied in the United States since the 1950’s. A multitude of segregation indexes have been elaborated to analyze all the aspects of this topic. In 1988, Massey and Denton synthetized the scientific knowledge by a 5-dimensional classification: evenness, exposure, concentration, centralization and clustering. This presentation displays us to demonstrate that these dimensions can be approached by a single index: the Duncan’s segregation index (1955). By using a method of spatial decomposition of the index, the proposed methodology allows to free itself from the aspatial character of the index and to create a map representation which visually reproduces the spatial structure of the phenomenon. Applied to the French urban areas this methodology allows demonstrating that the spatial structure of segregation is organized according to a sectorial model (Hoyt) for socioeconomic criteria, and follows a concentric model (Burgess) for sociodemographic criteria.

This paper analyses the causes of geographical fertility differences within metropolitan areas, by focusing on the metropolitan regions of Barcelona and Madrid. Our main hypothesis is that fertility differences among municipalities would be based on two main elements: 1) the degree to which the suburbanization process has developed –which, in the Spanish case, has rapidly expanded and has been highly selective– and 2) foreign immigration’s local level impact. Results from the descriptive analysis show that despite regional fertility levels have become increasingly similar within Spain, there are increasingly strong fertility variations within Barcelona and Madrid metropolitan regions. While the core cities have low, late and more stable fertility levels, the suburban periphery municipalities have earlier and higher fertility levels. This would confirm the fact that different areas within the metropolitan regions are increasingly specializing in a particular function –productive or residential. These settlement preferences are in turn dependent on nationality and the life cycle stage. Factor analysis using Movimiento Natural de la Población (vital statistics) data allow us to investigate the causes of such differences.

To understand the determinants of raising fertility rates after 2005 in Japan, we investigate the spatial variations of the relationship between fertility and their covariates using geographically weighted regression models in two time points. Our sample is 1,853 towns and villages based on 2010 administrative boundaries. Total fertility rate(TFR) of small areas at 2005 and 2010, which is adjusted by the child-woman ratio, is used as a dependent variable. As for the explanatory factors, we focus on female labor force participation, child-care availability, and economic conditions. We compare coefficients of the GWR between 2005 and 2010. All coefficients on TFR have statistically significant geographical variations. The regional fertility rates rose markedly in the urban areas, where the female labor force participants shows positive relationship with TFR and such areas spread out.

With an estimated 59,000 maternal deaths annually, Nigeria contributes about 10 percent of the world’s maternal deaths. Antenatal care is one of the major interventions recommended to reduce maternal mortality. Previous studies on antenatal care have focused on individual level determinants, but the role of community factors has been largely ignored. The study examined community contextual factors influencing the use of antenatal care in Nigeria using data from 2008 Nigeria Demographic and Health Survey. We employed multilevel logistic regression techniques at the individual and community levels. Results showed that women from communities with high proportion of women that delivered in a health facility (OR=4.2, p<0.001) and high proportion of educated women (OR=3.4, p<0.001) were more likely to attend four or more antenatal care visits, and in the first trimester compared with those residing in disadvantaged communities. Neighbourhood factors also acted as moderators on the association between individual level factors and antenatal care. Findings suggest the need to tailor interventions at the community context. Specifically, interventions aimed at improving antenatal care visits and in the first trimester, should focus on increasing women’s education and health facility delivery in disadvantaged communities.

For many countries, in particular in sub-Saharan Africa, Demographic and Health Surveys (DHS) are the main national source of data (depending on the subject). Several DHS collect latitude and longitude of surveyed clusters but the sampling method is not appropriate to derive local estimates: sample size is not large enough for a direct spatial interpolation.
We develop a methodological approach for estimating a proportion by using kernel density estimators with adaptive bandwidths of equal number of persons surveyed. The method was tested by creating a fictitious country from which survey datasets were produced. We compared the prevalence surface estimated from survey data with the model’s original prevalence surface.
This method makes it possible to achieve a smoothing effect that adapts to the high irregularity of spatial distribution among the survey clusters. The surfaces thus generated are relatively accurate for densely populated areas and strongly smoothed in sparsely surveyed areas. Although local variations were filtered out, the regional component in the spatial variation of prevalence was reproduced, and the estimated prevalence surfaces could be interpreted as regional trend surfaces.
Furthermore, this approach could be easily applied using prevR, a dedicated package for the statistical software R.

Malaria is a major obstacle to socio-economic development in Sub Sahara Africa, with about 90% of all recorded cases worldwide. The disease accounted for nearly one million deaths in 2008, mostly among children living in Africa. Furthermore malaria is a leading cause of under-five deaths in SSA where a child dies every 45 seconds of Malaria. It is of high importance to properly identify risk factors that are associated with the incidence of Malaria. Analyzing spatial data must be done with caution as observations may now be correlated, hence ordinary statistical methods assuming independence of observations are no longer valid. Ignoring the structure of the data may result in asymptotically biased parameter estimates. A crucial step in modeling spatial data is the specification of the spatial dependency, by choosing the correlation function. However, often the choice for a particular application is unclear and diagnostic tests will have to be carried out following fitting of a model. To resolve this problem, we adopt a more robust method for modeling spatial correlation by simultaneously solving the combined estimating equations using different working correlation structures. We illustrate our method by modeling the spatial correlation of malaria incidence in Liberia and Madagascar.