Spatial Demography最新文献

This study analyzes at a local level (i.e. census tract) the spatial patterns and main contextual factors related to the electoral resurgence of the extreme-right party (VOX) in Southern Spain (Andalusia) in 2018 and 2019. The 2019 electoral data was associated with the percentage of total foreign-born population, degree of territorial concentration of economic migrants, average income level, percentage of elderly people, urban/rural areas and the percentage of vote for VOX in 2018 (t − 1). We used a global and local spatial autocorrelation analysis to detect the spatial patterns of the vote for VOX and a spatial Durbin regression model to assess the role of contextual variables and spatial effects. The results underline the importance of space in modelling the vote for VOX and point to the existence of a spatial diffusion process. Previous electoral behavior and the urban milieu also play key roles in explaining the vote for VOX. Moreover, the territorial concentration of economic migrants is negatively related with the vote for VOX, which illustrates the positive character of interracial contact.

Studies have documented significant geographic divergence in U.S. mortality in recent decades. However, few studies have examined the extent to which county-level trends in mortality can be explained by national, state, and metropolitan-level trends, and which county-specific factors contribute to remaining variation. Combining vital statistics data on deaths and Census data with time-varying county-level contextual characteristics, we use a spatially explicit Bayesian hierarchical model to analyze the associations between working-age mortality, state, metropolitan status and county-level socioeconomic conditions, family characteristics, labor market conditions, health behaviors, and population characteristics between 2000 and 2017. Additionally, we employ a Shapley decomposition to illustrate the additive contributions of each changing county-level characteristic to the observed mortality change in U.S. counties between 1999-2001 and 2015-2017 over and above national, state, and metropolitan-nonmetropolitan mortality trends. Mortality trends varied by state and metropolitan status as did the contribution of county-level characteristics. Metropolitan status predicted more of the county-level variance in mortality than state of residence. Of the county-level characteristics, changes in percent college-graduates, smoking prevalence and the percent of foreign-born population contributed to a decline in all-cause mortality over this period, whereas increasing levels of poverty, unemployment, and single-parent families and declines manufacturing employment slowed down these improvements, and in many nonmetropolitan areas were large enough to overpower the positive contributions of the protective factors.

India has substantially reduced the burden of under-five child malnutrition over the last two decades. Despite this, it is still gigantic and differs remarkably across districts, while the demographic and socio-economic groups are most affected by it. This paper aimed to decrypt the place-specific spatial dependence and heterogeneity in associations between district-level nutritional status (stunting, wasting and underweight) and its considered correlates using a geocoded database for all 640 Indian districts from the latest fourth wave of the National Family Health Survey, 2015–16. Univariate Moran’s I and LISA statistics were used to confirm the spatial clustering and dependence in under-five nutritional status. The Ordinary Least Square (OLS), Geographically Weighted Regression (GWR), Spatial (lag/error) models were employed to examine the effects of correlates on the district-level nutritional status. The mean (Moran’s I) district-level stunting, wasting and underweight were 38% (0.634), 21% (0.488) and 36% (0.721), respectively. The GWR results disclosed that the spatial heterogeneity in relationships between district-level nutritional status and its driving forces were strongly location-based, altering their direction, magnitude and strength across districts. Overall, the localized model performed better, and best fit the data than the OLS and spatial (lag/error) models. This nationwide study confirmed that the spatial dependencies and heterogeneities in the district-level nutritional status indicators were strongly explained by a multitude of factors and thus can help policymakers in formulating effective nutrition-specific programmatic interventions to speed up the coverage of under-five malnutrition status in most priority districts and geographical hot spots across India.

Childhood diarrhoea accounts for over 15% of all under-five deaths in Africa. The disease is exacerbated by social vulnerability. This study operationalizes social vulnerability by using three indicators: water poverty, sanitation and assets, to capture social disadvantage, which measures individual or community resources to prevent or mitigate health effects. We particularly investigated the relationship between childhood diarrhoea and risks emanating from multiple stressors: water poverty, poor sanitation and low wealth status, which define social vulnerability. Using data from the 2013/14 Malawi MDG Endline Survey (MMES), we fitted spatial models assuming that the combined effect of social vulnerability indicators, together with individual covariates, exhibit spatial correlation and heterogeneity on the outcome-diarrhoea status. Findings showed evidence of spatially varying risk imposed by social vulnerability indicators on childhood diarrhoea. We established a positive relationship between diarrhoea and water poverty, and negative association with poor sanitation and low wealth status. Spatial characterization of health effects of social vulnerability presents an important step towards targeted interventions in diarrhoea management. Our use of district level mapping provides for optimal planning and implementation, particularly, for the lowly placed individuals who are geographically located in high risk areas, since most decentralized decision making processes are made at this level.

Small area population forecasts are widely used across the public and private sectors, with many users requiring forecasts broken down by sex and age group. The preparation of small area age-sex population forecasts across a whole country or State with a multiregional cohort-component model is usually a time-consuming and expensive task. It involves the purchase of large datasets, considerable amounts of complex data preparation and assumption-setting, and substantial amounts of staff time. A quicker and lower-cost alternative is to use a reduced form cohort projection model, such as the Hamilton-Perry model. This paper presents an evaluation of various implementations of the Hamilton-Perry model, including an alternative version employing a combination of Cohort Change Ratios and Cohort Change Differences. It also evaluates the effects on forecast accuracy of smoothing the age profiles of Cohort Change Ratios and Differences, and constraining to independent population forecasts. Population ‘forecasts’ were created for small areas of Australia over the horizon 2006–16 and compared against population estimates. The most accurate implementation is found to be the Hamilton-Perry model using a combination of Cohort Change Ratios and Cohort Change Differences, smoothed age profiles, and with constraining to independent forecasts.

The early stages of the COVID-19 pandemic required a dramatic change in social practices, including distancing from social settings, to limit its spread. While social capital has considerable potential in facilitating the adoption of these norms, it also comes with considerable limitations that potentially undermine its effectiveness. We draw upon recently released mobility data from Google, network data from Facebook, and demographic data from the 2018 American Community Survey to determine how both organizational and networked measures of social capital relate to different forms of distancing. In addition, we employ geographically weighted regression to identify how these relationships vary across the nation. Findings indicate that while both forms of social capital can positively relate to distancing, the impacts are spatially inconsistent and, in some locations, social capital can discourage distancing. In sum, more policy efforts are needed to address not only low-social capital, but also unhelpful social capital.