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Land use intensity determines the provision of multiple important ecosystem services of agriculture. In Switzerland, agricultural policy developments have aimed and still aim to extensify agricultural systems and especially grassland use. We here provide a spatial and temporal analysis of changes in grassland use intensity and discuss them in the context of agricultural policy developments to assess potential policy impacts. We use farm-level census data over a period of 19 years. Spatio-temporal patterns of in- and extensification are investigated visually and by global and local Moran’s I measures. We find that while average changes in grassland use intensity are small, there is a substantial increase in the heterogeneity of grassland use intensity strategies over time, as indicated by increasing interquantile ranges of yearly boxplots. Our results suggest that both in- and extensification are profitable strategies for farmers within the given policy framework. Furthermore, Moran’s I measures show the emergence of regional clusters of in- and extensification. These intensification clusters possibly amplify environmental problems. Our analysis therefore highlights the need for spatial assessments of agricultural policies, i.e. local adverse environmental effects of intensive grassland use should be targeted by spatially tailored policy measures.

High-resolution population grids built from historical census data can ease the analyses of geographical population changes, at the same time also facilitating the combination of population data with other GIS layers to perform analyses on a wide range of topics. This article reports on experiments with a hybrid spatial disaggregation technique that combines the ideas of dasymetric mapping and pycnophylactic interpolation, using modern machine learning methods to combine different types of ancillary variables, in order to disaggregate historical census data into a 200 m resolution grid. We specifically report on experiments related to the disaggregation of historical population counts from three different national censuses which took place around 1900, respectively in Great Britain, Belgium, and the Netherlands. The obtained results indicate that the proposed method is indeed highly accurate, outperforming simpler disaggregation schemes based on mass-preserving areal weighting or pycnophylactic interpolation. The best results were obtained using modern regression methods (i.e., gradient tree boosting or convolutional neural networks, depending on the case study), which previously have only seldom been used for spatial disaggregation.

In February 2020, Italy became the epicenter for COVID-19 in Europe, and at the beginning of March, the Italian Government put in place emergency measures to restrict population movement. Aim of our analysis is to provide a better understanding of the epidemiological context of COVID-19 in Italy, using commuting data at a high spatial resolution, characterizing the territory in terms of vulnerability. We used a Susceptible–Infectious stochastic model and we estimated a municipality-specific infection contact rate (β) to capture the susceptibility to the disease. We identified in Lombardy, Veneto and Emilia Romagna regions (52% of all Italian cases) significant clusters of high β, due to the simultaneous presence of connections between municipalities and high population density. Local simulated spreading in regions, with different levels of infection observed, showed different disease geographical patterns due to different β values and commuting systems. In addition, we produced a vulnerability map (in the Abruzzi region as an example) by simulating the epidemic considering each municipality as a seed. The result shows the highest vulnerability values in areas with commercial hubs, close to the highest populated cities and the most industrial area. Our results highlight how human mobility can affect the epidemic, identifying particular situations in which the health authorities can promptly intervene to control the disease spread.

After World War II, Japan’s policy makers believed that common forests were underutilized because of their legal status and organization method under customary iriai-type ownership and that modern ownership in the form of group ownership, such as forest producers’ cooperatives, or as individual, separate ownership, would improve the situation. Thus, the Common Forests Modernization Act of 1966 was enacted, following successive modernization policies since the Meiji Restoration in 1868. We evaluated the impacts of the past modernization policies on the management of common forests by statistically comparing the performance of modernized and non-modernized 19,690 common forests based on the World Census of Agriculture and Forestry 2000. The performance measures for comparison included planting, weeding, thinning, and harvesting activities. We found less modernized, customary holdings are more active in tending activities such as weeding and thinning, while modernized holdings may have an advantage in harvesting and timber sales.

Smoking is a leading cause of premature death across contemporary developed nations, but few longitudinal individual-level studies have examined the long-term health consequences of exposure to smoking. We examine the effect of fetal and infant exposure to exogenous variation in smoking, brought about by state-level cigarette taxation, on adulthood and old-age mortality (ages 55-73) among cohorts of boys born in the United States during the 1920s and 1930s. We use state-of-the-art methods of record linkage to match 1930 and 1940 US full-count census records to death records, identifying early life exposure to the implementation of state-level cigarette taxes through contemporary sources. We examine a population of 2.4 million boys, estimating age at death by means of OLS regression, with post-stratification weights to account for linking selectivity. Fetal or infant exposure to the implementation of state cigarette taxation delayed mortality by about two months. Analyses further indicate heterogenous effects that are consistent with theoretical expectations; the largest benefits are enjoyed by individuals with parents who would have been affected most by the tax implementation. Despite living in an era of continuously increasing cigarette consumption, cohorts exposed to a reduction in cigarette smoking during early life enjoyed a later age at death. While it is not possible to comprehensively assess the treatment effect on the treated, the magnitude of the effect should not be underestimated, as it is larger than the difference between having parents belonging to the highest and lowest socioeconomic groups. The study provides the first estimates of long-run health effects from early life exposure to cigarette smoking.

Mapping changes in carbon emissions and carbon storage (CECS) with high precision at a small scale (urban street-block level) can improve governmental policy decisions with respect to the construction of low-carbon cities. In this study, a methodological framework for assessing the carbon budget and its spatiotemporal changes from 2015 to 2017 in Wuhan is proposed, which is able to monitor a large area. To estimate the carbon storage, a comprehensive coefficient model was adopted with carbon density factors and corresponding land cover types. Details regarding land cover were extracted from the Geographic National Census Data (GNCD), including forests, grasslands, croplands, and gardens. For the carbon emissions, an emission-factor model was first used and a spatialization operation was subsequently performed using the geographic location that was obtained from the GNCD. The carbon emissions that were identified in the study are from fossil-fuel consumption, industrial production processes, disposal of urban domestic refuse, and transportation. The final dynamic changes in the CECS, in addition to the net carbon emissions, were monitored and analyzed, yielding temporal and spatial maps with a high-precision at a small scale. The results showed that the carbon storage in Wuhan declined by 2.70% over the three years, whereas the carbon emissions initially increased by 0.2%, and subsequently decreased by 3.1% over this period. The trend in the net carbon emission changes was similar to that of the carbon emissions, demonstrating that the efficiency of carbon reduction was improved during this period. Precise spatiotemporal results at the street-block level can offer insights to governments that are engaged in urban carbon cycle decision making processes, improving their capacities to more effectively manage the spatial distribution of CECS.

BackgroundDespite a concerted policy effort in Europe, social inequalities in health are a persistent problem. Developing a standardised measure of socioeconomic level across Europe will improve the understanding of the underlying mechanisms and causes of inequalities. This will facilitate developing, implementing and assessing new and more effective policies, and will improve the comparability and reproducibility of health inequality studies among countries. This paper presents the extension of the European Deprivation Index (EDI), a standardised measure first developed in France, to four other European countries—Italy, Portugal, Spain and England, using available 2001 and 1999 national census data.Methods and resultsThe method previously tested and validated to construct the French EDI was used: first, an individual indicator for relative deprivation was constructed, defined by the minimal number of unmet fundamental needs associated with both objective (income) poverty and subjective poverty. Second, variables available at both individual (European survey) and aggregate (census) levels were identified. Third, an ecological deprivation index was constructed by selecting the set of weighted variables from the second step that best correlated with the individual deprivation indicator.ConclusionsFor each country, the EDI is a weighted combination of aggregated variables from the national census that are most highly correlated with a country-specific individual deprivation indicator. This tool will improve both the historical and international comparability of studies, our understanding of the mechanisms underlying social inequalities in health and implementation of intervention to tackle social inequalities in health.

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics describing the human presence on the planet that is based mainly on two quantitative factors: (i) the spatial distribution (density) of built-up structures and (ii) the spatial distribution (density) of resident people. Both of the factors are observed in the long-term temporal domain and per unit area, in order to support the analysis of the trends and indicators for monitoring the implementation of the 2030 Development Agenda and the related thematic agreements. The GHSL uses various input data, including global, multi-temporal archives of high-resolution satellite imagery, census data, and volunteered geographic information. In this paper, we present a global estimate for the Land Use Efficiency (LUE) indicator—SDG 11.3.1, for circa 10,000 urban centers, calculating the ratio of land consumption rate to population growth rate between 1990 and 2015. In addition, we analyze the characteristics of the GHSL information to demonstrate how the original frameworks of data (gridded GHSL data) and tools (GHSL tools suite), developed from Earth Observation and integrated with census information, could support Sustainable Development Goals monitoring. In particular, we demonstrate the potential of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for Sustainable Development Goal 11. The results of our research demonstrate that there is potential to raise SDG 11.3.1 from a Tier II classification (manifesting unavailability of data) to a Tier I, as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.

Social networks form the backbone of social and economic life. Until recently, however, data have not been available to study the social impact of a national network structure. To that end, we combined the most complete record of a national communication network with national census data on the socioeconomic well-being of communities. These data make possible a population-level investigation of the relation between the structure of social networks and access to socioeconomic opportunity. We find that the diversity of individuals' relationships is strongly correlated with the economic development of communities.

Historical censuses are one of the most challenging datasets to compare over time. While many (successful) efforts have been made by researchers to harmonize these types of data, a lack of a generic workflow thwarts other researchers in their endeavors to do the same. In order to use historical census data for longitudinal analysis, a common process currently often loosely referred to as harmonization is inevitable. This process becomes even more challenging when dealing with aggregate data. Current approaches, whether focusing on micro or aggregate data, mainly provide specific, goal-oriented solutions to solve this problem. The nature of our data calls for an approach which allows different interpretations and preserves the link to the underlying sources at all times. To realize this we need a flexible, bottom-up harmonization process which allows us to iteratively discover the peculiarities of these types of data and provide different interpretations on the same data in an accountable way. In this article, we propose an approach which we refer to as source-oriented harmonization. We use the Resource Description Framework from (RDF) as the technological backbone of our efforts and aim to make the process of harmonization more graspable for others to stimulate similar efforts.