Exploration of ecological factors related to the spatial heterogeneity of tuberculosis prevalence in P. R. China

Background: The current prevalence of tuberculosis (TB) in the People's Republic of China (P. R. China) demonstrates geographical heterogeneities, which show that the TB prevalence in the remote areas of Western China is more serious than that in the coastal plain of Eastern China. Although a lot of ecological studies have been applied in the exploration on the regional difference of disease risks, there is still a paucity of ecological studies on TB prevalence in P. R. China. Objective: To understand the underlying factors contributing to the regional inequity of TB burden in P. R. China by using an ecological approach and, thus, aiming to provide a basis to eliminate the TB spatial heterogeneity in the near future. Design: Latent ecological variables were identified by using exploratory factor analysis from data obtained from four sources, i.e. the databases of the National TB Control Programme (2001–2010) in P. R. China, the China Health Statistical Yearbook during 2002–2011, the China Statistical Yearbook during 2002–2011, and the provincial government websites in 2013. Partial least squares path modelling was chosen to construct the structural equation model to evaluate the relationship between TB prevalence and ecological variables. Furthermore, a geographically weighted regression model was used to explore the local spatial heterogeneity in the relationships. Results: The latent ecological variables in terms of ‘TB prevalence’, ‘TB investment’, ‘TB service’, ‘health investment’, ‘health level’, ‘economic level’, ‘air quality’, ‘climatic factor’ and ‘geographic factor’ were identified. With the exception of TB service and health levels, other ecological factors had explicit and significant impacts on TB prevalence to varying degrees. Additionally, each ecological factor had different impacts on TB prevalence in different regions significantly. Conclusion: Ecological factors that were found predictive of TB prevalence in P. R. China are essential to take into account in the formulation of locally comprehensive strategies and interventions aiming to tailor the TB control and prevention programme into local settings in each ecozone

The ecology of outdoor rape: The case of Stockholm, Sweden

The objective of this article is to report the results of an ecological study into the geography of rape in Stockholm, Sweden, using small area data. In order to test the importance of factors indicating opportunity, accessibility and anonymity to the understanding of the geography of rape, a two-stage modelling approach is implemented. First, the overall risk factors associated with the occurrence of rape are identified using a standard Poisson regression, then a local analysis using profile regression is performed. Findings from the whole-map analysis show that accessibility, opportunity and anonymity are all, to different degrees, important in explaining the overall geography of rape - examples of these risk factors are the presence of subway stations or whether a basområde is close to the city centre. The local analysis reveals two groupings of high risk of rape areas associated with a variety of risk factors: city centre areas with a concentration of alcohol outlets, high residential population turnover and high counts of robbery; and poor suburban areas with schools and large female residential populations where subway stations are located and where people express a high fear of crime. The article concludes by reflecting upon the importance of these results for future research as well as indicating the implications of these results for policy

Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions

In any economic analysis, regions or municipalities should not be regarded as isolated spatial units, but rather as highly interrelated small open economies. These spatial interrelations must be considered also when the aim is to forecast economic variables. For example, policy makers need accurate forecasts of the unemployment evolution in order to design short- or long-run local welfare policies. These predictions should then consider the spatial interrelations and dynamics of regional unemployment. In addition, a number of papers have demonstrated the improvement in the reliability of long-run forecasts when spatial dependence is accounted for. We estimate a heterogeneouscoefficients dynamic panel model employing a spatial filter in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment, as well as a spatial vector-autoregressive (SVAR) model. We compare the short-run forecasting performance of these methods, and in particular, we carry out a sensitivity analysis in order to investigate if different number and size of the administrative regions influence their relative forecasting performance. We compute short-run unemployment forecasts in two countries with different administrative territorial divisions and data frequency: Switzerland (26 regions, monthly data for 34 years) and Spain (47 regions, quarterly data for 32 years)

Intra-week spatial-temporal patterns of crime

Since its original publication, routine activity theory has proven most instructive for understanding temporal patterns in crime. The most prominent of the temporal crime patterns investigated is seasonality: crime (most often assault) increases during the summer months and decreases once routine activities are less often outside. Despite the rather widespread literature on the seasonality of crime, there is very little research investigating temporal patterns of crime at shorter time intervals such as within the week or even within the day. This paper contributes to this literature through a spatial-temporal analysis of crime patterns for different days of the week. It is found that temporal patterns are present for different days of the week (more crime on weekends, as would be expected) and there is a spatial component to that temporal change. Specifically, aside from robbery and sexual assault at the micro-spatial unit of analysis (street segments) the spatial patterns of crime changed. With regard to the spatial pattern changes, we found that assaults and theft from vehicle had their spatial patterns change in predictable ways on Saturdays: assaults increased in the bar district and theft from vehicles increased in the downtown and recreational car park areas

Microbial regulation of the soil carbon cycle: evidence from gene-enzyme relationships.

A lack of empirical evidence for the microbial regulation of ecosystem processes, including carbon (C) degradation, hinders our ability to develop a framework to directly incorporate the genetic composition of microbial communities in the enzyme-driven Earth system models. Herein we evaluated the linkage between microbial functional genes and extracellular enzyme activity in soil samples collected across three geographical regions of Australia. We found a strong relationship between different functional genes and their corresponding enzyme activities. This relationship was maintained after considering microbial community structure, total C and soil pH using structural equation modelling. Results showed that the variations in the activity of enzymes involved in C degradation were predicted by the functional gene abundance of the soil microbial community (R2>0.90 in all cases). Our findings provide a strong framework for improved predictions on soil C dynamics that could be achieved by adopting a gene-centric approach incorporating the abundance of functional genes into process models

Superfund, Hedonics, and the Scales of Environmental Justice

Environmental justice (EJ) is prominent in environmental policy, yet EJ research is plagued by debates over methodological procedures. A well-established economic approach, the hedonic price method, can offer guidance on one contentious aspect of EJ research: the choice of the spatial unit of analysis. Environmental managers charged with preventing or remedying inequities grapple with these framing problems. This article reviews the theoretical and empirical literature on unit choice in EJ, as well as research employing hedonic pricing to assess the spatial extent of hazardous waste site impacts. The insights from hedonics are demonstrated in a series of EJ analyses for a national inventory of Superfund sites. First, as evidence of injustice exhibits substantial sensitivity to the choice of spatial unit, hedonics suggests some units conform better to Superfund impacts than others. Second, hedonic estimates for a particular site can inform the design of appropriate tests of environmental inequity for that site. Implications for policymakers and practitioners of EJ analyses are discussed

Local spatial regression models : a comparative analysis on soil contamination

Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have been evaluated. The experimental studies showed that integration of objective function based fuzzy clustering to geostatistics provides some accurate and general models structures. In particular, the estimation performance of the model established by combining the extended fuzzy clustering algorithm and standard regional dependence function is higher than that of the other regression models. Finally, it could be suggested that the hybrid regression models developed by combining soft computing and geostatistics could be used in spatial data analysis

Geographically weighted elastic net logistic regression

This paper develops a localized approach to elastic net logistic regression, extending previous research describing a localized elastic net as an extension to a localized ridge regression or a localized lasso. All such models have the objective to capture data relationships that vary across space. Geographically weighted elastic net logistic regression is first evaluated through a simulation experiment and shown to provide a robust approach for local model selection and alleviating local collinearity, before application to two case studies: county-level voting patterns in the 2016 USA presidential election, examining the spatial structure of socio-economic factors associated with voting for Trump, and a species presence–absence data set linked to explanatory environmental and climatic factors at gridded locations covering mainland USA. The approach is compared with other logistic regressions. It improves prediction for the election case study only which exhibits much greater spatial heterogeneity in the binary response than the species case study. Model comparisons show that standard geographically weighted logistic regression over-estimated relationship non-stationarity because it fails to adequately deal with collinearity and model selection. Results are discussed in the context of predictor variable collinearity and selection and the heterogeneities that were observed. Ongoing work is investigating locally derived elastic net parameters

Analyzing regional economic development patterns in a fast developing province of China through geographically weighted principal component analysis

Understanding the spatial structure of regional economic development is of importance for regional planning and provincial development strategy making. Taking Jiangsu Province in the economically richest Yangtze Delta as a case study, this paper aims to explore regional economic development level on a provincial scale. Using the data sets from provincial statistical yearbook of 2010, eleven variables are selected for statistical and spatial analyses at a county level. Both the traditional principal component analysis (PCA) and its local version—geographically weighted PCA (GWPCA)—are employed to these analyses for the purpose of comparison. The results have confirmed that GWPCA is an effective means of analyzing regional economic development level through mapping its local principal components. It is also concluded that the regional economic development in Jiangsu Province demonstrates spatial inequality between the North and South

A scalable analytical framework for spatio-temporal analysis of neighborhood change: A sequence analysis approach

© Springer Nature Switzerland AG 2020. Spatio-temporal changes reflect the complexity and evolution of demographic and socio-economic processes. Changes in the spatial distribution of population and consumer demand at urban and rural areas are expected to trigger changes in future housing and infrastructure needs. This paper presents a scalable analytical framework for understanding spatio-temporal population change, using a sequence analysis approach. This paper uses gridded cell Census data for Great Britain from 1971 to 2011 with 10-year intervals, creating neighborhood typologies for each Census year. These typologies are then used to analyze transitions of grid cells between different types of neighborhoods and define representative trajectories of neighborhood change. The results reveal seven prevalent trajectories of neighborhood change across Great Britain, identifying neighborhoods which have experienced stable, upward and downward pathways through the national socioeconomic hierarchy over the last four decades