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

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

Using geographically weighted regression to explore the spatially heterogeneous spread of bovine tuberculosis in England and Wales

An understanding of the factors that affect the spread of endemic bovine tuberculosis (bTB) is critical for the development of measures to stop and reverse this spread. Analyses of spatial data need to account for the inherent spatial heterogeneity within the data, or else spatial autocorrelation can lead to an overestimate of the significance of variables. This study used three methods of analysis—least-squares linear regression with a spatial autocorrelation term, geographically weighted regression (GWR) and boosted regression tree (BRT) analysis—to identify the factors that influence the spread of endemic bTB at a local level in England and Wales. The linear regression and GWR methods demonstrated the importance of accounting for spatial differences in risk factors for bTB, and showed some consistency in the identification of certain factors related to flooding, disease history and the presence of multiple genotypes of bTB. This is the first attempt to explore the factors associated with the spread of endemic bTB in England and Wales using GWR. This technique improves on least-squares linear regression approaches by identifying regional differences in the factors associated with bTB spread. However, interpretation of these complex regional differences is difficult and the approach does not lend itself to predictive models which are likely to be of more value to policy makers. Methods such as BRT may be more suited to such a task. Here we have demonstrated that GWR and BRT can produce comparable outputs

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

A framework for using self-organising maps to analyse spatiotemporal patterns, exemplified by analysis of mobile phone usage

We suggest a visual analytics framework for the exploration and analysis of spatially and temporally referenced values of numeric attributes. The framework supports two complementary perspectives on spatio-temporal data: as a temporal sequence of spatial distributions of attribute values (called spatial situations) and as a set of spatially referenced time series of attribute values representing local temporal variations. To handle a large amount of data, we use the self-organising map (SOM) method, which groups objects and arranges them according to similarity of relevant data features. We apply the SOM approach to spatial situations and to local temporal variations and obtain two types of SOM outcomes, called space-in-time SOM and time-in-space SOM, respectively. The examination and interpretation of both types of SOM outcomes are supported by appropriate visualisation and interaction techniques. This article describes the use of the framework by an example scenario of data analysis. We also discuss how the framework can be extended from supporting explorative analysis to building predictive models of the spatio-temporal variation of attribute values. We apply our approach to phone call data showing its usefulness in real-world analytic scenarios

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

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

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

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)