Spatial Heterogeneity In Relationship Research Articles

Spatial heterogeneity in relationship between district patterns of HIV incidence and covariates in Zimbabwe: a multi-scale geographically weighted regression analysis.

A study was conducted to investigate the district-level patterns of incidence of the human immunodeficiency virus (HIV) in Zimbabwe in the period 2005-2015 and explore variations in the relationship between covariates and HIV incidence across different districts. Demographic health survey data were analysed using hotspot analysis, spatial autocorrelation, and multi-scale geographically weighted regression (MGWR) techniques. The analysis revealed hotspots of the HIV epidemic in the southern and western regions of Zimbabwe in contrast to the eastern and northern regions. Specific districts in Matabeleland South and Matabeleland North provinces showed clusters of HIV incidence in 2005-2006, 2010-2011 and 2015. Variables studied were multiple sex partners and sexually transmitted infections (STI) condom use and being married. Recommendations include implementing targeted HIV prevention programmes in identified hotspots, prioritising interventions addressing the factors mentioned above as well as enhancing access to HIV testing and treatment services in high-risk areas, strengthening surveillance systems, and conducting further research to tailor interventions based on contextual factors. The study also emphasizes the need for regular monitoring and evaluation at the district level to inform effective responses to the HIV epidemic over time. By addressing the unique challenges and risk factors in different districts, significant progress can be made in reducing HIV transmission and improving health outcomes in Zimbabwe. These findings should be valuable for policymakers in resource allocation and designing evidence-based interventions.

Spatially Varying Relationships between Land Subsidence and Urbanization: A Case Study in Wuhan, China

Land subsidence has become an increasing global concern over the past few decades due to natural and anthropogenic factors. However, although several studies have examined factors affecting land subsidence in recent years, few have focused on the spatial heterogeneity of relationships between land subsidence and urbanization. In this paper, we adopted the small baseline subset-synthetic aperture radar interferometry (SBAS-InSAR) method using Sentinel-1 radar satellite images to map land subsidence from 2015 to 2018 and characterized its spatial pattern in Wuhan. The bivariate Moran’s I index was used to test and visualize the spatial correlations between land subsidence and urbanization. A geographically weighted regression (GWR) model was employed to explore the strengths and directions of impacts of urbanization on land subsidence. Our findings showed that land subsidence was obvious and unevenly distributed in the study area, the annual deformation rate varied from −42.85 mm/year to +29.98 mm/year, and its average value was −1.0 mm/year. A clear spatial pattern for land subsidence in Wuhan was mapped, and several apparent subsidence funnels were primarily located in central urban areas. All urbanization indicators were found to be significantly spatially correlated with land subsidence at different scales. In addition, the GWR model results showed that all urbanization indicators were significantly associated with land subsidence across the whole study area in Wuhan. The results of bivariate Moran’s I and GWR results confirmed that the relationships between land subsidence and urbanization spatially varied in Wuhan at multiple spatial scales. Although scale dependence existed in both the bivariate Moran’s I and GWR models for land subsidence and urbanization indicators, a “best” spatial scale could not be confirmed because the disturbance of factors varied over different sampling scales. The results can advance the understanding of the relationships between land subsidence and urbanization, and they will provide guidance for subsidence control and sustainable urban planning.

The Diversified Impacts of Urban Morphology on Land Surface Temperature among Urban Functional Zones.

Local warming induced by rapid urbanization has been threatening residents’ health, raising significant concerns among urban planners. Local climate zone (LCZ), a widely accepted approach to reclassify the urban area, which is helpful to propose planning strategies for mitigating local warming, has been well documented in recent years. Based on the LCZ framework, many scholars have carried out diversified extensions in urban zoning research in recent years, in which urban functional zone (UFZ) is a typical perspective because it directly takes into account the impacts of human activities. UFZs, widely used in urban planning and management, were chosen as the basic unit of this study to explore the spatial heterogeneity in the relationship between landscape composition, urban morphology, urban functions, and land surface temperature (LST). Global regression including ordinary least square regression (OLS) and random forest regression (RF) were used to model the landscape-LST correlations to screen indicators to participate in following spatial regression. The spatial regression including semi-parametric geographically weighted regression (SGWR) and multiscale geographically weighted regression (MGWR) were applied to investigate the spatial heterogeneity in landscape-LST among different types of UFZ and within each UFZ. Urban two-dimensional (2D) morphology indicators including building density (BD); three-dimensional (3D) morphology indicators including building height (BH), building volume density (BVD), and sky view factor (SVF); and other indicators including albedo and normalized difference vegetation index (NDVI) and impervious surface fraction (ISF) were used as potential landscape drivers for LST. The results show significant spatial heterogeneity in the Landscape-LST relationship across UFZs, but the spatial heterogeneity is not obvious within specific UFZs. The significant impact of urban morphology on LST was observed in six types of UFZs representing urban built up areas including Residential (R), Urban village (UV), Administration and Public Services (APS), Commercial and Business Facilities (CBF), Industrial and Manufacturing (IM), and Logistics and Warehouse (LW). Specifically, a significant correlation between urban 3D morphology indicators and LST in CBF was discovered. Based on the results, we propose different planning strategies to settle the local warming problems for each UFZ. In general, this research reveals UFZs to be an appropriate operational scale for analyzing LST on an urban scale.

Spatial heterogeneity of the association between temperature and hand, foot, and mouth disease risk in metropolitan and other areas

Interest in assessing the effects of temperature on hand, foot, and mouth disease (HFMD) has increased. However, little evidence is available on spatial heterogeneity in relationship to temperature and HFMD in metropolitan (capital city and municipal districts) and other areas where economic levels are significantly different. In this study, the Bayesian space-time hierarchy model was applied to identify the spatiotemporal heterogeneity of HFMD. GeoDetector was then used to quantify the determinant power of temperature to the disease in regions where the economic level has significant spatial heterogeneity. There was significant spatial heterogeneity in the influence of temperature on the incidence of HFMD in metropolitan and other areas. In metropolitan areas, where the disease risk is higher (hot spots), the HFMD incidence was higher alongside an increase in average temperature. However, in non-metropolitan areas, where the disease risk is lower (cold spots), there was an approximately S-shaped relationship between the temperature and the HFMD risk. More specifically, when the temperature was >25 °C, the HFMD incidence no longer increased monotonically with the increasing temperature. There was significant spatial heterogeneity in the effects of temperature on the HFMD incidence in metropolitan and non-metropolitan areas. This finding may serve as a suggestion and basis for the surveillance and control of this disease and it is conducive to the rational allocation of medical resources in different areas.

Built environment in local relation with walking: Why here and not there?

Walking, as both a major mode of transport and the most common form of every-day physical activity, deserves further attention in health-related transportation studies. In this paper, we focused on the built environmental correlates of walking for errands and leisure in a sample of 409 adults (Paris, France) through a cross-sectional study based on an internet survey. The main aims were (i) to delineate places with contrasting relationships between the built environment and walking, using geographically weighted regression models and (ii) to determine what differentiated the contexts we uncovered, in terms of both environmental and individual characteristics, using canonical discriminant analyses. Our results showed that the spatial heterogeneity of relationships between walking and the built environment occurred across the entire studied area and concerned the two walking outcomes, with odds-ratios (ORs) ranging from 1.01 to 131 and from 1.07 to 1.35 for walking for errands and leisure, respectively. We suggest that the spatial patterning of convergent relationships is due to contextual effects, i.e., the effect of places with specific intrinsic arrangement of environmental and individual features. Data-driven identification of local contexts should be a key step in future contextual analyses of walking and health related outcomes.

A statistical method based on remote sensing for the estimation of air temperature in China

ABSTRACTEnvironmental applications require accurate air temperature (Tair) datasets with different temporal and spatial resolutions. Existing methods generally improve the estimation accuracy ofTairusing environmental variables as auxiliary data to overcome problems related to sparse metrological stations. However, these data are always fixed and do not comprehensively explain the variations inTairvalues at all temporal and spatial scales. Moreover, these methods seldom consider the spatial heterogeneity of relationships betweenTairand auxiliary data. This heterogeneity is often caused by several factors, such as land type, topography, and climate. This study proposes an estimation method to produce maximum, minimum, and meanTair(Tmax,Tmin, andTmean) datasets at different temporal and spatial resolutions using satellite‐derived digital elevation model data and both nighttime and daytime land surface temperature data as auxiliary data. The method is based on the assumption that the relationships betweenTairand the chosen auxiliary data vary spatially. These relationships were further explored using geographically weighted regression with adaptive bi‐square kernel function. The derived relationships were used to construct aTairestimation model. MonthlyTairdata with 5‐km resolution and 8‐dayTairdata with 1‐km resolution were produced for 2010. The results show that the proposed method can accurately represent the variations inTair; theR2values were in the range of 0.95–0.99 for the monthlyTairdata and 0.93–0.99 for the 8‐dayTairdata. The root mean square errors (RMSEs) for the monthly and 8‐dayTmax,Tmin, andTmeandata of the year 2010 were 1.29 and 1.45 °C, 1.24 and 1.29 °C, and 0.8 and 1.2 °C, respectively. These results were compared with those from other estimation methods, specifically the estimation ofTairbased on multiple linear regression (EATMLR) and regression kriging (EATRK). The proposed method was found to produce RMSEs that were 25–26% smaller than EATMLR and 34–42% smaller than EATRK.

Does context matter for the relationship between deprivation and all-cause mortality? The West vs. the rest of Scotland

BackgroundA growing body of research emphasizes the importance of contextual factors on health outcomes. Using postcode sector data for Scotland (UK), this study tests the hypothesis of spatial heterogeneity in the relationship between area-level deprivation and mortality to determine if contextual differences in the West vs. the rest of Scotland influence this relationship. Research into health inequalities frequently fails to recognise spatial heterogeneity in the deprivation-health relationship, assuming that global relationships apply uniformly across geographical areas. In this study, exploratory spatial data analysis methods are used to assess local patterns in deprivation and mortality. Spatial regression models are then implemented to examine the relationship between deprivation and mortality more formally.ResultsThe initial exploratory spatial data analysis reveals concentrations of high standardized mortality ratios (SMR) and deprivation (hotspots) in the West of Scotland and concentrations of low values (coldspots) for both variables in the rest of the country. The main spatial regression result is that deprivation is the only variable that is highly significantly correlated with all-cause mortality in all models. However, in contrast to the expected spatial heterogeneity in the deprivation-mortality relationship, this relation does not vary between regions in any of the models. This result is robust to a number of specifications, including weighting for population size, controlling for spatial autocorrelation and heteroskedasticity, assuming a non-linear relationship between mortality and socio-economic deprivation, separating the dependent variable into male and female SMRs, and distinguishing between West, North and Southeast regions. The rejection of the hypothesis of spatial heterogeneity in the relationship between socio-economic deprivation and mortality complements prior research on the stability of the deprivation-mortality relationship over time.ConclusionsThe homogeneity we found in the deprivation-mortality relationship across the regions of Scotland and the absence of a contextualized effect of region highlights the importance of taking a broader strategic policy that can combat the toxic impacts of socio-economic deprivation on health. Focusing on a few specific places (e.g. 15% of the poorest areas) to concentrate resources might be a good start but the impact of socio-economic deprivation on mortality is not restricted to a few places. A comprehensive strategy that can be sustained over time might be needed to interrupt the linkages between poverty and mortality.