Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression

GIS-Based Assessments of Neighborhood Food Environments and Chronic Conditions: An Overview of Methodologies

The industrial revolution and urbanization fundamentally restructured populations' living circumstances, often with poor impacts on health. As an example, unhealthy food establishments may concentrate in some neighborhoods and, mediated by social and commercial drivers, increase local health risks. To understand the connections between neighborhood food environments and public health, researchers often use geographic information systems (GIS) and spatial statistics to analyze place-based evidence, but such tools require careful application and interpretation. In this article, we summarize the factors shaping neighborhood health in relation to local food environments and outline the use of GIS methodologies to assess associations between the two. We provide an overview of available data sources, analytical approaches, and their strengths and weaknesses. We postulate next steps in GIS integration with forecasting, prediction, and simulation measures to frame implications for local health policies.

The application of artificial intelligence in health financing: a scoping review

INTRODUCTION

Artificial Intelligence (AI) represents a significant advancement in technology, and it is crucial for policymakers to incorporate AI thinking into policies and to fully explore, analyze and utilize massive data and conduct AI-related policies. AI has the potential to optimize healthcare financing systems. This study provides an overview of the AI application domains in healthcare financing.

METHOD

We conducted a scoping review in six steps: formulating research questions, identifying relevant studies by conducting a comprehensive literature search using appropriate keywords, screening titles and abstracts for relevance, reviewing full texts of relevant articles, charting extracted data, and compiling and summarizing findings. Specifically, the research question sought to identify the applications of artificial intelligence in health financing supported by the published literature and explore potential future applications. PubMed, Scopus, and Web of Science databases were searched between 2000 and 2023.

RESULTS

We discovered that AI has a significant impact on various aspects of health financing, such as governance, revenue raising, pooling, and strategic purchasing. We provide evidence-based recommendations for establishing and improving the health financing system based on AI.

CONCLUSIONS

To ensure that vulnerable groups face minimum challenges and benefit from improved health financing, we urge national and international institutions worldwide to use and adopt AI tools and applications.

Individual and social determinants of adherence to sodium-glucose cotransporter 2 inhibitor therapy: A trajectory analysis

BACKGROUND: Sodium-glucose cotransporter 2 inhibitors (SGLT2is) are known to improve cardiovascular and renal outcomes in patients with type 2 diabetes (T2D). Understanding the longitudinal patterns of adherence and the associated predictors is critical to addressing the suboptimal use of this outcome-improving treatment. OBJECTIVE: To characterize the distinct trajectories of adherence to SGLT2is in patients with T2D and to identify patient characteristics and social determinants of health (SDOHs) associated with SGLT2i adherence. METHODS: In this retrospective cohort study, we identified patients with T2D who initiated and filled at least 1 SGLT2i prescription according to 2012-2016 national Medicare claims data. The monthly proportion of days covered with SGLT2is for each patient was incorporated into group-based trajectory models to identify groups with similar adherence patterns. A multinomial logistic regression model was constructed to examine the association between patient characteristics and group membership. In addition, the association between context-specific SDOHs (eg, neighborhood median income and neighborhood employment rate) and adherence to an SGLT2i regimen was explored in both the overall cohort and the racial and ethnic subgroups. RESULTS: The final sample comprised 6,719 patients with T2D. Four trajectories of SGLT2i adherence were identified: continuously adherent users (49.6%), early discontinuers (27.5%), late discontinuers (14.5%), and intermediately adherent users (8.4%). Patient age, sex, race, diabetes duration, and Medicaid eligibility were significantly associated with trajectory group membership. Areas with a higher unemployment rate, lower income level, lower high school education rate, worse nutrition environment, fewer health care facilities, and greater Area Deprivation Index scores were found to be associated with low adherence to SGLT2is. CONCLUSIONS: Four distinct trajectories of adherence to SGLT2is were identified, with only half of the patients remaining continuously adherent to their treatment regimen during the first year after initiation. Several contextual SDOHs were associated with suboptimal adherence to SGLT2is.

Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling

The onset of COVID-19 across the world has elevated interest in geographic information systems (GIS) for pandemic management. In Germany, however, most spatial analyses remain at the relatively coarse level of counties. In this study, we explored the spatial distribution of COVID-19 hospitalizations in health insurance data of the AOK Nordost health insurance. Additionally, we explored sociodemographic and pre-existing medical conditions associated with hospitalizations for COVID-19. Our results clearly show strong spatial dynamics of COVID-19 hospitalizations. The main risk factors for hospitalization were male sex, being unemployed, foreign citizenship, and living in a nursing home. The main pre-existing diseases associated with hospitalization were certain infectious and parasitic diseases, diseases of the blood and blood-forming organs, endocrine, nutritional and metabolic diseases, diseases of the nervous system, diseases of the circulatory system, diseases of the respiratory system, diseases of the genitourinary and symptoms, and signs and findings not classified elsewhere.

Impact of Contextual-Level Social Determinants of Health on Newer Antidiabetic Drug Adoption in Patients with Type 2 Diabetes

BACKGROUND

We aimed to investigate the association between contextual-level social determinants of health (SDoH) and the use of novel antidiabetic drugs (ADD), including sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a) for patients with type 2 diabetes (T2D), and whether the association varies across racial and ethnic groups.

METHODS

Using electronic health records from the OneFlorida+ network, we assembled a cohort of T2D patients who initiated a second-line ADD in 2015-2020. A set of 81 contextual-level SDoH documenting social and built environment were spatiotemporally linked to individuals based on their residential histories. We assessed the association between the contextual-level SDoH and initiation of SGTL2i/GLP1a and determined their effects across racial groups, adjusting for clinical factors.

RESULTS

Of 28,874 individuals, 61% were women, and the mean age was 58 (±15) years. Two contextual-level SDoH factors identified as significantly associated with SGLT2i/GLP1a use were neighborhood deprivation index (odds ratio [OR] 0.87, 95% confidence interval [CI] 0.81-0.94) and the percent of vacant addresses in the neighborhood (OR 0.91, 95% CI 0.85-0.98). Patients living in such neighborhoods are less likely to be prescribed with newer ADD. There was no interaction between race-ethnicity and SDoH on the use of newer ADD. However, in the overall cohort, the non-Hispanic Black individuals were less likely to use newer ADD than the non-Hispanic White individuals (OR 0.82, 95% CI 0.76-0.88).

CONCLUSION

Using a data-driven approach, we identified the key contextual-level SDoH factors associated with not following evidence-based treatment of T2D. Further investigations are needed to examine the mechanisms underlying these associations.

Spatial patterns in sociodemographic factors explain to a large extent the prevalence of hypertension and diabetes in Aragon (Spain)

Introduction

The global burden of multi-morbidity has become a major public health challenge due to the multi stakeholder action required to its prevention and control. The Social Determinants of Health approach is the basis for the establishment of health as a cross-cutting element of public policies toward enhanced and more efficient decision making for prevention and management.

Objective

To identify spatially varying relationships between the multi-morbidity of hypertension and diabetes and the sociodemographic settings (2015-2019) in Aragon (a mediterranean region of Northeastern Spain) from an ecological perspective.

Materials and methods

First, we compiled data on the prevalence of hypertension, diabetes, and sociodemographic variables to build a spatial geodatabase. Then, a Principal Component Analysis (PCA) was performed to derive regression variables, i.e., aggregating prevalence rates into a multi-morbidity component (stratified by sex) and sociodemographic covariate into a reduced but meaningful number of factors. Finally, we applied Geographically Weighted Regression (GWR) and cartographic design techniques to investigate the spatial variability of the relationships between multi-morbidity and sociodemographic variables.

Results

The GWR models revealed spatial explicit relationships with large heterogeneity. The sociodemographic environment participates in the explanation of the spatial behavior of multi-morbidity, reaching maximum local explained variance (R2) of 0.76 in men and 0.91 in women. The spatial gradient in the strength of the observed relationships was sharper in models addressing men's prevalence, while women's models attained more consistent and higher explanatory performance.

Conclusion

Modeling the prevalence of chronic diseases using GWR enables to identify specific areas in which the sociodemographic environment is explicitly manifested as a driving factor of multi-morbidity. This is step forward in supporting decision making as it highlights multi-scale contexts of vulnerability, hence allowing specific action suitable to the setting to be taken.

Analysis of Continuity of Care and Its Related Factors in Diabetic Patients: A Cross-Sectional Study

BACKGROUND

Continuity of care in primary care settings is crucial for managing diabetes. We aimed to statistically define and analyze continuity factors associated with demographics, clinical workforce, and geographical relationships.

METHODS

We used 2014-2015 National Health Insurance Service claims data from the Korean registry, with 39,096 eligible outpatient attendance. We applied multivariable logistic regression to analyze factors that may affect the continuity of care indices for each patient: the most frequent provider continuity index (MFPCI), modified-modified continuity index (MMCI), and continuity of care index (COCI).

RESULTS

The mean continuity of care indices were 0.90, 0.96, and 0.85 for MFPCI, MMCI and COCI, respectively. Among patient factors, old age >80 years (MFPCI: odds ratio [OR], 0.81; 95% confidence interval [CI], 0.74-0.89; MMCI: OR, 0.84; 95% CI, 0.76-0.92; and COCI: OR, 0.81; 95% CI, 0.74-0.89) and mild disability were strongly associated with lower continuity of care. Another significant factor was the residential area: the farther the patients lived from their primary care clinic, the lower the continuity of diabetes care (MFPCI: OR, 0.74; 95% CI, 0.70-0.78; MMCI: OR, 0.70; 95% CI, 0.66-0.73; and COCI: OR, 0.74; 95% CI, 0.70-0.78).

CONCLUSION

The geographical proximity of patients' residential areas and clinic locations showed the strongest correlation as a continuity factor. Further efforts are needed to improve continuity of care to address the geographical imbalance in diabetic care.

Psoriasis comorbidities in Germany: A population-based study on spatiotemporal variations

Psoriasis is a chronic disease with high impact on patients' health and their quality of life. Psoriasis often occurs along with other comorbidities, but it is not yet clear what role the comorbidities play in regional psoriasis prevalence. This study investigates the temporal and regional variation of the psoriasis comorbidities diabetes mellitus type II, obesity, hypertension, affective disorders in Germany and their association with psoriasis prevalence. This analysis based on the population set of ambulatory claims data (2010-2017) of the statutory health insurance (SHI) in Germany (approx. 70.3 million people in 2017). Psoriasis comorbidities rates were determined on county level. We performed descriptive spatiotemporal analyses of psoriasis comorbidity prevalence rates. In addition, we identified and compared spatial clusters and examined regional variations using spatial statistical methods. The results show strong regional variations (northeast to south gradient) and an increasing psoriasis prevalence (max. 28.8%) within the observation period. Considering the comorbidities, results indicate comparable spatial prevalence patterns for diabetes mellitus type II, obesity and hypertension. This means that the highest prevalence of comorbidities tends to be found where the psoriasis prevalence is highest. The spatiotemporal cluster analyses could once again confirm the results. An exception to this is to be found in the case of affective disorders with different spatial patterns. The results of the studies show the first spatiotemporal association between psoriasis prevalence and comorbidities in Germany. The causalities must be investigated in more detail in order to be able to derive measures for improved care.

The Role of Big Data in Aging and Older People’s Health Research: A Systematic Review and Ecological Framework

Big data has been prominent in studying aging and older people’s health. It has promoted modeling and analyses in biological and geriatric research (like cellular senescence), developed health management platforms, and supported decision-making in public healthcare and social security. However, current studies are still limited within a single subject, rather than flourished as interdisciplinary research in the context of big data. The research perspectives have not changed, nor has big data brought itself out of the role as a modeling tool. When embedding big data as a data product, analysis tool, and resolution service into different spatial, temporal, and organizational scales of aging processes, it would present as a connection, integration, and interaction simultaneously in conducting interdisciplinary research. Therefore, this paper attempts to propose an ecological framework for big data based on aging and older people’s health research. Following the scoping process of PRISMA, 35 studies were reviewed to validate our ecological framework. Although restricted by issues like digital divides and privacy security, we encourage researchers to capture various elements and their interactions in the human-environment system from a macro and dynamic perspective rather than simply pursuing accuracy.

Human health outcomes at the neighbourhood scale implications: Elderly's heat-related cardiorespiratory mortality and its influencing factors

The excessively warm weather, especially in cities, can lead to several adverse impacts, including heat-related mortality, becoming an increasingly important public health issue. Previous studies on heat-related mortality have assessed risk factors at the municipal scale, missing the intra-urban variability in heat risk and vulnerability. The knowledge of the spatial intra-variability can help to design spatially targeted measures to better protect citizens' health. Through hot spot analysis, we identified the neighbourhood-scale spatial pattern of heat-related cardiorespiratory mortality in the elderly, during the yearly warmest five months of a three years period. Potential associations between spatial variability in heat-related mortality and several independent factors in each neighbourhood were investigated and their predictions. Two approaches were adopted: one is eminently statistical, using Generalized Linear Models (GLM) and another using Geographically Weighted Regression (GWR). This new recent regression technique is increasing in international attention on spatial modelling. The spatial model explains about 60% of the spatial variations in elderly's heat-related cardiorespiratory mortality. The two-analyses produced an overlapping set of predictor variables, with emphasis on the elderly, vegetation cover and employment. The results also show that the areas where heat-related mortality is high, are also the areas where the number of deaths is higher than expected. These neighbourhoods should be considered as the most vulnerable to heat-related mortality. We concluded that studying human health outcomes at neighbourhood-scale is relevant for public health heat-related plans. Essential suggestions are provided to decision-making support and city planners designing strategies to reduce heat-related mortality.

Targeting the spatial context of obesity determinants via multiscale geographically weighted regression

BACKGROUND

Obesity rates are recognized to be at epidemic levels throughout much of the world, posing significant threats to both the health and financial security of many nations. The causes of obesity can vary but are often complex and multifactorial, and while many contributing factors can be targeted for intervention, an understanding of where these interventions are needed is necessary in order to implement effective policy. This has prompted an interest in incorporating spatial context into the analysis and modeling of obesity determinants, especially through the use of geographically weighted regression (GWR).

METHOD

This paper provides a critical review of previous GWR models of obesogenic processes and then presents a novel application of multiscale (M)GWR using the Phoenix metropolitan area as a case study.

RESULTS

Though the MGWR model consumes more degrees of freedom than OLS, it consumes far fewer degrees of freedom than GWR, ultimately resulting in a more nuanced analysis that can incorporate spatial context but does not force every relationship to become local a priori. In addition, MGWR yields a lower AIC and AICc value than GWR and is also less prone to issues of multicollinearity. Consequently, MGWR is able to improve our understanding of the factors that influence obesity rates by providing determinant-specific spatial contexts.

CONCLUSION

The results show that a mix of global and local processes are able to best model obesity rates and that MGWR provides a richer yet more parsimonious quantitative representation of obesity rate determinants compared to both GWR and ordinary least squares.

Geostatistical predictive modeling for asthma and chronic obstructive pulmonary disease using socioeconomic and environmental determinants

The spatial distribution of the prevalence of asthma and chronic obstructive pulmonary disease (COPD) remains under the influence of a wide array of environmental, climatic, and socioeconomic determinants. However, a large proportion of these influences remain unexplained. In completion, this study examined the spatial associations between asthma/COPD morbidity and their determinants using ordinary least squares (OLS) and geographically weighted regressions (GWR). Inpatient records collected from the secondary and tertiary care hospitals in Kandy from 2010 to 2014 were considered as the dependent variable. Potential risk factors (explanatory variables) were identified in four distinguished classes: 1) meteorological factors, (2) direct and indirect factors of air pollution, (3) socioeconomic factors, and (4) characteristics of the physical environment. All possible combinations of candidate explanatory variables were evaluated through an exploratory regression. A comparison between the regression models was also explored. The best OLS regression models revealed about 55% of asthma variation and 62% of COPD variation while GWR models yielded 78% and 74% of the variation of asthma and COPD occurrences respectively. Relative humidity, proximity to roads (0-200 m), road density, use of firewood as a source of fuel, and elevation play a vital role in predicting morbidity from asthma and COPD. Both local and global regression models are important in assessing spatial relationships of asthma and COPD. However, the local models exhibit a better prediction capability for assessing non-stationary relationships of asthma and COPD than global models. The geostatistical aspects used in this study may also provide insights for evaluating heterogeneous environmental risk factors in other epidemiological studies across different spatial settings.

Who is where at risk for Chronic Obstructive Pulmonary Disease? A spatial epidemiological analysis of health insurance claims for COPD in Northeastern Germany

Background

Chronic obstructive pulmonary disease (COPD) has a high prevalence rate in Germany and a further increase is expected within the next years. Although risk factors on an individual level are widely understood, only little is known about the spatial heterogeneity and population-based risk factors of COPD. Background knowledge about broader, population-based processes could help to plan the future provision of healthcare and prevention strategies more aligned to the expected demand. The aim of this study is to analyze how the prevalence of COPD varies across northeastern Germany on the smallest spatial-scale possible and to identify the location-specific population-based risk factors using health insurance claims of the AOK Nordost.

Methods

To visualize the spatial distribution of COPD prevalence at the level of municipalities and urban districts, we used the conditional autoregressive Besag–York–Mollié (BYM) model. Geographically weighted regression modelling (GWR) was applied to analyze the location-specific ecological risk factors for COPD.

Results

The sex- and age-adjusted prevalence of COPD was 6.5% in 2012 and varied widely across northeastern Germany. Population-based risk factors consist of the proportions of insurants aged 65 and older, insurants with migration background, household size and area deprivation. The results of the GWR model revealed that the population at risk for COPD varies considerably across northeastern Germany.

Conclusion

Area deprivation has a direct and an indirect influence on the prevalence of COPD. Persons ageing in socially disadvantaged areas have a higher chance of developing COPD, even when they are not necessarily directly affected by deprivation on an individual level. This underlines the importance of considering the impact of area deprivation on health for planning of healthcare. Additionally, our results reveal that in some parts of the study area, insurants with migration background and persons living in multi-persons households are at elevated risk of COPD.

Exploring the small-scale spatial distribution of hypertension and its association to area deprivation based on health insurance claims in Northeastern Germany

BACKGROUND

Hypertension is one of the most frequently diagnosed chronic conditions in Germany. Targeted prevention strategies and allocation of general practitioners where they are needed most are necessary to prevent severe complications arising from high blood pressure. However, data on chronic diseases in Germany are mostly available through survey data, which do not only underestimate the actual prevalence but are also only available on coarse spatial scales. The discussion of including area deprivation for planning of healthcare is still relatively young in Germany, although previous studies have shown that area deprivation is associated with adverse health outcomes, irrespective of individual characteristics. The aim of this study is therefore to analyze the spatial distribution of hypertension at very fine geographic scales and to assess location-specific associations between hypertension, socio-demographic population characteristics and area deprivation based on health insurance claims of the AOK Nordost.

METHODS

To visualize the spatial distribution of hypertension prevalence at very fine geographic scales, we used the conditional autoregressive Besag-York-Mollié (BYM) model. Geographically weighted regression modelling (GWR) was applied to analyze the location-specific association of hypertension to area deprivation and further socio-demographic population characteristics.

RESULTS

The sex- and age-adjusted prevalence of hypertension was 33.1% in 2012 and varied widely across northeastern Germany. The main risk factors for hypertension were proportions of insurants aged 45-64, 65 and older, area deprivation and proportion of persons commuting to work outside their residential municipality. The GWR model revealed important regional variations in the strength of the examined associations.

CONCLUSION

Area deprivation has only a significant and therefore direct influence in large parts of Mecklenburg-West Pomerania. However, the spatially varying strength of the association between demographic variables and hypertension indicates that there also exists an indirect effect of area deprivation on the prevalence of hypertension. It can therefore be expected that persons ageing in deprived areas will be at greater risk of hypertension, irrespective of their individual characteristics. The future planning and allocation of primary healthcare in northeastern Germany would therefore greatly benefit from considering the effect of area deprivation.

Environmental Risk Factors for Developing Type 2 Diabetes Mellitus: A Systematic Review

Different elements of the environment have been posited to influence type 2 diabetes mellitus (T2DM). This systematic review summarizes evidence on the environmental determinants of T2DM identified in four databases. It proposes a theoretical framework illustrating the link between environment and T2DM, and briefly discusses some methodological challenges and potential solutions, and opportunities for future research. Walkability, air pollution, food and physical activity environment and roadways proximity were the most common environmental characteristics studied. Of the more than 200 reported and extracted relationships assessed in 60 studies, 82 showed significant association in the expected direction. In general, higher levels of walkability and green space were associated with lower T2DM risk, while increased levels of noise and air pollution were associated with greater risk. Current evidence is limited in terms of volume and study quality prohibiting causal inferences. However, the evidence suggests that environmental characteristics may influence T2DM prevention, and also provides a reasonable basis for further investigation with better quality data and longitudinal studies with policy-relevant environmental measures. This pursuit of better evidence is critical to support health-orientated urban design and city planning.

[What potential do geographic information systems have for population-wide health monitoring in Germany? : Perspectives and challenges for the health monitoring of the Robert Koch Institute]

Geographic information systems (GISs) are computer-based systems with which geographical data can be recorded, stored, managed, analyzed, visualized and provided. In recent years, they have become an integral part of public health research. They offer a broad range of analysis tools, which enable innovative solutions for health-related research questions. An analysis of nationwide studies that applied geographic information systems underlines the potential this instrument bears for health monitoring in Germany. Geographic information systems provide up-to-date mapping and visualization options to be used for national health monitoring at the Robert Koch Institute (RKI). Furthermore, objective information on the residential environment as an influencing factor on population health and on health behavior can be gathered and linked to RKI survey data at different geographic scales. Besides using physical information, such as climate, vegetation or land use, as well as information on the built environment, the instrument can link socioeconomic and sociodemographic data as well as information on health care and environmental stress to the survey data and integrate them into concepts for analyses. Therefore, geographic information systems expand the potential of the RKI to present nationwide, representative and meaningful health-monitoring results. In doing so, data protection regulations must always be followed. To conclude, the development of a national spatial data infrastructure and the identification of important data sources can prospectively improve access to high quality data sets that are relevant for the health monitoring.

A new approach to spatial identification of potential health hazards associated with childhood asthma

RESEARCH BACKGROUND

Childhood asthma is a chronic disease, known to be linked to prolonged exposure to air pollution. However, the identification of specific health hazards, associated with childhood asthma is not always straightforward, due to the presence of multiple sources of air pollution in urban areas. In this study, we test a novel approach to the spatial identification of environmental hazards that have the highest probability of association with the observed asthma morbidity patterns.

METHODS

The effect of a particular health hazard on population morbidity is expected to weaken with distance. To account for this effect, we rank potential health hazards based on the strength of association between the observed morbidity patterns and wind-direction weighted proximities to these locations. We validate this approach by applying it to a study of spatial patterns of childhood asthma in the Greater Haifa Metropolitan Area (GHMA) in Israel, characterised by multiple health hazards.

RESULTS

We identified a spot in the local industrial zone as the primary risk source for the observed asthma morbidity patterns. Multivariate regressions, controlling for socio-economic and geographic variables, revealed that the observed incidence rates of asthma tend to decline as a function of distance from the identified industrial location.

CONCLUSION

The proposed identification approach uses disease patterns as its main input, and can be used by researches as a preliminary risk assessment tool, in cases in which specific sources of locally elevated morbidity are unclear or cannot be identified by traditional methods.

A modification to geographically weighted regression

Background

Geographically weighted regression (GWR) is a modelling technique designed to deal with spatial non-stationarity, e.g., the mean values vary by locations. It has been widely used as a visualization tool to explore the patterns of spatial data. However, the GWR tends to produce unsmooth surfaces when the mean parameters have considerable variations, partly due to that all parameter estimates are derived from a fixed- range (bandwidth) of observations. In order to deal with the varying bandwidth problem, this paper proposes an alternative approach, namely Conditional geographically weighted regression (CGWR).

Methods

The estimation of CGWR is based on an iterative procedure, analogy to the numerical optimization problem. Computer simulation, under realistic settings, is used to compare the performance between the traditional GWR, CGWR, and a local linear modification of GWR. Furthermore, this study also applies the CGWR to two empirical datasets for evaluating the model performance. The first dataset consists of disability status of Taiwan’s elderly, along with some social-economic variables and the other is Ohio’s crime dataset.

Results

Under the positively correlated scenario, we found that the CGWR produces a better fit for the response surface. Both the computer simulation and empirical analysis support the proposed approach since it significantly reduces the bias and variance of data fitting. In addition, the response surface from the CGWR reviews local spatial characteristics according to the corresponded variables.

Conclusions

As an explanatory tool for spatial data, producing accurate surface is essential in order to provide a first look at the data. Any distorted outcomes would likely mislead the following analysis. Since the CGWR can generate more accurate surface, it is more appropriate to use it exploring data that contain suspicious variables with varying characteristics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12942-017-0085-9) contains supplementary material, which is available to authorized users.