Presence Of Spatial Autocorrelation Research Articles

Association of Child Growth Failure Indicators With Household Sanitation Practices in India (1998-2021): Spatiotemporal Observational Study.

Undernutrition among children younger than 5 years is a subtle indicator of a country's health and economic status. Despite substantial macroeconomic progress in India, undernutrition remains a significant burden with geographical variations, compounded by poor access to water, sanitation, and hygiene services. This study aimed to explore the spatial trends of child growth failure (CGF) indicators and their association with household sanitation practices in India. We used data from the Indian Demographic and Health Surveys spanning 1998-2021. District-level CGF indicators (stunting, wasting, and underweight) were cross-referenced with sanitation and sociodemographic characteristics. Global Moran I and Local Indicator of Spatial Association were used to detect spatial clustering of the indicators. Spatial regression models were used to evaluate the significant determinants of CGF indicators. Our study showed a decreasing trend in stunting (44.9%-38.4%) and underweight (46.7%-35.7%) but an increasing prevalence of wasting (15.7%-21.0%) over 15 years. The positive values of Moran I between 1998 and 2021 indicate the presence of spatial autocorrelation. Geographic clustering was consistently observed in the states of Madhya Pradesh, Jharkhand, Odisha, Uttar Pradesh, Chhattisgarh, West Bengal, Rajasthan, Bihar, and Gujarat. Improved sanitation facilities, a higher wealth index, and advanced maternal education status showed a significant association in reducing stunting. Relative risk maps identified hotspots of CGF health outcomes, which could be targeted for future interventions. Despite numerous policies and programs, malnutrition remains a concern. Its multifaceted causes demand coordinated and sustained interventions that go above and beyond the usual. Identifying hotspot locations will aid in developing control methods for achieving objectives in target areas.

Spatial patterns of mortality among the able-bodied population in the Republic of Bashkortostan

BACKGROUND: Regional differentiation adds complexity to the issue of high mortality among the working-age population. The identified regional patterns of mortality causes are typically not confined to specific areas, but rather influenced by spatial factors.
 AIM: To study temporal trends in spatial patterns of mortality of the working-age population mortality from the main causes of death in the Republic of Bashkortostan. In addition, we identified territorial factors associated with the mortality patterns and trends.
 METHODS: Mortality data on 6 main causes of death from 54 municipal districts, 20 urban districts and urban settlements were collected for the period from 2002 through 2020. The analysis included estimation of the Moran's index and the spatial panel data modeling.
 RESULTS: Spatial mortality patterns were evident for mortality from diseases of the circulatory system, infectious and parasitic diseases, and external causes. The impact of crime levels in a specific region (male: p=0.046; female: p=0.019) negatively affected mortality rates from circulatory diseases among working-age men and women. Additionally, for men, the volume of gross municipal product (GMP) had an additional effect (p=0.046). Factors that contribute to a decrease in mortality from external causes among able-bodied men and women included the growth of GMP (male: p=0.010; female: p=0.003 — for women), an increase in the number of doctors per 10,000 people in the population (male: p=0.001; female: p=0.037), and the availability of nursing staff (male: p=0.026; female: p=0.005). Furthermore, crime rates had a significant impact on mortality from external causes for men (p=0.028), while the availability of hospital beds played a crucial role for women (p=0.030).
 CONCLUSION: The study provides the evidence on the presence of spatial autocorrelation in the context of causes of death, as well as common and differing factors influencing male and female mortality in Bashkortostan. The findings provide a solid foundation for further analysis of spatially determined factors contributing to mortality among working-age population.

Bayesian Spatial Split-Population Survival Model with Applications to Democratic Regime Failure and Civil War Recurrence

The underlying risk factors associated with the duration and termination of biological, sociological, economic, or political processes often exhibit spatial clustering. However, existing nonspatial survival models, including those that account for “immune” and “at-risk” subpopulations, assume that these baseline risks are spatially independent, leading to inaccurate inferences in split-population survival settings. In this paper, we develop a Bayesian spatial split-population survival model that addresses these methodological challenges by accounting for spatial autocorrelation among units in terms of their probability of becoming immune and their survival rates. Monte Carlo experiments demonstrate that, unlike nonspatial models, this spatial model provides accurate parameter estimates in the presence of spatial autocorrelation. Applying our spatial model to data from published studies on authoritarian reversals and civil war recurrence reveals that accounting for spatial autocorrelation in split-population models leads to new empirical insights, reflecting the need to theoretically and statistically account for space and non-failure inflation in applied research.

Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects

Fundamental to most classical data collection sampling theory development is the random drawings assumption requiring that each targeted population member has a known sample selection (i.e., inclusion) probability. Frequently, however, unrestricted random sampling of spatially autocorrelated data is impractical and/or inefficient. Instead, randomly choosing a population subset accounts for its exhibited spatial pattern by utilizing a grid, which often provides improved parameter estimates, such as the geographic landscape mean, at least via its precision. Unfortunately, spatial autocorrelation latent in these data can produce a questionable mean and/or standard error estimate because each sampled population member contains information about its nearby members, a data feature explicitly acknowledged in model-based inference, but ignored in design-based inference. This autocorrelation effect prompted the development of formulae for calculating an effective sample size (i.e., the equivalent number of sample selections from a geographically randomly distributed population that would yield the same sampling error) estimate. Some researchers recently challenged this and other aspects of spatial statistics as being incorrect/invalid/misleading. This paper seeks to address this category of misconceptions, demonstrating that the effective geographic sample size is a valid and useful concept regardless of the inferential basis invoked. Its spatial statistical methodology builds upon the preceding ingredients.

Epidemiological profile, spatial patterns and priority areas for surveillance and control of leishmaniasis in Brazilian border strip, 2009–2017

Leishmaniasis represents a major neglected public health problem and the control measures have not been successful in Brazil. Recent studies have shown leishmaniasis importance to Brazilian border zones which reinforces the need to investigate its spread in those regions. This study aimed to analyze epidemiologic profile and its spatial distribution or aggregation process in both tegumentary and visceral leishmaniasis in the Brazilian border strip from 2009 to 2017. This is an ecological study of the epidemiological profile and spatial patterns encompassing municipalities in the Brazilian border strip. The presence of spatial autocorrelation and determination of priority areas for disease control were performed using global (Moran I) and local (LISA) Moran spatial techniques. The annual mean coefficients of tegumentary and visceral leishmaniasis were 29.8 and 0.6 by 100,000 inhabitants in the border strip, respectively. The indigenous population rates of leishmaniasis in the border zone appears to be higher than in the rest of the country (cutaneous changed from 33.2% to 6.6% and visceral rising from 1.0% to 17.5%) in the period. The most affected municipalities were located in the North and Central arches of the border zone. The results can subsidize the development of more targeted and effective strategies that can contribute to the surveillance and control of leishmaniasis in border zones, as the provision of epidemiological and spatial data on the disease. For better control of the disease, we recommend and emphasize the need to integrate public health policies of neighboring countries.

Modelling land surface temperature in urban areas using spatial regression models

This study is aimed at modelling land surface temperature (LST) using the spatial lag model (SLM) and spatial error model (SEM). Landsat-8 OLI/TIRS data and digital elevation model were used to generate the dependent and explanatory variables. First, correlation between the variables and LST, and presence of spatial autocorrelation were assessed. Second, the modelled LST was validated. The findings revealed that built-up areas, green areas and water bodies exhibit lower LST compared to the non-urbanized areas around the city. A moderate inverse relationship (r2 = 0.6) is observed between LST and vegetation index with p-value = 4*10−11. In contrast, built-up and surface water indices, albedo, and elevation indicate a weak positive correlation with LST. The LST predicted by SLM ranged between 20 and 42.9 °C (0.5 °C and 0.7 °C below the minimum and maximum of the original data, respectively). In the case of SEM, the predicted LST ranged between 20.4 and 42.2 °C (only 0.1 °C below the minimum of the original data). At 0.01 level of significant, all the variables are significant predictor of the LST except elevation. Both models performed well but SEM showed more superiority. The outcome of this study will enable planners to obtain insight into interventions that are necessary in order to mitigate surface temperature in urban areas.

Factors Affecting Spatial Autocorrelation in Residential Property Prices

Within housing literature, the presence of spatial autocorrelation (S.A.) in housing prices is typically examined horizontally in a two-dimensional setting. However, in the context of apartment buildings, there is also a vertical component of S.A. for housing units located on different floor levels. This paper therefore explores the determinants of both horizontal and vertical S.A. within residential property prices. First, we posit that S.A. in housing prices is a consequence of the price discovery process of real estate, in which property traders acquire price information from recent market transactions (i.e., comparables) to value a subject property. Furthermore, we contend that the extent to which property traders rely on comparables to determine housing prices is governed by the liquidity and volatility conditions of the market, which in turn affects the magnitude of the S.A. By developing and testing several spatial autoregressive hedonic models using open market transaction data for the Hong Kong residential property market, we find that market liquidity tends to increase both vertical and horizontal S.A., whilst market volatility is more prone to increase vertical S.A. but depress horizontal S.A.

Detecting spatial flow outliers in the presence of spatial autocorrelation

Spatial flow outlier (SFO) detection aims to discover spatial flows whose non-spatial attribute values are significantly different from their neighborhoods. Different from spatial flow clusters, which are the main concern in the current literature, SFOs represent unusual local instabilities and are valuable for revealing anomalous spatial interactions between regions. Detecting SFOs is challenging because the underlying distribution of the flow data is unknown a priori, and inappropriate distribution assumptions may lead to misleading decisions on SFOs. Surprisingly, spatial autocorrelation, which is a common property of geographic data, has not been considered in the null hypothesis for testing spatial outliers. To solve this significant methodological issue, we propose a spatial-autocorrelation-aware detection method. This method detects SFOs by testing the local difference of attribute values in flow neighborhoods against the null hypothesis that neighboring flows are similar. To construct this null hypothesis, we develop a distribution-free model by reconstructing the observed spatial autocorrelation. Synthetic experiments and a case study using the journey-to-work flow data in Chicago demonstrate that the choice and modeling of the null hypothesis has a significant influence on the statistical inference of SFOs. By taking the inherent spatial autocorrelation into account, our method can more objectively assess the significance of SFOs than two baseline methods based on the normality and randomization hypotheses.

Quantifying spatial non-stationarity in the relationship between landscape structure and the provision of ecosystem services: An example in the New Zealand hill country

Knowing how landscape structure affects the provision of ecosystem services (ES) is an important first step toward better landscape planning. Because landscape structure is often heterogenous across space, modelling the relationship between landscape structure and the provision of ES must account for spatial non-stationarity. This paper examines the relationship between landscape structure and the provision of ES using a hill country and steep-land case farm in New Zealand. Indicators derived from land cover and topographical data such as Largest Patch Index (LPI), Contrast Class Edge (CCE), Edge Density (ED), and Terrain slope (SLOPE) were used to examine the landscape's structure and pattern. Measures of pasture productivity, soil erosion control, and water supply were derived with InVEST tools and spatial analysis in a GIS. Multiscale Geographically Weighted Regression (MGWR) was used to evaluate the relationship between indicators of landscape structure and the provisioning of ES. Other regression models, including Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR), were carried out to evaluate the performance of MGWR. Results showed that landscape patterns significantly affect the supply of all mapped ES, and this varies across the landscape, dependent on the pattern of topographical features and land cover pattern and structure. MWGR outperformed other OLS and GWR in terms of explanatory power of the ES determinants and had a better ability to deal with the presence of spatial autocorrelation. Spatially and quantitatively detailed variations of the relationship between landscape structure and the provision of ES provide a scientific basis to inform the design of sustainable multifunctional landscapes. Information derived from this analysis can be used for spatial planning of farmed landscapes to promote multiple ES which meet multiple sustainable development objectives.

Spatial Analysis of the Sociodemographic Characteristics, Comorbidities, Hospitalization, Signs, and Symptoms Among Hospitalized Coronavirus Disease 2019 Cases in the State of Rio De Janeiro, Brazil.

After more than 1 year from the beginning of the pandemic, the coronavirus disease 2019 (COVID-19) has reached all continents. The number of infected people is still increasing, and Brazil is among the countries with the highest number of registered cases in the world. In this study, we investigated the profile of hospitalized COVID-19 cases and the eventual clusters of similar areas, using geographic information systems. The study was conducted using secondary data. Variables such as sociodemographic characteristics, comorbidities, hospitalization, signs, and symptoms among confirmed cases were considered for each microregion/city of the state of Rio de Janeiro. These proportions were used when calculating the Global Moran's I. The local indicator of spatial association was used to identify local clusters. A significant global spatial auto correlation was found in 28% of the variables. The presence of spatial autocorrelation indicates that the proportions of patients with COVID-19 according to these characteristics are spatially oriented. Moran maps reveal 2 clusters, 1 of high proportions and 1 of low proportions. Understanding the geographic patterns of COVID-19 may assist public health investigators, contributing to actions to prevent and control the pandemic in the state.

Investigating associations between COVID-19 mortality and population-level health and socioeconomic indicators in the United States: A modeling study.

BackgroundWith the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines.Methods and findingsCounty-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R2); and (ii) effect estimates of each predictor.Adjusting for case rates, the selected indicators individually explain 24%–29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R2. Mortality is estimated to increase by 43 per thousand residents (95% CI: 37–49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34–44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors.ConclusionsSignificant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.

Reducing spatial autocorrelation in the dynamic simulation of urban growth using eigenvector spatial filtering

In cellular automata (CA) based modeling of dynamic urban growth, non-spatial statistical approaches are usually affected by spatial autocorrelation and fail to describe the spatially non-stationary relationships between urban growth and its drivers. The eigenvector spatial filtering (ESF) is a representative method that can well estimate the regression coefficients in the presence of spatial autocorrelation. In this study, we developed two CA models based on the linear ESF (LESF) and the non-stationary ESF (NESF), of which LESF is featured by spatially stationary coefficients while NESF is featured by spatially varying coefficients. These two models were compared to a conventional logistic regression (LOGR) CA model. The three models (i.e. CALOGR, CALESF and CANESF) were used to simulate rapid urban growth in Suzhou between 2009, 2014 and 2019. The statistics suggest significant reduction in the spatial autocorrelation of the residuals in the CALESF and CANESF land transition maps, as well as an increase in the model reliability. The results show that compared with CALOGR, CALESF and CANESF respectively improved the overall accuracy by 2.0% and 2.2% in 2014, and improved by 0.4% and 0.7% in 2019; they also hit more new-urban-cells, respectively increasing the figure-of-merit (FOM) by 3.8% and 4.3% in 2014, 0.3% and 0.9% in 2019. All three models performed better when integrated spatial heterogeneity into the CA neighborhoods, increasing overall accuracy by over 1% and FOM by over 2%. We concluded that the ESF-based CA models can effectively derive spatially varying land transition rules to capture the urban dynamics, and more accurately predict urban growth featured by spatial autocorrelation.

Spatial Analysis of Distribution of Inmates in Nigeria Prisons

The number of prison facilities available in Nigeria differs from state to state as well as across the regions. Hence the distribution of the inmates across these correctional facilities differs. This study investigates the distribution of the inmates across Nigeria correctional facilities (prisons) and the likely factors that may influence the distribution considering the spatial effect of the distribution. The considered factors are; year, month, state, region, gender and state population. Ordinary Least Square (OLS) was used to obtain the multiple regression model and Moran’s I test of spatial autocorrelation showed that there is presence of spatial autocorrelation in the distribution of inmates across Nigeria prisons. The Spatial Autocorrelation Regression Model (SARM) was therefore adopted to fit the regression model. While OLS approach indicated that all factors are significant except state, the SARM approach found both state and region not to be significant among the factors considered in the model. The SARM did not only give good compact spatial distribution of the inmates across Nigeria prisons it also gave a better predicted (forecast) values of inmates’ distribution than the OLS approach.

Detecting space\u2013time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities

The first case of COVID-19 in South America occurred in Brazil on February 25, 2020. By July 20, 2020, there were 2,118,646 confirmed cases and 80,120 confirmed deaths. To assist with the development of preventive measures and targeted interventions to combat the pandemic in Brazil, we present a geographic study to detect “active” and “emerging” space–time clusters of COVID-19. We document the relationship between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables. We used the prospective space–time scan statistic to detect daily COVID-19 clusters and examine the relative risk between February 25–June 7, 2020, and February 25–July 20, 2020, in 5570 Brazilian municipalities. We apply a Generalized Linear Model (GLM) to assess whether mortality rate, GINI index, and social inequality are predictors for the relative risk of each cluster. We detected 7 “active” clusters in the first time period, being one in the north, two in the northeast, two in the southeast, one in the south, and one in the capital of Brazil. In the second period, we found 9 clusters with RR > 1 located in all Brazilian regions. The results obtained through the GLM showed that there is a significant positive correlation between the predictor variables in relation to the relative risk of COVID-19. Given the presence of spatial autocorrelation in the GLM residuals, a spatial lag model was conducted that revealed that spatial effects, and both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk in Brazil. Our research can be utilized to improve COVID-19 response and planning in all Brazilian states. The results from this study are particularly salient to public health, as they can guide targeted intervention measures, lowering the magnitude and spread of COVID-19. They can also improve resource allocation such as tests and vaccines (when available) by informing key public health officials about the highest risk areas of COVID-19.

Configurational Entropy: An Application Involving Census Tract Data for the City of Birmingham, Alabama

A variation of Shannon's relative entropy statistic is presented as a measure of configurational entropy for variables known to exhibit spatial autocorrelation using census tract data for the city of Birmingham, Alabama. Standardized and non-standardized configurational entropy indices (CEIs) are introduced to measure the amount of spatial order in a geographic distribution. As the degree of spatial autocorrelation increases and the amount of entropy or uncertainty decreases, the CEIs produce values that diverge from Shannon's statistic, which tends to overstate the degree of disorder or uncertainty in the presence of spatial autocorrelation. The CEIs incorporate a spatial covariance approach to estimating spatial order based on connectivity and the differencing of values for adjacent areal units. While Shannon's entropy statistic is insensitive to spatial arrangement, the CEIs provide a scale-targeted quantification of the amount of inherent spatial order in a distribution as defined by the connective structure of the areal units and the degree to which a variable is spatially autocorrelated. The amount of spatial order, as manifested within an autocorrelated pattern at a given geographic scale and for a given connectivity structure, is directly proportional to the difference between Shannon's measure and the CEIs.

Spatial and spatio-temporal analysis of malaria cases in Zimbabwe

BackgroundAlthough effective treatment for malaria is now available, approximately half of the global population remain at risk of the disease particularly in developing countries. To design effective malaria control strategies there is need to understand the pattern of malaria heterogeneity in an area. Therefore, the main objective of this study was to explore the spatial and spatio-temporal pattern of malaria cases in Zimbabwe based on malaria data aggregated at district level from 2011 to 2016.MethodsGeographical information system (GIS) and spatial scan statistic were applied on passive malaria data collected from health facilities and aggregated at district level to detect existence of spatial clusters. The global Moran’s I test was used to infer the presence of spatial autocorrelation while the purely spatial retrospective analyses were performed to detect the spatial clusters of malaria cases with high rates based on the discrete Poisson model. Furthermore, space-time clusters with high rates were detected through the retrospective space-time analysis based on the discrete Poisson model.ResultsResults showed that there is significant positive spatial autocorrelation in malaria cases in the study area. In addition, malaria exhibits spatial heterogeneity as evidenced by the existence of statistically significant (P < 0.05) spatial and space-time clusters of malaria in specific geographic regions. The detected primary clusters persisted in the eastern region of the study area over the six year study period while the temporal pattern of malaria reflected the seasonality of the disease where clusters were detected within particular months of the year.ConclusionsGeographic regions characterised by clusters of high rates were identified as malaria high risk areas. The results of this study could be useful in prioritizing resource allocation in high-risk areas for malaria control and elimination particularly in resource limited settings such as Zimbabwe. The results of this study are also useful to guide further investigation into the possible determinants of persistence of high clusters of malaria cases in particular geographic regions which is useful in reducing malaria burden in such areas.

Drivers of plant diversity in Bulgarian dry grasslands vary across spatial scales and functional‐taxonomic groups

AbstractQuestions: Studying dry grasslands in a previously unexplored region, we asked: (a) which environmental factors drive the diversity patterns in vegetation; (b) are taxonomic groups (vascular plants, bryophytes, lichens) and functional vascular plant groups differently affected; and (c) how is fine‐grain beta diversity affected by environmental drivers?Location: Northwestern and Central Bulgaria.Methods: We sampled environmental data and vascular plant, terricolous bryophyte and lichen species in 97 10‐m2 plots and 15 nested‐plot series with seven grain sizes (0.0001–100 m2) of ten grassland sites within the two regions. We used species richness as measure of alpha‐diversity and the z‐value of the power‐law species–area relationship as measure of beta‐diversity. We analysed effects of landscape, topographic, soil and land‐use variables on the species richness of the different taxonomic and functional groups. We applied generalised linear models (GLMs) or, in the presence of spatial autocorrelation, generalised linear mixed‐effect models (GLMMs) in a multi‐model inference framework.Results: The main factors affecting total and vascular plant species richness in 10‐m2 plots were soil pH (unimodal) and inclination (negative). Species richness of bryophytes was positively affected by rock cover, sand proportion and negatively by inclination. Inclination and litter cover were also negative predictors of lichen species richness. Elevation negatively affected phanerophyte and therophyte richness, but positively that of cryptophytes. A major part of unexplained variance in species richness was associated with the grassland site. The z‐values for total richness showed a positive relationship with elevation and inclination.Conclusions: Environmental factors shaping richness patterns strongly differed among taxonomic groups, functional vascular plant groups and spatial scales. The disparities between our and previous findings suggest that many drivers of biodiversity cannot be generalised but rather depend on the regional context. The large unexplained variance at the site level calls for considering more site‐related factors such as land‐use history.

Economic Growth Patterns: Spatial Econometric Analysis for Russian Regions

In this article, we analyze the σ - and β -convergence, using the data of the socio-economic development of Russian areas, and discover the role of spatial autocorrelation in regional economic development. We are considering 80 areas of the Russian Federation for the period of 2010–2017. Moran coefficients were used to estimate spatial autocorrelation. We compare the Moran scatterplots for GDP per capita and GDP growth rates per capita in 2017 and in 2014. We study the impact on raising investment in leading capital and the costs of technological innovation. We evaluate a wide range of specifications of spatial econometric models for all kinds of weight matrices. We combine standard geographical proximity with specialization proximity to assess whether they are substitutes or additions to converging economic growth rates. The weight matrix of the neighborhood and specialization similarities are used. The weight matrix of specialization similarities of the regional economies is based on data on the structure of tax payments in 82 industries. The specialization structure of the region’s economy is related to its location. Clusters obtained by matrices of specialization proximity are well separable from each other in space. The connectivity within clusters and the boundaries between them become more apparent over time. It is shown that according to the results of estimation of conditional β -convergence models, the models of 2010–2014 and 2014–2017 differ significantly. There is a statistically significant β -convergence for the period 2010–2014. There is also the presence of spatial autocorrelation. Based on the results of valuation models constructed from data after 2014, it can be concluded that the coefficient estimates for the explanatory variables are not significantly different from zero, and accordingly there is no tendency towards regional convergence in terms of economic development. The results obtained in the work are stable for the proposed models and spatial weight matrices. Territorial proximity is a more important factor than the similarity of specialization for explanation the economic growth rates of Russian regions.

Spatial Regression with Multiple Dependent Variables: Principal Component Analysis and Spatial Autocorrelation

Simultaneous studies of multiple health conditions over geographic areas can be enhanced by the principal component analysis (PCA). However, the presence of spatial autocorrelation may induce nonlinearity that compromises PCA. This article presents an approach that combines the residual standardization method in PCA with a spatial regression method to account for spatial autocorrelation. It first estimates a multivariate simultaneous autoregressive model for parameter estimates and residuals. It then uses the residuals to formulate a standardized matrix for singular value decomposition. Simulation in various scenarios demonstrates that the proposed approach can effectively remove spatial dependent effects between spatial units to capture pure correlation effects within spatial units. Two case studies examine hospitalization and cancer data in Nebraska. The first demonstrates a way to account for spatial dependency in 39 hospitalization conditions over 156 census tracts. The second applies regression residuals to PCA to evaluate potentially elevated cancer risks near two nuclear power plants. The results show that accounting for spatial dependency and explanatory variables can help reveal information about multiple health outcomes.

Determinants of labour productivity growth in Spanish and Portuguese regions: a spatial shift-share approach

This article analyses the structural changes in labour productivity in the regions of Spain and Portugal during the period 2000–2015, by business sector contribution and spatial distribution. A shift-share empirical analysis was applied to identify the components of aggregate productivity growth and to ascertain the role played by the different sectors. Both the intra-sectoral component and the presence of spatial autocorrelation were found to be statistically significant, which led us to identify two clusters: one of high productivity, corresponding to regions in the eastern half of the Iberian peninsula, and the other of low productivity, in the west. For both clusters, various model specifications that have been proposed in spatial econometrics literature were used to estimate spatial spillovers of each sector and determine the contribution made to productivity growth by each sectoral component, and to identify any spatial feedbacks that may be generated in this process. The total effect of intra-sectoral component for all sectors in both clusters is positive. The highest values are obtained by the agriculture, construction, trade and public services sectors. Moreover, the total effects are greater in the high-productivity cluster, except for the industry sector.