Explore the vast range of titles available.

Interest in studying the urban real estate market, especially in investigating the relationship between house prices and related housing characteristics, is rapidly growing. However, this increasing attention is handicapped by a limited consideration of the multi-scale spatial heterogeneity in these relationships. This study uses the rental price data of 72,466 apartments in the Tokyo metropolitan area to examine spatial heterogeneity in the real estate market at multiple spatial scales. Within the framework of spatially varying coefficient (SVC) modeling, we utilized a random effect eigenvector spatial filtering-based SVC (RE-ESF-SVC) model, an approach not previously employed in real estate studies, and compared it with the traditional ESF-SVC model, which has no random effects. Our results show that: (1) except for one housing characteristic that impacts prices consistently throughout the Tokyo metropolitan area, relationships between other characteristics and prices vary from local to global spatial scales; (2) because of the utilization of random effects, RE-ESF-SVC has the unique advantage of making estimations flexibly while maintaining a high performance.

Although spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study is to show that capturing the \"spatial scale\" of each data relationship is crucially important to make SVC modeling more stable and, in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of scale. Models are examined through a series of Monte Carlo simulation experiments to assess the extent to which spatial scale influences model stability and the accuracy of their SVC estimates. The following models are studied: (1) geographically weighted regression (GWR) with a fixed distance or (2) an adaptive distance bandwidth (GWRa); (3) flexible bandwidth GWR (FB-GWR) with fixed distance or (4) adaptive distance bandwidths (FB-GWRa); (5) eigenvector spatial filtering (ESF); and (6) random effects ESF (RE-ESF). Results reveal that the SVC models designed to capture scale dependencies in local relationships (FB-GWR, FB-GWRa, and RE-ESF) most accurately estimate the simulated SVCs, where RE-ESF is the most computationally efficient. Conversely, GWR and ESF, where SVC estimates are naïvely assumed to operate at the same spatial scale for each relationship, perform poorly. Results also confirm that the adaptive bandwidth GWR models (GWRa and FB-GWRa) are superior to their fixed bandwidth counterparts (GWR and FB-GWR). Key Words: flexible bandwidth geographically weighted regression, Monte Carlo simulation, nonstationarity, random effects eigenvector spatial filtering, spatial scale.

To evaluate the relative importance of climate, productivity, environmental heterogeneity, biotic associations and habitat use by cattle to account for the species richness of trees, shrubs and herbs across the Subantarctic-Patagonian transition. An area of c. 150 x 150 km, within the transition zone between the Subantarctic and Patagonian subregions on the eastern slope of the Andes (c. 39-42° S, 70-72° W). All vascular plants found at each one of 50 (10 x 10 m) sampling plots were counted to estimate the local tree, shrub and herb species richness. Path analysis was used to evaluate the relationship between the richness of the three life-forms and plant cover, dried litter biomass, mean annual temperature, annual precipitation, daily temperature range, substrate heterogeneity and number of faecal pats. Principal coordinates of neighbour matrices was used to model the spatial autocorrelation of the data. Total plant species richness showed a unimodal pattern of spatial variation across the transition. Richness responded positively to indirect effects of precipitation mediated through plant cover, but there was a negative overall effect of precipitation on richness towards the west of the transition, most strongly for trees. An increase in substrate heterogeneity promoted a local increase in herb and shrub richness; the richness of trees increased in sites with steeper slopes. Canopy closure had a direct negative impact on herb richness; it also increased the local accumulation of litter, which negatively affected shrub and herb richness. The impact of habitat use by cattle negatively affected herb richness in areas to the east of the biogeographical transition. We suggest that the importance of indirect climatic effects mediated by vegetation cover can account for species richness patterns across this transition, most strongly for woody species, which supports the productivity hypothesis. The southern temperate forests towards the west may represent a deviation from the predictions of the water-energy dynamics hypothesis. Dissimilar spatial patterns of variation in the richness of woody and herbaceous species, and their different responses to climatic and heterogeneity variables across the transition, suggest that plant life-form influences the plant species richness-environment relationships.

Aim: Our goal was to use the occurrence of the influenza A virus in wild birds in Japan to create a potential risk map for the spread of avian influenza by migratory birds. Our modelling included a consideration of the multicollinearity and spatial autocorrelation of environmental variables and an examination of the reproducibility of the model results. Location: Japan. Methods: We used the maximum entropy approach to generate potential distribution models from presence-only data. Independent variables in the model included environmental factors such as winter temperature and precipitation, host factors such as duck population size and habitat abundance and artificial factors such as size of urban areas and poultry density. We used eigenvector-based spatial filters to alleviate spatial autocorrelation. To explore the reliability of the model, we compared the risk indices of localities positive in past winters for the influenza A virus in wild birds with those of all localities. Results: The model alleviated spatial autocorrelation with a high degree of accuracy. Dabbling duck population, size of urban area, diving duck population and altitude were the variables that were most strongly correlated with the potential distribution of avian influenza. We used the frequency of occurrence of the influenza A virus in five recent years in localities where wild birds were infected to estimate the repeatability of the high-risk indices; the potential risk indices for avian influenza in wild birds were high in localities where wild birds were infected in past. Main conclusions: The dabbling duck population in an area appeared to be the best indicator of high risk for the introduction of avian influenza from abroad. Priority monitoring localities for avian influenza carried by wild birds should be designated in western Japan and along the Pacific coast, which we estimated to be high-risk areas. Poultry farms in these areas should increase their biosecurity to prevent vectors from introducing avian influenza.

Linear regression models are commonly used for estimating ground PM2.5 concentrations, but the global spatial autocorrelation and local spatial heterogeneity of PM2.5 distribution are either ignored or only partially considered in commonly used models for estimating PM2.5 concentrations. Therefore, taking both global spatial autocorrelation and local spatial heterogeneity into consideration, a global-local regression (GLR) model is proposed for estimating ground PM2.5 concentrations in the Yangtze River Delta (YRD) and in the Beijing, Tianjin, Hebei (BTH) regions of China based on the aerosol optical depth data, meteorological data, remote sensing data, and pollution source data. Considering the global spatial autocorrelation, the GLR model extracts global factors by the eigenvector spatial filtering (ESF) method, and combines the fraction of them that passes further filtering with the geographically weighted regression (GWR) method to address the local spatial heterogeneity. Comprehensive results show that the GLR model outperforms the ordinary GWR and ESF models, and the GLR model has the best performance at the monthly, seasonal, and annual levels. The average adjusted R2 of the monthly GLR model in the YRD region (the BTH region) is 0.620 (0.853), which is 8.0% and 7.4% (6.8% and 7.0%) higher than that of the monthly ESF and GWR models, respectively. The average cross-validation root mean square error of the monthly GLR model is 7.024 μg/m3 in the YRD region, and 9.499 μg/m3 in the BTH region, which is lower than that of the ESF and GWR models. The GLR model can effectively address the spatial autocorrelation and spatial heterogeneity, and overcome the shortcoming of the ordinary GWR model that overfocuses on local features and the disadvantage of the poor local performance of the ordinary ESF model. Overall, the GLR model with good spatial and temporal applicability is a promising method for estimating PM2.5 concentrations.

Background In Ethiopia, acute respiratory infections (ARIs) are a leading cause of morbidity and mortality among children under five years. Geographically linked data analysis using nationally representative data is crucial to map spatial patterns of ARIs and identify spatially-varying factors of ARI. Therefore, this study aimed to investigate spatial patterns and spatially-varying factors of ARI in Ethiopia. Methods Secondary data from the Ethiopian Demographic Health Survey (EDHS) of 2005, 2011, and 2016 were used. Kuldorff’s spatial scan statistic using the Bernoulli model was used to identify spatial clusters with high or low ARI. Hot spot analysis was conducted using Getis-OrdGi statistics. Eigenvector spatial filtering regression model was carried out to identify spatial predictors of ARI. Results Acute respiratory infection spatially clustered in 2011 and 2016 surveys year (Moran’s I :-0.011621–0.334486 ) . The magnitude of ARI decreased from 12.6% (95%, CI: 0.113–0.138) in 2005 to 6.6% (95% CI: 0.055–0.077) in 2016. Across the three surveys, clusters with a high prevalence of ARI were observed in the North part of Ethiopia. The spatial regression analysis revealed that the spatial patterns of ARI was significantly associated with using biomass fuel for cooking and children not initiating breastfeeding within 1-hour of birth. This correlation is strong in the Northern and some areas in the Western part of the country. Conclusion Overall there has been a considerable decrease in ARI, but this decline in ARI varied in some regions and districts between surveys. Biomass fuel and early initiation of breastfeeding were independent predictors of ARI. There is a need to prioritize children living in regions and districts with high ARI.

Eigenvector spatial filtering (ESF) is becoming a popular way to address spatial dependence. Recently, a random effects specification of ESF (RE-ESF) is receiving considerable attention because of its usefulness for spatial dependence analysis considering spatial confounding. The objective of this study was to analyze theoretical properties of RE-ESF and extend it to overcome some of its disadvantages. We first compare the properties of RE-ESF and ESF with geostatistical and spatial econometric models. There, we suggest two major disadvantages of RE-ESF: it is specific to its selected spatial connectivity structure, and while the current form of RE-ESF eliminates the spatial dependence component confounding with explanatory variables to stabilize the parameter estimation, the elimination can yield biased estimates. RE-ESF is extended to cope with these two problems. A computationally efficient residual maximum likelihood estimation is developed for the extended model. Effectiveness of the extended RE-ESF is examined by a comparative Monte Carlo simulation. The main findings of this simulation are as follows: Our extension successfully reduces errors in parameter estimates; in many cases, parameter estimates of our RE-ESF are more accurate than other ESF models; the elimination of the spatial component confounding with explanatory variables results in biased parameter estimates; efficiency of an accuracy maximization-based conventional ESF is comparable to RE-ESF in many cases.

Habitat-use models are a powerful tool for improving our understanding of the relationships between animals and their environment. With the development of GIS, these models have been used increasingly for the analysis of ecological data. However, they often suffer from inappropriate model specifications, particularly the assumption of independence, which is essential in conventional statistical models, and may often be violated during the collection of spatial data. Spatial autocorrelation occurs when the values of variables sampled close to each other are not independent, representing a major problem that must be accounted for systematically. We used a spatial eigenvector (SEV) generalized linear model framework to investigate the distribution of Balaenoptera edeni off Cabo Frio, in southeastern Brazil, an upwelling area impacted by human activities (tourism and fisheries). Sighting data were collected during 94 boat trips conducted between December 2010 and November 2014. A quasi-Poisson model using SEV indicated that the use of habitat by the whales varied with depth and the distance from the coast, and predicted that whales would be found most frequently around Cabo Frio Island and along the coastline, apparently overlapping with their prey. We found that habitat use was better predicted with the inclusion of SEV and that it is also possible to produce predictions of habitat use by correcting for spatial autocorrelation without the use of expensive surveys conducted by specialized research ships. This study provides useful insights into the habitat use of B. edeni in the southwestern Atlantic Ocean, and represents an important contribution to the conservation of this data-deficient species.

Accurate snow water equivalent (SWE) products are vital for monitoring hydrological processes and managing water resources effectively. However, the coarse spatial resolution (typically at 25 km from passive microwave remote sensing images) of the existing SWE products cannot meet the needs of explicit hydrological modeling. Linear regression ignores the spatial autocorrelation (SA) in the variables, and the measure of SA in the data assimilation algorithm is not explicit. This study develops a Resolution-enhanced Multifactor Eigenvector Spatial Filtering (RM-ESF) method to estimate daily SWE in the western United States based on a 6.25 km enhanced-resolution passive microwave record. The RM-ESF method is based on a brightness temperature gradience algorithm, incorporating not only factors including geolocation, environmental, topographical, and snow features but also eigenvectors generated from a spatial weights matrix to take SA into account. The results indicate that the SWE estimation from the RM-ESF method obviously outperforms other SWE products given its overall highest correlation coefficient (0.72) and lowest RMSE (56.70 mm) and MAE (43.88 mm), compared with the AMSR2 (0.33, 131.38 mm, and 115.45 mm), GlobSnow3 (0.50, 100.03 mm, and 83.58 mm), NCA-LDAS (0.48, 98.80 mm, and 81.94 mm), and ERA5 (0.65, 67.33 mm, and 51.82 mm), respectively. The RM-ESF model considers SA effectively and estimates SWE at a resolution of 6.25 km, which provides a feasible and efficient approach for SWE estimation with higher precision and finer spatial resolution.

Moran’s I (Moran’s coefficient) is one of the most prominent measures of spatial autocorrelation. It is well known that Moran’s I has a representation that is similar to a Fourier series and is therefore useful for characterizing spatial data. However, the representation needs to be modified. This paper contributes to the literature by showing the necessary modification and presenting some further results. In addition, we provide the required MATLAB/GNU Octave and R user-defined functions.