Spatial Regression Models Research Articles

Divergent patterns of intraspecific trait variation among floral and vegetative characters in the hyperdominant dioecious Neotropical treeVirola sebifera(Myristicaceae)

AbstractMany Neotropical tree species are widespread at continental and even inter-continental scales, but phenotypic variation across their respective ranges has rarely been quantified. We measured traits in the widespread, unisexual Neotropical tree Virola sebifera (Myristicaceae) across its continental-scale range, and predicted that, given its hyperdominant status, generalist-pollinated flower type, high dispersal capacity and wide ecological amplitude there would be: (1) a weak relationship among and within reproductive and vegetative characters with environmental, spatial and biome correlates; (2) undetectable canalization of reproductive traits; and (3) high phenotypic variation of vegetative characters. We measured five foliar and seven floral traits from 187 male-expressing individuals of V. sebifera in specimens collected from Central to South America. Spatial regression models were employed to test for the importance of distance, elevation, mean annual rainfall and temperature on phenotypic variation. Pairwise correlations of vegetative and reproductive characters were calculated to examine intraspecific trait variation among and within these two classes. We found that most phenotypic variation was weakly explained by spatial predictors such as certain major South American domains like the Andes Cordillera and the Amazon Basin. Within-class comparisons of floral characters were no different than among-class (floral-foliar) correlates, suggesting the absence of canalization of reproductive structures. Greater phenotypic variation in flowers relative to leaves may be due to the generalist pollinator regime and high dispersal capacity thus relaxing divergences in the evolutionary process of its floral morphology. The results of these morphometric analyses (e.g. weak signals of isolation by distance) in combination with the biology of this Neotropical tree suggest that V. sebifera operates as a well-dispersed metapopulation across most of its range.

Risk exposure factors influencing the frequency of road crashes during the COVID-19 pandemic in Ciudad Juarez, Mexico. A negative binomial spatial regression model

The article aims to investigate the influence of risk exposure factors on the frequency of road crashes from January to August 2020 in Ciudad Juarez, Mexico. It is a longitudinal study with four data sets: road crashes, population and housing census, location of economic activities, and road network information. Specifically, this study investigates the relationship between exposure factors – demographics, main roads and land use – and road crashes. A mixed method analysis was employed, (1) spatial analysis using GIS techniques; and (2) a negative binomial spatial regression model. The results showed a strong spatial dependence (0.274; p-value 0.00) of road crashes in the census tracts, and this effect was statistically significant (0.007) in the spatial regression model. In the model, a high probability (<0.05) of road crashes in the census tracts was found with the population aged 15 to 65 years, the length of main roads and the level of road coverage (Engel index), land uses with economic activities of an industrial and commercial character. The findings of this study successfully capture the social, economic, and urban conditions during the January–August 2020 period in the context of the COVID-19 pandemic. This new knowledge could help create preventive plans and policies to address the frequency of road crashes.

Compliance of biosecurity practices for compartmentalization to foot-mouth disease and classical swine fever viruses in commercial swine companies from southern Brazil.

Classical swine fever (CSF) and foot-mouth disease (FMD) are both highly contagious disease and disruptive to commercial trades, but they are examples of foreign animal diseases that biosecurity-based compartmentalization could be used to support trade in free zones in response to an outbreak. This study aimed to evaluate biosecurity compliance to the Federal Normative Instruction #44 from December 4th, 2017 (BRAZIL, 2017) in commercial swine farms located in southern Brazil. A total of 604 swine farms from 10 commercial swine companies were sampled, from which 28.5% were breeding farms, 29.1% nursery, 32.8% finishing, 6.8% multipliers, and 2.8% farrow-to-finish. Cluster analyses revealed that farms with high compliance (n = 303, Cluster 1) performed 71% of the practices, moderate (n = 219, Cluster 2) 47%, and the low (n = 82, Cluster 3) 33%. A spatial logistic regression model estimated that biosecurity compliance was highest in only one of 10 commercial swine companies, and within a company, multipliers (when present) obtained the highest biosecurity compliance (p-value < 0.01). These results suggest that major improvements in biosecurity practices are needed in breeding herds, nursery, and grow-finish farms to be compliant to the Federal Instruction #44. Based on the combination of these analyses, only one commercial swine company was more suitable to establish compartments for CSF and FMD with minimal investments. Still, this study revealed that the majority of commercial swine companies needs to improve biosecurity practice protocols to then target compartmentalization.

Points of Interest and Housing Prices

ABSTRACT Points of Interest (POI) are an inherent element of the urban landscape, and their number and density reflect, among other things, the degree of urbanization and the city’s spatial structure. The very presence of POI in the closest vicinity of a residential property may indirectly or directly affect housing value. This paper aims to demonstrate the usefulness of POI density information of different categories in assessing the quality of a property’s immediate surroundings. While the mere presence of POIs in the nearest neighborhood may affect real estate prices, the influence of specific categories may not necessarily be positive. Therefore, the study attempted to classify POIs and determine their importance in the price formation process using spatial regression models. The results indicate that a high density of POIs in the immediate area is a stimulant for housing prices. The detailed analysis indicated that only some POI categories might be related to transaction prices, while in certain situations, some POI categories may negatively impact prices.

Car Ownership Modeling in Aceh Province Using a Spatial Regression Approach

The rise in car ownership in Aceh Province has been driven by population growth and economic development. The location of the owner's residence impacts vehicle ownership. There are common spatial dependencies in this area, meaning the significance of an observation in one area is influenced by its importance in another. Factors such as population, regional income, wages and salaries, area size, and road length are believed to impact vehicle ownership. This study aimed to determine the variables affecting vehicle ownership in Aceh Province. Spatial regression analysis was used to identify the determinants of vehicle ownership in Aceh Province. Secondary data was obtained from the Aceh Province One-Stop Administration System Office (SAMSAT) for vehicle ownership documentation, the Aceh Province Wealth and Income Service for vehicle ownership recap data, and other sources such as the Central Bureau of Statistics of Aceh Province for information on population, regional income, wages/salaries, area, road length, and ages. The results showed that car ownership is dependent on geography. The Spatial Error Model (SEM) with an Akaike Info Criterion (AIC) value of 107,919 was found to be the best spatial regression model. Population (X1), regional income (X2), road length (X5), and age over 17 (X6) were found to be the parameters with a significant level of 5% that impact vehicle ownership in Aceh Province.

The Spatial Pattern and Mechanism of Thermal Environment in Urban Blocks from the Perspective of Green Space Fractal

Land resources in cities are limited, and the cost of green space construction is high. Compared with increasing the amount of green space, maximizing the cooling effect of limited green space has important theoretical and practical significance. Green fractal is a new innovative branch of urban fractal that uses a fractal index to quantify the green space structural index in studying the thermal environmental effect. Multi-source data, such as high-resolution remote sensing images, were used, and spatial regression models and inconsistency indices were applied to explore the spatial pattern of the urban thermal environment at the block scale, and the mechanism of green space fractal characteristics in terms of correlation and spatial heterogeneity, to assess the quality of green space development. This study shows the following: (1) In 2019, the land surface temperature in Dalian formed a spatial distribution structure of “high in the central region and low in the surrounding region” at the block scale, and the fractal indices of different green spaces show the spatial distribution structure of “dual-core” and “multi-core” spaces. (2) The driving direction and force of the fractal index of green space on the spatial pattern of land surface temperature differs. The influence of the green space structure index (grid and boundary dimensions) is greater than that of the quantity index (area and circumference), and the influence of the grid dimension is the most significant. (3) The spatial heterogeneity between the fractal index of block-scale green space and land surface temperature in Dalian is significant, showing a centralized and contiguous spatial pattern, with a trapezoidal structure decreasing from north to south. (4) The spatial adaptation between the fractal and thermal environments of green spaces can be evaluated using the inconsistency index. The development quality of green space can be divided into three types: advanced, relative coordination, and lagged green spaces. Finally, this study proposes specific suggestions for the development of block-scale green spaces and thermal environment management in Dalian City.

Spatial Heterogeneity and Regional Clustering of Factors Influencing Chinese Adolescents’ Physical Fitness

There is often significant spatial heterogeneity in the factors influencing physical fitness in adolescents, yet less attention has been paid to this in established studies. Based on the 2018 Chinese National Student Physical Fitness Standard Test data, this study uses a multi-scale, geographically weighted regression (MGWR) model combined with a K-means clustering algorithm to construct a spatial regression model of the factors influencing adolescent physical fitness, and to investigate the degree of spatial variation in the physical fitness of Chinese adolescents from a socio-ecological perspective of health promotion. The following conclusions were drawn: the performance of the youth physical fitness regression model was significantly improved after taking spatial scale and heterogeneity into account. At the provincial scale, the non-farm output, average altitude, and precipitation of each region were strongly related to youth physical fitness, and each influencing factor generally showed a banded spatial heterogeneity pattern, which can be summarized into four types: N-S, E-W, NE-SW, and SE-NW. From the perspective of youth physical fitness, China can be divided into three regions of influence: the socio-economic-influenced region, mainly including the eastern region and some of the central provinces of China; the natural-environment-influenced region, which mainly includes the northwestern part of China and some provinces in the highland region; and the multi-factor joint-influenced region, which mainly includes the provinces in the central and northeastern regions of China. Finally, this study provides syndemic suggestions for physical fitness and health promotion for youths in each region.

Neighborhood Ecological Models of Alcohol Outlet Density and Male–on–Female Domestic Violence: Accounting for Adjacent Place and Neighborhood Characteristics

Alcohol availability is a consistent predictor of domestic violence, including intimate partner violence. Less is known about the effects of alcohol availability in neighboring units of analysis on domestic violence. This study examined whether alcohol outlet density in Milwaukee, Wisconsin is associated with Male-on-Female domestic violence (MFDV). Using block groups as the unit of analysis ( N = 571), spatial regression models were estimated to model the relationship between the density of total, on-premise, and off-premise alcohol outlets on MFDV, while accounting for the spatial spillover effect (i.e., alcohol availability and neighborhood characteristics in focal and surrounding block groups). At the focal level, off-premise alcohol outlets are associated with MFDV, net of concentrated disadvantage, lack of health insurance, MFDV lag and total population. Additionally, off-premise alcohol outlet density in surrounding units of analysis is a significant predictor of MFDV. However, total and on-premise densities did not demonstrate a relationship with MFDV.

Combining environmental and socioeconomic data to understand determinants of conflicts in Colombia.

Conflicts cause immense human suffering, violate human rights, and affect people's stability. Colombia is affected for decades by a high level of armed conflicts and violence. The political and socio-economic situation, drug trafficking in the Colombian economy, and natural disasters events affect the country and foster general violence. In this work, we aim to evaluate the role of the socioeconomic, political, financial, and environmental determinants of conflicts in the Colombian context. To achieve these aims, we apply a spatial analysis to explore patterns and identify areas that suffer from high levels of conflict. We investigate the role of determinants and their relationship with conflicts through spatial regression models. In this study, we do not consider only the entire Colombian territory, but we extend the analysis to a restricted area (Norte de Santander department) to explore the phenomena locally. Our findings indicate a possible diffusion process of conflicts and the presence of spillover effects among regions by comparing the two most known spatial regression models. As regards possible key drivers of conflicts, our results show that surprisingly socioeconomic variables present very little relationship with conflicts, while natural disasters and cocaine areas show a relevant impact on them. Despite some variables seeming to be the more informative to explain the process globally, they highlight a strong relationship for only a few specific areas while considering a local analysis. This result proves the importance of moving to a local investigation to strengthen our understanding and bring out additional interesting information. Our work emphasizes how the identification of key drivers of violence is crucial to have evidence to inform subnational governments and to support the decision-making policies that could assess targeted policy options.

Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM2.5 in the Yangtze River Economic Belt

The proposal of a “dual-carbon” goal puts forward higher requirements for air pollution control. Identifying the spatial-temporal characteristics, regional differences, dynamic evolution, and driving factors of PM2.5 are the keys to formulating targeted haze reduction measures and ameliorating air quality. Therefore, adopting the Dagum Gini Coefficient and its decomposition method, the Kernel Density Estimation model, and spatial quantile regression model, this study analyzes the regional differences, dynamic evolution, and driving factors of PM2.5 concentrations (PM2.5) in the Yangtze River Economic Belt (YREB) and the upstream, midstream, and downstream (the three regions) from 2003 to 2018. The study shows that: (1) PM2.5 in the YREB was characterized by increasing first and then decreasing, with evident heterogeneity and spatial agglomeration characteristics. (2) Inter-regional differences and intensity of trans-variation were the primary sources of PM2.5 differences. (3) The density curve of PM2.5 shifted to the left in the YREB and the upstream, midstream, and midstream, suggesting that PM2.5 has declined. (4) Industrial service level (IS) and financial expenditure scale (FES) exerted a significant and negative effect on PM2.5 across the quantiles. On the contrary, population density (PD) showed a significant and positive influence. Except for the 75th quantile, the technology level (TEC) significantly inhibited PM2.5. The remaining variables had a heterogeneous impact on PM2.5 at different quantiles. The above results suggest that regional joint prevention and control mechanisms, collaborative governance mechanisms, and comprehensive policy mix mechanisms should be established to cope with PM2.5 pollution and achieve green, sustainable economic development of the YREB.

Interaction between Development Intensity: An Evaluation of Alternative Spatial Weight Matrices

This paper investigates the spatial dependency of job and worker densities for the Minneapolis–St. Paul (Twin Cities) metropolitan area using census block level data from 2002 to 2017. A spatial weight matrix is proposed, considering the statistical expression of data, referred to as the correlation matrix, which detects the variations of dependencies among spatial units in both direction and level. The superior performance of the correlation matrix is demonstrated through a series of spatial regression models to predict land use patterns, in comparison with the conventionally used adjacency matrix as well as the accessibility matrix.

The determinants of Airbnb prices in New York City: a spatial quantile regression approach

AbstractIn this paper, we study the price determinants of Airbnb rentals, for the case of New York City, by developing a new dataset, which combines attributes of the property and of the related service, with other information available as open data. This dataset is employed within a spatial quantile semiparametric regression model, able to handle the intrinsic heterogeneity of house prices. The results confirm that property and service attributes play a significant role in determining rental prices, while some variables exert a different impact on prices in magnitude and sign, depending on the quantile considered.

Geographic Variation in Operative Management of Adhesive Small Bowel Obstruction

IntroductionVariation in surgical management exists nationally. We hypothesize that geographic variation exists in adhesive small bowel obstruction (aSBO) management. Materials and methodsA retrospective analysis of a national commercial insurance claims database (MarketScan) sample (2017-2019) was performed in adults with hospital admission due to aSBO. Geographic variation in rates of surgical intervention for aSBO was evaluated by state and compared to a risk-adjusted national baseline using a Bayesian spatial rates Poisson regression model. For individual-level analysis, patients were identified in 2018, with 365-d look back and follow-up periods. Logistic regression was performed for individual-level predictors of operative intervention for aSBO. ResultsTwo thousand one hundred forty-five patients were included. State-level analysis revealed rates of operative intervention for aSBO were significantly higher in Missouri and lower in Florida. On individual-level analysis, age (P < 0.01) and male sex (P < 0.03) but not comorbidity profile or prior aSBO, were negatively associated with undergoing operative management for aSBO. Patients presenting in 2018 with a history of admission for aSBO the year prior experienced a five-fold increase in odds of representation (odds ratio: 5.4, 95% confidence interval: 3.1–9.6) in 2019. Patients who received an operation for aSBO in 2018 reduced the odds of readmission in the next year by 77% (odds ratio: 0.23, 95% confidence interval: 0.1–0.5). The volume of operations performed within a state did not influence readmission. ConclusionsSurgical management of aSBO varies across the continental USA. Operative intervention is associated with decreased rates of representation in the following year. These data highlight a critical need for standardized guidelines for emergency general surgery patients.

Analysis of Spatial-Temporal Differentiation and Influencing Factors of Ecosystem Services in Resource-Based Cities in Semiarid Regions

The spatial-temporal differentiation characteristics and driving mechanisms of ecosystem services are of great significance for optimizing the pattern of land spatial protection and realizing regional sustainable development. Existing studies seldom consider the segmental influence mechanism of various influencing factors on different levels of ecosystem service value (ESV). Therefore, this paper analyzes the temporal and spatial differentiation evolution characteristics of ESV in semiarid regions through an improved ESV evaluation model. The spatial panel quantile regression (SPQR) model was introduced to explore the relationship between various types of influencing factors and ESV in different intervals. The results showed the following: (1) The changes in ESV in Baotou City from 2000 to 2018 tended to be stable, but the spatial differentiation of ESV intensified. The aggregation feature of the low-ESV region is significant and gradually expanding. (2) Precipitation was the dominant factor increasing the ESV in each interval, and temperature had a significant negative impact on the low-ESV area. (3) Higher land use integrity accelerates the decline of ESV in the surrounding areas of built-up areas. The high-ESV area was more sensitive to the intensity of human activity. The direction of human activities should be effectively controlled, and the structure of comprehensive land use should be optimized to enhance the service function of regional ecosystems. This research provides new thinking for the ecological restoration zoning of regional territorial spatial planning and the sustainable development of resource-based cities.

A nonparametric spatial regression model using partitioning estimators

Conventional spatial regression models are extended by modelling the spatial effects of the exogenous regressor model (SLX) as a functional coefficient. This coefficient is estimated by partitioning the domain of the spatial variable into a set of disjoint intervals and approximating the function using local Taylor expansions. The asymptotic properties of the proposed partitioning estimator are derived, and pointwise and uniform tests for the presence of spatial effects are developed. An empirical application of this work is used to study environmental Engel curves and provides strong evidence of neighbouring effects in the relationship between households’ income and the amount of pollution embodied in the goods and services they consume.

Dynamics of green spaces- Land surface temperature intensity nexus in cities of Ethiopia

In this study, the dynamics of green spaces and land surface temperature patterns in four cities in Ethiopia were investigated using Landsat imagery. The typical characteristics of LST over the past three decades (1990–2020) in relation to green space dynamics were first investigated; subsequently, the spatial distribution of LST was characterized based on hybrid geospatial techniques and mono-window algorithm analysis, in which the contributions of green spaces to LST were studied. In addition, the multiple linear regression method and spatial regression models (SRMs) were employed to investigate and predict the spatial dependence of LST and urbanization-induced green space dynamics. Results show that cities horizontally expanded unceasingly from 1990 to 2020, with a substantial discrepancy in expansion rates and the spatial patterns of UHI intensities among the cities (p < 0.05). Moreover, the area proportion of the UHI is significantly larger than that of the UGS, and the differences in the UGS cooling contribution were found in different land uses and zones of the cities. In the study periods, the spatial pattern of LST was significantly controlled by NDBI, and its coefficient in the OLS followed the pattern NDVI > MNDWI > latitudes > longitudes > population density > DEM. Due to the large proportions of buildings While green land and water bodies show significant capability to mitigate UHI effects, cooling effects are not apparent when their sizes are small. Besides, the SRMs show that UHI intensities were significantly influenced by MNDWI in Bahir Dar and Hawassa (p < 0.01).Cities' LAMBDA coefficients have a positive relationship with UHII (p < 0.01). Our study could help city planners and the government understand the current cooling potential of existing UGS to mitigate the dynamics of UHI and sustain the sustainability of green space management in cities.

How 2D and 3D built environments impact urban surface temperature under extreme heat: A study in Chengdu, China

Optimizing the built environment helps alleviate the adverse impact of extreme heat on public health and sustainable urban development. To this end, this study measured the daily land surface temperature (LST) in August 2022 (when extreme heat occurred) in Chengdu and introduced the 3D street view characteristics extracted from street view images. By employing OLS and spatial regression models, we probed the combined effects of 2D and 3D built environments on LST and their relationships under disparate circle spaces. The results show that: (1) compared with the 2D built environment, the 3D built environment presents a superior impact on LST. (2) urban land use that is 3D, compact, and has a high-floor area ratio diminishes LST, as evidenced by the finding that floor area ratio (FAR) reduces LST, but building density enhances it. (3) the green view index exhibits a stronger cooling effect than green space areas. (4) the built environment beneath distinct circle spaces displays a spatially varying impact on LST. For example, FAR is positively correlated with LST in the urban center, while in the urban periphery, it is prominently negative. Our empirical findings are conducive to formulating strategies from both 2D and 3D built environments for the formation and implementation of healthy urban design and development.

Poultry manure gleaned antibiotic residues in soil environment: A perspective of spatial variability and influencing factors

The antibiotics released by human and animals end up in the environmental sinks like soil and water to cause contamination and induce resistance in the microflora. The knowledge of fate and behavior of antibiotics in diverse geographical, climatological, and physicochemical is limited. Therefore, present study investigated the spatial distribution of antibiotics and their relationship with various factors and the source-sink relationship between soil and poultry manure. This was achieved by employing spatially constrained hierarchical clustering, global and local spatial autocorrelation, and spatial regression techniques. Most of the antibiotics co-occurred in both soil and poultry manure matrices, however antibiotic concentration in soil (1.20 μg kg−1 < antibiotics ≤21.38 μg kg−1) was lower than that in the poultry manure (7.05 μg kg−1< antibiotics ≤60.2 μg kg−1). Majority of the antibiotics showed spatial independence in both poultry manure and soil, except for sulfadiazine, sulfanilamide and sulfapyridine with Moran's I > − 0.111. Local indicator of spatial association indicated localized spatial clustering and outlier behavior of antibiotics. The underlying reasons for spatial heterogeneity of antibiotics resolved by spatial regression models indicated elevation, S%, C%, pH and mean annual temperature as the major factors. The influence of antibiotic concentration in poultry manure as a source was significant but marginal compared to the other predictors of spatial heterogeneity.

Comparison of Spatial Weight Matrices in Spatial Autoregressive Model: Case Study of Intangible Cultural Heritage in Indonesia

Intangible Cultural Heritage (ICH) can effectively contribute to Sustainable Development Goals (SDGs) in all economic, social, and environmental dimensions, along with peace and security. Studying ICH in Indonesia cannot be separated from the spatial aspect of how an area's attributes are related to other areas located close to each other. Spatial regression modeling needs to be done by considering the selection of spatial weight matrix. Using the wrong spatial weight matrix will increase the standard error in parameter estimation. Therefore, this study aims to determine: the best spatial weight matrix to accommodate the spatial autocorrelation in analyzing the description of the spread of ICH in Indonesia; and the variables that are thought to influence the number of ICH determination in Indonesia. The spatial regression modeling used in this study is the Spatial Autoregressive (SAR) model and the spatial weight matrices compared in this study are queen contiguity and inverse distance. The best model is the SAR model used the queen contiguity spatial weight matrix because it has minimum values of AIC, BIC, RMSE and MAPE which are 310.397, 319.555, 18.857 and 57.169 respectively. Simultaneously, involved in performing arts, wearing traditional dress, knowing Indonesian folklore and the spatial lag contribute significantly to number of ICH determination in Indonesia. Partially, only knowing Indonesian folklore have a significant effect on number of ICH determination in Indonesia at significance level α=5%. Each additional 1% of population that knowing Indonesian folklore in an area increases number of ICH determination in that area by 0.6719 units .

Reconstruction of Gap-Free Land Surface Temperature at a 100 m Spatial Resolution from Multidimensional Data: A Case in Wuhan, China.

Land surface temperatures (LST) are vital parameters in land surface-atmosphere interactions. Constrained by technology and atmospheric interferences, LST retrievals from various satellite sensors usually return missing data, thus negatively impacting analyses. Reconstructing missing data is important for acquiring gap-free datasets. However, the current reconstruction methods are limited for maintaining spatial details and high accuracies. We developed a new gap-free algorithm termed the spatial feature-considered random forest regression (SFRFR) model; it builds stable nonlinear relationships to connect the LST with related parameters, including terrain elements, land coverage types, spectral indexes, surface reflectance data, and the spatial feature of the LST, to reconstruct the missing LST data. The SFRFR model reconstructed gap-free LST data retrieved from the Landsat 8 satellite on 27 July 2017 in Wuhan. The results show that the SFRFR model exhibits the best performance according to the various evaluation metrics among the SFRFR, random forest regression and spline interpolation, with a coefficient of determination (R2) reaching 0.96, root-mean-square error (RMSE) of 0.55, and mean absolute error (MAE) of 0.55. Then, we reconstructed gap-free LST data gathered in Wuhan from 2016 to 2021 to analyze urban thermal environment changes and found that 2020 presented the coolest temperatures. The SFRFR model still displayed satisfactory results, with an average R2 of 0.91 and an MAE of 0.63. We further discuss and discover the factors affecting the visual performance of SFRFR and identify the research priority to circumvent these disadvantages. Overall, this study provides a simple, practical method for acquiring gap-free LST data to help us better understand the spatiotemporal LST variation process.