Spatial Statistical Methods Research Articles

Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North Spain

After two years of the COVID-19 pandemic, there is extensive research on the spread of the virus and geo-statistical analysis of spatial patterns. However, from the perspective of health geography, COVID-19 mortality is still under-studied. This research aims to provide a geographic profile of COVID-19 mortality, in terms of the space-time evolution and the relationship with individual and contextual variables. To this end, we geocoded the daily COVID-19 microdata of deceased persons provided by the Government of Cantabria (in northern Spain) from March 1, 2020 to March 31, 2022. The study also took cadastral variables, population records, and connections to geo-enrichment services accessed through ArcGIS Pro License (ESRI) into account. Using spatial statistics methods, such as 3D bins and emerging hot spots, local bivariate relationships, and ordinary least squares, we propose an exportable and scalable methodology to help policymakers cope with the current stage of living with the epidemic virus. Our results suggest that the spatial distribution of mortality is less clustered than that of contagion and shed light on differences in COVID-19 mortality profiles inside/outside nursing homes, such as higher age, and the temporal concentration of deaths in nursing homes. Spatial regimes showed hot spots of COVID-19 mortality in urban and metropolitan areas, with a pattern of repetition over time, such as sporadic hot spots that accounted for 36.28% of deaths in only 11.88% of the area with COVID-19 deaths. Despite immunization, periods of high contagion meant a subsequent increase in mortality, such as during the Omicron wave, where consecutive metropolitan hot spots accounted for 37.50% of the area and 51.45% of deaths were concentrated. Finally, there were interesting nuances in the significant local context variables of COVID-19 mortality compared with the explanatory factors of COVID-19 cases.

Spatial–temporal analysis and spatial drivers of hepatitis-related deaths in 183 countries, 2000–2019

Hepatitis is the seventh leading cause of mortality worldwide and is the only communicable disease where mortality is increasing, yet the long-term spatial–temporal variation at global scale and its possible causes, i.e., drivers, remain unknown. Firstly, this study employed the measure of spatial autocorrelation, Moran’s I, and the measure of local spatial cluster, Getis-Ord Gi*, to characterize the spatial variation of mortality due to hepatitis in 183 countries globally for years 2000, 2010, 2015 and 2019. Then, a novel spatial statistical method, named the Geographical Detector, was utilized to investigate eight possible influencing factors, i.e., risk factors, of the spatial–temporal variation of mortality due to hepatitis. The results showed significant disparities of hepatitis-related mortality rates among countries. Hot spots, representing locations with higher mortality rates, were consistently observed in Africa, East Asia, and Southeast Asia, while the cold spots, representing locations with lower mortality rates, were predominantly found in Europe and the Americas. Potential spatial drivers of hepatitis mortality, identified by geographical detector, include “health expenditure”, “universal health coverage”, and “per capita income”. However, “hepatitis B immunization” and “total population” were not identified as significant spatial drivers for hepatitis mortality The findings highlighted the critical role of socioeconomic factors in the variations in hepatitis mortality, and pointed out relative importance of increasing health expenditure, per capita income, and improve universal health coverage on alleviating global hepatitis-related mortality.

Spatial autocorrelation equation based on Moran’s index

Moran’s index is an important spatial statistical measure used to determine the presence or absence of spatial autocorrelation, thereby determining the selection orientation of spatial statistical methods. However, Moran’s index is chiefly a statistical measurement rather than a mathematical model. This paper is devoted to establishing spatial autocorrelation models by means of linear regression analysis. Using standardized vector as independent variable, and spatial weighted vector as dependent variable, we can obtain a set of normalized linear autocorrelation equations based on quadratic form and vector inner product. The inherent structure of the models’ parameters are revealed by mathematical derivation. The slope of the equation gives Moran’s index, while the intercept indicates the average value of standardized spatial weight variable. The square of the intercept is negatively correlated with the square of Moran’s index, but omitting the intercept does not affect the estimation of the slope value. The datasets of a real urban system are taken as an example to verify the reasoning results. A conclusion can be reached that the inner product equation of spatial autocorrelation based on Moran’s index is effective. The models extend the function of spatial analysis, and help to understand the boundary values of Moran’s index.

Evolution Characteristics and Main Influencing Factors of Carbon Dioxide Emissions in Chinese Cities from 2005 to 2020

Based on the carbon emission database of the China Urban Greenhouse Gas Working Group, this paper analyzed the spatiotemporal evolution characteristics and main influencing factors of urban carbon dioxide emissions in China using ArcGIS spatial analysis and SPSS statistical analysis methods, in order to provide a reference for the formulation of the national “double-carbon” strategy and the construction of low-carbon urbanization. The results showed that (1) the urban carbon dioxide emissions in China exhibit a “point-line-area” spreading spatial grid. Carbon dioxide emissions form a planar emission pattern surrounded by the Beijing–Tianjin–Hebei urban agglomeration, Yangtze River Delta urban agglomeration, and Central Plains urban agglomeration. A high per capita and high-intensity emission belt from Xinjiang to Inner Mongolia has been formed. (2) The proportion of industrial emissions continues to decrease, and the range of high industrial emissions has gradually crossed the “Hu Huan-yong Line”, spreading from eastern China to the whole country. The emissions from transportation, the service industry, and households have become new growth points, and high-value emissions from households have also shown a nationwide spreading trend. (3) The main factors influencing the spatial distribution of carbon dioxide emissions are urbanization, the economy, industry, investment, and household energy consumption.

Impact of built environment on urban surface temperature based on multi-source data at the community level in Beilin District, Xi'an, China.

With the global warming and rapid urbanization in China, the urban built environment has undergone rapid changes, and the land surface temperatures (LSTs) of urban communities have obvious spatial heterogeneity. To explore the key driving factors of community LSTs, the multi-source data and spatial statistical methods being jointly used to analyze the spatial characteristics and main influencing factors of LST at the community level in the Beilin District of Xi'an City, China. The results are as follows: (1) Compared with communities dominated by construction land, communities with large area of green space and water bodies have lower LST. (2) According to the Akaike's information criterion (AICc) and maximum of adjusted R2, and other parameters, the No.1236 model was selected as the optimal model to analyze the influencing factors of community LST by exploratory data analysis, including building density (BD), building height standard deviation (BHS), percentage of public administration and public services land (PASL), percentage of green space and square land (PGSL), population density (POPD), normalized difference impervious surface index (NDISI), and perimeter-area fractal dimension (PAFRAC). (3) For each increase of one unit in NDISI and BHS when other factors remain unchanged, the LST will increase by 0.569°C and decrease by 0.478°C, respectively. (4) From the spatial stability and distribution of Local-R2, the warming factors of community LST are mainly NDISI, PAFRAC, BD, and PASL, while the cooling factors are BHS and PGSL. The spatial heterogeneity of community LST is not only related to the change of underlying surface properties but is also affected by intra-urban architectural morphology. Therefore, reasonable planning of urban built environment is of great significance for mitigating heat islands.

A space-time flow LISA approach for panel flow data

Spatial flow data represent meaningful spatial interaction (SI) phenomena between geographic regions that are often highly dynamic. However, most existing flow analytical methods are cross-sectional, and there is a lack of methods to measure spatiotemporal autocorrelation of flow data. To fill this gap, we proposed a new localized spatial statistical method called Space-Time Flow LISA. The method design is a combination of two existing method families, namely space-time LISA and Spatial Flow LISA. A critical component of the method is the space-time weight matrix of flow data that blends pairwise spatial and temporal connectivities. We design three versions of the matrix, namely contemporaneous, lagged, and hybrid. We evaluate the method using both synthetic data and a case study of U.S. interstate migration from 2005 to 2017. The method is found to have high efficacy in finding spatiotemporal local autocorrelation patterns. Unlike the Spatial Flow LISA that tends to detect short-distance migration corridor havens (‘HH’ flows) and long-distance migration corridor deserts (‘LL’ flows), the Space-Time Flow LISA is less impeded by the distance between flow origin and destination, as they can pick up local patterns that are less spatially explicit but temporally dependent. In addition, the new method is able to detect time-sensitive patterns such as the outmigration from Louisiana forced by Hurricane Katrina in 2005. By integrating spatial, temporal, and attributive associations into a one-step analysis, the proposed Space-Time Flow LISA can illustrate the spatiotemporal structure of flow phenomena well, and reveal dynamic distribution changes over time.

Spatial correlation models of VS30 values: A case study of the Tehran region

The average shear wave velocity of the top 30 m of soil profile (VS30) is considered as an indicator of local-site effects in seismic hazard assessment (SHA) studies. Previous research works have demonstrated that in addition to actual VS30 values, their spatial correlation characteristics have considerable effects on the estimated values of earthquake intensity measurements, particularly when the seismic risk assessment of spatially distributed assets and infrastructure networks is of interest. Tehran city, in addition to its proximity to several faults, is situated on a variety of sedimentary textures and encompasses a large number of residential buildings and infrastructure networks. The current study utilizes advanced spatial statistics methods and investigates the spatial correlation characteristics of the random field of VS30 values in Tehran. Moreover, by employing a more comprehensive database of VS30 values measured using geoseismic approaches and using eight spatial interpolation methods, a new estimated VS30 map of Tehran city is provided. The geological formations of the study area are also investigated considering the distribution of sample points with different VS30 values. The findings of the current research can be utilized in spatially correlated SHA studies in the Tehran region and can be viewed as a step towards filling the data gap required for seismic risk assessment studies in this region. Moreover, by identifying the local variations in site classes, the findings of the current study can be used for further detailing of geological formations.

A question of dominance: Statistically approaching grammatical variation in German standard language across borders

AbstractThis paper revisits the question of national varieties in German, a concept that still has not found solid empirical proof, at least not from a quantitative-etic perspective. The question raised is how data collected in the course of the international research project, Variantengrammatik des Standarddeutschen (Handbook of Grammatical Variation in Standard German), and the application of spatial statistical methods to geographical variant distribution patterns can contribute to this discussion. With the aid of Factor Analysis and spatially modified clustering of variant distribution patterns it is shown that—at least from a quantitative/corpus linguistic point of view—there is no evidence for assuming nation-specific varieties. Importantly, this result does not imply the existence of a homogenous German standard language; rather, it is argued that geographical variation does not self-evidently legitimize the assumption of standard varieties.

The relationship between geological disasters with land use change, meteorological and hydrological factors: A case study of Neijiang City in Sichuan Province

Sichuan hilly area is located in southwest China, which have many hilly area, is a densely populated, economically active, frequent geological disasters area. In this paper, Neijiang City in the region is taken as the test sample. This article, which analyzes Land-Use and Land-Cover Changes (LUCC), meteorological and hydrological data from 2015 to 2020, takes Neijiang City in this region as the research object. We explored the relationship between geological disasters such as collapse and landslides that often occur in the natural ecological environment with LUCC and meteorological and hydrological factors, using GIS spatial analysis and statistical analysis methods. The research results indicate that geological disasters mainly occur in farmland areas. The density of geological disaster points is highest within urban areas, making them the most prone to geological disasters. Geological disasters are more densely distributed in the area within 600 to 800 m from the river. There is a strong correlation between the average rainfall amount and the probability of geological disasters occurring. This study provides a scientific basis for the geological disaster risk assessment, prevention and control in this region. It has certain reference significance for the ecological environment governance, land resources planning and management in other similar regions.

Spatial variations in the associations of surface water quality with roads and traffic across an urbanization gradient in northern Georgia, USA.

Roads and traffic are important elements of urbanization, but their spatial associations with surface water quality in watersheds have been seldom studied. In this study, the spatially varying associations of three urbanization indicators, including road density, traffic density, and percentages of urban land, with twenty water quality indicators, including dissolved oxygen (DO), specific conductance (SC), dissolved solids (DS), suspended solids (SS), biochemical oxygen demand (BOD), dissolved nutrients, dissolved ions, heavy metals, and coliform bacteria, across the watersheds in the northern part of the state of Georgia, USA, have been examined by a conventional statistical method, ordinary least squares regression (OLS), and a spatial statistical method, geographically weighted regression (GWR). The results from OLS show that the urbanization indicators all have significant positive associations with the majority of the studied water pollutants, indicating that water pollution is significantly contributed by human activities related to urbanization in northern Georgia. In contrast, GWR results show that the associations vary across the watersheds affected by their urbanization levels. Significant positive associations are found between each urbanization indicator and each of the studied water pollutants, but not in all watersheds. The associations of suspended solids, nitrogen nutrients, and coliform bacteria with all three urbanization indicators are more significant in less-urbanized watersheds, while the associations of dissolved ions, BOD, and orthophosphate (PO4) with road density and traffic density are more significant than those with urban land in more-urbanized watersheds, indicating that those water pollutants are more contributed by human activities associated with roads and traffic than other activities in more-urbanized areas. As a pilot study to explore how and why the associations of surface water quality with roads and traffic change across watersheds with different urbanization levels, its findings suggest that the policies of watershed management, land-use planning, and transportation planning should be tailored in local areas based on the locally important water pollutants and their associated urbanization indicators.

Examining Car Accident Prediction Techniques and Road Traffic Congestion: A Comparative Analysis of Road Safety and Prevention of World Challenges in Low-Income and High-Income Countries

Road accidents are a significant negative outcome of transportation systems, causing injuries, fatalities, traffic congestion, and economic losses. As cities expand and the number of vehicles on the road increases, traffic accidents (TAs) have become a significant problem. Studies have shown that urban development plays a more significant role in transportation safety than previously thought. Low-income countries have higher fatality rates than high-income countries, according to the Permanent International Association of Road Congress (PIARC) and the World Health Organization (WHO). Predicting and preventing the occurrence of accidents and congestion is necessary worldwide, especially in developing countries where fatality rates are higher. The objective of this study is to examine and make a comparative analysis in low-income and high-income countries of the existing literature on the global challenge of car accidents and use its prediction techniques to enhance road safety and reduce traffic congestion. The study evaluates various approaches such as logistic regression, decision tree, random forest, deep neural network, support vector machine, random forest, K-nearest neighbors, Naïve Bayes, empirical Bayes, geospatial analysis methods, and UIMA, NSGA-II, and MOPS algorithms. The research identifies current challenges, prevention ideas, and future directions for preventing accidents and congestion on the road network. Integrating GIS-based spatial statistical methods and temporal data and utilizing advanced optimization algorithms and machine learning methods can result in accurate prediction models that can help identify accident hotspots and reduce congestions and enhance traffic safety and mitigate their occurrence. Effectively preventing urban traffic congestion requires the integration of spatial data into precise accident prediction models. By employing spatial analysis, road safety planning can be enhanced, high-risk areas can be identified, interventions can be evaluated, and resources can be optimally allocated to facilitate effective road safety measures and decision-making, especially in settings with limited resources. Therefore, it is crucial to consider ML and spatial analysis techniques and advanced optimization algorithms to enhance traffic flow control, in road safety research and transport planning efforts.

Analyzing synergistic effects of tourist attractions on sustainable development of neighborhoods with emphasis on urban smart growth principles

ABSTRACT Despite the spatial effects of tourist attractions in promoting smart growth principles in tourist cities, it is necessary to pay attention to the spatial synergy between tourist attractions in order to control their destructive spatial effects and realize sustainable development (SD) in tourist cities. In this research, based on smart growth measures, the necessity of spatial synergy between tourist attractions in sustainable neighborhood development is explained using innovative spatial statistical analysis methods at the scale of 469 neighborhood units of Mashhad city. The findings of the research confirm significant synergistic effect of tourist attractions on urban smart growth indicators and composite indicator of smart growth of the surrounding neighborhood units. On the other hand, lack of spatial synergy between Mashhad’s main tourist attraction in the city center and other tourist attractions leads to unbalanced residential and activity development and unbalanced distribution of tourism services. This issue prevents integrated realization of smart growth and SD of all neighborhood units in Mashhad.

Density Based Spatial Clustering of Applications and Spatial Pattern Analysis In Mapping the Distribution of ISPA Disease in Bireuen Regency

ISPA is an infectious disease transmitted through the air. ISPA disease can be detected through the regional distribution maps of disease. ISPA disease can be prevented in the early detection before the disease gets worse. This can prevent the spread of the disease further. Data on ISPA cases in 2019 to 2021 in Bireuen Regency from the medical records of dr. Fauziah Bireuen from an average of 13.18 to 59.24 per year. This research aims to determine the clusters of ISPA spread areas across the district of Bireuen and to analyze the distribution of disease patterns with Spatial Pattern Analysis and to map the respiratory diseases in each area with Flexibly Shaped Spatial Scan Statistics. The methodology in this research are collecting the data at the hospital for each area of ISPA patients and the data processed by using DBSCAN to obtain the cluster points on the map. The result of the DBSCAN model are clusters. The cluster results are next processed with the distribution of hotspot point on the map using the Spatial Pattern Analysis model, and the map of the prone of the spread of ISPA in Bireuen Regency is processed by using the Flexibly Shaped Spatial Scan Statistic method. Information from the map obtained that the first 4 clusters of the area are formed, namely 3 clusters and 1 outlier. Cluster 1 and outliers consist of 6 sub-districts, cluster 2 consist of 4 sub-districts and cluster 3 only has 1 sub-district. There are 6 hotspot areas in 2019, 5 hotspot areas in 2020, and 6 hotspot areas in 2021. The results of this research are each ISPA disease clustering map shows the results of the ISPA distribution on the map and areas that are prone to ISPA disease in Bireuen Regency are Jeunieb and Peusangan

County-Level Geographic Disparities in Disabilities Among US Adults, 2018.

Local data are increasingly needed for public health practice. County-level data on disabilities can be a valuable complement to existing estimates of disabilities. The objective of this study was to describe the county-level prevalence of disabilities among US adults and identify geographic clusters of counties with a higher or lower prevalence of disabilities. We applied a multilevel logistic regression and poststratification approach to geocoded 2018 Behavioral Risk Factor Surveillance System data, Census 2018 county-level population estimates, and American Community Survey 2014-2018 poverty estimates to generate county-level estimates for 6 functional disabilities and any disability type. We used cluster-outlier spatial statistical methods to identify clustered counties. Among 3,142 counties, median estimated prevalence was 29.5% for any disability and differed by type: hearing (8.0%), vision (4.9%), cognition (11.5%), mobility (14.9%), self-care (3.7%), and independent living (7.2%). The spatial autocorrelation statistic, Moran's I, was 0.70 for any disability and 0.60 or greater for all 6 types of disability, indicating that disabilities were highly clustered at the county level. We observed similar spatial cluster patterns in all disability types except hearing disability. The results suggest substantial differences in disability prevalence across US counties. These data, heretofore unavailable from a health survey, may help with planning programs at the county level to improve the quality of life for people with disabilities.

Spatiotemporal variations in ambulance demand: towards equitable emergency services in Sweden

ABSTRACT This study investigates the spatiotemporal variations in ambulance demand in southern Sweden, and how land use factors and sociodemographic factors are associated with variations in demand. A standardized ambulance demand ratio (SADR) indicating whether the risk of requiring an ambulance exceeded the expected was calculated using ambulance dispatch data in southern Sweden in 2018. Spatial analysis using a geographic information system (GIS) and spatial statistical methods underlie the methodology. Findings show that although rural areas are associated with a higher risk of requiring an ambulance (especially in daytime), this risk may be obscured by the more numerous dispatches occurring in urban areas. The increased risk in areas with bars and nightclubs, schools, industrial areas or road crossings reflects temporal variations in people’s routine activities. By dictating how and when population’s reshuffle, land use factors can help explain variations in risk for requiring an ambulance. Confirming previous research, areas with a higher share of older adults are associated with a higher ambulance demand while areas with a high median income are associated with lower demand. Population size may be an inadequate indicator of ambulance demand. Planning should account for daily changes in population sizes and compositions as this impacts ambulance demand.

Dominant Modes of Agricultural Production Helped Structure Initial COVID-19 Spread in the U.S. Midwest

The spread of COVID-19 is geographically uneven in agricultural regions. Explanations proposed include differences in occupational risks, access to healthcare, racial inequalities, and approaches to public health. Here, we additionally explore the impacts of coexisting modes of agricultural production across counties from twelve midwestern U.S. states. In modeling COVID-19 spread before vaccine authorization, we employed and extended spatial statistical methods that make different assumptions about the natures and scales of underlying sociospatial processes. In the process, we also develop a novel approach to visualizing the results of geographically weighted regressions that allows us to identify distinctive regional regimes of epidemiological processes. Our approaches allowed for models using abstract spatial weights (e.g., inverse-squared distances) to be meaningfully improved by also integrating process-specific relations (e.g., the geographical relations of the food system or of commuting). We thus contribute in several ways to methods in health geography and epidemiology for identifying contextually sensitive public engagements in socio-eco-epidemiological issues. Our results further show that agricultural modes of production are associated with the spread of COVID-19, with counties more engaged in modes of regenerative agricultural production having lower COVID-19 rates than those dominated by modes of conventional agricultural production, even when accounting for other factors.

Quantifying the Impact of Hydrological Connectivity on Salt Marsh Vegetation in the Liao River Delta Wetland.

The online version contains supplementary material available at 10.1007/s13157-023-01693-4.

Determination of short-range order in TiVNbHf(Al)

The presence of short-range chemical order can be a key factor in determining the mechanical behavior of metals, but directly and unambiguously determining its distribution in complex concentrated alloy systems can be challenging. Here, we directly identify and quantify chemical order in the globally single phase BCC-TiVNbHf(Al) system using aberration corrected scanning transmission electron microscopy (STEM) paired with spatial statistics methods. To overcome the difficulties of short-range order (SRO) quantification with STEM when the components of an alloy exhibit large atomic number differences and near equiatomic ratios, “null hypothesis” tests are used to separate experiment from a random chemical distribution. Experiment is found to deviate from both the case of an ideal random solid solution and a fully ordered structure with statistical significance. We also identify local chemical order in TiVNbHf and confirm and quantify the enhancement of SRO with the addition of Al. These results provide insight into local chemical order in the promising TiVNbHf(Al) refractory alloys while highlighting the utility of spatial statistics in characterizing nanoscale SRO in compositionally complex systems.

Zero-Watermarking for Vector Maps Combining Spatial and Frequency Domain Based on Constrained Delaunay Triangulation Network and Discrete Fourier Transform.

With its lossless properties, zero-watermarking has attracted a lot of attention in the field of copyright protection for vector maps. However, the common zero-watermarking algorithm puts too much emphasis on mining for global features, making it vulnerable to cropping attacks, and the robustness is not comprehensive enough. This study provides a vector map zero-watermarking scheme that utilizes spatial statistical information and frequency domain transformation methods in an effort to solve the aforementioned issue. In order to make the scheme more resistant to cropping and compression, it is constructed on the basis of feature point extraction and point constraint blocking of the original vector map. Within each sub-block, feature points are used to build constraint Delaunay triangulation networks (CDTN), and the angular values within the triangle networks are then extracted as spatial statistics. The angle value sequence is further transformed by discrete Fourier transform (DFT), and the binarized phase sequence is used as the final feature information to build a zero watermark by executing an exclusive disjunction operation with the encrypted copyright watermark image, both of which contribute to the scheme's robustness and security. The results of the attack experiments show that the proposed vector map zero-watermarking can restore identifiable copyright images under common geometric attacks, cropping attacks, and coordinate system transformations, demonstrating a high level of robustness. The theoretical basis for the robustness of this watermarking scheme is the stability of CDTN and the geometric invariance of DFT coefficients, and both theory and experiment validate the method's validity.

A machine learning-based spatial statistical method for modelling different phases of gully development in South-Eastern Nigeria

Gully erosion is an environmental problem that causes topsoil loss and ecological damage. Understanding its formative processes and predisposing factors is crucial for effective management. However, few studies have focused on the spatial modelling of these factors. In the current study, machine learning (ML)-based spatial statistics were used to identify the predisposing factors of gully formation processes in south-eastern Nigeria. A geospatial database was created using 262 gully locations, fragmented into four categories as dependent variables. A total of 20 predictor variables, including geomorphometry, terrain, geo-environmental factors, proximity, and climate, were fused into a multinomial logistic regression model. The model was then fitted using the nnet package in R after testing for multi-collinearity and spatial autocorrelation. The results revealed that the study area, which is a 7th-order drainage basin, is vulnerable to gullying, as expressed by Horton's laws of geomorphometry. The highest simulated probability range was observed for moderate gully (0.5512–0.7175), with terrain and climate as predictive factors, whereas the lowest simulated probability range was observed for full gully (0.1136–0.4107) as influenced by basin geomorphometry. Soil creep (0.3869–0.5317) and low gully (0.1025–0.5107), both driven by climate, proximity, and terrain factors, exhibited a moderate tendency for occurrence. The findings reported in this paper can aid in decision-making for sustainable environmental management of gully cycles and provide a novel approach for global gully-active drainage basins.