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PubMed2006

Spatial analysis for spring bloom and nutrient limitation in Xiangxi bay of three Gorges Reservoir.

The spatial and temporal dynamics of physical variables, inorganic nutrients and phytoplankton chlorophyll a were investigated in Xiangxi Bay from 23 Feb. to 28 Apr. every six days, including one daily sampling site and one bidaily sampling site. The concentrations of nutrient variables showed ranges of 0.02-3.20 mg/L for dissolved silicate (Si); 0.06-2.40 mg/L for DIN (NH(4)N + NO(2)N + NO(3)N); 0.03-0.56 mg/L for PO(4)P and 0.22-193.37 microg/L for chlorophyll a, respectively. The concentration of chlorophyll a and inorganic nutrients were interpolated using GIS techniques. The results indicated that the spring bloom was occurred twice in space during the whole monitoring period (The first one: 26 Feb.-23 Mar.; the second one: 23 Mar.-28 Apr.). The concentration of DIN was always high in the mouth of Xiangxi Bay, and PO(4)P was high in the upstream of Xiangxi Bay during the whole bloom period. Si seems no obvious difference in space in the beginning of the spring bloom, but showed high heterogeneity in space and time with the development of spring bloom. By comparing the interpolated maps of chlorophyll a and inorganic variables, obvious consumptions of Si and DIN were found when the bloom status was serious. However, no obvious depletion of PO(4)P was found. Spatial regression analysis could explained most variation of Chl-a except at the begin of the first and second bloom. The result indicated that Si was the factor limiting Chl-a in space before achieved the max area of hypertrophic in the first and second bloom period. When Si was obviously exhausted, DIN became the factor limiting the Chl-a in space. Daily and bidaily monitoring of Site A and B, representing for high DIN: PO(4)P ratio and low DIN:PO(4)P ratio, indicated that the concentration of Si was decreased with times at both site A and B, and the dramatically drop of DIN was found in the end monitoring at site B. Multiple stepwise regression analysis indicated that Si was the most important factor affect the development of spring bloom both at site A and B in time series.

China↗
PubMed2026

Ten quick tips for spatial transcriptomics analysis.

Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery.

Spatial Transcriptomics↗
PubMed2001

Correlation of sensorimotor activation with functional magnetic resonance imaging and magnetoencephalography in presurgical functional imaging: a spatial analysis.

In this study we investigated the spatial heterotopy of MEG and fMRI localizations after sensory and motor stimulation tasks. Both methods are frequently used to study the topology of the primary and secondary motor cortex, as well as a tool for presurgical brain mapping. fMRI was performed with a 1.5T MR system, using echo-planar imaging with a motor and a sensory task. Somatosensory and motor evoked fields were recorded with a biomagnetometer. fMRI activation was determined with a cross-correlation analysis. MEG source localization was performed with a single equivalent current dipole model and a current density localization approach. Distances between MEG and fMRI activation sites were measured within the same anatomical 3-D-MR image set. The central region could be identified by MEG and fMRI in 33 of 34 cases. However, MEG and fMRI localization results showed significantly different activation sites for the motor and sensory task with a distance of 10 and 15 mm, respectively. This reflects the different neurophysiological mechanisms: direct neuronal current flow (MEG) and secondary changes in cerebral blood flow and oxygenation level of activated versus non activated brain structures (fMRI). The result of our study has clinical implications when MEG and fMRI localizations are used for pre- and intraoperative brain mapping. Although both modalities are useful for the estimation of the motor cortex, a single modality may err in the exact topographical labeling of the motor cortex. In some unclear cases a combination of both methods should be used in order to avoid neurological deficits.

Adolescent↗
PubMedDate not supplied

Descriptive spatial analysis of BSE in western France.

The spatial heterogeneity of Bovine Spongiform Encephalopathy (BSE) was analysed on the 84 cases confirmed in western France (WF) between August and December 2000, when both the Mandatory Reporting System and an active surveillance on cattle at risk were running. Ninety-four percent of these cases were born between June 1993 and June 1996, and we analysed the location at birth. One disease mapping and two clustering methods (Scan of Kulldorff and the method of Besag and Newell) were used. In order to attenuate the contrasts artificially created by the standard disease mapping method (over-dispersion), we estimated the Standard Incidence Ratio (SIR) with a Bayesian method (Poisson-Gamma model) allowing a smoothing of the estimators. The geographical location of interest was the "canton", that divided the total area into 526 geographical units. The background population (2.6 million cattle) was obtained from the Agricultural Census 2000. We tested the hypothesis of a homogenous spatial distribution of the BSE risk where the expected number of BSE cases per unit area was obtained by applying the overall BSE rate in WF to each "canton", standardised on the type of breed, dairy versus beef suckler. The SIR ranged from 0.80 to 2.18 and the spatial distribution of BSE cases was significantly heterogeneous. Two spatial clusters were detected with the spatial scan statistics of Kulldorff and the method of Besag and Newell (18 to 20 observed BSE-cases per cluster with a radius of 45 km) centred on the "département" of Côtes-d'Armor and Mayenne. Another cluster was detected with the method of Besag and Newell (9 observed BSE-cases) in the "département" of Finistère. The results proved that the risk of BSE is linked to the geographical location in the area of the study.

Animals↗
PubMed2001

Real-time strain rate echocardiographic imaging: temporal and spatial analysis of postsystolic compression in acutely ischemic myocardium.

Postsystolic compression (PSC) is a sensitive indicator of regional left ventricular ischemic diastolic dysfunction. Quantitative assessment of compression patterns by strain rate imaging could determine the presence and spatial extent of PSC for the detection and analysis of acute ischemic diastolic dysfunction. With the use of a segmental left ventricular model, we evaluated time to compression/expansion crossover (T-CEC) in standard apical views. Data at baseline and after acute left anterior descending coronary artery occlusion were collected from 18 open-chest pigs. We found significant mean prolongation of T-CEC, ranging from 43.9 +/- 48.6 ms to 110.8 +/- 73.8 ms, in all apical segments and in 2 midventricular (anterior and anteroseptal) segments. Analysis of variance demonstrated that the prolonged T-CEC is spatially consistent with perfusion defect. The temporal and spatial analysis of T-CEC with the use of strain rate imaging is a new noninvasive technique for identification and topographic quantitation of ischemic diastolic dysfunction expressed by PSC.

Animals↗
PubMed2000

New Frontiers in the Study of Dispersal and Spatial Analysis of Epidemics Caused by Species in the Genus Phytophthora.

Diseases caused by species in the genus Phytophthora are responsible for significant economic losses on a wide range of host plants. Spatial pattern is one of the most characteristic ecological properties of a species, and reflects environmental and genetic heterogeneity and reproductive population growth acting on the processes of reproduction, dispersal, and mortality. Species of Phytophthora can be dispersed either in soil, via surface water movement down rows, from rain splash dispersal, by air, or via movement by humans or invertebrate activity. Dispersal results in patchiness in patterns of disease or inoculum in soil. In this chapter we discuss the mechanisms of dispersal of members of this important genus and describe several methods that can be used to statistically analyze data for which spatial coordinates are known. The methods include testing spatial autocorrelation for binary data or continuous data, semivariograms, and regression models for spatial data. The goal of spatial pattern analysis is to gain an understanding of the mechanisms of dispersal of propagules and to sort out the physical and biological factors that are important for spread of plant pathogens and ultimately, for disease management.

Phytophthora diseases↗
PubMed2003

Multi-scale spatial analysis of human alveolar echinococcosis risk in China.

Risk factors for the transmission of Echinococcus multilocularis to humans operate at a range of spatial scales. Over a large area, such as China, regional scale risk is correlated with variation in climatic conditions because of its effect on the spatial distribution of landscapes that can support E. multilocularis transmission in wildlife hosts and the probability of egg survival. At a local scale of a few kilometres, or tens of kilometres, transmission risk is related to the spatial proximity of human populations and landscapes with active transmission. At the patch scale, when considering individual villages or households, human behavioural factors are important and for individuals genetic and immunological factors play a role. Satellite remote sensing can provide landscape information at a range of spatial scales and provide a spatial framework within which to examine transmission patterns. This paper reviews the application of remotely sensed data and spatial data analysis to develop a better understanding of disease transmission and shows how such data have been used to examine human alveolar echinococcosis infection patterns, at a range of spatial scales, in an endemic area in central China.

Animals↗
PubMed2004

Spatial analysis of alcohol-related motor vehicle crash injuries in southeastern Michigan.

Temporal, behavioral and social risk factors that affect injuries resulting from alcohol-related motor vehicle crashes have been characterized in previous research. Much less is known about spatial patterns and environmental associations of alcohol-related motor vehicle crashes. The aim of this study was to evaluate geographic patterns of alcohol-related motor vehicle crashes and to determine if locations of alcohol outlets are associated with those crashes. In addition, we sought to demonstrate the value of integrating spatial and traditional statistical techniques in the analysis of this preventable public health risk. The study design was a cross-sectional analysis of individual-level blood alcohol content, traffic report information, census block group data, and alcohol distribution outlets. Besag and Newell's spatial analysis and traditional logistic regression both indicated that areas of low population density had more alcohol-related motor vehicle crashes than expected (P < 0.05). There was no significant association between alcohol outlets and alcohol-related motor vehicle crashes using distance analyses, logistic regression, and Chi-square. Differences in environmental or behavioral factors characteristic of areas of low population density may be responsible for the higher proportion of alcohol-related crashes occurring in these areas.

Accidents, Traffic↗
PubMedDate not supplied

[Differential patterns of neonatal and post-neonatal mortality rates in Goiânia, Brazil, 1992-1996: use of spatial analysis to identify high-risk areas].

The aim of this study was to investigate the spatial pattern of neonatal and post-neonatal mortality in the city of Goiânia, Central Brazil. Analyses were based on linked birth and death certificates relating to 101,000 in-hospital live births from mothers residing in the city of Goiânia over the 1992-1996 period. Overall neonatal and post-neonatal mortality probabilities were calculated using the linked database. The empirical Bayes method was applied to smooth the estimated rates and minimize random fluctuation. Spatial units of analysis were 65 urban districts, corresponding to the urban planning sectors. The following exploratory spatial analyses were applied: "global" Moran's I statistic, local Moran LISA map, and Gi* local statistics. For both neonatal and post-neonatal mortality there was statistically significant spatial autocorrelation. Results of post-neonatal mortality showed a high-risk cluster located on the outskirts of the city. For the neonatal period, a heterogeneous mortality pattern was found with high-risk districts in all regions, including central areas.

Brazil↗
PubMed2026

Spatial analysis reveals the evolving organization of IDH-mutant glioma.

Adult diffuse gliomas are composed of malignant cell states interwoven with the non-malignant brain microenvironment. Here, we combine spatial transcriptomics and spatial proteomics of isocitrate dehydrogenase (IDH)-mutant gliomas to define organizational principles across histological grades. In low-grade tumors, spatial organization is shaped by underlying brain anatomy. We identify a functional white-gray matter junction that restricts cortical invasion and is associated with marked changes in tumor composition and cellular phenotypes. This junction is preferentially traversed by oligodendrocyte progenitor (OPC)-like malignant cells, suggesting a role in tumor expansion. In contrast, tumors with intermediate histological features are largely disorganized, with few recurring interactions between cancer cell states and microenvironmental cell types. In high-grade tumors, hypoxia-associated structure emerges, resembling IDH-wild-type glioblastoma. Together, these findings reveal two independent axes of spatial organization-from anatomy-driven structure in low-grade tumors to hypoxia-driven organization in high-grade tumors-and establish a framework linking tumor grade to recurrent spatial interactions.

Isocitrate Dehydrogenase↗
PubMed2005

[Spatial analysis of socioeconomic determinants of homicide in Brazil].

OBJECTIVE: To investigate the association between homicide rates and socio-economic variables taking into account the spatial site of the indicators. METHODS: An ecological study was conducted. The dependent variable was the rate of homicides among the male population aged 15 to 49 years, residing in the districts of the State of Pernambuco from 1995 to 1998. The independent variables were an index of the living conditions, per capita family income, Theil inequality index, Gini index, average income of the head of the family, poverty index, rate of illiteracy, and demographic density. The following techniques were used in the analysis: a spatial autocorrelation test determined by the Moran index, multiple linear regression, a spatial regression model (CAR) and a generalized additive model for the detection of spatial trend (LOESS). RESULTS: The illiteracy and the poverty index explained 24.6% of the total variability of the homicide rates and there was an inverse relationship. Moran's I statistics indicated spatial autocorrelation between municipalities. The multiple linear regression model best fitted for the purposes of this study was the Conditional Auto Regressive (CAR) model. The latter confirmed the association between the poverty index, illiteracy and homicide rates. CONCLUSIONS: The inverse association observed between socio-economic indicators and homicides may be expressing a process that propitiates improvement in living conditions and that is linked predominantly to conditions that generate violence, such as drug traffic.

Adolescent↗
PubMedDate not supplied

A spatial analysis of 100 surnames in England and Wales.

Spatial patterns are described and analysed for the 84 most common surnames in England and Wales, as well as 16 others selected for various reasons. At least three-quarters of the surname frequencies show spatial structure and are heterogeneous over the area of study. While they do not exhibit clines extending over the entire area of study, they do divide into four characteristics patterns. Spatial autocorrelation, while significant, is relatively low; similarity in surname frequency does not extend much beyond 100 km. Correlograms could be clustered to yield groups of surfaces denoting partial clines, isolation by distance, and differentiation at far distances. A method for detecting zones of rapid change found 21 such zones, mostly near the periphery of the study area. These boundaries do not indicate barriers to gene flow, but appear to be patterns brought about by historical factors. There are diffusion patterns between areas that differ greatly in surname composition, such as Wales and central England. There is little evidence of long-distance movements involving several surnames. At least three characteristic migration patterns, east-west and north-south diffusion and local dispersal, were found.

England↗
PubMed2025

SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis.

Spatial transcriptomics (ST) integrates spatial information into genomics, yet methods for generating spatially-informed gene representations are limited and computationally intensive. We present SIGEL, a cost-effective framework that derives gene manifolds from ST data by exploiting spatial genomic context. The resulting SIGEL-generated gene representations (SGRs) are context-aware, biologically meaningful, and robust across samples, making them highly effective for key downstream tasks, including imputing missing genes, detecting spatial expression patterns, identifying disease-related genes and interactions, and improving spatial clustering. Extensive experiments across diverse ST datasets validate SIGEL's effectiveness and highlight its potential in advancing spatial genomics research.

Genomics↗
PubMed2001

[Empirical bayesian model applied to the spatial analysis of leprosy occurrence].

OBJECTIVE: To analyze the spatial distribution of leprosy, identify areas of potential case underreporting or high transmission risk, and to assess the ecological association of leprosy distribution with multibacillary cases. METHODS: This study was carried out in 94 neighborhoods of Recife, Brazil. Data was obtained from the Ministry of Health's Disease Reporting System. An ecological approach with the empirical Bayesian method was applied for local rate flattening, using data from a neighborhood matrix. RESULTS: The mean annual occurrence was 17.3% of new cases in individuals under the age of 15 (28.3% corresponded to multibacillary forms), revealing an intense disease transmission. The spatial distribution of leprosy indicated three areas where there was a concentration of high detection rates and low-income neighborhoods. CONCLUSIONS: The Bayesian method allowed to reassess epidemiological indicators based on data from neighboring spatial units. This enabled to identify areas that should be prioritized in municipal control programs, either because of underreporting of cases or the higher number of occurrences related to multibacillary forms in individuals under 15.

Adolescent↗
PubMed2006

Validating crash locations for quantitative spatial analysis: a GIS-based approach.

In this paper, the spatial variables of the crash database in Hong Kong from 1993 to 2004 are validated. The proposed spatial data validation system makes use of three databases (the crash, road network and district board databases) and relies on GIS to carry out most of the validation steps so that the human resource required for manually checking the accuracy of the spatial data can be enormously reduced. With the GIS-based spatial data validation system, it was found that about 65-80% of the police crash records from 1993 to 2004 had correct road names and district board information. In 2004, the police crash database contained about 12.7% mistakes for road names and 9.7% mistakes for district boards. The situation was broadly comparable to the United Kingdom. However, the results also suggest that safety researchers should carefully validate spatial data in the crash database before scientific analysis.

Accidents, Traffic↗
PubMed2004

[Statistical models for spatial analysis in parasitology].

The simplest way to study the spatial pattern of a disease is the geographical representation of its cases (or some indicators of them) over a map. Maps based on raw data are generally "wrong" since they do not take into consideration for sampling errors. Indeed, the observed differences between areas (or points in the map) are not directly interpretable, as they derive from the composition of true, structural differences and of the noise deriving from the sampling process. This problem is well known in human epidemiology, and several solutions have been proposed to filter the signal from the noise. These statistical methods are usually referred to as Disease Mapping. In geographical analysis a first goal is to evaluate the statistical significance of the heterogeneity between areas (or points). If the test indicates rejection of the hypothesis of homogeneity the following task is to study the spatial pattern of the disease. The spatial variability of risk is usually decomposed into two terms: a spatially structured (clustering) and a non spatially structured (heterogeneity) one. The heterogeneity term reflects spatial variability due to intrinsic characteristics of the sampling units (e.g. igienic conditions of farms), while the clustering term models the association due to proximity between sampling units, that usually depends on ecological conditions that vary over the study area and that affect in similar way breedings that are close to each other. Hierarchical bayesian models are the main tool to make inference over the clustering and heterogeneity components. The results are based on the marginal posterior distributions of the parameters of the model, that are approximated by Monte Carlo Markov Chain methods. Different models can be defined depending on the terms that are considered, namely a model with only the clustering term, a model with only the heterogeneity term and a model where both are included. Model selection criteria based on a compromise between degree of complexity and goodness of fit are then needed to discriminate among them, because each specification has a different biological meaning. Our aim is to demonstrate that these techniques can be used to study the geographical distribution of a parasite infection. Our analyses are based on data collected in 142 farms of the province of Latina. In each breeding a fixed number of sheeps has been sampled (20) and checked for the presence of C. daubneyi. We have specified a Binomial model for the proportion of infected animals in each breeding. The heterogeneity component is modelled in a standard way, while we have used different prior specifications for the clustering term to show how they affect the results. When we use the usual specification also for clustering, the two models show a completely different spatial pattern of infection, probably because the intrinsic spatial structure of the clustering term tend to bias our inferences. The selection criterion indicates in this case the heterogeneity model as the "best" one. However, if we modify the prior so that a lower degree of spatial interaction is assumed, the clustering model is less complex and its goodness of fit better and it should be preferred.

Animal Husbandry↗
PubMedDate not supplied

Geocomputation techniques for spatial analysis: are they relevant to health data?

Geocomputation is an emerging field of research that advocates the use of computationally intensive techniques such as neural networks, heuristic search, and cellular automata for spatial data analysis. Since increasing amounts of health-related data are collected within a geographical frame of reference, geocomputational methods show increasing potential for health data analysis. This paper presents a brief survey of the geocomputational field, including some typical applications and references for further reading.

Cluster Analysis↗