Journal articles: 'Spatial analysis (Statistics)'

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Unwin, David J. "GIS, spatial analysis and spatial statistics." Progress in Human Geography 20, no. 4 (December 1996): 540–51. http://dx.doi.org/10.1177/030913259602000408.

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Riffo-Campos, Angela L., Guillermo Ayala, and Francisco Montes. "Gene Set Analysis Using Spatial Statistics." Mathematics 9, no. 5 (March 3, 2021): 521. http://dx.doi.org/10.3390/math9050521.

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Gene differential expression consists of the study of the possible association between the gene expression, evaluated using different types of data as DNA microarray or RNA-Seq technologies, and the phenotype. This can be performed marginally for each gene (differential gene expression) or using a gene set collection (gene set analysis). A previous (marginal) per-gene analysis of differential expression is usually performed in order to obtain a set of significant genes or marginal p-values used later in the study of association between phenotype and gene expression. This paper proposes the use of methods of spatial statistics for testing gene set differential expression analysis using paired samples of RNA-Seq counts. This approach is not based on a previous per-gene differential expression analysis. Instead, we compare the paired counts within each sample/control using a binomial test. Each pair per gene will produce a p-value so gene expression profile is transformed into a vector of p-values which will be considered as an event belonging to a point pattern. This would be the first component of a bivariate point pattern. The second component is generated by applying two different randomization distributions to the correspondence between samples and treatment. The self-contained null hypothesis considered in gene set analysis can be formulated in terms of the associated point pattern as a random labeling of the considered bivariate point pattern. The gene sets were defined by the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The proposed methodology was tested in four RNA-Seq datasets of colorectal cancer (CRC) patients and the results were contrasted with those obtained using the edgeR-GOseq pipeline. The proposed methodology has proved to be consistent at the biological and statistical level, in particular using Cuzick and Edwards test with one realization of the second component and between-pair distribution.

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NL, Julian Besag, and James Simpson. "Spatial Statistics and Digital Image Analysis." Journal of the American Statistical Association 89, no. 427 (September 1994): 1149. http://dx.doi.org/10.2307/2290961.

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Unwin, A., and D. Unwin. "Spatial Data Analysis with Local Statistics." Journal of the Royal Statistical Society: Series D (The Statistician) 47, no. 3 (September 1998): 415–21. http://dx.doi.org/10.1111/1467-9884.00143.

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Lee, E. Stanley. "Neuro-Fuzzy Estimation in Spatial Statistics." Journal of Mathematical Analysis and Applications 249, no. 1 (September 2000): 221–31. http://dx.doi.org/10.1006/jmaa.2000.6938.

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Yang, Hong, Xiao Ya Dong, Min Wang, and Yu Guo. "GIS-Aided Evolvement Analysis of Spatial-Temporal Pattern of Regional Tourism Industry Environment." Advanced Materials Research 726-731 (August 2013): 4690–93. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4690.

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Regional tourism is a part of China's economic income. It is of great significance for tourism economy development to study spatial-temporal evolvement. This study analyzed time-based characteristics and spatial cluster characteristics through methods including Spatial Weight Matrix, global spatial autocorrelation (Morans I) statistic, spatial Statistics (Getis-Ord Gi*) and local spatial autocorrelation calculations. Results show that the overall spatial autocorrelation model changed slowly from negative (-0.05) to positive (0.08) while eastern part of study area clustered as hotspot and western part clustered as coldspot. It can be concluded that the spatial distribution pattern of the tourism economy in study area from 2002 to 2007 was increasingly clustered and the tourism development of each units in study area will be much more spatially inter-dependent.

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Wagner, Helene H., and Marie-Josée Fortin. "SPATIAL ANALYSIS OF LANDSCAPES: CONCEPTS AND STATISTICS." Ecology 86, no. 8 (August 2005): 1975–87. http://dx.doi.org/10.1890/04-0914.

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Javizadeh, Saeed, and Zahra Hejazizadeh. "Analysis of Drought Spatial Statistics in Iran." Journal of Applied researches in Geographical Sciences 19, no. 53 (July 1, 2019): 251–77. http://dx.doi.org/10.29252/jgs.19.53.251.

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Smith, Chad M., Daniel C. Brown, Anthony P. Lyons, and Thomas Gabrielson. "Analysis of split-beam spatial coherence statistics for discerning compact from non-compact littoral sonar clutter." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A280. http://dx.doi.org/10.1121/10.0011341.

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Active sonar systems operating in the littoral environment are often reverberation-limited, which impacts detection performance by reducing the effective signal-to-noise ratio. Additionally, irregularities in the environment lead to excessive false alarms commonly referred to as sonar clutter. Clutter is found in all environments, but shallow-water littoral regions have been observed to be especially challenging. The irregularities that cause sonar clutter can have spatial scales ranging from much smaller to much greater than the dimensions of the sonar resolution cell. This talk will discuss physical interpretation and experimental assessment of signal statistics related to the transverse horizontal spatial coherence of reverberation estimated via split-beam processed data obtained from a line array. Multiple boundary interaction in shallow water depths and spatial variability randomize reverberation and may allow the use of Gaussian noise models. While match-filtered signal intensity is an optimal detection statistic in a Gaussian noise environment, spatial reverberation coherence statistics may provide complimentary information of the spatial scale of clutter features relative to the width of the beampattern. It is found these statistics may be used to help discern clutter types that are spatially compact from those that are non-compact when compared to the width of the sonar resolution cell.

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Wang, Fen-Jiao, Chang-Lin Mei, Zhi Zhang, and Qiu-Xia Xu. "Testing for Local Spatial Association Based on Geographically Weighted Interpolation of Geostatistical Data with Application to PM2.5 Concentration Analysis." Sustainability 14, no. 21 (November 7, 2022): 14646. http://dx.doi.org/10.3390/su142114646.

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Using local spatial statistics to explore local spatial association of geo-referenced data has attracted much attention. As is known, a local statistic is formulated at a particular sampling unit based on a prespecific proximity relationship and the observations in the neighborhood of this sampling unit. However, geostatistical data such as meteorological data and air pollution data are generally collected from meteorological or monitoring stations which are usually sparsely located or highly clustered over space. For such data, a local spatial statistic formulated at an isolate sampling point may be ineffective because of its distant neighbors, or the statistic is undefinable in the sub-regions where no observations are available, which limits the comprehensive exploration of local spatial association over the whole studied region. In order to overcome the predicament, a local-linear geographically weighted interpolation method is proposed in this paper to obtain the predictors of the underlying spatial process on a lattice spatial tessellation, on which a local spatial statistic can be well formulated at each interpolation point. Furthermore, the bootstrap test is suggested to identify the locations where local spatial association is significant using the interpolated-value-based local spatial statistics. Simulation with comparison to some existing interpolation and test methods is conducted to assess the performance of the proposed interpolation and the suggested test methods and a case study based on PM2.5 concentration data in Guangdong province, China, is used to demonstrate their applicability. The results show that the proposed interpolation method performs accurately in retrieving an underlying spatial process and the bootstrap test with the interpolated-value-based local statistics is powerful in identifying local patterns of spatial association.

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Fisher, Martin. "Fine-scale distributions of tropical animal mounds: a revised statistical analysis." Journal of Tropical Ecology 9, no. 3 (August 1993): 339–48. http://dx.doi.org/10.1017/s0266467400007392.

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ABSTRACTDescriptions of the fine scale distribution of organisms have frequently been used to investigate various ecological phenomena. Unfortunately, the most widely used spatial analysis techniques are based on single index statistics, which convey only minimal information about the biological processes underlying the studied distributions. Such statistics cannot detect changes in pattern over different scales, and cannot identify some types of distribution. Additionally, both the use of such statistics on the distribution of individuals which have a non-negligible size, and the frequent failure to use an edge correction for points close to the boundaries of a sampled area, have led to the over-reporting of ‘spaced out’ (‘regular’) distributions. Using two spatial distributions recently analysed with a single index statistic (termite mounds, and earthmounds created by termites), I illustrate the benefits gained from using the spatial functions K(t), G(y) and F(x) to analyse both ‘point events’ and events which have a non-negligible size. These functions are considerably more informative about the nature of a spatial pattern and offer wide scope for the fitting of spatial models to biological distributions.

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Menafoglio, Alessandra, and Piercesare Secchi. "Statistical analysis of complex and spatially dependent data: A review of Object Oriented Spatial Statistics." European Journal of Operational Research 258, no. 2 (April 2017): 401–10. http://dx.doi.org/10.1016/j.ejor.2016.09.061.

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Choi, SeungBae, and Yutaka Tanaka. "SENSITIVITY ANALYSIS IN SPATIAL STATISTICS: DETECTING INFLUENTIAL OBSERVATIONS IN SPATIAL PREDICTION." Journal of the Japanese Society of Computational Statistics 13, no. 1 (2000): 25–39. http://dx.doi.org/10.5183/jjscs1988.13.25.

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Ręklewski, Marek. "The use of spatial statistics in the analysis of salaries at poviat level in Poland." Wiadomości Statystyczne. The Polish Statistician 67, no. 1 (January 31, 2022): 38–56. http://dx.doi.org/10.5604/01.3001.0015.7087.

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The spatial differentiation of salaries is the subject of many scientific studies, both theoretical and empirical. One of the factors determining remuneration in Poland is the structure and type of business activity, specific for a given region and depending on its poviats (counties) in terms of the level of the average gross monthly salary by means of spatial autocorrelation statistical methods. The analysed statistical data for 2010–2019 come from the Local Data Bank (Bank Danych Lokalnych – BDL) of Statistics Poland. Global and local measures were used in the analysis. The calculation of the global parameters of spatial autocorrelation was based on the I Moran and C Geary statistics, while the Ii Moran statistic, which belongs to local spatial indicators from the LISA group (Local Indicators of Spatial Association), was used to identify the local autocorrelation. The statistical significance of the global statistics was verified by means of a randomisation approach based on theoretical moments. The I Moran and C Geary global statistics indicated a significant (very weak or weak) and positive spatial autocorrelation between poviats in terms of the level of average gross monthly salaries in 2010–2019, which shows the existence of spatial poviat structures of similar values, i.e. clusters with high or low values of average salaries. The increase in I Moran’s statistics and the growth of the C Geary in the analysed period indicate a decrease in the differentiation of average monthly salaries between poviats, thus signifying an increase in the dependence of spatial autocorrelation. The analysis of the results of the obtained local statistics allowed the determination of clusters of similar poviats in Poland, e.g. Mazowiecki, Pomorski and Śląski. Furthermore, the results of the analysis indicated the presence of outlier poviats.

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Cressie, Noel, Matthew Sainsbury-Dale, and Andrew Zammit-Mangion. "Basis-Function Models in Spatial Statistics." Annual Review of Statistics and Its Application 9, no. 1 (March 7, 2022): 373–400. http://dx.doi.org/10.1146/annurev-statistics-040120-020733.

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Spatial statistics is concerned with the analysis of data that have spatial locations associated with them, and those locations are used to model statistical dependence between the data. The spatial data are treated as a single realization from a probability model that encodes the dependence through both fixed effects and random effects, where randomness is manifest in the underlying spatial process and in the noisy, incomplete measurement process. The focus of this review article is on the use of basis functions to provide an extremely flexible and computationally efficient way to model spatial processes that are possibly highly nonstationary. Several examples of basis-function models are provided to illustrate how they are used in Gaussian, non-Gaussian, multivariate, and spatio-temporal settings, with applications in geophysics. Our aim is to emphasize the versatility of these spatial-statistical models and to demonstrate that they are now center-stage in a number of application domains. The review concludes with a discussion and illustration of software currently available to fit spatial-basis-function models and implement spatial-statistical prediction.

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Sun, Qian, Yong Tang, and Aimee Yang. "The Spatial Statistics Analysis of Housing Market Bubbles." Journal of Systems Science and Information 5, no. 3 (August 1, 2017): 250–66. http://dx.doi.org/10.21078/jssi-2017-250-17.

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Abstract With the incorporation of spatial statistic method, this paper constructs a state-space model of housing market bubbles, discussing the spatial pattern of housing market bubbles in China, and identifying the dynamic evolution process. The results show that: The bubbles of housing market walked along a path from low level to high level and then downsized to a low level during the period of 2009 and 2014, and the highest level stayed at 2011. From overall, the level of housing market bubbles had shown significant spatial autocorrelation and spatial agglomeration. In detail, the direction of North-South in China showed the inverted U shape, i.e., Central region was with high bubbles, and two ends contained low bubbles; from East-West direction, the East had high bubbles and the West contained comparatively low bubbles. Local spatial test indicates that there were some approximate spatial features in housing market bubbles among the adjacent regions. Observed from the level of housing market bubbles, China contained 3 plates: The first was the plate with low bubble level, including 3 provinces in North-East China (provinces of Jilin, Heilongjiang and Liaoning were included, but Dalian in Liaoning province was excluded; the second was the Central and West plate (the provinces of Yunnan, Guizhou, Sichuan, Guangdong, Guangxi, Hunan, Hubei, Gansu, Fujian, Jiangxi and Hainan were included in this plate), which was also featured with low bubble; and the third was Central East plate (provinces or provincial regions of Beijing, Tianjin, Hebei, Jiangsu, Zhejiang, Shanghai, Shandong, Anhui, Shanxi, Shaanxi and Inner Mongolia were included), which was characterized as high bubble region.

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Fassò, Alessandro, Alessio Pollice, and Barbara Cafarelli. "Spatial statistics for environmental studies." AStA Advances in Statistical Analysis 97, no. 2 (April 2013): 89–91. http://dx.doi.org/10.1007/s10182-013-0209-x.

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Yu, Jianming, and Yuan Ma. "Population Data Spatial Analysis in the Border Area of Shanxi, Hebei and Inner Mongolia in China." International Journal of Geology and Earth Sciences 6, no. 3 (September 2020): 27–34. http://dx.doi.org/10.18178/ijges.6.3.27-34.

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Spatialization of population statistics is an effective way to solve the problem of data fusion between statistics and natural factors. In order to solve the problems of data aging and inadequate spatial precision of population data in Inner Mongolia Bureau, this study used NPP/VIIRS night light data, residential population statistics and land use data as data sources, and selected appropriate models to simulate population spatial distribution at county level in the border area of Shanxi, Hebei and Mongolia. Based on land use data, a stepwise regression model is established to generate spatialized population data of each county in the border area, and then the accuracy of the evaluation data is tested with the standard of resident population statistics. Finally, the goal of the transformation of population statistics from administrative divisions to kilometer grids is realized.

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Griffith, Daniel A. "Articulating Spatial Statistics and Spatial Optimization Relationships: Expanding the Relevance of Statistics." Stats 4, no. 4 (October 19, 2021): 850–67. http://dx.doi.org/10.3390/stats4040050.

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Both historically and in terms of practiced academic organization, the anticipation should be that a flourishing synergistic interface exists between statistics and operations research in general, and between spatial statistics/econometrics and spatial optimization in particular. Unfortunately, for the most part, this expectation is false. The purpose of this paper is to address this existential missing link by focusing on the beneficial contributions of spatial statistics to spatial optimization, via spatial autocorrelation (i.e., dis/similar attribute values tend to cluster together on a map), in order to encourage considerably more future collaboration and interaction between contributors to their two parent bodies of knowledge. The key basic statistical concept in this pursuit is the median in its bivariate form, with special reference to the global and to sets of regional spatial medians. One-dimensional examples illustrate situations that the narrative then extends to two-dimensional illustrations, which, in turn, connects these treatments to the spatial statistics centrography theme. Because of computational time constraints (reported results include some for timing experiments), the summarized analysis restricts attention to problems involving one global and two or three regional spatial medians. The fundamental and foundational spatial, statistical, conceptual tool employed here is spatial autocorrelation: geographically informed sampling designs—which acknowledge a non-random mixture of geographic demand weight values that manifests itself as local, homogeneous, spatial clusters of these values—can help spatial optimization techniques determine the spatial optima, at least for location-allocation problems. A valuable discovery by this study is that existing but ignored spatial autocorrelation latent in georeferenced demand point weights undermines spatial optimization algorithms. All in all, this paper should help initiate a dissipation of the existing isolation between statistics and operations research, hopefully inspiring substantially more collaborative work by their professionals in the future.

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Zhu, Jun. "Statistical Methods for Spatial Data Analysis." Journal of the American Statistical Association 101, no. 473 (March 2006): 389–40. http://dx.doi.org/10.1198/jasa.2006.s66.

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Katti, S. K., Peter J. Diggle, and Brian D. Ripley. "Statistical Analysis of Spatial Point Patterns." Journal of the American Statistical Association 81, no. 393 (March 1986): 263. http://dx.doi.org/10.2307/2288020.

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Martin, R. J., and F. Droesbeke. "Spatial Processes and Spatial Time Series Analysis." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 3 (1988): 553. http://dx.doi.org/10.2307/2983011.

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Amoako, Esther Akoto. "A Spatial Analysis of Robbery Rate in the City of Detroit using Exploratory Data Analysis Approach." Proceedings of the ICA 4 (December 3, 2021): 1–8. http://dx.doi.org/10.5194/ica-proc-4-6-2021.

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Abstract. Many U.S. cities have experienced rising crime rates in recent years. Crime has inherent geographic quality and tend to concentrate in certain places within the city. To prioritize public safety and crime prevention strategies, it is important to identify where crime is occurring and with what severity. Using spatial statistics including the average nearest neighbour index, Moran’s I, Getis-Ord Gi* statistic, and Anselin Cluster and Outlier Analysis, this study investigates robbery locations within the city of Detroit over 5-year period, 2016 to 2020 to identify hot spots, cold spots and spatial patterns across two different spatial scale – block group and census tracts. The study seeks to understand the effect of data aggregation on each spatial scale on the outcome of the analysis to determine the most optimum spatial scale to study robbery rates. The study concludes that, spatial analysis at small scale like block group level is most informative. Policy implications and areas for further research are provided.

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Litvinenko, Alexander, David Keyes, Venera Khoromskaia, Boris N. Khoromskij, and Hermann G. Matthies. "Tucker Tensor Analysis of Matérn Functions in Spatial Statistics." Computational Methods in Applied Mathematics 19, no. 1 (January 1, 2019): 101–22. http://dx.doi.org/10.1515/cmam-2018-0022.

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AbstractIn this work, we describe advanced numerical tools for working with multivariate functions and for the analysis of large data sets. These tools will drastically reduce the required computing time and the storage cost, and, therefore, will allow us to consider much larger data sets or finer meshes. Covariance matrices are crucial in spatio-temporal statistical tasks, but are often very expensive to compute and store, especially in three dimensions. Therefore, we approximate covariance functions by cheap surrogates in a low-rank tensor format. We apply the Tucker and canonical tensor decompositions to a family of Matérn- and Slater-type functions with varying parameters and demonstrate numerically that their approximations exhibit exponentially fast convergence. We prove the exponential convergence of the Tucker and canonical approximations in tensor rank parameters. Several statistical operations are performed in this low-rank tensor format, including evaluating the conditional covariance matrix, spatially averaged estimation variance, computing a quadratic form, determinant, trace, loglikelihood, inverse, and Cholesky decomposition of a large covariance matrix. Low-rank tensor approximations reduce the computing and storage costs essentially. For example, the storage cost is reduced from an exponential{\mathcal{O}(n^{d})}to a linear scaling{\mathcal{O}(drn)}, wheredis the spatial dimension,nis the number of mesh points in one direction, andris the tensor rank. Prerequisites for applicability of the proposed techniques are the assumptions that the data, locations, and measurements lie on a tensor (axes-parallel) grid and that the covariance function depends on a distance,{\|x-y\|}.

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Gibeau, Rose-Marie, Erin A. Maloney, Sébastien Béland, Daniel Lalande, Michael Cantinotti, Alexandre Williot, Lucile Chanquoy, Jessica Simon, Marie-Aude Boislard-Pépin, and Denis Cousineau. "The correlates of statistics anxiety: Relationships with spatial anxiety, mathematics anxiety and gender." Journal of Numerical Cognition 9, no. 1 (March 31, 2023): 16–43. http://dx.doi.org/10.5964/jnc.8199.

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This study investigates the correlates of statistics anxiety. Considering that statistics anxiety and spatial anxiety have been separately correlated with related constructs (e.g., mathematics anxiety, academic performance, etc.), the possibility that spatial anxiety plays a role in statistics anxiety is explored. When facing statistics or mathematics operations, people may imagine or visualize the task operations they must do to obtain the result. To examine this hypothesis, 778 students in a Social or Health Sciences program, enrolled in a –often mandatory– statistics course from Canadian, French and Belgian universities completed an online survey. The results show moderate to strong positive correlations between all three types of anxiety (spatial, mathematics, and statistics). In addition, a mediation analysis reveals the intermediate role played by mathematics anxiety in the relationship between spatial and statistics anxieties. Nonetheless, the direct link from spatial anxiety to statistics anxiety is non-negligible in the model. Finally, the results also indicate that women report higher levels of statistics anxiety, which may be partly explained by their higher level of spatial anxiety.

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Rahman, S., L. C. Munn, R. Zhang, and G. F. Vance. "Rocky Mountain forest soils: Evaluating spatial variability using conventional statistics and geostatistics." Canadian Journal of Soil Science 76, no. 4 (November 1, 1996): 501–7. http://dx.doi.org/10.4141/cjss96-062.

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Spatial variability of soils is a landscape attribute which soil scientists must identify and understand if they are to construct useful soils maps. This paper describes the spatial variability of soils in a forested watershed in the Medicine Bow Mountains, Wyoming, using both conventional statistics and geostatistics. Principle Components Analysis indicated that flow accumulation and aspect were the two terrain attributes that most economically described terrain variability. Thickness of A and B horizons, organic carbon and solum coarse fragments were variable in the study area (CVs of 40 to 58%). Simple correlation and regression analyses suggested there were no statistically significant relationships between soil properties (texture, pH, coarse fragments, organic carbon content) and terrain attributes (elevation, slope gradient, slope shape, flow accumulation, aspect). Geostatistical analysis indicated thickness and coarse fragment contents of the A and B horizons, and solum thickness were spatially independent variables; however, pH, organic carbon content, and solum coarse fragment content were spatially correlated. Spatial variability was described by both linear (pH and organic carbon content) and spherical (solum coarse fragment) models. Use of geostatistics provided insight into the nature of variability in soil properties across the landscape of the Libby Creek watershed when conventional statistics (analysis of variance and regression analysis) did not. Key words: Rocky Mountains, Medicine Bow Mountains, forest soils, spatial variability, principle component analysis, geostatistics

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Titterington, D. M., and M. N. M. van Lieshout. "Stochastic Geometry Models in Image Analysis and Spatial Statistics." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 3 (1996): 627. http://dx.doi.org/10.2307/2983347.

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LePage, Kevin, and Henrik Schmidt. "Analysis of spatial reverberation statistics in the central Arctic." Journal of the Acoustical Society of America 99, no. 4 (April 1996): 2033–47. http://dx.doi.org/10.1121/1.415390.

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congxin, Li, and Zhang zaisheng. "Spatial Statistics Analysis of Regional Environmental pollution In China." Energy Procedia 5 (2011): 163–68. http://dx.doi.org/10.1016/j.egypro.2011.03.029.

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Khoo, Tzung Hsuen, Dharini Pathmanathan, and Sophie Dabo-Niang. "Spatial Autocorrelation of Global Stock Exchanges Using Functional Areal Spatial Principal Component Analysis." Mathematics 11, no. 3 (January 28, 2023): 674. http://dx.doi.org/10.3390/math11030674.

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This work focuses on functional data presenting spatial dependence. The spatial autocorrelation of stock exchange returns for 71 stock exchanges from 69 countries was investigated using the functional Moran’s I statistic, classical principal component analysis (PCA) and functional areal spatial principal component analysis (FASPCA). This work focuses on the period where the 2015–2016 global market sell-off occurred and proved the existence of spatial autocorrelation among the stock exchanges studied. The stock exchange return data were converted into functional data before performing the classical PCA and FASPCA. Results from the Monte Carlo test of the functional Moran’s I statistics show that the 2015–2016 global market sell-off had a great impact on the spatial autocorrelation of stock exchanges. Principal components from FASPCA show positive spatial autocorrelation in the stock exchanges. Regional clusters were formed before, after and during the 2015–2016 global market sell-off period. This work explored the existence of positive spatial autocorrelation in global stock exchanges and showed that FASPCA is a useful tool in exploring spatial dependency in complex spatial data.

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khodadad bonab, mahdi. "Pattern Analysis of City-Spatial Growth by Spatial Statistics (Case Study: Gorgan City)." Haft Hesar Journal of Environmental Studies 8, no. 32 (July 1, 2020): 29–40. http://dx.doi.org/10.29252/hafthesar.8.32.5.

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李, 文雯. "Spatial-Temporal Evolution Analysis of Provincial Common Prosperity Level Based on Spatial Statistics." Operations Research and Fuzziology 13, no. 01 (2023): 259–70. http://dx.doi.org/10.12677/orf.2023.131028.

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Bertorelle, G., and G. Barbujani. "Analysis of DNA diversity by spatial autocorrelation." Genetics 140, no. 2 (June 1, 1995): 811–19. http://dx.doi.org/10.1093/genetics/140.2.811.

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Abstract Two statistics are proposed for summarizing spatial patterns of DNA diversity. These autocorrelation indices for DNA analysis, or AIDAs, can be applied to RFLP and sequence data; the resulting set of autocorrelation coefficients, or correlogram, measures whether, and to what extent, individual DNA sequences or haplotypes resemble the haplotypes sampled at arbitrarily chosen spatial distances. Analyses of computer-generated sets of data, and of RFLP data from two natural populations, show that AIDAs allow one to objectively and simply identify basic patterns in the spatial distribution of haplotypes. These statistics, therefore, seem to be a useful tool both to explore the genetic structure of a population and to suggest hypotheses on the evolutionary processes that shaped the observed patterns.

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Dusek, Tamás. "Bidimensional Regression in Spatial Analysis." Regional Statistics 2, no. 1 (2012): 61–73. http://dx.doi.org/10.15196/rs02105.

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VASCONCELOS, RITA. "THE RELEVANCE OF SPATIAL STATISTICS ON THE STATISTICAL MODEL BUILDING FOR CORONARY HEART DISEASE." Journal of Biological Systems 03, no. 03 (September 1995): 661–75. http://dx.doi.org/10.1142/s0218339095000617.

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For a long time, it has been widely acknowledged that putting data on a map underlines important features, and helps in the understanding and interpretation of the real world. Recent and extensive developments of spatial statistics and of geostatistics show the growing importance of this field. Our aim was to help physicians to interpret a very large database on heart diseases (acute myocardial infarction and angina pectoris) on the Madeira Islands. Besides standard techniques, such as loglinear models fitting, we decided to explore the spatial aspect of the question, and to bring in to the analysis recent advances in exploratory and robust data analysis. We show the relevance of spatial statistics on the detection of "hidden" variables.

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Cardina, John, Denise H. Sparrow, and Edward L. McCoy. "Analysis of Spatial Distribution of Common Lambsquarters (Chenopodium album) in No-Till Soybean (Glycine max)." Weed Science 43, no. 2 (June 1995): 258–68. http://dx.doi.org/10.1017/s0043174500081157.

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The nonuniform spatial distribution of weeds complicates sampling, modeling, and management of weed populations. Principles of a rational approach to analysis of weed spatial distribution, combining classical and spatial statistics, are presented using data for cumulative emergence of common lambsquarters in no-tillage soybean fields in 1990 and 1993. Classical statistics, dispersion indices, mean/variance relationships, and frequency histograms confirmed that raw and loge-transformed data were not normally distributed, that populations were aggregated, and that large-scale trends in population means violated assumptions of spatial statistics. Detrending was accomplished by median polishing loge-transformed data and confirmed by evaluation of standardized residuals and frequency histograms. Detrended residuals were used to construct omni-directional and uni-directional semivariograms to describe the spatial structure of the populations. A spherical model fit to the data was verified by cross validation. Semivariograms showed that common lambsquarters density was spatially autocorrelated at distances to 16 m, with more than 30% of the variance in density due to distance between field locations. Comparisons of kriged estimates and their standard deviations with and without detrending indicated that estimates using detrended data were more appropriate and more precise. Kriged estimates of common lambsquarters density were used to draw contour maps of the populations.

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Mathur, Manish. "Spatial autocorrelation analysis in plant population: An overview." Journal of Applied and Natural Science 7, no. 1 (June 1, 2015): 501–13. http://dx.doi.org/10.31018/jans.v7i1.639.

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Analysis of spatial distribution in ecology is often influenced by spatial autocorrelation. In present paper various techniques related with quantification of spatial autocorrelation were categorized. Three broad categories namely global, local and variogram were identified and mathematically explained. Local measurers captures the many local spatial variation and spatial dependency while global measurements provide only one set of values that represent the extent of spatial autocorrelation across the entire study area. Global spatial autocorrelation measures the overall clustering of data and it included six well defines methods, namely, Global index of spatial autocorrelation, Joint count statistics, Moran’s I, Geary’s C ration, General G-statistics and Getis and Ord’s G. The study revealed that out of the six methods Moran’s I index was most frequently utilized in plant population study. Based on their similarity degree, local indicator of spatial association (LISA) can differentiate the neighbors in to hot and cold spots. Correlogram and variogram approaches are also given.

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Wang, Defeng, Yishan Luo, Vincent C. T. Mok, Winnie C. W. Chu, and Lin Shi. "Tractography atlas-based spatial statistics: Statistical analysis of diffusion tensor image along fiber pathways." NeuroImage 125 (January 2016): 301–10. http://dx.doi.org/10.1016/j.neuroimage.2015.10.032.

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Symanzik, Jürgen. "Statistical Analysis of Spatial Point Patterns." Technometrics 47, no. 4 (November 2005): 516–17. http://dx.doi.org/10.1198/tech.2005.s318.

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Wei, Y., M. Lu, and W. Wu. "A COMPARATIVE ANALYSIS OF FIVE CROPLAND DATASETS IN AFRICA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1863–70. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1863-2018.

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The food security, particularly in Africa, is a challenge to be resolved. The cropland area and spatial distribution obtained from remote sensing imagery are vital information. In this paper, according to cropland area and spatial location, we compare five global cropland datasets including CCI Land Cover, GlobCover, MODIS Collection 5, GlobeLand30 and Unified Cropland in circa 2010 of Africa in terms of cropland area and spatial location. The accuracy of cropland area calculated from five datasets was analyzed compared with statistic data. Based on validation samples, the accuracies of spatial location for the five cropland products were assessed by error matrix. The results show that GlobeLand30 has the best fitness with the statistics, followed by MODIS Collection 5 and Unified Cropland, GlobCover and CCI Land Cover have the lower accuracies. For the accuracy of spatial location of cropland, GlobeLand30 reaches the highest accuracy, followed by Unified Cropland, MODIS Collection 5 and GlobCover, CCI Land Cover has the lowest accuracy. The spatial location accuracy of five datasets in the Csa with suitable farming condition is generally higher than in the Bsk.

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Cressie, Noel, and Timothy R. C. Read. "Spatial Data Analysis of Regional Counts." Biometrical Journal 31, no. 6 (January 1989): 699–719. http://dx.doi.org/10.1002/bimj.4710310607.

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Puttanapong, Nattapong, Amornrat Luenam, and Pit Jongwattanakul. "Spatial Analysis of Inequality in Thailand: Applications of Satellite Data and Spatial Statistics/Econometrics." Sustainability 14, no. 7 (March 26, 2022): 3946. http://dx.doi.org/10.3390/su14073946.

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To formulate and monitor the progress of development policies, acquiring data with sufficient spatiotemporal details is inevitable. With the increasing availability of open remote-sensing data and open-source software packages, this research suggested the novelty integration of satellite data and spatial analytical methods, enabling a timely and costless framework for assessing the nationwide socioeconomic condition. Specifically, the spatial statistical and spatial econometrical methods were applied to geospatial data to identify the clustering patterns and the localized associations of inequality in Thailand. The spatial statistical results showed that Bangkok and its vicinity had been a cluster of high socioeconomic conditions, representing the spatial inequality of development. In addition, results of the spatial econometrical models showed that the satellite-based indicators could identify the socioeconomic condition (with p-value < 0.010 and R-squared ranging between 0.345 and 0.657). Inequality indicators (i.e., Gini, Thiel and Atkinson) were then constructed by using survey-based and satellite-based data, informing that spatial inequality has been slowly declining. These findings recommended the new establishment of polycentric growth poles that offer economic opportunities and reduce spatial inequality. In addition, in accordance with Sustainable Development Goal 10 (reduced inequalities), this analytical framework can be applied to country-specific implications along with the global scale extensions.

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salahi, boromand, and mojtaba faridpour. "Spatial analysis of climatic drought in North West of Iran using spatial autocorrelation statistics." Journal of Spatial Analysis Environmental Hazarts 3, no. 3 (October 1, 2016): 1–20. http://dx.doi.org/10.18869/acadpub.jsaeh.3.3.1.

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Manzini, Reinaldo Belickas, and Di Serio Carlos Luiz. "Cluster identification." Competitiveness Review: An International Business Journal 29, no. 4 (July 15, 2019): 401–15. http://dx.doi.org/10.1108/cr-01-2018-0001.

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Purpose This paper aims to contribute to the approaches based on traditional industry concentration statistics for identifying clusters by complementing them with the techniques of exploratory spatial data analysis (ESDA). Design/methodology/approach Using a sample with 34,500 observations retrieved from the social information annual report released by Brazil Ministry of Labor and Employment, the methodology was designed to make a comparison between the application of industry concentration statistics and ESDA statistics. Findings As the results show, the geographic distribution measures proved to be fundamental for longitudinal studies on regional dynamics and industrial agglomerations, and the local indicator of spatial association statistic tends to overcome the limitation of the industry concentration approach. Research limitations/implications In the period considered, due to economic, structural and circumstantial questions, activities linked to the transformation industry have been losing ground in the value creation process in Brazil. In this sense, the study of other industries may generate other types of insights that should be considered in the process of regional development. Originality/value This paper offers a critical analysis of empirical approaches and methodological advances with an emphasis on the treatment of special effects: spatial dependence, spatial heterogeneity and spatial scale. However, the regional dynamic presents a temporal dimension and a spatial dimension. The role of space has increasingly attracted attention in the analysis of economic changes. This work has identified opportunities for incorporating spatial effects in regional analysis over time.

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Sain, Stephan R. "Analysis and Modelling of Spatial Environmental Data." Journal of the American Statistical Association 101, no. 475 (September 2006): 1312. http://dx.doi.org/10.1198/jasa.2006.s125.

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Cracolici, Maria Francesca, Miranda Cuffaro, and Peter Nijkamp. "A spatial analysis on Italian unemployment differences." Statistical Methods and Applications 18, no. 2 (January 3, 2008): 275–91. http://dx.doi.org/10.1007/s10260-007-0087-z.

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Kavousi, Amir, Mohammad Reza Meshkani, and Mohsen Mohammadzadeh. "Spatial analysis of auto-multivariate lattice data." Statistical Papers 52, no. 4 (January 14, 2010): 937–52. http://dx.doi.org/10.1007/s00362-009-0302-0.

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Chen, Hao, and Lynna Chu. "Graph-Based Change-Point Analysis." Annual Review of Statistics and Its Application 10, no. 1 (March 10, 2023): 475–99. http://dx.doi.org/10.1146/annurev-statistics-122121-033817.

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Recent technological advances allow for the collection of massive data in the study of complex phenomena over time and/or space in various fields. Many of these data involve sequences of high-dimensional or non-Euclidean measurements, where change-point analysis is a crucial early step in understanding the data. Segmentation, or offline change-point analysis, divides data into homogeneous temporal or spatial segments, making subsequent analysis easier; its online counterpart detects changes in sequentially observed data, allowing for real-time anomaly detection. This article reviews a nonparametric change-point analysis framework that utilizes graphs representing the similarity between observations. This framework can be applied to data as long as a reasonable dissimilarity distance among the observations can be defined. Thus, this framework can be applied to a wide range of applications, from high-dimensional data to non-Euclidean data, such as imaging data or network data. In addition, analytic formulas can be derived to control the false discoveries, making them easy off-the-shelf data analysis tools.

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Rumpf, Jonas, Simon Pickl, Stephan Elspass, Werner König, and Volker Schmidt. "Structural analysis of dialect maps using methods from spatial statistics." Zeitschrift für Dialektologie und Linguistik 76, no. 3 (2009): 280–308. http://dx.doi.org/10.25162/zdl-2009-0010.

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Okunola, Olasunkanmi Habeeb. "Spatial analysis of disaster statistics in selected cities of Nigeria." International Journal of Emergency Management 15, no. 4 (2019): 299. http://dx.doi.org/10.1504/ijem.2019.10025949.

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Journal articles on the topic 'Spatial analysis (Statistics)'

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