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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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Gamerman, Dani, and Ajax R. B. Moreira. "Multivariate spatial regression models." Journal of Multivariate Analysis 91, no. 2 (November 2004): 262–81. http://dx.doi.org/10.1016/j.jmva.2004.02.016.
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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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Bernardi, Mara S., Michelle Carey, James O. Ramsay, and Laura M. Sangalli. "Modeling spatial anisotropy via regression with partial differential regularization." Journal of Multivariate Analysis 167 (September 2018): 15–30. http://dx.doi.org/10.1016/j.jmva.2018.03.014.
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SALTYTE-BENTH, JURATE, and KESTUTIS DUCINSKAS. "Linear Discriminant Analysis of Multivariate Spatial-Temporal Regressions." Scandinavian Journal of Statistics 32, no. 2 (June 2005): 281–94. http://dx.doi.org/10.1111/j.1467-9469.2005.00421.x.
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Fried, Roland. "An investigation of humus disintegration by spatial-temporal regression analysis." Journal of Agricultural, Biological, and Environmental Statistics 9, no. 2 (June 2004): 138–57. http://dx.doi.org/10.1198/1085711043127_a.
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Hepple, Leslie W. "Multiple Regression and Spatial Policy Analysis: George Udny Yule and the Origins of Statistical Social Science." Environment and Planning D: Society and Space 19, no. 4 (August 2001): 385–407. http://dx.doi.org/10.1068/d291.
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Studies on the history of statistics by MacKenzie and on quantitative geography by Barnes have suggested that the lineaments and assumptions of statistical methods such as correlation and regression are closely related to their origin in biometrics and eugenics. This paper challenges that view by examining in detail the work of George Udny Yule. Yule was a colleague of Karl Pearson in the 1890s, but was interested in social science and social policy applications, not eugenics. In the late 1890s he constructed both the theory and application of multiple regression analysis, using geographical data. The paper examines Yule's work and its context, relating it to debates on the history of statistics, and traces the subsequent early diffusion of regression and correlation into the social sciences. The paper concludes by arguing for greater recognition of Yule's pivotal role, and also for further studies on the history of quantitative social science.
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Rojas-Gualdrón, Diego Fernando. "Comparing definitions of spatial relations for the analysis of geographic disparities in mortality within a Bayesian mixed-effects framework." Revista Brasileira de Epidemiologia 20, no. 3 (July 2017): 487–500. http://dx.doi.org/10.1590/1980-5497201700030011.
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ABSTRACT: Objective: To analyze the conceptual and technical differences between three definitions of spatial relations within a Bayesian mixed-effects framework: classical multilevel definition, spatial multiple membership definition and conditional autoregressive definition with an illustration of the estimate of geographic disparities in early neonatal mortality in Colombia, 2011-2014. Methods: A registry based cross-sectional study was conducted. Births and early neonatal deaths were obtained from the Colombian vital statistics registry for 2011-2014. Crude and adjusted Bayesian mixed effects regressions were performed for each definition of spatial relation. Model fit statistics, spatial autocorrelation of residuals and estimated mortality rates, geographic disparity measures, relative ratios and relative differences were compared. Results: The definition of spatial relations between municipalities based on the conditional autoregressive prior showed the best performance according to both fit statistics and residual spatial pattern analyses. Spatial multiple membership definition had a poor performance. Conclusion: Bayesian mixed effects regression with conditional autoregressive prior as an analytical framework may be an important contribution to epidemiological design as an improved alternative to ecological methods in the analyses of geographic disparities of mortality, considering potential ecological bias and spatial model misspecification.
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Sifriyani, Sifriyani, Ruslan Ruslan, and Susanty, F. H. "Mapping and Analysis Factors of Affecting Productivity Tropical Rain Forests in East Kalimantan." Modern Applied Science 13, no. 10 (September 24, 2019): 112. http://dx.doi.org/10.5539/mas.v13n10p112.
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Up to 2019, tropical rainforests in East Kalimantan has been experiencing very rapid degradation and continues to shrink. Therefore, it is necessary to evaluate mapping and analysis of factors affecting the productivity of tropical rain forests in East Kalimantan. The purpose of this study was to determine the factors that cause shrinkage of tropical rainforests in East Kalimantan based on spatial statistical perspectives. The data used were secondary data from the Indonesian Ministry of Forestry and the Central Bureau of Statistics. The data consisted of 10 districts/cities from East Kalimantan Province. Those data were influenced by spatial dependence and spatial heterogeneity. Nonparametric Geospatial Regression (NGR) is one of the spatial statistical methods used to overcome spatial dependence and spatial heterogeneity. The results of the study obtained was a Nonparametric Geospatial Regression modeling using the Gaussian Kernel geographical weighting function with a minimum CV value of 1.48. The model had R2 values for each district/city ranging from 74.39% - 88.65%. The goodness of fit of the NGR model was shown by the value of R2 = 0.8865, which stated that the variables that significantly affect the preservation of tropical rainforest by 88.65% were the area of protected forests, nature reserves and nature preservation, production forests, area of each district/city, economic growth rate and regional development index.
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Su, Liyun, Changhai Liang, Xiaohe Yang, and Yang Liu. "Influence Factors Analysis of Provincial Divorce Rate Spatial Distribution in China." Discrete Dynamics in Nature and Society 2018 (July 16, 2018): 1–11. http://dx.doi.org/10.1155/2018/6903845.
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Divorce is the primary factor affecting the harmony and stability of the family and society. This paper uses spatial statistics to analyze the potential social causes of influencing the spatial distribution of divorce rates in various provinces of China. Firstly, the factors of social influence, family cohesion, and ethnic customs are constructed by factor analysis, then the spatial interaction effect of divorce rate in each province is brought into the model, and the spatial regression analysis of these three factors is carried out. The results show that social influence, especially the tertiary industry share of GDP, has a significant influence on the divorce rate, family cohesion has a distinct negative effect on the divorce rate, and ethnic customs have a noteworthy impact on the divorce rate. It is reflected in the high divorce rate of the majority of ethnic minority population, while, in the spatial data processing, the factor spatial lag model (FSLM) is better than the ordinary least square (OLS) regression model.
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de Souza, Suzana, Oscar Kenji Nihei, and Cezar Rangel Pestana. "High prevalence of gastroschisis in Brazilian triple side border: A socioenvironmental spatial analysis." PLOS ONE 16, no. 2 (February 26, 2021): e0247863. http://dx.doi.org/10.1371/journal.pone.0247863.
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This research investigated the spatial association between socioenvironmental factors and gastroschisis in Brazilian triple side border. A geographic analysis for gastroschisis prevalence was performed considering census sector units using Global Moran Index, Local Indicator of Spatial Association Analysis and Getis Ord statistics. Sociodemographic factors included rate of adolescent and parturients over 35 years; population with no income and above 5 minimum wages; rate of late prenatal; and proximity to power transmission lines. Logistic regression models were applied to verify the association between socio-environmental factors and prevalence of gastroschisis. No global spatial correlation was observed in the distribution of gastroschisis (Moran´s I = 0.006; p = 0.319). However, multiple logistic regression showed census sectors with positive cases had higher probability to power transmission lines proximity (OR 3,47; CI 95% 1,11–10,79; p = 0,031). Yet, spatial scan statistic showed low risk for gastroschisis in southern city region (OR = 0; p = 0.035) in opposite to power transmission lines location. The study design does not allow us to attest the causality between power transmission lines and gastroschisis but these findings support the potential exposure risk of pregnant to electromagnetic fields.
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Lewandowska-Gwarda, Karolina. "Spatial Analysis Of Foreign Migration In Poland In 2012 Using Geographically Weighted Regression." Comparative Economic Research. Central and Eastern Europe 17, no. 4 (December 30, 2014): 137–54. http://dx.doi.org/10.2478/cer-2014-0037.
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Migration has a principal influence on countries’ population changes. Thus, the issues connected with the causes, effects and directions of people’s movements are a common topic of political and academic discussions. The aim of this paper is to analyse the spatial distribution of officially registered foreign migration in Poland in 2012. GIS tools are implemented for data visualization and statistical analysis. Geographically weighted regression (GWR) is used to estimate the impact of unemployment, wages and other socioeconomic variables on the foreign emigration and immigration measure. GWR provides spatially varying estimates of model parameters that can be presented on a map, giving a useful graphical representation of spatially varying relationships.
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Di Salvatore, Jessica, and Andrea Ruggeri. "Spatial analysis for political scientists." Italian Political Science Review/Rivista Italiana di Scienza Politica 51, no. 2 (May 11, 2021): 198–214. http://dx.doi.org/10.1017/ipo.2021.7.
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AbstractHow does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.
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Ingram, Matthew C., and Imke Harbers. "Spatial Tools for Case Selection: Using LISA Statistics to Design Mixed-Methods Research." Political Science Research and Methods 8, no. 4 (May 6, 2019): 747–63. http://dx.doi.org/10.1017/psrm.2019.3.
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AbstractMixed-methods designs, especially those in which case selection is regression-based, have become popular across the social sciences. In this paper, we highlight why tools from spatial analysis—which have largely been overlooked in the mixed-methods literature—can be used for case selection and be particularly fruitful for theory development. We discuss two tools for integrating quantitative and qualitative analysis: (1) spatial autocorrelation in the outcome of interest; and (2) spatial autocorrelation in the residuals of a regression model. The case selection strategies presented here enable scholars to systematically use geography to learn more about their data and select cases that help identify scope conditions, evaluate the appropriate unit or level of analysis, examine causal mechanisms, and uncover previously omitted variables.
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Zhu, Jun, Yanbing Zheng, Allan L. Carroll, and Brian H. Aukema. "Autologistic regression analysis of spatial-temporal binary data via Monte Carlo maximum likelihood." Journal of Agricultural, Biological, and Environmental Statistics 13, no. 1 (March 2008): 84–98. http://dx.doi.org/10.1198/108571108x273566.
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Montagna, Silvia, Tor Wager, Lisa Feldman Barrett, Timothy D. Johnson, and Thomas E. Nichols. "Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data." Biometrics 74, no. 1 (May 12, 2017): 342–53. http://dx.doi.org/10.1111/biom.12713.
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Nikolova, Valentina, and Aleksandar Penkov. "GIS spatial analysis of the distribution of snow depth: A study of western Rhodopes, Bulgaria." Glasnik Srpskog geografskog drustva 96, no. 1 (2016): 46–55. http://dx.doi.org/10.2298/gsgd1601046n.
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The aim of the present research is to show the advantages of information technology in investigating the snow cover. The snow data is usually taken from the measurement in meteorological stations which are often sparsely and insufficient. The problem in the analysis of the snow cover is how to present point data spatially and what is the most appropriate model. The area of the present research is the western part of Rhodopes mountain (Southern Bulgaria). The relief is variable from low to high mountainous and the climate is influenced by the high altitude and Mediterranean air advections. The spatial analysis of the distribution of snow depth is done in ArcGIS by application of Spatial Statistics Tools and Geostatistical Analyst. We considered altitude, aspect and slope as explanatory variables that could be used for determination of the territorial distribution of the snow depth. These factors are determined on the base of digital elevation model and the relationship between variables is evaluated by application of regression analysis, ordinary less squares (OLS) analysis and geographically weighted regression (GWR). The high values of R2 (above 0.7) show the representativeness of the model. A map of spatial distribution of snow depth is created by Map algebra in GIS environment, applying the regression equation of the relation snow depth - altitude. Inverse distance weighted and ordinary kriging interpolation are also carried out. The research shows that spatial presentation of point snow data and its interpretation should be done taking into account the relief and the exposition of the territory.
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Dučinskas, K. "Statistical Classification of the Observation of Nuggetless Spatial Gaussian Process with Unknown Sill Parameter." Nonlinear Analysis: Modelling and Control 14, no. 2 (April 25, 2009): 155–63. http://dx.doi.org/10.15388/na.2009.14.2.14518.
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The problem of classification of spatial Gaussian process observation into one of two populations specified by different regression mean models and common stationary covariance with unknown sill parameter is considered. Unknown parameters are estimated from training sample and these estimators are plugged in the Bayes discriminant function. The asymptotic expansion of the expected error rate associated with Bayes plug-in discriminant function is derived. Numerical analysis of the accuracy of approximation based on derived asymptotic expansion in the small training sample case is carried out. Comparison of two spatial sampling designs based on values of this approximation is done.
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Barnes, T. J. "A History of Regression: Actors, Networks, Machines, and Numbers." Environment and Planning A: Economy and Space 30, no. 2 (February 1998): 203–23. http://dx.doi.org/10.1068/a300203.
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In this paper the history of correlation and regression analyses, both in the discipline of statistics generally and in human geography particularly, is examined. It is argued that correlation and regression analysis emerged from a particular social and cultural context, and that this context entered into the very nature of those techniques. The paper is divided into three sections. First, to counter the idea that mathematics and statistics are somehow outside the social, the arguments put forward by David Bloor and Bruno Latour suggesting that mathematical propositions arc socially constructed are briefly reviewed. Second, using the ideas of both Bloor and Latour I turn to the development of statistics as an intellectual discipline during the 19th century, and specifically to the invention of correlation and regression at the end of that period. It is argued that the development of statistics as a discipline and its associated techniques are both stamped by, but also leave their stamp on, the wider society in which they are set. Last, the importation of correlation and regression analyses into human geography which occurred in the 1950s is examined. Following my general social constructionist argument, it is suggested that because of the difference in context the correlation and regression analyses devised in the late 19th century were often inappropriate for mid-20th century spatial science.
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Karimi, Sadegh, Hamid Nazaripour, and Mohsen Hamidianpour. "Spatial and temporal variability of precipitation extreme indices in arid and semi-arid regions of Iran for the last half-century." Időjárás 125, no. 1 (2021): 83–104. http://dx.doi.org/10.28974/idojaras.2021.1.4.
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Precipitation variability analysis, on different spatial and temporal scales, has been of great concern during the past century because of the attention given to global climate change by the scientific community. According to some recent studies, the Iranian territory has been experienced a precipitation variability, especially in the last 50 years, and the arid and semi-arid areas seem to be more affected. The present study aims to analyze precipitation extreme indices over a wide time interval and a wide area, detecting potential trends and assessing their significance. The investigation is based on a wide range of daily and multi-day precipitation statistics encompassing basic characteristics and heavy precipitation. Two different methods of trend analysis and statistical testing are applied, depending on the nature of the statistics. Linear regression is used for statistics with a continuous value range, and logistic regression is used for statistics with a discrete value range. The trends are calculated on annual and seasonal bases for the years 1951–2007. Statistical analysis of the database highlight that a clear trend signal is found with a high number of sites with a statistically significant trend. In winter, significant increases are found for all statistics related to precipitation strength and occurrence. In spring, statistically, significant increases are found only for the statistics related to heavy precipitation, whereas precipitation frequency and occurrence statistics show little systematic change. The trend signal is strongest in highlands and mountainous terrains. In autumn and summer, the heavy and basic precipitation statistics did not show statistically significant trends.
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Denton, Shirley R., and Burton V. Barnes. "Spatial distribution of ecologically applicable climatic statistics in Michigan." Canadian Journal of Forest Research 17, no. 7 (July 1, 1987): 598–612. http://dx.doi.org/10.1139/x87-101.
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The paper addresses the problem of selecting appropriate climatic variables from readily available weather data for use in studies of species ranges and ecosystem site classification. Principal component variables and more traditional climatic variables such as heat sums are presented. To facilitate comparison of spatially distributed variables, a procedure is described for estimation of climatic statistics for areas between weather stations. The procedure is objective and provides a measure of its error. Like any regression procedure, it improves with availability of more data. Extension of climatic statistics to a 5 × 5 km grid covering the state of Michigan was used to create contour maps for a number of climatic statistics with potential relevance for plant growth. In addition, a large number of climatic statistics were summarized using principal component analysis. Separate analyses were made for winter temperature and precipitation, growing season temperature, growing season precipitation, and a combination of variables possibly related to stressful conditions. There was a high degree of correlation among many of the statistics. The correlations were due to global climatic controls and to moderation due to the Great Lakes. Principal component variables successfully presented major climatic trends. However, for ecological use they appeared to offer few advantages over more traditional climatic statistics.
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Harini, Sri. "Estimation of Error Variance-Covariance Parameters Using Multivariate Geographically Weighted Regression Model." Abstract and Applied Analysis 2020 (February 1, 2020): 1–5. http://dx.doi.org/10.1155/2020/4657151.
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The Multivariate Geographically Weighted Regression (MGWR) model is a development of the Geographically Weighted Regression (GWR) model that takes into account spatial heterogeneity and autocorrelation error factors that are localized at each observation location. The MGWR model is assumed to be an error vector ε that distributed as a multivariate normally with zero vector mean and variance-covariance matrix Σ at each location ui,vi, which Σ is sized qxq for samples at the i-location. In this study, the estimated error variance-covariance parameters is obtained from the MGWR model using Maximum Likelihood Estimation (MLE) and Weighted Least Square (WLS) methods. The selection of the WLS method is based on the weighting function measured from the standard deviation of the distance vector between one observation location and another observation location. This test uses a statistical inference procedure by reducing the MGWR model equation so that the estimated error variance-covariance parameters meet the characteristics of unbiased. This study also provides researchers with an understanding of statistical inference procedures.
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Liyew, Alemneh Mekuriaw, Malede Mequanent Sisay, and Achenef Asmamaw Muche. "Spatial distribution and factors associated with low birth weight in Ethiopia using data from Ethiopian Demographic and Health Survey 2016: spatial and multilevel analysis." BMJ Paediatrics Open 5, no. 1 (May 2021): e000968. http://dx.doi.org/10.1136/bmjpo-2020-000968.
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ObjectiveThis study aimed to assess the spatial distribution, individual and community-level factors associated with low birth weight in Ethiopia.MethodSecondary data analysis was conducted using the 2016 Ethiopian Demographic and Health Survey data. A total of 2110 neonates were included in this study. Spatial autocorrelation analysis was conducted to assess the spatial clustering of LBW. Besides, the spatial scan statistics and ordinary kriging interpolation were done to detect the local level clusters and to assess predicted risk areas, respectively. Furthermore, a multilevel logistic regression model was fitted to determine individual and community-level factors associated with LBW. Finally, most likely clusters with log-likelihood ratio (LLR), relative risk and p value from spatial scan statistics and adjusted OR (AOR) with 95% CI for multilevel logistic regression model were reported.ResultsLBW was spatially clustered in Ethiopia. Primary (LLR=11.57; p=0.002) clusters were detected in the Amhara region. Neonates within this spatial window had a 2.66 times higher risk of being LBW babies as compared with those outside the window. Besides, secondary (LLR=11.4; p=0.003; LLR=10.14, p=0.0075) clusters were identified at southwest Oromia, north Oromia, south Afar and southeast Amhara regions. Neonates who were born from severely anaemic (AOR=1.40, 95% CI (1.03 to 2.15)), and uneducated (AOR=1.90, 95% CI (1.23 to 2.93)) mothers, those who were born before 37 weeks of gestation (AOR=5.97, 95% CI (3.26 to 10.95)) and women (AOR=1.41, 95% CI (1.05 to 1.89)), had significantly higher odds of being LBW babies.ConclusionThe high-risk areas of LBW were detected in Afar, Amhara and Oromia regions. Therefore, targeting the policy interventions in those hotspot areas and focusing on the improvement of maternal education, strengthening anaemia control programmes and elimination of modifiable causes of prematurity could be vital for reducing the LBW disparity in Ethiopia.
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Cichulska, Aneta, and Radosław Cellmer. "Analysis of Prices in the Housing Market Using Mixed Models." Real Estate Management and Valuation 26, no. 4 (December 1, 2018): 102–11. http://dx.doi.org/10.2478/remav-2018-0040.
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Abstract Hedonic models, commonly applied for analyzing prices in the property market, do not always fulfil their role, mainly due to the application of simplified assumptions concerning the distribution of variables, the nature of relations or spatial heterogeneity. Classical regression models assumed that the variation of the explained variable (price) is explained by the effect of market features (fixed effects) and the residual component. The hierarchical structure of market data, both as regards market segments and the spatial division, suggests that statistical models of prices should also include random effects for selected subgroups of properties and interactions between variables. The mixed model provides an alternative for constructing various regression models for individual groups or for using binary variables within one model. With its appropriate structure, it makes it possible to take into account both the spatial heterogeneity and to examine the effects of individual features on prices within various property groups. It can also identify synergy effects. The article presents the issue of mixed modelling in the property market and an example of its application in a market of dwellings in Olsztyn. The research used transaction data from the price and value register, supplemented with spatial data. The obtained model was compared with classical regression models and geographically weighted regression. The study also covered the usefulness of mixed models in the mass evaluation of properties, and the possibility of using them in spatial analyses and for the development of property value maps.
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Litwińska, Elżbieta. "Spatial Analysis of the Labour Market by Using Econometric Tools. The Case of Lower Silesia Region (Dolnośląskie voivodship)." Comparative Economic Research. Central and Eastern Europe 15, no. 4 (March 8, 2013): 147–60. http://dx.doi.org/10.2478/v10103-012-0032-8.
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This paper presents the application of econometric techniques to examine the labour market in Lower Silesia. First the analysis was performed on a data set for variables connected with labour market recorded in poviats (NUTS 4). In order to determine the existence of spatial autocorrelation Moran’s statistics I was calculated. Then the spatial regression model was used to describe the relationship between the rate of unemployment and other variables. Next, LISA cluster maps were generated for units at NUTS 5 level. The results indicate the spatial dimension of the unemployment and its tendency to creating concentrations.
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Lipi, Nasrin, Mohammad Samsul Alam, and Syed Shahadat Hossain. "A Generalized Estimating Equations Approach for Modeling Spatially Clustered Data." Austrian Journal of Statistics 50, no. 4 (July 15, 2021): 36–52. http://dx.doi.org/10.17713/ajs.v50i4.1097.
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Clustering in spatial data is very common phenomena in various fields such as disease mapping, ecology, environmental science and so on. Analysis of spatially clustered data should be different from conventional analysis of spatial data because of the nature of clusters in the data. Because it is expected that the observations of same cluster are more similar than the observations from different clusters. In this study, a method has been proposed for the analysis of spatially clustered areal data based on generalized estimating equations which were originally developed for analyzing longitudinal data. The performance of the model for known clusters is tested in terms of how well it estimates the regression parameters and how well it captures the true spatial process. These results are presented and compared with the conditional auto-regressive model which is the most frequently used spatial model. In the simulation study, the proposed generalized estimating equations approach yields better results than the popular conditional auto-regressive model from the both perspectives of parameter estimation and spatial process capturing. A real life data on the vitamin A supplement coverage among postpartum women in Bangladesh is then analyzed for demonstration of the method. The existing divisional clustering behavior of vitamin A supplement coverage in Bangladesh is identified more accurately by the proposed approach than that by the conditional auto-regressive model.
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Samadi, Dr. "PENGARUH PERILAKU BERTANGGUNG JAWAB DAN PEMAHAMAN SISWA SMA DI DKI JAKARTA TERHADAP KETAHANAN SOSIAL EKOLOGIS LOKAL." Jurnal SPATIAL Wahana Komunikasi dan Informasi Geografi 18, no. 1 (July 5, 2018): 72–76. http://dx.doi.org/10.21009/spatial.181.04.
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This study aims to determine empirically the influence of responsible behavior toward ecological social resilience in the framework of environmental education in senior high school students. The research was conducted during August-December 2017. This research used experimental method with 2 X 2 factorial design. The population in this study is the affordable population with random sampling method. The test of statistical hypothesis used ANAVA (analysis of variance) by comparing the Fcount with Ftable on each treatment factor (A and B), and interaction between factors (Ax B). Data analysis in this study using Pearson correlation analysis and regression analysis where the normality test using Kolmogorov-Smirnov method. The result of the research concludes that the stronger the responsible behavior toward the environment, the more its ecological socio-economic resilience in the framework of applying of environmental education to high school students in DKI Jakarta.
Keyword: Rresponsible behavior, Ecological social resilience, Environmental education in senior high school, Jakarta.
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Mastuti, Winda Chairani, Anik Djuraidah, and Erfiani Erfiani. "ROBUST SPATIAL REGRESSION MODEL ON ORIGINAL LOCAL GOVERNMENT REVENUE IN JAVA 2017." Indonesian Journal of Statistics and Its Applications 4, no. 1 (February 28, 2020): 68–79. http://dx.doi.org/10.29244/ijsa.v4i1.573.
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Spatial regression measures the relationship between response and explanatory variables in the regression model considering spatial effects. Detecting and accommodating outliers is an important step in the regression analysis. Several methods can detect outliers in spatial regression. One of these methods is generating a score test statistics to identify outliers in the spatial autoregressive (SAR) model. This research applies a robust spatial autoregressive (RSAR) model with S- estimator to the Original Local Government Revenue (OLGR) data. The RSAR model with the 4-nearest neighbor weighting matrix is the best model produced in this study. The coefficient of the RSAR model gives a more relevant result. Median absolute deviation (MdAD) and median absolute percentage error (MdAPE) values in the RSAR model with 4-nearest neighbor give smaller results than the SAR model.
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Fatima, Munazza, Kara J. O’Keefe, Wenjia Wei, Sana Arshad, and Oliver Gruebner. "Geospatial Analysis of COVID-19: A Scoping Review." International Journal of Environmental Research and Public Health 18, no. 5 (February 27, 2021): 2336. http://dx.doi.org/10.3390/ijerph18052336.
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The outbreak of SARS-CoV-2 in Wuhan, China in late December 2019 became the harbinger of the COVID-19 pandemic. During the pandemic, geospatial techniques, such as modeling and mapping, have helped in disease pattern detection. Here we provide a synthesis of the techniques and associated findings in relation to COVID-19 and its geographic, environmental, and socio-demographic characteristics, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology for scoping reviews. We searched PubMed for relevant articles and discussed the results separately for three categories: disease mapping, exposure mapping, and spatial epidemiological modeling. The majority of studies were ecological in nature and primarily carried out in China, Brazil, and the USA. The most common spatial methods used were clustering, hotspot analysis, space-time scan statistic, and regression modeling. Researchers used a wide range of spatial and statistical software to apply spatial analysis for the purpose of disease mapping, exposure mapping, and epidemiological modeling. Factors limiting the use of these spatial techniques were the unavailability and bias of COVID-19 data—along with scarcity of fine-scaled demographic, environmental, and socio-economic data—which restrained most of the researchers from exploring causal relationships of potential influencing factors of COVID-19. Our review identified geospatial analysis in COVID-19 research and highlighted current trends and research gaps. Since most of the studies found centered on Asia and the Americas, there is a need for more comparable spatial studies using geographically fine-scaled data in other areas of the world.
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Yang, Rong-Cai, and Patricia Juskiw. "Analysis of covariance in agronomy and crop research." Canadian Journal of Plant Science 91, no. 4 (July 2011): 621–41. http://dx.doi.org/10.4141/cjps2010-032.
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Yang, R.-C. and Juskiw, P. 2011. Analysis of covariance in agronomy and crop research. Can. J. Plant Sci. 91: 621–641. Analysis of covariance (ANCOVA) is a statistical technique that combines the methods of the analysis of variance (ANOVA) and regression analysis. However, ANCOVA is an advanced topic that often appears towards the end of many textbooks, and thus, it is either taught cursorily or ignored completely in many statistics classes. Additionally, many elaborated applications of ANCOVA to agronomy and crop research along with uses of the latest statistical software are rarely described in textbooks or classes. The objectives of this paper are to provide an overview on conventional ANCOVA and to introduce more advanced uses of ANCOVA under mixed models. We describe three elaborate applications including (i) the use of ANCOVA for dissecting dosage responses for different treatments, (ii) stability of treatments across multiple environments and (iii) removal of spatial variation that is not effectively controlled by blocking. These analyses illustrate that ANCOVA is either a simpler analysis or provides more information than conventional statistical methods. We provide a technical appendix ( Appendix A ) on principles and theory underlying mixed-model analysis of ANCOVA along with SAS programs ( Appendix B ) for more uses and in-depth understanding of this powerful technique in agronomy and crop research.
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Hovenden, Elizabeth, and Gang-Jun Liu. "Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne." Journal of Road Safety 31, no. 4 (November 1, 2020): 36–58. http://dx.doi.org/10.33492/jrs-d-19-00249.
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Understanding where, when, what type and why crashes are occurring can help determine the most appropriate initiatives to reduce road trauma. Spatial statistical analysis techniques are better suited to analysing crashes than traditional statistical techniques as they allow for spatial dependency and non-stationarity. For example, crashes tend to cluster at specific locations (spatial dependency) and vary from one location to another (non-stationarity). Several spatial statistical methods were used to examine crash clustering in metropolitan Melbourne, including Global Moran’s I statistic, Kernel Density Estimation and Getis-Ord Gi* statistic. The Global Moran’s I statistic identified statistically significant clustering on a global level. The Kernel Density Estimation method showed clustering but could not identify the statistical significance. The Getis-Ord Gi* method identified local crash clustering and found that 15.7 per cent of casualty crash locations in metropolitan Melbourne were statistically significant hot spots at the 95 per cent confidence level. The degree, location and extent of clustering was found to vary for different crash categories, with fatal crashes exhibiting the lowest level of clustering and bicycle crashes exhibiting the highest level of clustering. Temporal variations in clustering were also observed. Overlaying the results with land use and road classification data found that hot spot clusters were in areas with a higher proportion of commercial land use and with a higher proportion of arterial and sub-arterial roads. Further work should investigate network based hot spot analysis and explore the relationship between crash clusters and influencing factors using spatial techniques such as Geographically Weighted Regression.
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Solomon, Malebogo, Luis Furuya-Kanamori, and Kinley Wangdi. "Spatial Analysis of HIV Infection and Associated Risk Factors in Botswana." International Journal of Environmental Research and Public Health 18, no. 7 (March 25, 2021): 3424. http://dx.doi.org/10.3390/ijerph18073424.
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Botswana has the third highest human immunodeficiency virus (HIV) prevalence globally, and the severity of the epidemic within the country varies considerably between the districts. This study aimed to identify clusters of HIV and associated factors among adults in Botswana. Data from the Botswana Acquired Immunodeficiency Syndrome (AIDS) Impact Survey IV (BIAS IV), a nationally representative household-based survey, were used for this study. Multivariable logistic regression and Kulldorf’s scan statistics were used to identify the risk factors and HIV clusters. Socio-demographic characteristics were compared within and outside the clusters. HIV prevalence among the study participants was 25.1% (95% CI 23.3–26.4). HIV infection was significantly higher among the female gender, those older than 24 years and those reporting the use of condoms, while tertiary education had a protective effect. Two significant HIV clusters were identified, one located between Selibe-Phikwe and Francistown and another in the Central Mahalapye district. Clusters had higher levels of unemployment, less people with tertiary education and more people residing in rural areas compared to regions outside the clusters. Our study identified high-risk populations and regions with a high burden of HIV infection in Botswana. This calls for focused innovative and cost-effective HIV interventions on these vulnerable populations and regions to curb the HIV epidemic in Botswana.
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Hou, Dingchen, Mike charles, Yan Luo, Zoltan Toth, Yuejian Zhu, Roman Krzysztofowicz, Ying Lin, et al. "Climatology-Calibrated Precipitation Analysis at Fine Scales: Statistical Adjustment of Stage IV toward CPC Gauge-Based Analysis." Journal of Hydrometeorology 15, no. 6 (December 1, 2014): 2542–57. http://dx.doi.org/10.1175/jhm-d-11-0140.1.
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Abstract Two widely used precipitation analyses are the Climate Prediction Center (CPC) unified global daily gauge analysis and Stage IV analysis based on quantitative precipitation estimate with multisensor observations. The former is based on gauge records with a uniform quality control across the entire domain and thus bears more confidence, but provides only 24-h accumulation at ⅛° resolution. The Stage IV dataset, on the other hand, has higher spatial and temporal resolution, but is subject to different methods of quality control and adjustments by different River Forecasting Centers. This article describes a methodology used to generate a new dataset by adjusting the Stage IV 6-h accumulations based on available joint samples of the two analyses to take advantage of both datasets. A simple linear regression model is applied to the archived historical Stage IV and the CPC datasets after the former is aggregated to the CPC grid and daily accumulation. The aggregated Stage IV analysis is then adjusted based on this linear model and then downscaled back to its original resolution. The new dataset, named Climatology-Calibrated Precipitation Analysis (CCPA), retains the spatial and temporal patterns of the Stage IV analysis while having its long-term average and climate probability distribution closer to that of the CPC analysis. The limitation of the methodology at some locations is mainly associated with heavy to extreme precipitation events, which the Stage IV dataset tends to underestimate. CCPA cannot effectively correct this because of the linear regression model and the relative scarcity of heavy precipitation in the training data sample.
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Trzpiot, Grażyna, and Agnieszka Orwat-Acedańska. "Spatial Quantile Regression In Analysis Of Healthy Life Years In The European Union Countries." Comparative Economic Research. Central and Eastern Europe 19, no. 5 (March 30, 2017): 179–99. http://dx.doi.org/10.1515/cer-2016-0044.
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The paper investigates the impact of the selected factors on the healthy life years of men and women in the EU countries. The multiple quantile spatial autoregression models are used in order to account for substantial differences in the healthy life years and life quality across the EU members. Quantile regression allows studying dependencies between variables in different quantiles of the response distribution. Moreover, this statistical tool is robust against violations of the classical regression assumption about the distribution of the error term. Parameters of the models were estimated using instrumental variable method (Kim, Muller 2004), whereas the confidence intervals and p-values were bootstrapped.
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Root, Elisabeth Dowling, Emelie D. Bailey, Tyler Gorham, Christopher Browning, Chi Song, and Pamela Salsberry. "Geovisualization and Spatial Analysis of Infant Mortality and Preterm Birth in Ohio, 2008-2015: Opportunities to Enhance Spatial Thinking." Public Health Reports 135, no. 4 (June 18, 2020): 472–82. http://dx.doi.org/10.1177/0033354920927854.
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Objectives Geovisualization and spatial analysis are valuable tools for exploring and evaluating the complex social, economic, and environmental interactions that lead to spatial inequalities in health. The objective of this study was to describe spatial patterns of infant mortality and preterm birth in Ohio by using interactive mapping and spatial analysis. Methods We conducted a retrospective cohort study using Ohio vital statistics records from 2008-2015. We geocoded live births and infant deaths by using residential address at birth. We used multivariable logistic regression to adjust spatial and space–time cluster analyses that examined the geographic clustering of infant mortality and preterm birth and changes in spatial distribution over time. Results The overall infant mortality rate in Ohio during the study period was 6.55 per 1000 births; of 1 097 507 births, 10.3% (n = 112 552) were preterm. We found significant geographic clustering of both infant mortality and preterm birth centered on large urban areas. However, when known demographic risk factors were taken into account, urban clusters disappeared and, for preterm birth, new rural clusters appeared. Conclusions Although many public health agencies have the capacity to create maps of health outcomes, complex spatial analysis and geovisualization techniques are still challenging for public health practitioners to use and understand. We found that actively engaging policymakers in reviewing results of the cluster analysis improved understanding of the processes driving spatial patterns of birth outcomes in the state.
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De Ridder, David, Fabiën N. Belle, Pedro Marques-Vidal, Belén Ponte, Murielle Bochud, Silvia Stringhini, Stéphane Joost, and Idris Guessous. "Geospatial Analysis of Sodium and Potassium Intake: A Swiss Population-Based Study." Nutrients 13, no. 6 (May 25, 2021): 1798. http://dx.doi.org/10.3390/nu13061798.
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Inadequate sodium and potassium dietary intakes are associated with major, yet preventable, health consequences. Local public health interventions can be facilitated and informed by fine-scale geospatial analyses. In this study, we assess the existence of spatial clustering (i.e., an unusual concentration of individuals with a specific outcome in space) of estimated sodium (Na), potassium (K) intakes, and Na:K ratio in the Bus Santé 1992–2018 annual population-based surveys, including 22,495 participants aged 20–74 years, residing in the canton of Geneva, using the local Moran’s I spatial statistics. We also investigate whether socio-demographic and food environment characteristics are associated with identified spatial clustering, using both global ordinary least squares (OLS) and local geographically weighted regression (GWR) modeling. We identified clear spatial clustering of Na:K ratio, Na, and K intakes. The GWR outperformed the OLS models and revealed spatial variations in the associations between explanatory and outcome variables. Older age, being a woman, higher education, and having a lower access to supermarkets were associated with higher Na:K ratio, while the opposite was seen for having the Swiss nationality. Socio-demographic characteristics explained a major part of the identified clusters. Socio-demographic and food environment characteristics significantly differed between individuals in spatial clusters of high and low Na:K ratio, Na, and K intakes. These findings could guide prioritized place-based interventions tailored to the characteristics of the identified populations.
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Matos, Cátia, Neftalí Sillero, and Elena Argaña. "Spatial analysis of amphibian road mortality levels in northern Portugal country roads." Amphibia-Reptilia 33, no. 3-4 (2012): 469–83. http://dx.doi.org/10.1163/15685381-00002850.
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Animal mortality caused by vehicle collisions is one of the main ecological impacts of roads. Amphibians are the most affected group and road fatalities have a significant impact on population dynamics and viability. Several studies on Iberian amphibians have shown the importance of country roads on amphibian road mortality, but still, little is known about the situation in northern Portugal. By being more permeable to amphibian passage, country roads represent a greater source of mortality than highways, which act as barriers. Thus, mitigation measures should be applied, but due to the extensive road network, the identification of precise locations (hotspots) and variables related to animal-vehicle collision is needed to plan these measures successfully. The aim of the study was to analyse the spatial occurrence and related factors linked to amphibian mortality on a number of country roads in northern Portugal, using spatial statistics implemented in GIS and applying a binary logistical regression. We surveyed 631 km of road corresponding to seven transects, and observed 404 individual amphibians: 74 (18.3%) alive and 330 (81.7%) road-killed. Bufo bufo represented 80% of the mortality records. Three transects showed clustered distribution of road-kills, and broadleaved forests and road ditches were the most important factors associated with hotspots of road-kill. Logistic regression models showed that habitat quality, Bufo bufo’s habitat preferences, and road ditches are positively associated with amphibians’ road mortality in northern Portugal, whereas average altitude and length of walls were negatively associated. This study is a useful tool to understand spatial occurrence of amphibian road-kills in the face of applying mitigation measures on country roads from northern Portugal. This study also considers the necessity of assessing the condition of amphibian local populations to understand their road-kills spatial patterns and the urgency to apply mitigation measures on country roads.
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Salsavira, Salsavira, Jahra Afifah, Fiqih Tri Mahendra, and Lathifah Dzakiyah. "Spatial Analysis of Prevalence of Early Marriage and HDI in Indonesia." Jurnal Matematika, Statistika dan Komputasi 18, no. 1 (September 2, 2021): 31–41. http://dx.doi.org/10.20956/j.v18i1.13975.
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Early marriage has become an important issue in Indonesia. Even though the rate of early marriage shows a decline until 2020, the number still makes Indonesia become the country with the second highest early marriage in Southeast Asia. Early marriage that occurs can hinder the achievement of the Sustainable Development Goals (SDG) and can have an impact on the Human Development Index. The existence of a relationship between early marriage and HDI encourages researchers to conduct studies that aimed at examining the effect of the prevalence of early marriage on HDI in each district/city in Indonesia on 2020. This study uses the Geographically Weighted Logistic Regression (GWLR) analysis method with the data sourced from the National Socio-Economic Survey (SUSENAS) raw data in March 2020 and publication data on the website of The Central Bureau of Statistics. The results of the analysis found that the prevalence of early marriage has a negative and significant effect in several districts/cities in the Provinces of Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Bangka Belitung Islands, Riau Islands, West Java, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Maluku, and West Papua. This research is expected to be a recommendation for the government and community organizations to conduct socialization regarding the maturity age of marriage and the adverse effects that can be caused by early marriage.
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Malinowski, Mariusz, and Joanna Smoluk-Sikorska. "Spatial Relations between the Standards of Living and the Financial Capacity of Polish District-Level Local Government." Sustainability 12, no. 5 (February 28, 2020): 1825. http://dx.doi.org/10.3390/su12051825.
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The objective of the presented article is the identification of spatial relations between the inhabitants’ standards of living and the districts’ financial capacity basing on data for 2017. The investigation comprised all of the 380 Polish districts. In regard to the multidimensionality of economic occurrences analyzed, the TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) approach to measure the inhabitants’ standards of living and the financial ability of districts was applied in the research. A spatial autocorrelation analysis between the taxonomic (synthetic) indexes was performed using local and global Moran’s I statistics in order to determine the districts’ clusters, demonstrating a comparable degree of occurrences analyzed. A spatial regression analysis was conducted to find the strength of spatial relations between the taxonomic index of the standards of living and the districts’ financial ability. Diagnostic variables were chosen according to substantive, statistical and formal criteria. The outcomes of the spatial regression analysis allowed it to be concluded that about 1% increase of the taxonomic indicator of the districts’ financial ability is reflected in about 0.4% growth of the taxonomic index of the standards of living of the inhabitants of different districts (other things being equal). The results of analyses can be applied indirectly by a number of stakeholders, e.g., local authorities responsible for local and regional development, when creating the development strategies at local government unit (LGU) level. The knowledge on spatial development structures can enhance the formation of the strategic management process (for instance, redefining the objectives and tasks set out in local strategies; restructuring the expenditure to meet the local population’s needs).
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Somers, Keith M., and Donald A. Jackson. "Adjusting Mercury Concentration for Fish-Size Covariation: A Multivariate Alternative to Bivariate Regression." Canadian Journal of Fisheries and Aquatic Sciences 50, no. 11 (November 1, 1993): 2388–96. http://dx.doi.org/10.1139/f93-263.
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Regression-based methods like analysis of covariance (ANCOVA) are frequently used to adjust one variable for the correlated influence of a second less interesting variable (e.g., mercury concentration and fish size). However, the influence of the covariate (i.e., fish size) is not removed unequivocally when regression slopes are not parallel. Using data on tissue-mercury concentration and fish size from 30 populations of lake trout (Salvelinus namaycush), we show that data adjusted to a common size with bivariate regression can retain information associated with the original size differences. As an alternative, we use univariate and bivariate summary statistics from each population as raw data in a multivariate analysis to search for differences among populations. Ordination axes resulting from this analysis exhibited both small- and large-scale spatial autocorrelation. Localized spatial patterns probably reflect similar geochemical features of the watersheds of neighbouring lakes in small geographic areas. In contrast, regional spatial autocorrelation suggested broad-scale patterns that may implicate atmospheric inputs of mercury. As an extension of this multivariate approach, both regional and local patterns could be compared with environmental variables to reveal correlations that may suggest new cause-and-effect hypotheses.
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Bertazzon, Stefania, Caitlin Calder-Bellamy, Rizwan Shahid, Isabelle Couloigner, and Richard Wong. "A Preliminary Spatial Analysis of the Association of Asthma and Traffic-Related Air Pollution in the Metropolitan Area of Calgary, Canada." Atmosphere 11, no. 10 (October 8, 2020): 1066. http://dx.doi.org/10.3390/atmos11101066.
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We performed a preliminary spatial analysis to assess the association of asthma emergency visits (AEV) with ambient air pollutants (NO2, PM2.5, PM10, Black Carbon, and VOCs) over Calgary, Canada. Descriptive analyses identify spatial patterns across the city. The spatial patterns of AEV and air pollutants were analyzed by descriptive and spatial statistics (Moran’s I and Getis G). The association between AEV, air pollutants, and socioeconomic status was assessed by correlation and regression. A spatial gradient was identified, characterized by increasing AEV incidence from west to east; this pattern has become increasingly pronounced over time. The association of asthma and air pollution is consistent with the location of industrial areas and major traffic corridors. AEV exhibited more significant associations with BTEX and PM10, particularly during the summer. Over time, AEV decreased overall, though with varying temporal patterns throughout Calgary. AEV exhibited significant and seasonal associations with ambient air pollutants. Socioeconomic status is a confounding factor in AEV in Calgary, and the AEV disparities across the city are becoming more pronounced over time. Within the current pandemic, this spatial analysis is relevant and timely, bearing potential to identify hotspots linked to ambient air pollution and populations at greater risk.
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Arnone, Eleonora, Laura Azzimonti, Fabio Nobile, and Laura M. Sangalli. "Modeling spatially dependent functional data via regression with differential regularization." Journal of Multivariate Analysis 170 (March 2019): 275–95. http://dx.doi.org/10.1016/j.jmva.2018.09.006.
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Labron Carter, Perry, Jason M. Post, and Cynthia L. Sorrensen. "Spatial Environmental Inequality in Lubbock, Texas." Current Research Journal of Social Sciences and Humanities 1, no. 1 (June 25, 2018): 01–12. http://dx.doi.org/10.12944/crjssh.1.1.01.
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Environmental inequality assumes a near proximity of environmental health hazards, hazardous waste processing and releasing facilities to minority and low-income communities. Research in environmental inequality and environment justice over the past twenty years suggests that hazardous waste facilities are often located near minority and low-income neighborhoods. We conducted a study evaluating and quantifying environmental inequality in Lubbock County, Texas. Our study analyzed both spatial and statistical relationships between population demographics and spatial proximity to hazardous waste releasing facilities. Hazardous waste facility data used in the study were collected from the Environmental Protection Agency’s Toxic Release Inventory (TRI). Population statistics from the U.S. Census comprise the demographic data for this analysis. Spatial regression models were estimated to evaluate the relationship between distance from TRI sites and neighborhood / census block group demographics. A statistically significant relationship with proximity to hazardous waste facilities was found in communities having significant minority populations.
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Verma, Siddhartha, Alena Bartosova, Momcilo Markus, Richard Cooke, Myoung-Jin Um, and Daeryong Park. "Quantifying the Role of Large Floods in Riverine Nutrient Loadings Using Linear Regression and Analysis of Covariance." Sustainability 10, no. 8 (August 13, 2018): 2876. http://dx.doi.org/10.3390/su10082876.
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This study analyzes the role of large river flow events in annual loads, for three constituents and for up to 32 years of daily data at multiple watersheds with different land-uses. Prior studies were mainly based on simple descriptive statistics, such as the percentage of nutrient loadings transported during several of the largest river flows, while this study uses log-regression and analysis of covariance (ANCOVA) to describe and quantify the relationships between large flow events and nutrient loadings. Regression relationships were developed to predict total annual loads based on loads exported by the largest events in a year for nitrate plus nitrite nitrogen (NO3-N + NO2-N, indicated as total oxidized nitrogen; TON), total phosphorus (TP), and suspended solids (SS) for eight watersheds in the Lake Erie and Ohio River basins. The median prediction errors for annual TON, TP, and SS loads from the top five load events for spatially aggregated watersheds were 13.2%, 18.6%, and 13.4%, respectively, which improve further on refining the spatial scales. ANCOVA suggests that the relationships between annual loads and large load events are regionally consistent. The findings outline the dominant role of large hydroclimatic events, and can help to improve the design of pollutant monitoring and agricultural conservation programs.
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Jordan, Nicholas. "A Statistical Analysis for Area-of-Influence Experiments." Weed Technology 3, no. 1 (March 1989): 114–21. http://dx.doi.org/10.1017/s0890037x00031444.
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Area-of-influence (AOI3) experiments measure the effect of a single weed on crop growth at intervals away from the weed plant. Effects of treatment variables, e.g., weed species or control measures, on the AOI of a single weed can be estimated. AOI experiments can be analyzed by regression of crop growth on distance from the weed plant, but this analysis violates an important regression assumption: independece of observations. Statistical dependence can occur among successive observations along the row because uncontrolled sources of variation are likely to act in similar ways on adjacent individuals. Multivariate analysis of variance (MANOVA) is a statistical technique that accounts for dependencies among crop growth measurements along the row. The technique tests three hypotheses: first, that different treatments cause weed AOI to differ in spatial distribution of competitive effects; second, that different treatments cause weed AOI to differ in size; and third, that the weed has an effect, i.e., crop growth near the weed differs from growth away from weed. MANOVA can be applied to most common experimental designs, e.g., randomized blocks or split plots, and can be implemented on various mainframe and microcomputer statistical packages.
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Kisiała, Wojciech, and Izabela Rącka. "Spatial and Statistical Analysis of Urban Poverty for Sustainable City Development." Sustainability 13, no. 2 (January 16, 2021): 858. http://dx.doi.org/10.3390/su13020858.
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One of the main pillars of sustainable urban development at the local scale is to control the social aspect of urban equality of socio-economic systems. A number of studies confirm that poverty in urban space is accompanied by negative phenomena, such as high unemployment, social pathologies, increased crime rate, or the high level of the decapitalization of space, including the poor condition of housing and municipal infrastructure. However, there is a gap in defining the relation between urban poverty and city structure to control and preferably minimize social inequalities. The aim of the study was to empirically verify the impact of the location of residential properties in relation to poverty-stricken areas in the city. The research covered the housing market in one Polish city (Kalisz) in the years 2006–2018. By applying GIS technologies, we identified the location of each property in relation to poverty areas. The data was subjected to regression analysis, with the use of the hedonic approach based on exponential models. The analysis of data allowed us to conclude that location in a poorer area does affect the prices of new flats, which is not only a contribution to the development of science, but is also information that could be used by developers or property valuers to establish the prices of flats, as well as city managers to avoid pauperization of urban districts.
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Amrhein, C. G. "Searching for the Elusive Aggregation Effect: Evidence from Statistical Simulations." Environment and Planning A: Economy and Space 27, no. 1 (January 1995): 105–19. http://dx.doi.org/10.1068/a270105.
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The past few years have seen a resurging interest in the modifiable areal unit problem, or aggregation effects. The new evidence, however, both supports and conflicts with previous work. This paper represents the first stage in a series of numerical experiments designed to explore the nature and extent of scale and zonation effects. Results from a series of carefully controlled statistical simulations are reported. It is concluded that there definitely are aggregation effects separate from effects that can be attributed to changing the definition of the spatial process. These effects, however, vary with the statistic calculated. Means and variances are resistant to aggregation effects, whereas regression coefficients and correlation statistics exhibit dramatic effects. In summary, the world of spatial analysis as it relates to the modifiable areal unit problem is not entirely well-behaved, but neither is it completely random and ill-defined.
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Bunch, Rick L., Christine Murray, Xiaoli Gao, and Eleazer D. Hunt. "Geographic Analysis of Domestic Violence Incident Locations and Neighborhood Level Influences." International Journal of Applied Geospatial Research 9, no. 2 (April 2018): 14–32. http://dx.doi.org/10.4018/ijagr.2018040102.
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Domestic violence is an important public health issue, and there is limited research to date that examines community-level influences on this serious form of violence. This article investigates the neighborhood characteristics of domestic violence incidents in the city of Greensboro, North Carolina. US Census block group boundaries and corresponding tables were used as proxies for neighborhoods. The article addresses an important gap in domestic violence research by combining geographic and statistical analyses at the block group level. Geographic data were analyzed using an Optimized Hot Spot Analysis (OHA) along with features selected by penalized Poisson regression model. The OHA was used to identify spatial clusters of high and low values while the penalized Poisson regression model was used to select the important variables from over 7000 candidates. The results of high-dimensional analysis produced six categories and 20 variables that were used to examine the characteristics of spatial clusters.
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Adamiak, Maciej, Iwona Jażdżewska, and Marta Nalej. "Analysis of Built-Up Areas of Small Polish Cities with the Use of Deep Learning and Geographically Weighted Regression." Geosciences 11, no. 5 (May 20, 2021): 223. http://dx.doi.org/10.3390/geosciences11050223.
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Small cities are an important part of the settlement system, a link between rural areas and large cities. Although they perform important functions, research focuses on large cities and metropolises while marginalizing small cities, the study of which is of great importance to progress in social sciences, geography, and urban planning. The main goal of this paper was to verify the impact of selected socio-economic factors on the share of built-up areas in 665 small Polish cities in 2019. Data from the Database of Topographic Objects (BDOT), Sentinel-2 satellite imagery from 2015 and 2019, and Local Data Bank by Statistics Poland form 2019 were used in the research. A machine learning segmentation procedure was used to obtain the data on the occurrence of built-up areas. Hot Spot (Getis-Ord Gi*) analysis and geographically weighted regression (GWR) was applied to explain spatially varying impact of factors related to population, spatial and economic development, and living standards on the share of built-up areas in the area of small cities. Significant association was found between the population density and the share of built-up areas in the area of the cities studied. The influence of the other socio-economic factors examined, related to the spatial and economic development of the cities and the quality of life of the inhabitants, showed great regional variation. The results also indicated that the share of built-up areas in the area of the cities under study is a result of the conditions under which they were established and developed throughout their existence, and not only of the socio-economic factors affecting them at present.
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Nurpadilah, Winda, I. Made Sumertajaya, and Muhamad Nur Aidi. "Geographically Weighted Regression with Kernel Weighted Function on Poverty Cases in West Java Province." Indonesian Journal of Statistics and Its Applications 5, no. 1 (March 31, 2021): 173–81. http://dx.doi.org/10.29244/ijsa.v5i1p173-181.
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Spatial regression analysis is a form of regression model that considers spatial effects. Geographically weighted regression (GWR) is the spatial regression methods that can be used to deal with the problem of spatial diversity. This method generates local model parameter estimates for each observation location. The application of spatial statistics can be done in all areas such as the problem of poverty. Poverty can be influenced by factors of proximity between regions, so that in determining the poverty factor, the proximity factor of the region cannot be ignored. West Java Province is a province with the largest population, so this study aims to model the poverty data in West Java Province by incorporating spatial effects. The weighting function used for the GWR model is the function of the fixed and adaptive kernels. The analysis results show that the fixed exponential kernel function has the smallest cross validation (CV) value, so the weighting matrix used in the model is determined by the exponential kernel function. The largest value and the smallest AIC value are owned by the GWR model with an exponential kernel function. Based on the results obtained by the the ANOVA table to test GWR's global goodness, the GWR model is more effective than global regression. Therefore, the GWR model is the best model when it used in West Java’s poverty cases. The effect of each explanatory variable on the percentage of poverty varies in each district/city in West Java Province.