Journal articles: 'Spatial analysis (Statistics) Geography'

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Páez. "Spatial statistics for urban analysis: A review of techniques with examples." GeoJournal 61, no. 1 (2005): 53. http://dx.doi.org/10.1007/sgejo-004-0877-x.

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Ceccato, Vânia, Robert Haining, and Paola Signoretta. "Exploring Offence Statistics in Stockholm City Using Spatial Analysis Tools." Annals of the Association of American Geographers 92, no. 1 (March 2002): 29–51. http://dx.doi.org/10.1111/1467-8306.00278.

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Coulston, John W., and Kurt H. Riitters. "Geographic Analysis of Forest Health Indicators Using Spatial Scan Statistics." Environmental Management 31, no. 6 (June 1, 2003): 764–73. http://dx.doi.org/10.1007/s00267-002-0023-9.

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Rosenberg, Michael S., and Corey Devin Anderson. "PASSaGE: Pattern Analysis, Spatial Statistics and Geographic Exegesis. Version 2." Methods in Ecology and Evolution 2, no. 3 (December 15, 2010): 229–32. http://dx.doi.org/10.1111/j.2041-210x.2010.00081.x.

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Leung, Yee, Chang-Lin Mei, and Wen-Xiu Zhang. "Statistical Test for Local Patterns of Spatial Association." Environment and Planning A: Economy and Space 35, no. 4 (April 2003): 725–44. http://dx.doi.org/10.1068/a3550.

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In recent years, there has been a growing interest in the use of local measures such as Anselin's LISAs and Ord and Getis G statistics to identify local patterns of spatial association. The statistical significance test based on local statistics is one of the most important aspects in performing this kind of analysis, and a randomized permutation approach and normal approximation are commonly used to derive the p-values of the statistics. To circumvent some of the shortcomings of these existing methods and to offer a more formal approach in line with classical statistical framework, we develop in this paper an exact method for computing the p-values of the local Moran's Ii, local Geary's ci, and the modified Ord and Getis G statistics based on the distributional theory of quadratic forms in normal variables. Furthermore, an approximate method, called three-moment χ2 approximation, with explicit calculation formulae is also proposed to achieve a computational cost lower than the exact method. Numerical evaluation on the accuracy of the approximate null distributions of the local statistics demonstrates that the proposed three-moment χ2 method is useful in some situations although it is inappropriate for approximating the null distribution of Ii. The study not only provides an exact test for local patterns of spatial association, but also put the tests of several local statistics within a unified statistical framework.

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Кандрычын and S. Kandrychyn. "Geography of ascariasis in Belarus: cultural and anthropological issues (Part 1)." Russian Journal of Parasitology 3, no. 3 (September 25, 2016): 337–44. http://dx.doi.org/10.12737/21658.

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Objective of research: The analysis of spatial distribution of ascariasis among the population in different regions of the republic of Belarus with respect to anthropological and cultural aspects. Investigations for regional differences in ascariasis conducted in the USSR were focused rather on climatic, hygienic and economic factors and didn’t consider the causative role of cultural and anthropological factors in epidemiological process. The study is aimed at correlation of regional indices of ascariasis with the previously defined spatial historical gradient manifested in Belarus. Materials and methods: The effect of social and cultural spatial gradient was assessed by comparison of difference in ascariasis morbidity between eastern and western regions of Belarus. Thus, the effect of ethnic and anthropological spatial gradient was considered in comparison of epidemiological indices between the Brest region (south-western part of Belarus) and Vitebsk region (north-eastern part). The analysis was conducted with the historical perspective; the regional epidemiological statistics were provided for two periods 1970-1989 and 2000-2014. Regional statistical materials of the republic of Belarus presented in the annual reports of the national Ministry of Health were used as an empirical basis. Results and discussion: The analysis showed, that the differentiation trend in distribution of ascariasis in Belarus correlates both with historical-cultural and anthropological spatial gradients. The identified pattern of regional differentiation allows to evaluate the complex effect of sociocultural and anthropogenic components on epidemiological features of ascariasis.

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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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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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Griffith, Daniel A. "A Spatial Analysis of Selected Art: a GIScience-Humanities Interface." International Journal of Humanities and Arts Computing 14, no. 1-2 (March 2020): 154–75. http://dx.doi.org/10.3366/ijhac.2020.0250.

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This exposition presents little-known connections between geography, through geographic information systems (GISs), mathematics, through matrix algebra, and art, through paintings and images, adding to the geo-humanities, spatial humanities, and humanistic mathematics literature. To this end, findings summarized for spatial statistical analyses of selected Susie Rosmarin paintings (which are reminiscent of visualizations of certain mathematical quantities known as eigenvectors), remotely sensed images that have appeared in art exhibits, and selected famous paintings by historically renowned artists reveal that spatial autocorrelation constitutes a fundamental element of art. These analyses extend the tradition of visualizing fractals as art, and interfacing cartography with art. This paper promotes analytical art, and establishes additional commonalities for GIScience, mathematics, and art.

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Kim, Minho. "Linked Micromap: Exploratory Data Analysis and Geographic Visualization of Spatial Statistics Data." Journal of the Korean Cartographic Association 15, no. 2 (August 30, 2015): 39–50. http://dx.doi.org/10.16879/jkca.2015.15.2.039.

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Spasovski, Milena, and Danica Santic. "Development of population geography from antropogeography to spatial-analitical approach." Stanovnistvo 51, no. 2 (2013): 1–22. http://dx.doi.org/10.2298/stnv1302001s.

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Population geography is a subdiscipline of Human geography and studies the distribution, concentration and density of population over the terestrial surface, as well as differences in population size, changes and characteristics, like structures, migrations, activity etc, among some places present compared to others. Population geography has had a perscientific stage as long as human history. First modern scientific treatis of population in geography was the F. Ratzels book Antropogeography in 1882. During the first half of the XX century, French geographer Vidal de la Blanche gave a capital importance of population studies in his work Principes de Geographie Humaine. In interwar years, various aspects of population were studied. After The Second World War started the renovating movement of geography and new tendencies appear in human geography and, consequently in population geography. Attempts were made to define population geography as a separate sub-discipline. The world wide trend of treating population geography as separate discipline was expressed by publishing monographs, bibliographies and textbooks. The most significant authors who worked on defining population geography were French geographers P. George (1951, 1959), Beaujen-Garnier (1965, 1966); North-american geographers: G. Trewarta (1953, 1969), W. Bunge (1962), J. Clance (1965, 1971), W. Zelinski (1966); in Great Britain: J.I. Clarke (1965); in USSR: Ju.G. Sauskin i D.N. Anucin (1950), V.V. Poksisevskij (1966), D.I. Valentej (1973); in Poland V. Ormotski (1931), L. Kosinski (1967) A. Jagelski (1980). Those authors and their works had the significant influence on the development of population science in the world and also in Serbia. Although the development of population geography was different in different countries and scientific research centers, we can clearly defined four stages. First stage lasted untill 1960s and was characterised by works of G.Trewarta, H. Doerres Ju.G. Sauskin, D.N. Anucin, J. Beaujeu-Gariner. G. Trewarta argued that the population is the point of reference from which all other elements are observed and from which all derive significance and meaning. This view was adopted and shared by authors dealing with population items, explicitly or implicitly. Second stage lasted from 1960s till 1970s and the most significant authors dealing with population problems were W. Zelinsky, W. Bunge; H.Bobek, W. Hartke, K.Ruppert, F.Schaffer; D.I. Valentej, K.Korcak. This phase was characterized by the application of quantitative methods and efforts for understanding the spatial structure of the population. Many scientists see this development phase as a particularly prosperous period, because it carried more intensive relations of geography and demography through the introduction of statistical, mathematical and demographic methods and techniques in studies of population geography. Third phase lasted from 1970s to 1980s, and was characterized by close relations between population geography and formal demography. Development and application of GIS and computer data, have made population studies more complex and applicable in practice, through population policy and population projections. The most significant authors in this period were L. Kosinski, A. Jagelski, H?gerstrand. And at last, fourth stage started in 1980s and in many countries lastes untill present days. In population geography appeared new tendencies associated with the critique of positivism, the establishment of humanistic approaches and modifications of general geographic concepts. In this period, spatial analysis and quantitative scientific methods were reaffirmed, and because of that some population studies were redefined in spatial demography, a time dimension advocated in historical demography. In this context, we emphasize the work of D. Plane and P. Rogerson. Population geography is viewed differently from one country to another. Its definition differs from too narrow to overly broad. But two research areas were of particular interest to geographers - population distribution and migration. Both items acquired an international dimension. Recently, eminent population geographers exchanged various view points in an attempt to provoke new thinking on subject and define the answers of new fields research in population geography. Population geography in the XXI Century is no longer a field comprised of spatial applications of fertility, mortality and migration only. Contemporary population geography is theoretically sophisticated, integrating spatial analysis, GIS and geo-referenced data. Future progress in the field of population geography will derive from more research at the intersections of population processes and societal issues and concerns. Major themes of future empirical researches in population geography should be: global population growth, studies of migration, transnationalism, human security issues, population-health-environment nexus, human-environment sustainability, economic development and poverty issues.

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Nethery, Rachel C., Dale P. Sandler, Shanshan Zhao, Lawrence S. Engel, and Richard K. Kwok. "A joint spatial factor analysis model to accommodate data from misaligned areal units with application to Louisiana social vulnerability." Biostatistics 20, no. 3 (April 5, 2018): 468–84. http://dx.doi.org/10.1093/biostatistics/kxy016.

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SummaryWith the threat of climate change looming, the public health community has an interest in identifying communities at the highest risk of devastation based not only on geographic features but also on social characteristics. Indices of community social vulnerability can be created by applying a spatial factor analysis to a set of relevant social variables measured for each community; however, current spatial factor analysis methodology is ill-equipped to handle spatially misaligned data. We introduce a joint spatial factor analysis model that can accommodate spatial data from two distinct partitions of a geographic space and identify a common set of latent factors underlying them. By defining the latent factors over the intersection of the two partitions, the model minimizes loss of information. Using simulated data constructed to mimic the spatial structure of our real data, we confirm the reliability of the model and demonstrate its superiority over competing ad hoc methods for dealing with misaligned data in spatial factor analysis. Finally, we construct an index of community social vulnerability for each census tract in Louisiana, a state prone to environmental disasters, which could be exacerbated by climate change, by applying the joint spatial factor analysis model to a set of misaligned social indicator data from the state. To demonstrate the utility of this index, we integrate it with Louisiana flood insurance claims data to identify communities that may be at particularly high risk during natural disasters, based on both social and geographic features.

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Kalabikhina, Irina E., and Alexander N. Panin. "Spatial choreography of the coronavirus." Population and Economics 4, no. 2 (June 1, 2020): 123–52. http://dx.doi.org/10.3897/popecon.4.e54487.

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This paper offers spatial analysis of the coronavirus spread, testing and implementation of restrictive measures in the framework of social distancing within countries and Russian regions. The geography of the spread of the disease in the municipalities of the Central Federal District is presented, the speed and direction of the shift of the «center» of disease concentration are estimated. The paper uses geographical data, operational statistics on morbidity, deaths from COVID-19 and tests, official documents on restrictive measures within the framework of social distancing (on 10 April 2020). The mapping is performed on the ArcGIS geo-information platform. Some interpretations of spatial differentiation of traceable indicators are proposed.

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Shan, Weifeng, Zhihao Wang, Yuntian Teng, and Maofa Wang. "Temporal and Spatial Evolution Analysis of Earthquake Events in California and Nevada Based on Spatial Statistics." ISPRS International Journal of Geo-Information 10, no. 7 (July 7, 2021): 465. http://dx.doi.org/10.3390/ijgi10070465.

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Studying the temporal and spatial evolution trends in earthquakes in an area is beneficial for determining the earthquake risk of the area so that local governments can make the correct decisions for disaster prevention and reduction. In this paper, we propose a new method for analyzing the temporal and spatial evolution trends in earthquakes based on earthquakes of magnitude 3.0 or above from 1980 to 2019 in California and Nevada. The experiment’s results show that (1) the frequency of earthquake events of magnitude 4.5 or above present a relatively regular change trend of decreasing–rising in this area; (2) by using the weighted average center method to analyze the spatial concentration of earthquake events of magnitude 3.0 or above in this region, we find that the weighted average center of the earthquake events in this area shows a conch-type movement law, where it moves closer to the center from all sides; (3) the direction of the spatial distribution of earthquake events in this area shows a NW–SE pattern when the standard deviational ellipse (SDE) method is used, which is basically consistent with the direction of the San Andreas Fault Zone across the north and south of California; and (4) the spatial distribution pattern of the earthquake events in this region is found to be clustered using the global spatial autocorrelation analysis method. This study provides a new perspective for the exploration of the temporal and spatial evolution trends in earthquakes and understanding the earthquake risk in an area.

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Dobryakova, Valentina, Natalya Moskvina, Andrey Dobryakov, Lilia Zhegalina, and Ildar Idrisov. "Getis-Ord Gi* statistics for hydrocarbons content analysis in the Tromjegan river basin." InterCarto. InterGIS 26, no. 2 (2020): 151–60. http://dx.doi.org/10.35595/2414-9179-2020-2-26-151-160.

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The information content and effectiveness of ecological research of the territory can be improved using the methods of multivariate analysis and mapping of the results. The article presents the analysis and mapping results of spatial and temporal trends of hydrocarbon pollution in the Tromjegan river basin for the period 2006–2018 using the tools of ArcGIS Pro. The informational and basic research is the data of local environmental monitoring of licensed blocks of the Khanty-Mansiysk Autonomous Okrug — Ugra. Pollution analysis was carried out on the basis of a detailed study of the geography of the source data using statistical calculations (minimum, average, maximum distances between sampling points, Getis-Ord Gi* index). Thematic maps were constructed using data averaged over the year. The spatial and temporal dynamics of hydrocarbons concentration in surface waters for 2006–2018 is analyzed using the “Hot Spot Analysis” tool. A temporary cluster section of hydrocarbons average annual concentration according to the Getis-Ord Gi* indicator allowed us to identify trends in the dynamics of indicators. Maps of hydrocarbons average annual concentration were compiled and the results of a spatial-temporal analysis of hydrocarbons average annual concentration in surface waters were presented. The identification of patterns in large arrays of long-term data and the consideration of the spatial component are necessary elements of modern environmental research. Analysis of the time series of average annual concentrations in the Tromjegan river basin showed a clear trend in the dynamics of hydrocarbon pollution. The findings can be the basis for making managerial decisions in the environmental monitoring of licensed blocks of the Khanty-Mansiysk Autonomous Okrug — Ugra.

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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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Battey, C. J., Peter L. Ralph, and Andrew D. Kern. "Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data." Genetics 215, no. 1 (March 24, 2020): 193–214. http://dx.doi.org/10.1534/genetics.120.303143.

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Real geography is continuous, but standard models in population genetics are based on discrete, well-mixed populations. As a result, many methods of analyzing genetic data assume that samples are a random draw from a well-mixed population, but are applied to clustered samples from populations that are structured clinally over space. Here, we use simulations of populations living in continuous geography to study the impacts of dispersal and sampling strategy on population genetic summary statistics, demographic inference, and genome-wide association studies (GWAS). We find that most common summary statistics have distributions that differ substantially from those seen in well-mixed populations, especially when Wright’s neighborhood size is < 100 and sampling is spatially clustered. “Stepping-stone” models reproduce some of these effects, but discretizing the landscape introduces artifacts that in some cases are exacerbated at higher resolutions. The combination of low dispersal and clustered sampling causes demographic inference from the site frequency spectrum to infer more turbulent demographic histories, but averaged results across multiple simulations revealed surprisingly little systematic bias. We also show that the combination of spatially autocorrelated environments and limited dispersal causes GWAS to identify spurious signals of genetic association with purely environmentally determined phenotypes, and that this bias is only partially corrected by regressing out principal components of ancestry. Last, we discuss the relevance of our simulation results for inference from genetic variation in real organisms.

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Mastalerz-Kodzis, Adrianna Damiana, and Ewa Katarzyna Pośpiech. "Application of Hölder Function to Expansion Intensity of Spatial Phenomena Analysis." Acta Universitatis Lodziensis. Folia Oeconomica 3, no. 335 (May 16, 2018): 49–61. http://dx.doi.org/10.18778/0208-6018.335.04.

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The development of methods describing time series using stochastic processes took place in the 20th century. Among others, stationary processes were modelled with Hurst exponent, whereas non‑stationary processes with Hölder function. The characteristic feature of this type of processes is the analysis of the memory present in the time series. At the turn of the 21st century interest in statistics and spatial econometrics, as well as analyses carried out within the new economic geography arose. In this article, we have proposed the implementation of methods taken from the analysis of time series in the modelling of spatial data and the application of selected measures in studying the intensity of expansion in spatial phenomena. As the intensity measure we use Hölder point exponents. The article is composed of two parts. The first one contains the description of study methodology, the second – examples of application.

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Beenstock, Michael, Daniel Felsenstein, and Dai Xieer. "Spatial econometric analysis of spatial general equilibrium." Spatial Economic Analysis 13, no. 3 (December 7, 2017): 356–78. http://dx.doi.org/10.1080/17421772.2018.1403645.

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Xie, Hualin, Guiying Liu, Qu Liu, and Peng Wang. "Analysis of Spatial Disparities and Driving Factors of Energy Consumption Change in China Based on Spatial Statistics." Sustainability 6, no. 4 (April 17, 2014): 2264–80. http://dx.doi.org/10.3390/su6042264.

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Huang, Zechun. "Spatiotemporal Evolution Patterns of the COVID-19 Pandemic Using Space-Time Aggregation and Spatial Statistics: A Global Perspective." ISPRS International Journal of Geo-Information 10, no. 8 (July 31, 2021): 519. http://dx.doi.org/10.3390/ijgi10080519.

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Unlike previous regionalized studies on a worldwide crisis, this study aims to analyze spatial distribution patterns and evolution characteristics of the COVID-19 pandemic, using space-time aggregation and spatial statistics from a global perspective. Hence, various spatial statistical methods, such as the heat map, global Moran’s I, geographic mean center, and emerging hot spot analysis were utilized comprehensively to mine and analyze spatiotemporal evolution patterns. The main findings were as follows: Overall, the spatial autocorrelation of confirmed cases gradually increased from the initial outbreak until September 2020 and then decreased slightly. The geographic centroid migration ranges of the pandemic in Asia, Europe, and Africa are wider than those in South America, Oceania, and North America. The spatiotemporal evolution pattern of the global pandemic mainly consisted of oscillating hot spots, intensifying cold spots, persistent cold spots, and diminishing cold spots. This study provides auxiliary decision-making information for pandemic prevention and control.

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Yang, Jianxin, Jian Gong, and Wenwu Tang. "Prioritizing Spatially Aggregated Cost-Effective Sites in Natural Reserves to Mitigate Human-Induced Threats: A Case Study of the Qinghai Plateau, China." Sustainability 11, no. 5 (March 4, 2019): 1346. http://dx.doi.org/10.3390/su11051346.

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Anthropogenic activities often lead to the degradation of valuable natural habitats. Many efforts have been taken to counteract this degradation process, including the mitigation of human-induced stressors. However, knowing-doing gaps exist in stakeholder’s decision-making of prioritizing sites to allocate limited resources in these mitigation activities in both spatially aggregated and cost-effective manner. In this study, we present a spatially explicit prioritization framework that integrates basic cost effectiveness analysis (CEA) and spatial clustering statistics. The advantages of the proposed framework lie in its straightforward logic and ease of implementation to assist stakeholders in the identification of threat mitigation actions that are both spatially clumped and cost-effective using innovative prioritization indicators. We compared the utility of three local autocorrelation-based clustering statistics, including local Moran’s I, Getis-Ord Gi*, and AMOEBA, in quantifying the spatial aggregation of identified sites under given budgets. It is our finding that the CEA method produced threat mitigation sites that are more cost-effective but are dispersed in space. Spatial clustering statistics could help identify spatially aggregated management sites with only minor loss in cost effectiveness. We concluded that integrating basic CEA with spatial clustering statistics provides stakeholders with straightforward and reliable information in prioritizing spatially clustered cost-effective actions for habitat threat mitigation.

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Dawson, MPH, Emily, Julia Lew, MD Candidate, Dane Mauer-Vakil, BKin, Adam Van Dijk, MSc, Paul Belanger, PhD, and Kieran M. Moore, MD. "A longitudinal analysis of temporal and spatial incidence of neonatal abstinence syndrome in Ontario: 2003-2016." Journal of Opioid Management 15, no. 3 (May 1, 2019): 205–12. http://dx.doi.org/10.5055/jom.2019.0504.

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Objective: This study describes the incidence of neonatal abstinence syndrome (NAS) in Ontario, Canada by year and health region from 2003 to 2016. Design: The incidence of NAS diagnoses per 1,000 live births was calculated for the 36 local public health agency regions in Ontario from 2003 to 2016 using retrospective hospital admissions data. Infants with a diagnosis of NAS were identified using ICD-10 code P961. Local public health agency level data were aggregated and analyzed by geographic region and by Statistics Canada 2015 Peer Groups.Results: The incidence of NAS in Ontario increased from 0.99 per 1,000 live births in 2003 to 5.94 per 1,000 live births in 2016. There were major differences in NAS incidence by geography, North Western Ontario had the greatest incidence across all years. Health regions with a rural and population center mix or mostly rural population had greater incidence rate of NAS compared to health regions with high density population centers.Conclusions: The incidence of NAS has dramatically increased across Ontario in the last decade. Actions should be taken to combat the continued increase in NAS rates, especially in health regions with disproportionately high incidence of NAS.

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Boots, B., and A. Okabe. "Local statistical spatial analysis: Inventory and prospect." International Journal of Geographical Information Science 21, no. 4 (April 2007): 355–75. http://dx.doi.org/10.1080/13658810601034267.

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Vadrevu, Krishna Prasad. "Analysis of fire events and controlling factors in eastern india using spatial scan and multivariate statistics." Geografiska Annaler: Series A, Physical Geography 90, no. 4 (December 2008): 315–28. http://dx.doi.org/10.1111/j.1468-0459.2008.00348.x.

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Vilalta Perdomo, Carlos J. "¿Se pueden predecir geográficamente los resultados electorales? Una aplicación del análisis de clusters y outliers espaciales / Can Electoral Results Be Geographically Predicted? A Spatial Clusters and Outliers Analysis." Estudios Demográficos y Urbanos 23, no. 3 (September 1, 2008): 571. http://dx.doi.org/10.24201/edu.v23i3.1322.

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Los resultados de este estudio demuestran que al aplicar la estadística espacial en la geografía electoral es posible predecir los resultados electorales. Se utilizan los conceptos geográficos de cluster y outlier espaciales, y como variable predictiva la segregación espacial socioeconómica. Las técnicas estadísticas que se emplean son los índices globales y locales de autocorrelación espacial de Moran y el análisis de regresión lineal. Sobre los datos analizados se encuentra: 1) que la Ciudad de México posee clusters espaciales de apoyo electoral y de marginación, 2) outliers espaciales de marginación, 3) que los partidos electorales se excluyen geográficamente, y 4) que sus resultados dependen significativamente de los niveles de segregación espacial en la ciudad. AbstractThe results of this study show that using spatial statistics in electoral geography can predict electoral results. The geographical concepts of spatial cluster and spatial outlier are applied, and a local spatial segregation measure used as the predictor variable. The statistical techniques employed are Moran´s global and local spatial autocorrelation indexes, and linear regression. The analysis shows: 1) that Mexico City contains spatial clusters of electoral support and marginality, 2) spatial outliers of marginality, 3) political parties exclude each other geographically, and 4) electoral results to be significantly dependent on the levels of spatial segregation within the city.

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Fingleton, Bernard. "INDUSTRIAL LOCATION AND POLICY: A SPATIAL STATISTICAL ANALYSIS." Papers in Regional Science 70, no. 3 (January 14, 2005): 267–85. http://dx.doi.org/10.1111/j.1435-5597.1991.tb01732.x.

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Fingleton, Bernard. "Industrial location and policy: A spatial statistical analysis." Papers in Regional Science 70, no. 3 (July 1991): 267–85. http://dx.doi.org/10.1007/bf01434422.

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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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NAZARENKO, Tetiana, Oleh TOPUZOV, Olena CHASNIKOVA, and Iryna DUBROVINA. "Role of geography teacher in forming the pupils’ cartographic competence." Prace i Studia Geograficzne 66, no. 2 (September 1, 2021): 43–53. http://dx.doi.org/10.48128/pisg/2021-66.2-03.

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Cartographiccompetence has been determined as a very important one in the list of subject geographical competencies recorded in the State Standard of Secondary Basic Education of Ukraine. Since a geographic map is a system of landmarks with specific information based on field studies, aerial photographs and satellite images, and other cartographic sources, statistics and literature, the task of a geography teacher is to instil in pupils the necessary skills, which will result in cartographic competencies. Since it is impossible to study geography without a geographic map as a forming factor for spatial thinking, the authors of the article conducted an empirical research. The research and comparative analysis were conducted among the pupils, their parents and geography teachers in order to identify their cartographic knowledge, concepts and cartographic skills of the pupils. The aim of the article was to determine the role of the geography teacher as a consultant who forms the cartographic competence, the content of which consists of cartographic knowledge and map reading skills. The objectives of the paper includes evaluation of the pupils’ levels of cartographic competence, and determination of the correlation between the levels of the pupils’ cartographic competence and factors influencing its development. The theoretical analysis and empirical study resulted in developing the updated methodology for working with the maps on geography classes, and determining the role of a teacher who guarantees the formation of subject competence in cartography among pupils with the use of proper and relevant methodological instruments for teaching pupils’ cartographic competence. The results of the correlations that we received highlighted the important role of a geography teacher in forming the average and high level of the pupils’ cartographic competence.

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Owczarz, Karolina, and Jan Blachowski. "Application of DInSAR and Spatial Statistics Methods in Analysis of Surface Displacements Caused by Induced Tremors." Applied Sciences 10, no. 21 (October 29, 2020): 7660. http://dx.doi.org/10.3390/app10217660.

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Induced seismicity is one of the negative phenomena caused by anthropogenic activities that include mining of minerals. This phenomenon manifests itself as sudden and unpredictable shocks of rock mass, which can cause surface deformation and damage to ground infrastructure. Until the advent of satellite radar interferometry that enables analysis of historical events, the characteristics of these unexpected surface deformations were difficult to assess. The main aim of the research was the spatial analysis of the geometry of surface displacements caused by eight induced tremors in the Rudna copper mine (SW Poland) and the dependence of deformation characteristics (vertical displacements, extent) on the induced shock energy. For this purpose, Sentinel-1 satellite imagery, the differential radar satellite interferometry (DInSAR) method and geographic information systems (GIS) based spatial statistics were used. Vertical displacements were mapped on the basis of 37 calculated interferograms. Spatial statistics on the pixel-to-pixel level were performed in the GIS Map Algebra environment. In the result, descriptive and spatial statistics characterizing deformations caused by individual shocks were calculated. The average values of vertical displacements ranged from −44 to −119 mm. Strong, statistical correlation between the extent, maximum vertical displacement, and energy values was determined. In addition, geometries of the formed deformation areas were analyzed and presented graphically. The results obtained in this research constitute development of a knowledge base on surface displacements caused by induced tremors in underground copper mining.

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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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KubešCDFMR, Jan. "European post-socialist cities and their near hinterland in intra-urban geography literature." Bulletin of Geography. Socio-economic Series 19, no. 19 (June 1, 2013): 19–43. http://dx.doi.org/10.2478/bog-2013-0002.

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AbstractOver 180 articles concerning urban geography aspects of European post-socialist cities and their near hinterland, published between 1990-2012 in international journals, were selected for the analysis in this paper. Statistics of articles by journals, analysed cities (and their countries) and authors proves the preponderance of research on post-socialist Central European capitals, especially Berlin, Prague and Budapest, followed by Leipzig and Łodź, as well as Moscow and Tallinn. It also proves that the number of domestic authors and those who moved from post-socialist Europe to Western universities gradually increased, while the number of authors from the West decreased. The analysis of representations of article topics, their explanations and justifications were carried out in the second part of the paper. The most frequent article topics include ‘social spatial structure of the city and its transformation’, followed by ‘urban planning and management in the city’ and ’suburbanisation and urban sprawl in the near hinterland of the city’. A smaller number of articles refers to ‘physical spatial structure of the city and its transformation’, ‘housing structure in urban neighbourhoods in connection with changes in housing policy and market’ and ‘functional spatial structure of the city and its transformation’. Indexes of the articles are part of this paper

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Bivand, Roger, and Albrecht Gebhardt. "Implementing functions for spatial statistical analysis using the language." Journal of Geographical Systems 2, no. 3 (September 19, 2000): 307–17. http://dx.doi.org/10.1007/pl00011460.

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Abreu, Maria, Jessie Poon, and Paul Elhorst. "Women in spatial economic analysis." Spatial Economic Analysis 16, no. 2 (April 3, 2021): 241–46. http://dx.doi.org/10.1080/17421772.2021.1909892.

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Rayushkin, V. B., and V. P. Vogachev. "CARTOGRAPHIC-STATISTICAL ANALYSIS OF THE SPATIAL-TEMPORAL VARIABILITY OF INFECTIOUS DISEASES." Mapping Sciences and Remote Sensing 28, no. 1 (January 1991): 70. http://dx.doi.org/10.1080/07493878.1991.10641849.

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Veiga Gonçalves, Nelson, Claudia do Socorro Carvalho Miranda, Rodrigo Junior Farias da Costa, Juan Andrade Guedes, Erica Silva de Souza Matsumura, Simone Beverly Nascimento da Costa, Selma Kazumi da Trindade Noguchi, et al. "Cutaneous leishmaniasis: Spatial distribution and environmental risk factors in the state of Pará, Brazilian Eastern Amazon." Journal of Infection in Developing Countries 13, no. 10 (October 31, 2019): 939–44. http://dx.doi.org/10.3855/jidc.11573.

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Introduction: Cutaneous leishmaniasis is an infectious disease transmitted by phlebotomine sandflies and is considered a great environmental and public health problem. Thus, this work presents initial results of the analyses about the relationship between the spatial distribution of this disease and its environmental risk factors in three municipalities, in the state of Pará, Brazil, from 2012 to 2016. Methodology: It was used data from the Ministry of Health, the National Institute for Space Research and the Brazilian Institute of Geography and Statistics. The statistical and spacial analysis of the variables were done using G-test goodness-of-fit, kernel interpolation technique and the Bivariate Global Moran Index (I). Results: The analyses showed that the most affected individuals were males, adults, low schooling, residents in rural areas and small farmers. The disease spatial distribution was not homogeneous in the municipalities and it was associated to different relationships between the land use and occupation and the notificated cases density, with direct spatial autocorrelation. Conclusions: The deforestation was the most significant risk factor linked to the cases occurrence in all the studied area. We emphasize the need of intensification of epidemiological and environmental surveillance actions in the studied areas.

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Havas, Clemens, Lorenz Wendlinger, Julian Stier, Sahib Julka, Veronika Krieger, Cornelia Ferner, Andreas Petutschnig, Michael Granitzer, Stefan Wegenkittl, and Bernd Resch. "Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics." ISPRS International Journal of Geo-Information 10, no. 8 (July 23, 2021): 498. http://dx.doi.org/10.3390/ijgi10080498.

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In 2015, within the timespan of only a few months, more than a million people made their way from Turkey to Central Europe in the wake of the Syrian civil war. At the time, public authorities and relief organisations struggled with the admission, transfer, care, and accommodation of refugees due to the information gap about ongoing refugee movements. Therefore, we propose an approach utilising machine learning methods and publicly available data to provide more information about refugee movements. The approach combines methods to analyse the textual, temporal and spatial features of social media data and the number of arriving refugees of historical refugee movement statistics to provide relevant and up to date information about refugee movements and expected numbers. The results include spatial patterns and factual information about collective refugee movements extracted from social media data that match actual movement patterns. Furthermore, our approach enables us to forecast and simulate refugee movements to forecast an increase or decrease in the number of incoming refugees and to analyse potential future scenarios. We demonstrate that the approach proposed in this article benefits refugee management and vastly improves the status quo.

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Kim, Chunil, and Choongik Choi. "Towards Sustainable Urban Spatial Structure: Does Decentralization Reduce Commuting Times?" Sustainability 11, no. 4 (February 15, 2019): 1012. http://dx.doi.org/10.3390/su11041012.

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This paper contributes to the existing debate on the co-location hypothesis, by devising a proximity measure and controlling for a set of other urban form measures. Utilizing the LEHD (Longitudinal Employer–Household Dynamics) Origin-Destination Employment Statistics (LODES) data that provide the number of jobs by a finer geography, this paper measured the degree of centralization, proximity, and job–housing mismatch. Multiple regression analysis revealed that the job–worker proximity leads to a shorter commuting time. In addition, the results focusing on suburban areas revealed that the impact of the job–worker imbalance and the impact of job–worker mismatch on the commuting time are greater in the suburb in comparison with the city center.

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Fedorko, Viktor N., Sergey L. Yanchuk, and Shukhrat B. Kurbanov. "THE MODERN ETHNIC GEOGRAPHY OF UZBEKISTAN." Географический вестник = Geographical bulletin, no. 4(55) (2020): 63–80. http://dx.doi.org/10.17072/2079-7877-2020-4-63-80.

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Spatial analysis of the population ethnic structure is a poorly developed area of socio-geographical research in Uzbekistan. The authors of the article are the first to develop a multi-level scheme of ethno-geographical zoning of Uzbekistan based on the current statistical data on the ethnic composition of the population in the republic. These statistical materials are the only source of information on the ethnic structure of the population of the country and its regions, taking into account the fact that the census in Uzbekistan has not been conducted after 1989. In the course of research, the methodological foundations of ethno-geographical zoning of Uzbekistan were developed, in particular, its principles, criteria, and taxonomic units. Ethno-geographical zoning was carried out in accordance with the grid of administrative-territorial division based on spatial grouping of rural areas and cities of regional and republican subordination; 33 types of ethnic environments were distinguished. Zoning is three-level and includes three taxonomic stages: 4 ethnogeographic provinces, 6 ethnogeographic districts and 32 ethnogeographic areas. In addition, there were identified ‘through’ spatial units − ethnogeographic tiers: upper (mountain), middle (foothill-plain) and lower (desert-oasis). The main ethnogeographic boundaries within the territory of Uzbekistan, tied to the orographic and landscape boundaries, were determined. The features of the population ethnic structure in all the ethnogeographic provinces, districts and areas are characterized. An ethno-geographical zoning map of the republic has been compiled.

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Fiorentin, Ailton Moisés Xavier, Renato Billia Miranda, Dalva Maria de Castro Vitti, Gustavo D’Almeida Scarpinella, and Frederico Fabio Mauad. "Spatial distribution of areas susceptible to sheet erosion in computing environment." Management of Environmental Quality: An International Journal 28, no. 3 (April 10, 2017): 414–29. http://dx.doi.org/10.1108/meq-07-2015-0156.

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Purpose Erosion is the main cause of soil and water degradation, which demands investments for the recovery of such natural resources. The purpose of this paper is to quantify and spatialize soil losses in a municipality with large areas of monoculture of sugarcane. Design/methodology/approach The Universal Soil Loss Equation was applied for the identification of areas susceptible to erosion in the municipality of Jahu, SP, Brazil. Soil maps, statistical census data from Brazilian Institute of Geography and Statistics, radar images from SRTM, rainfall data from Fatec-Jahu hydrometeorological station and digital satellite images from Landsat 5 TM were used for the analyses. Four basic parameters were evaluated: topography of the terrain, rainfall erosivity, soil erodibility, and soil use and management factors. The analyses were conducted in the Geographic Information System (ArcView software). Findings The results show most of the municipality (91 percent) consists of areas of erosion potential. However, the remaining 9 percent can potentially produce 40 percent of the sediment load, which indicates the need for conservation practices and environmental recovery for a reduction in the erosion process acceleration. Originality/value In view of the expansion of sugarcane culture in the cities of São Paulo, the results show the importance of a master plan for new areas of sugarcane planting and the need for restoration of riparian vegetation in the surrounding streams and springs for the reduction of erosion.

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Zhu, Wei, Ding Ma, Zhigang Zhao, and Renzhong Guo. "Investigating the Complexity of Spatial Interactions between Different Administrative Units in China Using Flickr Data." Sustainability 12, no. 22 (November 23, 2020): 9778. http://dx.doi.org/10.3390/su12229778.

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Location-based social media have facilitated us to bridge the gap between virtual and physical worlds through the exploration of human online dynamics from a geographic perspective. This study uses a large collection of geotagged photos from Flickr to investigate the complexity of spatial interactions at the country level. We adopted three levels of administrative divisions in mainland China—province, city, and county—as basic geographic units and established three types of topology—province–province network, city–city network, and county–county network—from the extracted user movement trajectories. We conducted the scaling analysis based on heavy-tailed distribution statistics including power law exponents, goodness of fit index, and ht-index, by which we characterized a great complexity of the trajectory lengths, spatial distribution of geotagged photos, and the related metrics of built networks. The great complexity indicates the highly imbalanced ratio of populated-to-unpopulated areas or large-to-small flows between areas. More interestingly, all power law exponents were around 2 for the networks at various spatial and temporal scales. Such a recurrence of scaling statistics at multiple resolutions can be regarded a statistical self-similarity and could thus help us to reveal the fractal nature of human mobility patterns.

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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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Kitron, Uriel, Jack Swanson, Linn Haramis, and James Michael. "Spatial Analysis of the Distribution of Lacrosse Encephalitis in Illinois, Using a Geographic Information System and Local and Global Spatial Statistics." American Journal of Tropical Medicine and Hygiene 57, no. 4 (October 1, 1997): 469–75. http://dx.doi.org/10.4269/ajtmh.1997.57.469.

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O'Loughlin, John. "The Electoral Geography of Weimar Germany: Exploratory Spatial Data Analyses (ESDA) of Protestant Support for the Nazi Party." Political Analysis 10, no. 3 (2002): 217–43. http://dx.doi.org/10.1093/pan/10.3.217.

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For more than half a century, social scientists have probed the aggregate correlates of the vote for the Nazi party (NSDAP) in Weimar Germany. Since individual-level data are not available for this time period, aggregate census data for small geographic units have been heavily used to infer the support of the Nazi party by various compositional groups. Many of these studies hint at a complex geographic patterning. Recent developments in geographic methodologies, based on Geographic Information Science (GIS) and spatial statistics, allow a deeper probing of these regional and local contextual elements. In this paper, a suite of geographic methods—global and local measures of spatial autocorrelation, variography, distance-based correlation, directional spatial correlograms, vector mapping, and barrier definition (wombling)—are used in an exploratory spatial data analysis of the NSDAP vote. The support for the NSDAP by Protestant voters (estimated using King's ecological inference procedure) is the key correlate examined. The results from the various methods are consistent in showing a voting surface of great complexity, with many local clusters that differ from the regional trend. The Weimar German electoral map does not show much evidence of a nationalized electorate, but is better characterized as a mosaic of support for “milieu parties,” mixed across class and other social lines, and defined by a strong attachment to local traditions, beliefs, and practices.

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Tamás, János, István Buzás, and Ildikó Nagy. "Spatially Discrete GIS Analysis of Sampling Points Based on Yield and Quality Analysis of Sugar Beet (Beta vulgaris L.)." Acta Agraria Debreceniensis, no. 19 (March 4, 2006): 32–37. http://dx.doi.org/10.34101/actaagrar/19/3144.

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Fulfilment of the increasing quality requirements of sugar beet production can be analysed with sampling of plants and soil at the cultivated area. Analyses of the spatial characteristics of samples require exact geodetic positioning. This is applied in practice using GPS in precision agriculture. The examinations were made in a sample area located in north-western Hungary with sugar beet test plant. According to the traditional sample taking procedure N=60 samples were taken in regular 20 x 20 m grid, where besides the plant micro and macro elements, the sugar industrial quality parameters (Equations 1-2) and the agro-chemical parameters of soils were analysed. Till now, to gain values of mean, weighted mean and standard variance values, geometric analogues used in geography were adapted, which correspond to the mean centre (Equation 3), the spatially weighted mean centre (Equation 4), the standard distance (Equation 5), and the standard distance circle values. Robust spatial statistical values provide abstractions, which can be visually estimated immediately, and applied to analyse several parameters in parallel or in time series (Figure 1). This interpretation technique considers the spatial position of each point to another individually (distance and direction), and the value of the plant and soil parameters. Mapping the sample area in GIS environment, the coordinates of the spatially weighted mean centre values of the measured plant and soil parameters correlated to the mean centre values showed a northwest direction. Exceptions were the total salt and calcium-carbonate contents, and the molybdenum concentration of the soil samples (Table 1). As a new visual analysis, the spatially weighted mean centre values of the parameters as eigenvectors were projected to the mean centre values as origin. To characterize the production yield, the raw and digested sugar contents of the sample area, the absolute rotation angles of the generated vectors were determined, which indicate numerically the inhomogenity of the area (Figure 2). The generated spatial analogues are applicable to characterise visually and quantitatively the spatial positions of sampling points and the measured parameters in a quick way. However, their disadvantage is that they do not provide information on the tightness and direction of the spatial correlation similarly to the original statistical parameters.

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Csillag, Ferenc, and Barry Boots. "A framework for statistical inferential decisions in spatial pattern analysis." Canadian Geographer/Le G�ographe canadien 49, no. 2 (June 2005): 172–79. http://dx.doi.org/10.1111/j.0008-3658.2005.00087.x.

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Dark, Shawna J., and Danielle Bram. "The modifiable areal unit problem (MAUP) in physical geography." Progress in Physical Geography: Earth and Environment 31, no. 5 (October 2007): 471–79. http://dx.doi.org/10.1177/0309133307083294.

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Of particular importance to the study of large-scale phenomena in physical geography is the modifiable areal unit problem ( MAUP). While often viewed as only a problem in human geography (particularly demographic studies), the MAUP is an issue for all quantitative studies in geography of spatial phenomena (Openshaw and Taylor, 1979). Increasingly, remote sensing and Geographic Information Systems ( GIS) are being used to assess the distribution of phenomena from a large scale. These phenomena are modelled using areal units that can take any shape or size resulting in complications with statistical analysis related to both the scale and method used to create the areal units. In this paper, we define the modifiable areal unit problem, present examples of when it is a problem in physical geography studies, and review some potential solutions to the problem. Our aim is to increase awareness of this complicated issue and to promote further discussion and interest in this topic.

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Veiga Gonçalves, Nelson, João Simão De Melo Neto, Selma Kazumi da Trindade Noguchi, Andrey Silva Machado, Alcindo Da Silva Martins Junior, Maira Cibelle Da Silva Peixoto, Alba Lúcia Ribeiro Raithy, et al. "COVID-19 in socially vulnerable quilombola populations in Salvaterra, Pará, Eastern Amazon, Brazil." Journal of Infection in Developing Countries 15, no. 08 (August 31, 2021): 1066–73. http://dx.doi.org/10.3855/jidc.14420.

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Introduction: COVID-19 is a severe respiratory syndrome caused by the SARS-CoV-2 virus. In Brazil the highest infection rates are associated with socially vulnerable populations. This study therefore sought to analyze the spatial distribution of the disease and its relation with geographic, socioeconomic and public health policy characteristics associated with quilombola communities in Salvaterra municipality, state of Pará, for the period of March to September, 2020. Methodology: This cross-sectional and ecological study used data from the Disease Notification System and the National Registry of Health Establishments of the Ministry of Health, the Income Transfer Registry of the Ministry of Citizenship and the 2010 census of the Brazilian Institute of Geography and Statistics. Statistical and spatial analysis of the data was done through percentages of cases and Flow and Kernel map techniques. Results: Seventy-five notified cases of COVID-19 distributed among 7 quilombola communities in the municipality were analyzed. The epidemiological profile followed a national trend, with a higher percentage of cases among persons who were female, adults with low schooling levels, working as family farmers and with an outcome ending in recovery. The spatial distribution of the disease was not homogenous and showed clusters of cases and high incidence rates, especially in communities close to the municipal seat or to highways. Conclusions: The use of data analysis techniques was satisfactory for providing an understanding of the socioeconomic production of the disease in the areas studied. Accordingly, the need for intensifying epidemiological survey actions in the quilombola communities of the municipality is emphasized.

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Li, Hongxing, Qi Zhang, Weiwei Li, Qing Luo, Kaitai Liu, and Yong Tao. "Spatial analysis of rural drinking water supply in China." Water Policy 17, no. 3 (September 29, 2014): 441–53. http://dx.doi.org/10.2166/wp.2014.193.

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A spatial autocorrelation analysis method was employed to process the spatial change of rural water supply over the past 19 years in the People's Republic of China. Statistical analyses indicate great achievements in rural water supply construction. Two main indices describing rural drinking water supply status, the Rural Popularization Rate of Tap Water and the Rural Popularization Rate of Water Improvement Beneficiaries, were found to be spatially auto-correlated. The Global Moran's I of the latter decreased generally, and local spatial autocorrelation analysis showed that the regional gap of rural water supply infrastructure is declining. The main factors affecting the spatial pattern of rural water supply were analyzed through the mean centre method. Our research shows that the spatial pattern of economic development and government investment has had a decisive role in the formation and evolution of rural water supply.

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

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