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7th Brazilian Technology Symposium (BTSym) - Emerging Trends and Challenges in Technology

作者： Gonzalez Cusi, Bryan Martin Solano Davila, Olga Lidia Univ Nacl Mayor San Marcos Fac Ciencias Matemat Lima Peru

ISBN: (纸本)9783031044359;9783031044342

This research presents the application of the exploratory spatial data analysis to assess the type of spatial pattern that follows the cases of patients with malnutrition in children under 5 years who were attended in the Ministry of Health of Peru (MINSA, after its Spanish abbreviation) in 2017. The results demonstrated that malnutrition cases in children under 5 years were non-homogeneously distributed throughout Peru in 2017. The boxplot identified that a larger number of malnutrition cases in girls under five years was presented in the departments of Piura, Loreto, and Ucayali;and in the case of the boys was in Piura, Cajamarca, and Ucayali. The rates map evidenced that a higher rate of malnutrition was presented in the departments of Piura, Cajamarca, Loreto, Ucayali, Madre de Dios, and Ayacucho. The LISA indexes found the presence of significant clusters in departments with a high demand for this disease as in Loreto, as well as significant clusters in departments with less demand as in Lima, Moquegua, and Puno. The Moran's Index indicated that the spatial dependence is positive and almost nil.

关键词： exploratory spatial data analysis Malnutrition LISA map Moran's Index

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spatial Patterns of Population Growth and Social Indicators' Change in Korea : an exploratory spatial data analysis

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INTERNATIONAL JOURNAL OF URBAN SCIENCES 2008年第2期12卷 61-72页

作者： Bae, Yujin Yoon, Minkyoung Kim, Euijune Seoul Natl Univ Dept Agr Econ & Rural Dev Reg Informat Seoul South Korea

The purpose of this paper is to identify regional concentration in terms of population and social indicators such as employment and infrastructure, at a disaggregated level using the exploratory spatial data analysis (ESDA). Through an investigation of the change of Moran-I in 1995-2007, we found two phenomena, dramatic deconcentmtion in 1998, and gradually increasing concentration after 1999 due to the IMF financial crisis and its recovery pace. analysis of local spatial autocorrelation revealed a concentration of regions with positive population growth in SMA and near-SMA and that with negative population growth in the south of Gangwon. Gyeongbuk and the midst of Jeolla in 2000-2005. The distribution of employment growth follows the spatial patterns of population growth among various social indicators. We can conclude that the concentration of population spread out over SMA and the gap of population between growing regions and declining ones widened after the financial crisis. The gap of in population numbers between growing and declining regions is not expected to be reduced within a short period due to simultaneous employment and population growth and past regulatory policies in SMA cannot achieve population deconcentration.

关键词： population concentration spatial patterns exploratory spatial data analysis spatial autocorrelation

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Quality of life in the regions: an exploratory spatial data analysis for West German labor markets

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REVIEW OF REGIONAL RESEARCH-JAHRBUCH FUR REGIONALWISSENSCHAFT 2010年第1期30卷 1-22页

作者： Rusche, Karsten ILS Inst Landes & Stadtentwicklungsforsch gGmbH Forschungsfeld Regionalisierung Stadt 2 Postfach 101764 D-44017 Dortmund Germany

Which of Germany's regions is the most attractive? Where is it best to live and work-on objective grounds? These questions are summed up in the concept "quality of life". This paper uses recent research projects that determine this parameter to examine the spatial distribution of quality of life in Germany. For this purpose, an exploratory spatial data analysis is conducted which focuses on identifying statistically significant (dis-) similarities in space. An initial result of this research is that it is important to choose the aggregation level of administrative units carefully when considering a spatial analysis. The level plays a crucial role in the strength and impact of spatial effects. In concentrating on various labor market areas, this paper identifies a significant spatial autocorrelation in the quality of life, which seems to be contradictory to the hypothesis of a North-South divide. Instead, the German regions in the geographical mid show the highest clustering of low quality of life. In addition, the ESDA results are used to augment the regression specifications, which helps to avoid the occurrence of spatial dependencies in the residuals. The regression results indicate that soft location factors have half the impact on quality of life than hard economic factors.

关键词： Quality of life exploratory spatial data analysis Functional economic areas spatial econometrics and statistics LISA dummies

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Evaluation of Agricultural Modernization Development Level based on exploratory spatial data analysis and Back Propagation Neural Network 1

Evaluation of Agricultural Modernization Development Level b...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Liu, Wenjie Aba Teachers University Aba Prefecture China

ISBN: (纸本)9798350352931

Recent days, agricultural modernization represents a transformative shift from traditional farming practices to a modern, science-driven agricultural system. The existing approaches for assessing agricultural modernization development level have encountered significant challenges which includes, model interoperability, identification of patterns and relationships, variability in temporal and spatial variables. Therefore, this paper proposes exploratory spatial data analysis-Backpropagation Neural Network (ESDABPNN) for Evaluation of Agricultural Modernization Development Level which is employed on statistical yearbook of Chinese government. Then, the data is preprocessed by using data normalization which effectively improved the model interoperability. After that, the ESDA-BPNN is used for analyzing the agricultural development which effectively identified the patterns and relationships between neighboring regions, highlighted the areas of high agricultural modernization. Finally, obstacle degree model is used for identifying key constraints and quantifies the obstacle impact. The proposed ESDA has increased in the year 2024 C 1 has increased 22%, C2 increased 24%, C3 increased 25%, C4 increases 22% and C5 increases 24%, where, C1, C2, C3, C4 and C 5 are per unit arable machinery, irrigation rate, per capita electricity consumption, agriculture results of expenditure, area of arable land per household when compared with existing ESDA. © 2024 IEEE.

关键词： backpropagation neural network evaluation of agricultural modernization development level exploratory spatial data analysis obstacle degree model statistical yearbook of chinese government

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Mapping mHealth (mobile health) and mobile penetrations in sub-Saharan Africa for strategic regional collaboration in mHealth scale-up: an application of exploratory spatial data analysis

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GLOBALIZATION AND HEALTH 2017年第1期13卷 63-63页

作者： Lee, Seohyun Cho, Yoon-min Kim, Sun-Young Univ Texas Hlth Sci Ctr Houston Sch Publ Hlth Dept Management Policy & Community Hlth 1200 Pressler St Houston TX 77030 USA Seoul Natl Univ Grad Sch Publ Hlth Dept Publ Hlth 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Grad Sch Publ Hlth Ctr Global Hlth Res 1 Gwanak Ro Seoul 08826 South Korea

Background: Mobile health (mHealth), a term used for healthcare delivery via mobile devices, has gained attention as an innovative technology for better access to healthcare and support for performance of health workers in the global health context. Despite large expansion of mHealth across sub-Saharan Africa, regional collaboration for scale-up has not made progress since last decade. Methods: As a groundwork for strategic planning for regional collaboration, the study attempted to identify spatial patterns of mHealth implementation in sub-Saharan Africa using an exploratory spatial data analysis. In order to obtain comprehensive data on the total number of mHelath programs implemented between 2006 and 2016 in each of the 48 sub-Saharan Africa countries, we performed a systematic data collection from various sources, including: the WHO eHealth database, the World Bank Projects & Operations database, and the USAID mHealth database. Additional spatial analysis was performed for mobile cellular subscriptions per 100 people to suggest strategic regional collaboration for improving mobile penetration rates along with the mHealth initiative. Global Moran's I and Local Indicator of spatial Association (LISA) were calculated for mHealth programs and mobile subscriptions per 100 population to investigate spatial autocorrelation, which indicates the presence of local clustering and spatial disparities. Results: From our systematic data collection, the total number of mHealth programs implemented in sub-Saharan Africa between 2006 and 2016 was 487 (same programs implemented in multiple countries were counted separately). Of these, the eastern region with 17 countries and the western region with 16 countries had 287 and 145 mHealth programs, respectively. Despite low levels of global autocorrelation, LISA enabled us to detect meaningful local clusters. Overall, the eastern part of sub-Saharan Africa shows high-high association for mHealth programs. As for mobile subscription r

关键词： mHealth exploratory spatial data analysis spatial autocorrelation Sub-Saharan Africa

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Monitoring county-level chlamydia incidence in Texas, 2004-2005: application of empirical Bayesian smoothing and exploratory spatial data analysis (ESDA) methods

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INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS 2009年第1期8卷 12-12页

作者： Owusu-Edusei, Kwame, Jr. Owens, Chantelle J. Ctr Dis Control & Prevent Div STD Prevent Atlanta GA 30333 USA

Background: Chlamydia continues to be the most prevalent disease in the United States. Effective spatial monitoring of chlamydia incidence is important for successful implementation of control and prevention programs. The objective of this study is to apply Bayesian smoothing and exploratory spatial data analysis (ESDA) methods to monitor Texas county-level chlamydia incidence rates by examining spatiotemporal patterns. We used county-level data on chlamydia incidence (for all ages, gender and races) from the National Electronic Telecommunications System for Surveillance (NETSS) for 2004 and 2005. Results: Bayesian-smoothed chlamydia incidence rates were spatially dependent both in levels and in relative changes. Erath county had significantly (p < 0.05) higher smoothed rates (> 300 cases per 100,000 residents) than its contiguous neighbors (195 or less) in both years. Gaines county experienced the highest relative increase in smoothed rates (173% - 139 to 379). The relative change in smoothed chlamydia rates in Newton county was significantly (p < 0.05) higher than its contiguous neighbors. Conclusion: Bayesian smoothing and ESDA methods can assist programs in using chlamydia surveillance data to identify outliers, as well as relevant changes in chlamydia incidence in specific geographic units. Secondly, it may also indirectly help in assessing existing differences and changes in chlamydia surveillance systems over time.

关键词： spatial Dependence Local Outlier Chlamydia Screening exploratory spatial data analysis Monte Carlo Randomization

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Drivers of winegrowers' decision on land use abandonment based on exploratory spatial data analysis and multilevel models

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LAND USE POLICY 2023年 132卷

作者： Calafat-Marzal, Consuelo Sanchez-Garcia, Mercedes Gallego-Salguero, Aurea Pineiro, Veronica Univ Politecn Valencia Dept Econ & Ciencias Sociales Valencia Spain Univ Publ Navarra Dept Gest Empresas Campus Arrosadia Pamplona Spain Univ Politecn Valencia Dept Ingn Cartograf Geodesia & Fotogrametria Valencia Spain Univ Nacl Dept Agron Bahia Blanca Argentina

The frequency of producers opting to abandon agricultural land has become increasingly, highlighting the sig-nificance of this phenomenon due to its environmental, landscape, and socio-economic impacts. The decisions of producers to abandon or maintain/improve their farms depend on individual and contextual factors. The aims of this research are twofold. Firstly, to evaluate the influence of the neighbours on the winegrowers' decisions, using spatial analysis. Secondly, to clarify the specific importance of each of the individual and contextual drivers in farmers' decisions to improve their farms, to keep them unchanged or to abandon them, using multilevel models. The results obtained for the case study of vineyards in Spain, reveal a strong agglomeration phenomenon in farmers' decisions indicating that producers make land use decisions influenced by what their neighbours do. A multilevel analysis identifies that individual factors are determinant and that the influence of contextual factors is conditioned by the innovation process at farm level. Individual drivers, such as size, innovation, Protected Designations of Origin and irrigation influence vineyard area, with irrigation having the greatest overall influence, and is expected to be decisive in climate change projections. The Protected Designations of Origin are driving forces that dynamize the territory and achieve productive concentrations, encouraging winegrowers to replant, but they are not enough to halt abandonment. The elements that slow down the abandonment of plots are irrigation and the combination of innovation and context variables, mainly the combination of modernised plots in the municipalities with trading options.

关键词： Multinomial multilevel analysis exploratory spatial data analysis Wine sector Abandonment land

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Assessment of an exploratory spatio-temporal data analysis method: Using a dynamic multi-variate geo-visualization of spatio-temporal electric vehicle charge data in an information rich online spatial environment to explore the public charge behaviour in Amsterdam

Assessment of an exploratory spatio-temporal data analysis m...

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作者： Daniel Scheerooren Wageningen University And Research

学位级别：硕士

The thesis research describes the development and assessment of a new method for exploratory Spatio- temporal data analysis (ESDA). The question of "why there?" has occupied the minds of the map users ever since maps were used as a research tool. Exploration of spatial and spatio-temporal data using maps can be a first step in constructing new knowledge and generating hypotheses for further research. New – mostly digital- technologies have expanded the map possibilities for exploration. Over the last decade several researchers developed different software tools to explore spatio-temporal data. These tools allow researchers to use their visual-cognitive capacities to visually explore spatio-temporal data and discover patterns and relationships between variables. Current ESDA's have several limitations: they often use aggregated data, static time frames, visualize only one or two attributes of the data and they anonymize space. To make better use of human visual-cognitive and analytical capacities in the exploratory phase, an ESDA tool was developed which enabled the user to interactively explore a dynamic multivariate geo- visualization of spatio-temporal point data in a highly detailed and information rich spatial context. With the plotKML R package, a geo-visualization was developed to be presented in Google Earth (GE) interface environment. The research used the electric vehicle charge session data from the municipality of Amsterdam, the Netherlands, as case study data. Users were asked to generate hypotheses on the "why there?" and "why then?" by relating their observations in the geo-visualization to spatial context information. Results showed that the new ESDA method was suitable to generate hypotheses on spatio- temporal point data. The combination of plotKML and GE was able to facilitate an interactive exploration of spatio-temporal data. The ability of the GE interface to visualize day-night transitions is unique and proved very useful. The assessment also s

关键词： exploratory spatial data analysis geo-visualization hypothesis generation spatio-temporal data plotKML Google Earth electric vehicle charging

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Spatiotemporal Dynamics of Maize, Wheat, and Paddy Production in China During 1950-2022

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INTERNATIONAL JOURNAL OF PLANT PRODUCTION 2025年 1-15页

作者： Yu, Xuan Zhao, Chenyu Zhang, Fangmin Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast Evaluat Meteorol D Sch Ecol & Appl Meteorol Jiangsu Key Lab Agr Meteorol Nanjing 210044 Peoples R China

Understanding the spatiotemporal dynamics of grain production is crucial for sustainable agricultural development and food security. This study employs linear regression, Moran's I, the gravity model, and yield-area decomposition, considering both temporal and spatial dimensions, to comprehensively examine the temporal trends, spatial patterns, and interregional relationships of maize, wheat, and paddy production. The findings are intuitive, widely applicable, and have strong spatial explanatory power, providing a scientific foundation for policy formulation and regional optimization. Results indicate that: (1) Maize output and sown area experienced stable growth (1950-1999) and rapid growth (2000-2022). Wheat and paddy outputs grew during 1950-1997 and 2004-2022, but declined between 1998 and 2003, primarily due to grain policy reforms and falling crop prices, with sown areas fluctuating significantly. All crop yields steadily increased. (2) Grain production exhibited significant spatial clustering (P < 0.1). Maize exhibited a "higher in the north, lower in the south" pattern, with significant growth across most provinces. Wheat was concentrated in the Huang-Huai-Hai Plain but declined in northern and southern regions. Paddy exhibited a "higher in the south, lower in the north" pattern, with stronger growth in the south. (3) Production centroids for maize and paddy migrated northeastward, while wheat shifted southeastward. (4) Maize production was driven by the interaction of sown area and yield, while wheat and paddy were yield-dominant. Over time, the expansion of sown area became the primary contributor to output growth for all three crops. More attention should be devoted to stabilizing and expanding sown areas to enhance grain production.

关键词： Crop production Spatiotemporal dynamics exploratory spatial data analysis Production centroid Food security

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Spatiotemporal Differentiation Recognition of Dam Deformation using Gaussian-ESDA model

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KSCE Journal of Civil Engineering 2025年

作者： Wenlong Chen Xiaohu Du Xingbo Zhou Xiaoling Wang Chaoning Lin Huaiyu Xie State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation Tianjin University Tianjin 300350 China China Renewable Energy Engineering Institute Beijing 100120 China College of Water Conservancy and Hydropower Engineering Hohai University Nanjing 210098 China Fujian Shuikou Power Generation Group Co. Ltd Fujian 350000 China

The reasonable interpretation of deformation behavior is critical for the health diagnosis and safety assessment of dams. Existing studies often focus on the time-domain characteristics of single-point deformation or spatial averaging characteristics of the multi-point deformations, and fail to reveal and explain the deformation spatiotemporal differentiation. To address these issues, a deformation spatiotemporal differentiation recognition method is proposed based on Gaussian-corrected exploratory spatial data analysis. The application results of a real concrete dam show that the dam deformation share significant spatiotemporal differentiation since its Z-score exceeding 2.58 at 1% significance level, the center of gravity and hot (cold) spots changing seasonally, as well as spatial stability indices exceeding 90%, which verifies the feasibility of the proposed method for the interpretation of deformation behavior. Compared with the natural breaks(jenks) method and the classical ESDA method, the proposed method is superior in time effect recognition of dam deformation, with the 10%∼20% shrinkage in hot (cold)-spot area and 5% improvement in significance level.

关键词： Dam deformation behavior Spatiotemporal differentiation recognition exploratory spatial data analysis Gaussian spatial weight spatial autocorrelation

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