Search results for: geographically weighted regressions

Authors: Silvia Lorenzo, Hongmian Gong

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With the influx of research assessing the economic impact of the global financial crisis of 2007-8, a spatial analysis based on empirical data is needed to better understand the spatial significance of the financial crisis in New York, a key international financial center also considered the origin of the crisis. Using spatial statistics, the existence of financial clusters specializing in credit and securities throughout the New York metropolitan area are identified for 2000 and 2010, the time period before and after the height of the global financial crisis. Geographically Weighted Regressions are then used to examine processes underlying the formation and movement of financial geographies across state, county and ZIP codes of the New York metropolitan area throughout the 2000s with specific attention to tax regimes, employment, household income, technology, and transportation hubs. This analysis provides useful inputs for financial risk management and public policy initiatives aimed at addressing regional economic sustainability across state boundaries, while also developing the groundwork for further research on a spatial analysis of the global financial crisis.

Keywords: financial clusters, New York, global financial crisis, geographically weighted regression

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Authors: Ana Paula Camelo, Keila Sanches

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The Geographically Weighted Regression (GWR) was proposed in geography literature to allow relationship in a regression model to vary over space. In Brazil, the agricultural exploitation of the Cerrado Biome is the main cause of deforestation. In this study, we propose a methodology using geostatistical methods to characterize the spatial dependence of deforestation in the Cerrado based on agricultural production indicators. Therefore, it was used the set of exploratory spatial data analysis tools (ESDA) and confirmatory analysis using GWR. It was made the calibration a non-spatial model, evaluation the nature of the regression curve, election of the variables by stepwise process and multicollinearity analysis. After the evaluation of the non-spatial model was processed the spatial-regression model, statistic evaluation of the intercept and verification of its effect on calibration. In an analysis of Spearman’s correlation the results between deforestation and livestock was +0.783 and with soybeans +0.405. The model presented R²=0.936 and showed a strong spatial dependence of agricultural activity of soybeans associated to maize and cotton crops. The GWR is a very effective tool presenting results closer to the reality of deforestation in the Cerrado when compared with other analysis.

Keywords: deforestation, geographically weighted regression, land use, spatial analysis

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Authors: Sifriyani Sifriyani, I Nyoman Budiantara, Sri Haryatmi, Gunardi Gunardi

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East Java Province has a first rank as a province that has the most counties and cities in Indonesia and has the largest population. In 2015, the population reached 38.847.561 million, this figure showed a very high population growth. High population growth is feared to lead to increase the levels of unemployment. In this study, the researchers mapped and modeled the unemployment rate with 6 variables that were supposed to influence. Modeling was done by nonparametric geographically weighted regression methods with truncated spline approach. This method was chosen because spline method is a flexible method, these models tend to look for its own estimation. In this modeling, there were point knots, the point that showed the changes of data. The selection of the optimum point knots was done by selecting the most minimun value of Generalized Cross Validation (GCV). Based on the research, 6 variables were declared to affect the level of unemployment in eastern Java. They were the percentage of population that is educated above high school, the rate of economic growth, the population density, the investment ratio of total labor force, the regional minimum wage and the ratio of the number of big industry and medium scale industry from the work force. The nonparametric geographically weighted regression models with truncated spline approach had a coefficient of determination 98.95% and the value of MSE equal to 0.0047.

Keywords: East Java, nonparametric geographically weighted regression, spatial, spline approach, unemployed rate

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Authors: Elvira Mustikawati P.H., Iis Dewi Ratih, Dita Amelia

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Bojonegoro has proclaimed a policy of zero malnutrition. Therefore, as an effort to solve the cases of malnutrition children in Bojonegoro, this study used the approach geographically Mixed Weighted Regression (MGWR) to determine the factors that influence the percentage of malnourished children under five in which factors can be divided into locally influential factor in each district and global factors that influence throughout the district. Based on the test of goodness of fit models, R2 and AIC values in GWR models are better than MGWR models. R2 and AIC values in MGWR models are 84.37% and 14.28, while the GWR models respectively are 91.04% and -62.04. Based on the analysis with GWR models, District Sekar, Bubulan, Gondang, and Dander is a district with three predictor variables (percentage of vitamin A, the percentage of births assisted health personnel, and the percentage of clean water) that significantly influence the percentage of malnourished children under five.

Keywords: GWR, MGWR, R2, AIC

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Authors: Luh Eka Suryani, Purhadi

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Poisson regression is a non-linear regression model with response variable in the form of count data that follows Poisson distribution. Modeling for a pair of count data that show high correlation can be analyzed by Poisson Bivariate Regression. Data, the number of infant mortality and maternal mortality, are count data that can be analyzed by Poisson Bivariate Regression. The Poisson regression assumption is an equidispersion where the mean and variance values are equal. However, the actual count data has a variance value which can be greater or less than the mean value (overdispersion and underdispersion). Violations of this assumption can be overcome by applying Generalized Poisson Regression. Characteristics of each regency can affect the number of cases occurred. This issue can be overcome by spatial analysis called geographically weighted regression. This study analyzes the number of infant mortality and maternal mortality based on conditions in East Java in 2016 using Geographically Weighted Bivariate Generalized Poisson Regression (GWBGPR) method. Modeling is done with adaptive bisquare Kernel weighting which produces 3 regency groups based on infant mortality rate and 5 regency groups based on maternal mortality rate. Variables that significantly influence the number of infant and maternal mortality are the percentages of pregnant women visit health workers at least 4 times during pregnancy, pregnant women get Fe3 tablets, obstetric complication handled, clean household and healthy behavior, and married women with the first marriage age under 18 years.

Keywords: adaptive bisquare kernel, GWBGPR, infant mortality, maternal mortality, overdispersion

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Authors: Mwehe Mathenge, Ben G. J. S. Sonneveld, Jacqueline E. W. Broerse

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Smallholder farmers are important drivers of agriculture productivity, food security, and poverty reduction in Sub-Saharan Africa. However, they are faced with myriad challenges in their efforts at participating in agribusiness markets. How the geographic explicit factors existing at the local level interact to impede smallholder farmers' decision to participates (or not) in agribusiness markets is not well understood. Deconstructing the spatial complexity of the local environment could provide a deeper insight into how geographically explicit determinants promote or impede resource-poor smallholder farmers from participating in agribusiness. This paper’s objective was to identify, map, and analyze local spatial autocorrelation in factors that impede poor smallholders from participating in agribusiness markets. Data were collected using geocoded researcher-administered survey questionnaires from 392 households in Western Kenya. Three spatial statistics methods in geographic information system (GIS) were used to analyze data -Global Moran’s I, Cluster and Outliers Analysis (Anselin Local Moran’s I), and geographically weighted regression. The results of Global Moran’s I reveal the presence of spatial patterns in the dataset that was not caused by spatial randomness of data. Subsequently, Anselin Local Moran’s I result identified spatially and statistically significant local spatial clustering (hot spots and cold spots) in factors hindering smallholder participation. Finally, the geographically weighted regression results unearthed those specific geographic explicit factors impeding market participation in the study area. The results confirm that geographically explicit factors are indispensable in influencing the smallholder farming decisions, and policymakers should take cognizance of them. Additionally, this research demonstrated how geospatial explicit analysis conducted at the local level, using geographically disaggregated data, could help in identifying households and localities where the most impoverished and resource-poor smallholder households reside. In designing spatially targeted interventions, policymakers could benefit from geospatial analysis methods in understanding complex geographic factors and processes that interact to influence smallholder farmers' decision-making processes and choices.

Keywords: agribusiness markets, GIS, smallholder farmers, spatial statistics, disaggregated spatial data

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Authors: Liang Weichien

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Without adequate information and mitigation plan for natural disaster, the risk to urban populated areas will increase in the future as populations grow, especially in Taiwan. Taiwan is recognized as the world's high-risk areas, where an average of 5.7 times of floods occur per year should seek to strengthen coherence and consensus in how cities can plan for flood and climate change. Therefore, this study aims at understanding the vulnerability to flooding in Taipei city, Taiwan, by creating indicators and calculating the vulnerability of each study units. The indicators were grouped into sensitivity and adaptive capacity based on the definition of vulnerability of Intergovernmental Panel on Climate Change. The indicators were weighted by using Principal Component Analysis. However, current researches were based on the assumption that the composition and influence of the indicators were the same in different areas. This disregarded spatial correlation that might result in inaccurate explanation on local vulnerability. The study used Geographically Weighted Principal Component Analysis by adding geographic weighting matrix as weighting to get the different main flood impact characteristic in different areas. Cross Validation Method and Akaike Information Criterion were used to decide bandwidth and Gaussian Pattern as the bandwidth weight scheme. The ultimate outcome can be used for the reduction of damage potential by integrating the outputs into local mitigation plan and urban planning.

Keywords: flood vulnerability, geographically weighted principal components analysis, GWPCA, highly urbanized area, spatial correlation

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Authors: Wenting Zhang, Shishi Liu, Peihong Fu

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As an indicator of air quality and directly related to concentration of ground PM2.5, the spatial-temporal variation and impact factor analysis of Aerosol Optical Depth (AOD) have been a hot spot in air pollution. This paper concerns the non-stationarity and the autocorrelation (with Moran’s I index of 0.75) of the AOD in Wuhan agglomeration (WHA), in central China, uses the geographically weighted regression (GRW) to identify the spatial relationship of AOD and its impact factors. The 3 km AOD product of Moderate Resolution Imaging Spectrometer (MODIS) is used in this study. Beyond the economic-social factor, land use density factors, vegetable cover, and elevation, the landscape metric is also considered as one factor. The results suggest that the GWR model is capable of dealing with spatial varying relationship, with R square, corrected Akaike Information Criterion (AICc) and standard residual better than that of ordinary least square (OLS) model. The results of GWR suggest that the urban developing, forest, landscape metric, and elevation are the major driving factors of AOD. Generally, the higher AOD trends to located in the place with higher urban developing, less forest, and flat area.

Keywords: aerosol optical depth, geographically weighted regression, land use change, Wuhan agglomeration

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Authors: Shams Alyusof

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This work is to enrich the studies of the frames due to their prominent role in pure mathematics as well as in applied mathematics and many applications in computer science and engineering. Recently, there are remarkable studies of operators that preserve frames on some spaces, and this research could be considered as an extension of such studies. Indeed, this paper is to we characterize weighted composition operators that preserve frames in weighted Hardy spaces on the open unit disk. Moreover, it shows that this characterization does not apply to generalized weighted composition operators on such spaces. Nevertheless, this study could be extended to provide more specific characterizations.

Keywords: frames, generalized weighted composition operators, weighted Hardy spaces, analytic functions

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Authors: M. Abdelmalek

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In this work, we study the hypersurfaces of constant weighted mean curvature embedded in weighted manifolds. We give a condition about these hypersurfaces to be minimal. This condition is given by the ellipticity of the weighted Newton transformations. We especially prove that two compact hypersurfaces of constant weighted mean curvature embedded in space forms and with the intersection in at least a point of the boundary must be transverse. The method is based on the calculus of the matrix of the second fundamental form in a boundary point and then the matrix associated with the Newton transformations. By equality, we find the weighted elementary symmetric function on the boundary of the hypersurface. We give in the end some examples and applications. Especially in Euclidean space, we use the above result to prove the Alexandrov spherical caps conjecture for the weighted case.

Keywords: weighted mean curvature, weighted manifolds, ellipticity, Newton transformations

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Authors: Hung-Chu Chen, Han Qi, Bauke de Vries

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Energy consumption in the urban environment has attracted numerous researches in recent decades. However, it is comparatively rare to find literary works which investigated 3D spatial analysis of urban energy demand modelling. In order to analyze the spatial correlation between urban morphology and energy demand comprehensively, this paper investigates their relation by using the spatial regression tool. In addition, the spatial regression tool which is applied in this paper is ordinary least squares regression (OLS) and geographically weighted regression (GWR) model. Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and building volume are explainers of urban morphology, which act as independent variables of Energy-land use (E-L) model. NDBI and NDVI are used as the index to describe five types of land use: urban area (U), open space (O), artificial green area (G), natural green area (V), and water body (W). Accordingly, annual electricity, gas demand and energy demand are dependent variables of the E-L model. Based on the analytical result of E-L model relation, it revealed that energy demand and urban morphology are closely connected and the possible causes and practical use are discussed. Besides, the spatial analysis methods of OLS and GWR are compared.

Keywords: energy demand model, geographically weighted regression, normalized difference built-up index, normalized difference vegetation index, spatial statistics

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Authors: Fangzheng Li, Xiong Li

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Significant differences in urban greening have occurred in Chinese cities, which accompanied with China's rapid urbanization. However, few studies focused on the spatial differentiation of urban greening in China with large amounts of data. The spatial differentiation pattern, spatial correlation characteristics and the distribution shape of urban green space ratio, urban green coverage rate and public green area per capita were calculated and analyzed, using Global and Local Moran's I using data from 289 cities in 2014. We employed Spatial Lag Model and Spatial Error Model to assess the impacts of urbanization process on urban greening of China. Then we used Geographically Weighted Regression to estimate the spatial variations of the impacts. The results showed: 1. a significant spatial dependence and heterogeneity existed in urban greening values, and the differentiation patterns were featured by the administrative grade and the spatial agglomeration simultaneously; 2. it revealed that urbanization has a negative correlation with urban greening in Chinese cities. Among the indices, the the proportion of secondary industry, urbanization rate, population and the scale of urban land use has significant negative correlation with the urban greening of China. Automobile density and per capita Gross Domestic Product has no significant impact. The results of GWR modeling showed that the relationship between urbanization and urban greening was not constant in space. Further, the local parameter estimates suggested significant spatial variation in the impacts of various urbanization factors on urban greening.

Keywords: China’s urbanization, geographically weighted regression, spatial differentiation pattern, urban greening

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Authors: Jingjue Bao, Ye Li, Yujie Qi

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The explosive growth of ride-hailing has reshaped residents' travel behavior and plays a crucial role in urban mobility within the built environment. Contributing to the research of the spatial variation of ride-hailing demand and its relationship to the built environment and socioeconomic factors, this study utilizes multi-source data from Haikou, China, to construct a Multi-scale Geographically Weighted Regression model (MGWR), considering spatial scale heterogeneity. The regression results showed that MGWR model was demonstrated superior interpretability and reliability with an improvement of 3.4% on R2 and from 4853 to 4787 on AIC, compared with Geographically Weighted Regression model (GWR). Furthermore, to precisely identify the surrounding environment of sampling point, DeepLabv3+ model is employed to segment street-level images. Features extracted from these images are incorporated as variables in the regression model, further enhancing its rationality and accuracy by 7.78% improvement on R2 compared with the MGWR model only considered region-level variables. By integrating multi-scale geospatial data and utilizing advanced computer vision techniques, this study provides a comprehensive understanding of the spatial dynamics between ride-hailing demand and the urban built environment. The insights gained from this research are expected to contribute significantly to urban transportation planning and policy making, as well as ride-hailing platforms, facilitating the development of more efficient and effective mobility solutions in modern cities.

Keywords: travel behavior, ride-hailing, spatial relationship, built environment, street-level image

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Authors: Madhushi Bandara, Dharshana Kasthurirathna, Danaja Maldeniya, Mahendra Piraveenan

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Community detection is an extremely useful technique in understanding the structure and function of a social network. Louvain algorithm, which is based on Newman-Girman modularity optimization technique, is extensively used as a computationally efficient method extract the communities in social networks. It has been suggested that the nodes that are in close geographical proximity have a higher tendency of forming communities. Variants of the Newman-Girman modularity measure such as dist-modularity try to normalize the effect of geographical proximity to extract geographically dispersed communities, at the expense of losing the information about the geographically proximate communities. In this work, we propose a method to extract geographically dispersed communities while preserving the information about the geographically proximate communities, by analyzing the ‘community network’, where the centroids of communities would be considered as network nodes. We suggest that the inter-community link strengths, which are normalized over the community sizes, may be used to identify and extract the ‘overlay communities’. The overlay communities would have relatively higher link strengths, despite being relatively apart in their spatial distribution. We apply this method to the Gowalla online social network, which contains the geographical signatures of its users, and identify the overlay communities within it.

Keywords: social networks, community detection, modularity optimization, geographically dispersed communities

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Authors: Lin-Han Chiang Hsieh, Hsiao-Yi Lin

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This study aims to determine the impact of the disclosure of flood potential map on housing prices. The disclosure is supposed to mitigate the market failure by reducing information asymmetry. On the other hand, opponents argue that the official disclosure of simulated results will only create unnecessary disturbances on the housing market. This study identifies the impact of the disclosure of the flood potential map by comparing the hedonic price of flood potential before and after the disclosure. The flood potential map used in this study is published by Taipei municipal government in 2015, which is a result of a comprehensive simulation based on geographical, hydrological, and meteorological factors. The residential property sales data of 2013 to 2016 is used in this study, which is collected from the actual sales price registration system by the Department of Land Administration (DLA). The result shows that the impact of flood potential on residential real estate market is statistically significant both before and after the disclosure. But the trend is clearer after the disclosure, suggesting that the disclosure does have an impact on the market. Also, the result shows that the impact of flood potential differs by the severity and frequency of precipitation. The negative impact for a relatively mild, high frequency flood potential is stronger than that for a heavy, low possibility flood potential. The result indicates that home buyers are of more concern to the frequency, than the intensity of flood. Another contribution of this study is in the methodological perspective. The classic hedonic price analysis with OLS regression suffers from two spatial problems: the endogeneity problem caused by omitted spatial-related variables, and the heterogeneity concern to the presumption that regression coefficients are spatially constant. These two problems are seldom considered in a single model. This study tries to deal with the endogeneity and heterogeneity problem together by combining the spatial fixed-effect model and geographically weighted regression (GWR). A series of literature indicates that the hedonic price of certain environmental assets varies spatially by applying GWR. Since the endogeneity problem is usually not considered in typical GWR models, it is arguable that the omitted spatial-related variables might bias the result of GWR models. By combing the spatial fixed-effect model and GWR, this study concludes that the effect of flood potential map is highly sensitive by location, even after controlling for the spatial autocorrelation at the same time. The main policy application of this result is that it is improper to determine the potential benefit of flood prevention policy by simply multiplying the hedonic price of flood risk by the number of houses. The effect of flood prevention might vary dramatically by location.

Keywords: flood potential, hedonic price analysis, endogeneity, heterogeneity, geographically-weighted regression

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Authors: Sujith Rajashekar Swamy, Meghana Rajashekara Swamy

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Although T1-weighted gadolinium-enhanced imaging (T1-Gd) has its established clinical role in diagnosing brain lesions of infectious and metastatic origins, the use of post-contrast susceptibility-weighted imaging (SWI) has been understudied. This observational study aims to explore and compare the prominence of brain parenchymal lesions between T1-Gd and SWI-Gd images. A cross-sectional study design was utilized to analyze 58 patients with brain parenchymal lesions using T1-Gd and SWI-Gd scanning techniques. Our results indicated that SWI-Gd enhanced the conspicuity of metastatic as well as infectious brain lesions when compared to T1-Gd. Consequently, it can be used as an adjunct to T1-Gd for post-contrast imaging, thereby avoiding additional contrast administration. Improved conspicuity of brain lesions translates directly to enhanced patient outcomes, and hence SWI-Gd imaging proves useful to meet that endpoint.

Keywords: susceptibility weighted, T1 weighted, brain lesions, gadolinium contrast

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Authors: Rajeshwar Singh

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The presence of multicollinearity is common in handling with several explanatory variables simultaneously due to exhibiting a linear relationship among them. A great problem arises in understanding the impact of explanatory variables on the dependent variable. Thus, the method of least squares estimation gives inexact estimates. In this case, it is advised to detect its presence first before proceeding further. Using the ridge regression degree of its occurrence is reduced but principal components regression gives good estimates in this situation. This paper discusses well-known techniques of the ridge and principal components regressions and applies to get the estimates of coefficients by both techniques. In addition to it, this paper also discusses the conflicting claim on the discovery of the method of ridge regression based on available documents.

Keywords: conflicting claim on credit of discovery of ridge regression, multicollinearity, principal components and ridge regressions, variance inflation factor

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Authors: M. Budakoglu, M. Karaman, A. Abdelnasser, M. Kumral

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The spatial interpolation and spatial correlation of the rare earth elements (REE) of lake surface sediments of Lake Acıgöl and its surrounding lithological units is carried out by using GIS techniques like Inverse Distance Weighted (IDW) and Geographically Weighted Regression (GWR) techniques. IDW technique which makes the spatial interpolation shows that the lithological units like Hayrettin Formation at north of Lake Acigol have high REE contents than lake sediments as well as ∑LREE and ∑HREE contents. However, Eu/Eu* values (based on chondrite-normalized REE pattern) show high value in some lake surface sediments than in lithological units and that refers to negative Eu-anomaly. Also, the spatial interpolation of the V/Cr ratio indicated that Acıgöl lithological units and lake sediments deposited in in oxic and dysoxic conditions. But, the spatial correlation is carried out by GWR technique. This technique shows high spatial correlation coefficient between ∑LREE and ∑HREE which is higher in the lithological units (Hayrettin Formation and Cameli Formation) than in the other lithological units and lake surface sediments. Also, the matching between REEs and Sc and Al refers to REE abundances of Lake Acıgöl sediments weathered from local bedrock around the lake.

Keywords: spatial geochemical modeling, IDW, GWR techniques, REE, lake sediments, Lake Acıgöl, Turkey

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Authors: Hazem Al-Mofleh, John Daniels, Joseph McKean

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Within geostatistics research, effective estimation of the variogram points has been examined, particularly in developing robust alternatives. The parametric fit of these variogram points which eventually defines the kriging weights, however, has not received the same attention from a robust perspective. This paper proposes the use of the non-linear Wilcoxon norm over weighted non-linear least squares as a robust variogram fitting alternative. First, we introduce the concept of variogram estimation and fitting. Then, as an alternative to non-linear weighted least squares, we discuss the non-linear Wilcoxon estimator. Next, the robustness properties of the non-linear Wilcoxon are demonstrated using a contaminated spatial data set. Finally, under simulated conditions, increasing levels of contaminated spatial processes have their variograms points estimated and fit. In the fitting of these variogram points, both non-linear Weighted Least Squares and non-linear Wilcoxon fits are examined for efficiency. At all levels of contamination (including 0%), using a robust estimation and robust fitting procedure, the non-weighted Wilcoxon outperforms weighted Least Squares.

Keywords: non-linear wilcoxon, robust estimation, variogram estimation, wilcoxon norm

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Authors: Rob Gesley Capistrano, Jasper James Isaac, Rose Mae Moralda, Therese Anne Peleo, Danica Rillo, Maria Virginia Santillian

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One of the valuable things that shows the intelligence among individuals is the academic background specifically their Grade Weighted Average and the significant result of the Comprehensive Examination. The general objective of the researchers to this study is to determine if there is a significant difference between General Weighted Average and Comprehensive Examination Result of Psychometrician Board Passers and Non-Board Passers. The respondents of this study composed of board passers and non-board passers. The researchers used purposive sampling technique. The result utilized by using T-test Independent Sample to determine the comparison of General Weighted Average and Comprehensive Examination Result of Board Passers and Non Board Passers. At the end, it concluded that the General Weighted Average of Board Passers and Non-Board Passers shows that there is no significant difference, but the average showed a minimal variation. The Comprehensive Examination Result of Board Passers and Non-Board Passers result revealed that there is a significant difference. The performance of comprehensive examination that will test the overall knowledge of an individual and will determine whose more proficient will likely to have a higher score. The result of the comprehensive examination had an impact in the passing performance of board examination.

Keywords: board passers, comprehensive examination result, grade weighted average, non board passers

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Authors: Salisu ibrahim, Abdalla Rababah

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In this research, we use Weighted G2-Multi-degree reduction of Bezier curve of degree n to a Bezier curve of degree m, m < n. The degree reduction of Bezier curves is used to represent a given Bezier curve of n by a Bezier curve of degree m, m < n. Exact degree reduction is not possible, and degree reduction is approximate process in nature. We derive a weighted degree reducing method that is geometrically continuous at the end points. Different norms will be considered, several error minimizations will be given. The proposed methods produce error function that are less than the errors of existing methods.

Keywords: Bezier curves, multiple degree reduction, geometric continuity, error function

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Authors: Halil Ibrahim Demir, Abdullah Hulusi Kokcam, Fuat Simsir, Özer Uygun

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Traditionally, three important manufacturing functions which are process planning, scheduling and due-date assignment are performed sequentially and separately. Although there are numerous works on the integration of process planning and scheduling and plenty of works focusing on scheduling with due date assignment, there are only a few works on integrated process planning, scheduling and due-date assignment. Although due-dates are determined without taking into account of weights of the customers in the literature, here weighted due-date assignment is employed to get better performance. Jobs are scheduled according to weighted earliest due date dispatching rule and due dates are determined according to some popular due date assignment methods by taking into account of the weights of each job. Simulated Annealing, Evolutionary Strategies, Random Search, hybrid of Random Search and Simulated Annealing, and hybrid of Random Search and Evolutionary Strategies, are applied as solution techniques. Three important manufacturing functions are integrated step-by-step and higher integration levels are found better. Search meta-heuristics are found to be very useful while improving performance measure.

Keywords: process planning, weighted scheduling, weighted due-date assignment, simulated annealing, evolutionary strategies, hybrid searches

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Authors: Halil Ibrahim Demir, Caner Erden, Abdullah Hulusi Kokcam, Mumtaz Ipek

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Process planning, scheduling, and due date assignment are three important manufacturing functions which are studied independently in literature. There are hundreds of works on IPPS and SWDDA problems but a few works on IPPSDDA problem. Integrating these three functions is very crucial due to the high relationship between them. Since the scheduling problem is in the NP-Hard problem class without any integration, an integrated problem is even harder to solve. This study focuses on the integration of these functions. Sum of weighted tardiness, earliness, and due date related costs are used as a penalty function. Random search and hybrid metaheuristics are used to solve the integrated problem. Marginal improvement in random search is very high in the early iterations and reduces enormously in later iterations. At that point directed search contribute to marginal improvement more than random search. In this study, random and genetic search methods are combined to find better solutions. Results show that overall performance becomes better as the integration level increases.

Keywords: process planning, genetic algorithm, hybrid search, random search, weighted due-date assignment, weighted scheduling

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Authors: Preeti Sharma

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This paper deals with the Stancu type generalization of Lupaş-Durrmeyer operators. We establish some direct results in the polynomial weighted space of continuous functions defined on the interval [0, 1]. Also, Voronovskaja type theorem is studied.

Keywords: Lupas-Durrmeyer operators, polya distribution, weighted approximation, rate of convergence, modulus of continuity

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Authors: Halil Ibrahim Demir, Tarık Cakar, Ibrahim Cil, Muharrem Dugenci, Caner Erden

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Conventionally, process planning, scheduling, and due-date assignment functions are performed separately and sequentially. The interdependence of these functions requires integration. Although integrated process planning and scheduling, and scheduling with due date assignment problems are popular research topics, only a few works address the integration of these three functions. This work focuses on the integration of process planning, WMS scheduling, and WPPW due date assignment. Another novelty of this work is the use of a weighted due date assignment. In the literature, due dates are generally assigned without considering the importance of customers. However, in this study, more important customers get closer due dates. Typically, only tardiness is punished, but the JIT philosophy punishes both earliness and tardiness. In this study, all weighted earliness, tardiness, and due date related costs are penalized. As no customer desires distant due dates, such distant due dates should be penalized. In this study, various levels of integration of these three functions are tested and genetic search and random search are compared both with each other and with ordinary solutions. Higher integration levels are superior, while search is always useful. Genetic searches outperformed random searches.

Keywords: process planning, weighted scheduling, weighted due-date assignment, genetic algorithm, random search

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Authors: Vallence Ngabo Maniragaba

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This study aimed at examining the variations of undernutrition among children below 5 years of age in Uganda. The approach of spatial and spatiotemporal analysis helped in identifying cluster patterns, hot spots and emerging hot spots. Data from the 6 Uganda Demographic and Health Surveys spanning from 1990 to 2016 were used with the main outcome variable being undernutrition among children <5 years of age. All data that were relevant to this study were retrieved from the survey datasets and combined with the 214 shape files for the districts of Uganda to enable spatial and spatiotemporal analysis. Spatial maps with the spatial distribution of the prevalence of undernutrition, both in space and time, were generated using ArcGIS Pro version 2.8. Moran’s I, an index of spatial autocorrelation, rules out doubts of spatial randomness in order to identify spatially clustered patterns of hot or cold spot areas. Furthermore, space-time cubes were generated to establish the trend in undernutrition as well as to mirror its variations over time and across Uganda. Moreover, emerging hot spot analysis was done to help identify the patterns of undernutrition over time. The results indicate a heterogeneous distribution of undernutrition across Uganda and the same variations were also evident over time. Moran’s I index confirmed spatial clustered patterns as opposed to random distributions of undernutrition prevalence. Four hot spot areas, namely; the Karamoja, the Sebei, the West Nile and the Toro regions were significantly evident, most of the central parts of Uganda were identified as cold spot clusters, while most of Western Uganda, the Acholi and the Lango regions had no statistically significant spatial patterns by the year 2016. The spatio-temporal analysis identified the Karamoja and Sebei regions as clusters of persistent, consecutive and intensifying hot spots, West Nile region was identified as a sporadic hot spot area while the Toro region was identified with both sporadic and emerging hotspots. In conclusion, undernutrition is a silent pandemic that needs to be handled with both hands. At 31.2 percent, the prevalence is still very high and unpleasant. The distribution across the country is nonuniform with some areas such as the Karamoja, the West Nile, the Sebei and the Toro regions being epicenters of undernutrition in Uganda. Over time, the same areas have experienced and exhibited high undernutrition prevalence. Policymakers, as well as the implementers, should bear in mind the spatial variations across the country and prioritize hot spot areas in order to have efficient, timely and region-specific interventions.

Keywords: undernutrition, spatial autocorrelation, hotspots analysis, geographically weighted regressions, emerging hotspots analysis, under-fives, Uganda

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Authors: Halil Ibrahim Demir, Caner Erden, Mumtaz Ipek, Ozer Uygun

Abstract:

Traditionally, the three important manufacturing functions, which are process planning, scheduling and due-date assignment, are performed separately and sequentially. For couple of decades, hundreds of studies are done on integrated process planning and scheduling problems and numerous researches are performed on scheduling with due date assignment problem, but unfortunately the integration of these three important functions are not adequately addressed. Here, the integration of these three important functions is studied by using genetic, random-genetic hybrid, simulated annealing, random-simulated annealing hybrid and random search techniques. As well, the importance of the integration of these three functions and the power of meta-heuristics and of hybrid heuristics are studied.

Keywords: process planning, weighted scheduling, weighted due-date assignment, genetic search, simulated annealing, hybrid meta-heuristics

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Authors: Kang-Mo Jung

Abstract:

The use of regularization for statistical methods has become popular. The least absolute shrinkage and selection operator (LASSO) framework has become the standard tool for sparse regression. However, it is well known that the LASSO is sensitive to outliers or leverage points. We consider a new robust estimation which is composed of the weighted loss function of the pairwise difference of residuals and the adaptive penalty function regulating the tuning parameter for each variable. Rank regression is resistant to regression outliers, but not to leverage points. By adopting a weighted loss function, the proposed method is robust to leverage points of the predictor variable. Furthermore, the adaptive penalty function gives us good statistical properties in variable selection such as oracle property and consistency. We develop an efficient algorithm to compute the proposed estimator using basic functions in program R. We used an optimal tuning parameter based on the Bayesian information criterion (BIC). Numerical simulation shows that the proposed estimator is effective for analyzing real data set and contaminated data.

Keywords: adaptive penalty function, robust penalized regression, variable selection, weighted rank regression

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Authors: Fatma Abdedayem

Abstract:

The most effective and efficient methods in image categorization are almost based on bag-of-words (BOW) which presents image by a histogram of occurrence of visual words. In this paper, we propose a novel extension to this method. Firstly, we extract features in multi-scales by applying a color local descriptor named opponent-SIFT. Secondly, in order to represent image we use Spatial Pyramid Representation (SPR) and an extension to the BOW method which based on weighting visual words. Typically, the visual words are weighted during histogram assignment by computing the ratio of their occurrences in the image to the occurrences in the background. Finally, according to classical BOW retrieval framework, only a few words of the vocabulary is useful for image representation. Therefore, we select the useful weighted visual words that respect the threshold value. Experimentally, the algorithm is tested by using different image classes of PASCAL VOC 2007 and is compared against the classical bag-of-visual-words algorithm.

Keywords: BOW, useful visual words, weighted visual words, bag of visual words

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Authors: Seema Vaidya

Abstract:

Mining association rule is a key issue in data mining. In any case, the standard models ignore the distinction among the exchanges, and the weighted association rule mining does not transform on databases with just binary attributes. This paper proposes a novel continuous example and executes a tree (FP-tree) structure, which is an increased prefix-tree structure for securing compacted, discriminating data about examples, and makes a fit FP-tree-based mining system, FP enhanced capacity algorithm is used, for mining the complete game plan of examples by illustration incessant development. Here, this paper handles the motivation behind making remarkable and weighted item sets, i.e. rare weighted item set mining issue. The two novel brightness measures are proposed for figuring the infrequent weighted item set mining issue. Also, the algorithm are handled which perform IWI which is more insignificant IWI mining. Moreover we utilized the rare item set for choice based structure. The general issue of the start of reliable definite rules is troublesome for the grounds that hypothetically no inciting technique with no other person can promise the rightness of influenced theories. In this way, this framework expects the disorder with the uncommon signs. Usage study demonstrates that proposed algorithm upgrades the structure which is successful and versatile for mining both long and short diagnostics rules. Structure upgrades aftereffects of foreseeing rare diseases of patient.

Keywords: association rule, data mining, IWI mining, infrequent item set, frequent pattern growth

Procedia PDF Downloads 376