scholarly journals A GIS-Based Bivariate Logistic Regression Model for the Site-Suitability Analysis of Parcel-Pickup Lockers: A Case Study of Guangzhou, China

2021 ◽  
Vol 10 (10) ◽  
pp. 648
Author(s):  
Zilai Zheng ◽  
Takehiro Morimoto ◽  
Yuji Murayama
Keyword(s):  
Logistic Regression ◽  
Site Selection ◽  
Residential Buildings ◽  
Supervised Machine Learning ◽  
Small Scale ◽  
Search Problem ◽  
Suitability Analysis ◽  
Site Suitability ◽  
Transport Services ◽  
Bivariate Logistic Regression

The site-suitability analysis (SSA) of parcel-pickup lockers (PPLs) is becoming a critical problem in last-mile logistics. Most studies have focused on the site-selection problem to identify the best site from given potential sites in specific areas, while few have solved the site-search problem to determine the boundary of the suitable area. A GIS-based bivariate logistic regression (LR) model using the supervised machine-learning (ML) algorithm was developed for suitability classification in this study. Eight crucial factors were selected from 27 candidate variables using stepwise methods with a training dataset in the best LR model. The variable of the proximity to residential buildings was more important than that to various commercial buildings, transport services, and roads. Among the four types of residential buildings, the most crucial factor was the proximity to residential quarters. A test dataset was employed for the validation process, showing that the best LR model had excellent performance. The results identified the suitable areas for PPLs, accounting for 8% of the total area of Guangzhou (GZ). A decision-maker can focus on these suitable areas as the site-selection ranges for PPLs, which significantly reduces the difficulty of analysis and time costs. This method can quickly decompose a large-scale area into several small-scale suitable areas, with relevance to the problem of selecting sites from various candidate sites.

scholarly journals A geospatial approach for site suitability and accessibility for healthcare services in Bankura district, West Bengal, India

2021 ◽  
Vol 15 (2) ◽  
pp. 81-91
Author(s):  
Shruti Kanga ◽  
Nikola Kranjčić ◽  
Suraj Kumar Singh ◽  
Selim Raja ◽  
Bojan Durin
Keyword(s):  
Spatial Data ◽  
Site Selection ◽  
Healthcare Services ◽  
Healthcare Management ◽  
Second Phase ◽  
Suitability Analysis ◽  
Site Suitability ◽  
Medical College ◽  
Shortest Network ◽  
Bankura District

Healthcare site selection assumes an imperative part in healthcare development and management. From part of the public authority, proper medical site selection will help the distribution of clinical assets, coordinating with the arrangement of medical care with the social and economic demands, organizing the metropolitan and rural healthcare administration advancement, and facilitating social logical inconsistencies. Site suitability analysis is a variety of analysis utilized in GIS to work out the simplest place or site for one thing. The main objective of the current study was to select a site for new healthcare services with geospatial technologies to intermix spatial and non-spatial data to create a weighted result. The current study had been done into three phases, where many processes are intermixed into a single phase. In the first phase of analysis, distance, density, and proximity were mapped to seek out poor and lower accessible areas of healthcare from existing healthcare. To selecting new healthcare sites, four-factor criteria (Buffer around road and rail, land use land cover and buffer around settlement,) and some constrain criteria considered in the second phase of analysis. Finally, the shortest network path analysis has been done in the third phase to determine the shortest and best route from selected healthcare sites towards district medical college. The current study presents some suitable sites in the poor and inaccessible areas of the district. This study will be very helpful for the decision support system of healthcare management in the future.

scholarly journals SITE SUITABILITY ANALYSIS FOR COMMUNITY-BASED TROPICAL SEA CUCUMBER HOLOTHURIA SCABRA GROW-OUT IN NORTH SULAWESI WATERS

2019 ◽  
Vol 44 (1) ◽  
pp. 1-10
Author(s):  
Marenda Pandu Rizqi ◽  
Supono
Keyword(s):  
Sea Cucumber ◽  
Community Support ◽  
Ex Situ ◽  
Small Scale ◽  
Suitability Analysis ◽  
Site Suitability ◽  
Holothuria Scabra ◽  
North Sulawesi ◽  
Recent Developments ◽  
Substrate Protection

North Sulawesi, a region that has experienced a decreasing population of commercial sea cucumbers in Indonesia, has recently been designated an aquaculture development area. Recent developments of sea cucumber Holothuria scabra hatchery technology have successfully enabled the small-scale enterprise to produce hatcheryreared sandfish for restocking purposes. However, the grow-out phase requires considerable resources including ex-situ grow-out sites, labor, and distribution. This study aims to investigate the suitability of some potential grow-out sites for juvenile H. scabra in North Sulawesi waters. Three traditional sea cucumber fishing grounds in coastal areas of North Minahasa regency in North Sulawesi, i.e., Tanjung Merah, Tasikoki and Makalisung, were examined to determine their suitability as a grow-out site. Several criteria encompassing bioecological (native sea cucumber population, seagrass bed population, competitor/predator), environmental (substrate, protection to weather, water quality, tidal cycle), and technical/support (access, safety, labor) were assessed in each site. Site suitability analysis using a multi-criteria decision-making tool, Analytical Hierarchy Process (AHP), shows that Tasikoki has the highest suitability score (48.94%) followed by Tanjung Merah (27.18%) and Makalisung (23.88%). Tasikoki scores higher than the two other sites in 7 out of 11 sub-criteria, including native sandfish population, seagrass species, substrate, weather protection, safety, and community support. Our finding suggests that Tasikoki is bioecologically, environmentally, and technically the best potential site to be developed for H. scabra juvenile grow-out.

Site Suitability Analysis for Water Conservation Using AHP and GIS Techniques: A Case Study of Upper Sina River Catchment, Ahmednagar (India)

2020 ◽  
Vol 3 (2) ◽  
pp. 49-59 ◽  
Author(s):  
Yogesh Badhe ◽  
Ravindra Medhe ◽  
Tushar Shelar
Keyword(s):  
Water Conservation ◽  
Soil Depth ◽  
Land Use Land Cover ◽  
Suitability Analysis ◽  
Site Suitability ◽  
Gis Techniques ◽  
Multi Criteria Evaluation ◽  
Conservation Activity ◽  
Suitable Site

The site suitability for construction of water conservation structures is an important step towards groundwater conservation in arid and semi-arid regions. Water is the most crucial for maintaining an environment and ecosystem which is helpful to sustaining all forms of the life. The increasing water scarcity day to day has been one of the common problems over a period of time. On top of it, when the area is a part of rain shadow zone like Ahmednagar district, water conservation activities are become more important. The present study aims to identify the suitable zones for water conservation activity. Multi- criteria evaluation is carried out using Geographic Information System (GIS) techniques to help the choice makers in defining suitable site for construction of water conservation structures. Different layers which were considered for multi-criteria evaluation: slope, land use land cover, soil texture, lithology, soil depth, soil erosion, wells, lineaments and drainage network. Analytical Hierarchy Processes (AHP) is used for weighted sum to find suitable sites for implementation of water conservation activity using selected criterions. The site suitability map was classified into four classes: highly suitable, moderately suitable, less suitable and not suitable with area of 19.19%, 26%, 49.03% and 5.78, respectively. This map will help for selection of suitable sites for construction of Mati Nala Bund (MNB), Check Dam, Cement Nala Bund (CNB) and Continuous Contour Trenches (CCT) for conservation of groundwater resource in the region.

scholarly journals Online health information-seeking behaviors and skills of Chinese college students

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dangui Zhang ◽  
Weixin Zhan ◽  
Chunwen Zheng ◽  
Jinsheng Zhang ◽  
Anqi Huang ◽  
...  
Keyword(s):  
College Students ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Health Information ◽  
Information Seeking ◽  
Online Health Information ◽  
Chinese College Students ◽  
Link Type ◽  
Bivariate Logistic Regression ◽  
Information Seeking Behaviors

Abstract Background Seeking online health information (OHI) has become a common practice globally. The information seekers could face health risks if they are not proficient in OHI literacy. The OHI-seeking behaviors and skills of Chinese college students, the largest proportion of college students in the world, are understudied. This study was aimed to describe OHI-seeking behaviors and skills of college students in Guangdong, China. Methods College students in the Guangdong province with OHI-seeking experience were invited via WeChat, QQ, and Sina Weibo using QR code posters and flyers for participation in this online anonymized questionnaire-based study. Data on demographics, OHI literacy, information resources, search approaches, and behaviors were collected. The relationship between perceived OHI literacy and high-risk behaviors was investigated by bivariate logistic regression analysis. Results Respondents were 1203 college students with a mean age of 20.6 years, females (60.2%), and undergraduates (97.2%). They sought health information via websites (20.3%), WeChat (2.6%), or both (77.1%). Baidu was the main search engine, and baike.baidu.com (80.3%), Zhihu.com (48.4%), and Zhidao.baidu.com (35.8%) were top three among 20 searched websites for information about self-care (80.7%), general health (79.5%), disease prevention (77.7%), self-medication (61.2%), family treatment (40.9%), drugs (37.7%), western medications (26.6%), hospitals (22.7%), physicians (21.4%), and Traditional Chinese Medicine (15.6%). Despite most respondents (78%) lacked confidence in the evidence quality and satisfaction with the results, only 32.4% further consulted doctors. Many (> 50%) would recommend the retrieved information to others. About 20% experienced hacking/Internet fraud. Cronbach’s alpha for the internal consistency of OHI literacy was 0.786. Bivariate logistic regression analysis showed that students who believed they can judge the evidence level of OHI were more likely to self-diagnose (OR = 2.2, 95%CI, 1.6–3.1) and look for drug usage (OR = 3.1, 95%CI, 1.9–5.0). Conclusions This study reveals Chinese college students’ heavy reliance on OHI to manage their own and others’ health without sufficient knowledge/skills to identify misinformation and disinformation. The apparent risky information-seeking behaviors of Chinese college students warrant the provision of regulated, accurate, and actionable health information; assurance of cybersecurity; and health information literacy promotion in colleges by concerned authorities.

scholarly journals Supervised machine learning and logistic regression identifies novel epistatic risk factors with PTPN22 for rheumatoid arthritis

2010 ◽  
Vol 11 (3) ◽  
pp. 199-208 ◽  
Author(s):  
F B S Briggs ◽  
P P Ramsay ◽  
E Madden ◽  
J M Norris ◽  
V M Holers ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Supervised Machine Learning

Site suitability analysis of biogas digester plant for municipal waste using GIS and multi-criteria analysis

2018 ◽  
Vol 3 (1) ◽  
pp. 61-93 ◽  
Author(s):  
Afsana Akther ◽  
Tofael Ahamed ◽  
Ryozo Noguchi ◽  
Takuma Genkawa ◽  
Tomohiro Takigawa
Keyword(s):  
Municipal Waste ◽  
Suitability Analysis ◽  
Site Suitability ◽  
Multi Criteria Analysis

scholarly journals Comparison of the Performance of Machine Learning Algorithms in Predicting Heart Disease

2021 ◽  
Vol 10 (1) ◽  
pp. 99
Author(s):  
Sajad Yousefi
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Heart Disease ◽  
Decision Tree ◽  
Roc Curve ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Algorithm Performance ◽  
Machine Learning Models

Introduction: Heart disease is often associated with conditions such as clogged arteries due to the sediment accumulation which causes chest pain and heart attack. Many people die due to the heart disease annually. Most countries have a shortage of cardiovascular specialists and thus, a significant percentage of misdiagnosis occurs. Hence, predicting this disease is a serious issue. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning models for disease prediction.Material and Methods: Several algorithms were utilized to predict heart disease among which Decision Tree, Random Forest and KNN supervised machine learning are highly mentioned. The algorithms are applied to the dataset taken from the UCI repository including 294 samples. The dataset includes heart disease features. To enhance the algorithm performance, these features are analyzed, the feature importance scores and cross validation are considered.Results: The algorithm performance is compared with each other, so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, Accuracy, AUC ROC are 83% and 99% respectively for Decision Tree algorithm. Logistic Regression algorithm with accuracy and AUC ROC are 88% and 91% respectively has better performance than other algorithms. Therefore, these techniques can be useful for physicians to predict heart disease patients and prescribe them correctly.Conclusion: Machine learning technique can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the prediction of heart disease is compared to determine the most appropriate classification. As a result of evaluation, better performance was observed in both Decision Tree and Logistic Regression models.

scholarly journals Cambridge Servicemen’s Memorial (Breuer, 1945): espacio público para el recuerdo

2019 ◽  
Vol 6 (2) ◽  
pp. 27
Author(s):  
Salvador José Sanchís Gisbert ◽  
Pedro Ponce Gregorio ◽  
Ignacio Peris Blat
Keyword(s):  
Public Space ◽  
Large Scale ◽  
Residential Buildings ◽  
Small Scale ◽  
First Year ◽  
Residential Land ◽  
The Public ◽  
International Recognition ◽  
The World ◽  
Strong Commitment

Marcel Breuer was in the first year of architectural technicians to graduate from Bauhaus School. The peculiar education he received there allowed him to explore the concept of design in its broadest sense. In his European stage we find, on the most private and small scale, unique solutions for furniture. In his first American stage we see a strong commitment with solutions related to the residential land and, when he earned international recognition, he developed large scale solutions for his public non-residential buildings and urban equipments in locations all over the world. It is strange to see that an architect like him did not have the opportunity to materialize any of his proposals associated with the public space. The 1945 Cambridge Servicemen’s Memorial project, also known as the Memorial War, is the most significant one he developed in his last years in Cambridge. Had it been built, it would have been a valuable example of modernity and contemporary reinterpretation of the monument in the public space.

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