Study on drivers of cultivated land change in urban fringe area based on the logistic regression model

2011 ◽  
Author(s):  
Shiquan Hou ◽  
Yun Liu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Urban Fringe ◽  
Cultivated Land ◽  
Land Change ◽  
Fringe Area

scholarly journals Response of Land Use Change to the Grain for Green Program and Its Driving Forces in the Loess Hilly-Gully Region

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 194
Author(s):  
Xiao Zhang ◽  
Yuanjie Deng ◽  
Mengyang Hou ◽  
Shunbo Yao
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Population Density ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Driving Forces ◽  
Cultivated Land ◽  
Forest Land ◽  
Land Area ◽  
Grain For Green

Implementation of the Grain for Green program (GGP) intensifies land use/cover change (LUCC) in the loess hilly-gully region. Clarifying the response of LUCC to the GGP and its driving forces are basic premises to implement the GGP more effectively for alleviating soil erosion in this region. This study analyzed the spatio-temporal characteristics of conversion of cultivated land to forest land and grassland in two study periods of 2000–2010 and 2010–2018. The transition matrix model and the dynamic degree model were utilized to explore changes among cultivated land, forest land, and grassland based on the remote sensing (RS) and monitoring data of land use in 2000, 2010, and 2018. Secondly, further detection on driving forces of increase of forest land and grassland was conducted through the logistic regression model. Fourteen driving factors were selected: the GGP, elevation, slope, population density, GDP per land area, distance to city, distance to residential area, etc. The results revealed that: (1) Area of cultivated land was mainly transferred to forest land and grassland during two study periods. The conversion of cultivated land to forest land and grassland occupied 21.48% and 68.01% of outward-transferring area of cultivated land from 2000 to 2010, and accounted for 13.26% and 74.3% from 2010 to 2018; (2) From the results of the logistic regression model, elevation, the GGP, annual mean temperature, slope III (6–15°), and GDP per land area were the main driving forces from 2000 to 2010. Moreover, the most prominent driving forces were the GGP, elevation, rural population density, slope III (6–15°), and soil pH from 2010 to 2018. The findings of this study can help us better understand the conversion of cultivated land to forest land and grassland under the GGP and provide a scientific basis to facilitate sustainable development of land resources in the study area.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

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