scholarly journals A Method of Population Spatialization Considering Parametric Spatial Stationarity: Case Study of the Southwestern Area of China

2019 ◽  
Vol 8 (11) ◽  
pp. 495 ◽  
Author(s):  
Xiong ◽  
Li ◽  
Cheng ◽  
Ye ◽  
Zhang
Keyword(s):  
Regression Models ◽  
Population Distribution ◽  
Percentage Error ◽  
Night Time ◽  
Geographically Weighted Regression Model ◽  
Southwestern Area ◽  
The Mean ◽  
Gwr Model ◽  
Better Than

Population is a crucial basis for the study of sociology, geography, environmental studies, and other disciplines; accurate estimates of population are of great significance for many countries. Many studies have developed population spatialization methods. However, little attention has been paid to the differential treatment of the spatial stationarity and non-stationarity of variables. Based on a semi-parametric, geographically weighted regression model (s-GWR), this paper attempts to construct a novel, precise population spatialization method considering parametric stationarity to enhance spatialization accuracy; the southwestern area of China is used as the study area for comparison and validation. In this study, the night-time light and land use data were integrated as weighting factors to establish the population model; based on the analysis of variables characteristics, the method uses an s-GWR model to deal with the spatial stationarity of variables and reduce regional errors. Finally, the spatial distribution of the population (SSDP) of the study area in 2010 was obtained. When assessed against the traditional regression models, the model that considers parametric stationarity is more accurate than the models without it. Furthermore, the comparison with three commonly-used population grids reveals that the SSDP has a percentage error close to zero at the county level, while at the township level, the mean relative error of SSDP is 33.63%, and that is >15% better than other population grids. Thus, this study suggests that the proposed method can produce a more accurate population distribution.

scholarly journals The updated Charlson comorbidity index is a useful predictor of mortality in patients with Staphylococcus aureus bacteraemia

2018 ◽  
Vol 146 (16) ◽  
pp. 2122-2130 ◽  
Author(s):  
H. G. Ternavasio-de la Vega ◽  
F. Castaño-Romero ◽  
S. Ragozzino ◽  
R. Sánchez González ◽  
M. P. Vaquero-Herrero ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Charlson Comorbidity Index ◽  
Regression Models ◽  
Model Fit ◽  
Logistic Regression Models ◽  
Relevant Variables ◽  
Comorbidity Index ◽  
The Mean ◽  
Related Mortality ◽  
Better Than

AbstractThe objective was to compare the performance of the updated Charlson comorbidity index (uCCI) and classical CCI (cCCI) in predicting 30-day mortality in patients with Staphylococcus aureus bacteraemia (SAB). All cases of SAB in patients aged ⩾14 years identified at the Microbiology Unit were included prospectively and followed. Comorbidity was evaluated using the cCCI and uCCI. Relevant variables associated with SAB-related mortality, along with cCCI or uCCI scores, were entered into multivariate logistic regression models. Global model fit, model calibration and predictive validity of each model were evaluated and compared. In total, 257 episodes of SAB in 239 patients were included (mean age 74 years; 65% were male). The mean cCCI and uCCI scores were 3.6 (standard deviation, 2.4) and 2.9 (2.3), respectively; 161 (63%) cases had cCCI score ⩾3 and 89 (35%) cases had uCCI score ⩾4. Sixty-five (25%) patients died within 30 days. The cCCI score was not related to mortality in any model, but uCCI score ⩾4 was an independent factor of 30-day mortality (odds ratio, 1.98; 95% confidence interval, 1.05–3.74). The uCCI is a more up-to-date, refined and parsimonious prognostic mortality score than the cCCI; it may thus serve better than the latter in the identification of patients with SAB with worse prognoses.

scholarly journals The Perception of Tone and Focus in Mandarin by Indonesian Learners: A Case Study

2019 ◽  
Vol 4 (1) ◽  
pp. 11-26
Author(s):  
Hana Nurul Hasanah
Keyword(s):  
Foreign Language ◽  
Language Learners ◽  
First Language ◽  
Identification Accuracy ◽  
Tone Language ◽  
Mandarin Language ◽  
Perceptual Ability ◽  
The Mean ◽  
Better Than

 In a tone language, the interface between tone, intonation, and focus will affect the pitch height and contour of tones. Previous perceptual studies revealed the potential conflicts in perceiving pitch variations at lexical and post-lexical levels that were experienced by either native listeners or listeners who speak Mandarin language as a second or foreign language. Rarely we find research in Indonesia that provides evidence for Mandarin language learners’ perceptual ability at a post-lexical level. This paper investigated how well learners with distinct first language (L1) background identify tones that are affected by the realization of focus and the presence and location of focus in distinct intonation types. Perceptual experiments were conducted towards two groups of listeners: Mandarin learners with Indonesian L1 and learners with a tone language L1 background (Hakka or Hokkien). Their identification accuracy (IA) rate in recognizing the tone type for the last syllable with a narrow focus was compared with their IA in identifying the location of the focus. In general, identifying tone type was easier than identifying focus position for both groups. However, the Mean from each group showed that learners with a tone language L1 were slightly better than the other group. Results exhibited more similarities between the two groups of the listener, which indicates that L1 background only has a mild effect on the perceptual ability of Indonesian learners of Mandarin as a foreign language.  

scholarly journals Prediction by using Artificial Neural Networks and Box-Jenkins methodologies: Comparison Study

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Mohammed Habib Al- Sharoot ◽  
Emaan Yousif Abdoon
Keyword(s):  
Neural Networks ◽  
Exchange Rate ◽  
Absolute Error ◽  
Percentage Error ◽  
Neural Network Approach ◽  
Decision Making Processes ◽  
The Mean ◽  
Us Dollar ◽  
Stable Economy ◽  
Better Than

The variations in exchange rate, especially the sudden unexpected increases and decreases, have significant impact on the national economy of any country. Iraq is no exception; therefore, the accurate forecasting of exchange rate of Iraqi dinar to US dollar plays an important role in the planning and decision-making processes as well as the maintenance of a stable economy in Iraq. This research aims to compare Box-Jenkins methodology to neural networks in terms of forecasting the exchange rate of Iraqi dinar to US dollar based on data provided by the Iraqi Central Bank for the period  30/01/2004 and 30/12/2014. Based on the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE) as criteria to compare the two methodologies, it was concluded that Box-Jenkins is better than neural network approach in forecasting.

Implementasi Metode Double Exponential Smoothing dalam Memprediksi Pertambahan Jumlah Penduduk di Wilayah Kabupaten Karawang

2020 ◽  
Vol 12 (2) ◽  
pp. 95-103
Author(s):  
Andini Diyah Pramesti ◽  
Mohamad Jajuli ◽  
Betha Nurina Sari
Keyword(s):  
Population Distribution ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Percentage Error ◽  
Population Census ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Better Than ◽  
Population Numbers

The density and uneven distribution of the population in each area must be considered because it will cause problems such as the emergence of uninhabitable slums, environmental degradation, security disturbances, and other population problems. In the data obtained from the 2010 population census based on the level of population distribution in Karawang District, the area of West Karawang, East Karawang, Rengasdengklok, Telukjambe Timur, Klari, Cikampek and Kotabaru are zone 1 regions which are the densest zone with a population of 76,337 people up to 155,471 inhabitants. This research predicts / forecasting population growth in the 7 most populated areas for the next 1 year using Double Exponential Smoothing Brown and Holt methods. This study uses Mean Absolute Percentage Error (MAPE) to evaluate the performance of the double exponential smoothing method in predicting per-additional population numbers. Forecasting results from the two methods place the Districts of East Telukjambe, Cikampek, Kotabaru, East Karawang, and Rengasdengklok in 2020 to remain in zone 1 with a range of 76,337 people to 155,471 inhabitants. Whereas in the Districts of Klari and West Karawang are outside the range in zone 1 because both districts have more population than the range in zone 1. From the results of MAPE both methods are found that 6 out of 7 districts in the method Holt's double exponential smoothing produces a smaller MAPE value compared to the MAPE value generated from Brown's double exponential smoothing method. It was concluded that in this study the Holt double exponential smoothing method was better than Brown's double exponential smoothing method.

scholarly journals Forecasting and Mapping Coffee Borer Beetle Attacks Using GSTAR-SUR Kriging and GSTARX-SUR Kriging Models

2020 ◽  
Vol 11 (2) ◽  
pp. 65-73
Author(s):  
Henny Pramoedyo ◽  
Arif Ashari ◽  
Alfi Fadliana
Keyword(s):  
Spatial Clustering ◽  
Kriging Model ◽  
Seemingly Unrelated Regression ◽  
Kriging Interpolation ◽  
Percentage Error ◽  
Eastern Region ◽  
Unrelated Regression ◽  
Interpolation Technique ◽  
The Mean

The research aimed to use Generalized Space Time Autoregressive (GSTAR) and GSTARX modeling with the Seemingly Unrelated Regression (SUR) approach and combine them with the Kriging interpolation technique in an unobserved location. The case study was coffee borer beetle forecasting in Probolinggo Regency, East Java, Indonesia, with Watupanjang Village as the unobserved location. The results show that GSTAR-SUR Kriging and GSTARX-SUR Kriging models can predict coffee borer beetle attacks in unobserved areas with high accuracy. It is indicated by the Mean Absolute Percentage Error (MAPE) values of less than 10%. The addition of exogenous variables (rainfall) into the model is proven to improve the accuracy of the model. The Root-Mean-Square Error (RMSE) value of the GSTARX-SUR Kriging model is smaller than the GSTAR-SUR Kriging model. The structure of the model produced from the research, GSTARX-SUR (1,[1,12])(10,0,0), can be used as a reference in modeling coffee borer beetle attacks in other regencies. Map of forecasting coffee borer beetle attack shows that the spread of coffee borer beetle attack is spatial clustering with the attack center located in the eastern region of Probolinggo Regency.

scholarly journals A Wavelet-Based Robust Relevance Vector Machine Based on Sensor Data Scheduling Control for Modeling Mine Gas Gushing Forecasting on Virtual Environment

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Wang Ting ◽  
Cai Lin-qin ◽  
Fu Yao ◽  
Zhu Tingcheng
Keyword(s):  
Prediction Error ◽  
Relevance Vector Machine ◽  
Wavelet Function ◽  
Sensor Data ◽  
Percentage Error ◽  
Data Scheduling ◽  
The Mean ◽  
Mine Gas ◽  
Mean Prediction Error ◽  
Better Than

It is wellknown that mine gas gushing forecasting is very significant to ensure the safety of mining. A wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting is presented in the paper. Morlet wavelet function can be used as the kernel function of robust relevance vector machine. Mean percentage error has been used to measure the performance of the proposed method in this study. As the mean prediction error of mine gas gushing of the WRRVM model is less than 1.5%, and the mean prediction error of mine gas gushing of the RVM model is more than 2.5%, it can be seen that the prediction accuracy for mine gas gushing of the WRRVM model is better than that of the RVM model.

scholarly journals EFFECTS OF CHANGING SCALES ON LANDSCAPE PATTERNS AND SPATIAL MODELING UNDER URBANIZATION

2020 ◽  
Vol 28 (2) ◽  
pp. 62-73
Author(s):  
Jinming Yang ◽  
Shimei Li ◽  
Jingwei Xu ◽  
Xiaojie Wang ◽  
Xiaoguang Zhang
Keyword(s):  
Spatial Scale ◽  
Landscape Pattern ◽  
Spatial Modeling ◽  
Regression Models ◽  
Block Size ◽  
Landscape Patterns ◽  
The Mean ◽  
Patch Density ◽  
Variation Trends ◽  
Better Than

Spatial scale is an eternal topic in landscape pattern related analysis. This paper examined the spatial scale effect of landscape pattern changes and their relationships with urbanization indicators in Qingdao using a series of sampling blocks. The results indicated that, with the increasing block scale, the mean patch density and aggregation within a block decreased, whereas the diversity increased. Furthermore, the expanding scale amplified the mean change ratio of landscape metrics and eliminated local drastic changes and regional variation trends along an urban-to-rural gradient, which would be obvious at a finer block scale. Meanwhile, the adjusted R2 of GWR (Geographically Weighted Regression) models increased with an increasing block size, especially when the block scale changed from 1 km to 5 km. Odd-numbered block scales performed better than even-numbered block scales.

An economical, environmental, and social comparison between vacuum and gravity sewers in decentralized sanitation systems, with Egypt as a case study

2015 ◽  
Vol 5 (4) ◽  
pp. 614-619 ◽  
Author(s):  
Abdelsalam Elawwad ◽  
Mostafa Ragab ◽  
Hisham Abdel-Halim
Keyword(s):  
Regression Models ◽  
Prediction Models ◽  
Vacuum System ◽  
Quadratic Regression ◽  
Maintenance Costs ◽  
Rural Villages ◽  
Gravity Sewer ◽  
Sanitation Systems ◽  
Better Than

The conventional gravity sewer is the most commonly used rural sewerage system in developing countries. However, this system has many technical, economic, environmental, and social disadvantages. Vacuum sewers could serve as a good competitor as an alternative system to conventional gravity sewers. A sample of 33 rural villages with populations of <10,000 people is selected from Egypt. A statistical analysis was done using SPSS and STATISTICA software where population and area variables had the most significant effect on the calculation of investment, operation, and maintenance costs. It was found that investment costs for the vacuum system were mostly lower than for the conventional one, while operational and maintenance costs played significant roles. Prediction models were obtained based on multiple quadratic regression models. It was found that the vacuum system was economically competitive in large villages with low population densities. Environmentally and socially, the vacuum sewers proved to be better than gravity sewers.

Pedestrian visibility at night: The effect of solid state streetlights

2016 ◽  
Vol 48 (8) ◽  
pp. 976-991 ◽  
Author(s):  
R Saraiji ◽  
D Younis ◽  
MT Madi ◽  
RB Gibbons
Keyword(s):  
High Pressure ◽  
Solid State ◽  
Metal Halide ◽  
Night Time ◽  
Detection Distance ◽  
Different Types ◽  
The Mean ◽  
Metal Halide Lamps ◽  
Better Than

This study examines the effect of different types of lamps on pedestrian night time visibility. Detection distance was used as a measure of visibility. The detection distance was measured in the presence and in the absence of on-coming car headlamps in an unlit street. Subsequently, the street was lit using metal halide, high-pressure sodium or LED luminaires. A pedestrian who changed his clothing colour randomly was used as a target. The results showed that the detection distance on the unlit road was 52% shorter in the presence of on coming car headlamps than when the oncoming car headlamps were off. A person wearing black clothing was harder to see and their mean detection distance was 60% less than when the observer was not dazzled by the oncoming car headlights. When the street was lit, the detection distance was doubled. The mean detection distance using LED lamps was statistically similar to that obtained using metal halide lamps, both of which were better than the detection distance obtained under high pressure sodium lighting.

scholarly journals IMPLEMENTATION OF LOCALLY COMPENSATED RIDGE-GEOGRAPHICALLY WEIGHTED REGRESSION MODEL IN SPATIAL DATA WITH MULTICOLLINEARITY PROBLEMS (Case Study: Stunting among Children Aged under Five Years in East Nusa Tenggara Province)

2020 ◽  
Vol 13 (2) ◽  
pp. 125-135
Author(s):  
Alfi Fadliana ◽  
Henny Pramoedyo ◽  
Rahma Fitriani
Keyword(s):  
Spatial Data ◽  
Factors Affecting ◽  
Under Five ◽  
Capita Expenditure ◽  
Geographically Weighted Regression Model ◽  
Per Capita ◽  
Gwr Model ◽  
Baseline Health ◽  
Model Approach

East Nusa Tenggara Province, according to the findings of 2013 Baseline Health Research and 2016 and 2017 Nutritional Status Surveys, was recorded as the province with the highest prevalence of stunting in Indonesia. Efforts should be made to formulate policies that are integrated with spatial aspects in order to reduce the prevalence of stunting. The LCR-GWR model approach is used by using locally compensated ridge, which were meant to adjusts to the effect of collinearity between predictor variables (i.e., the factors affecting the prevalence of stunting) in each area. Results of the analysis showed that factors affecting the prevalence of stunting in all districts/cities in East Nusa Tenggara Province are the percentage of children aged under five who were weighed ≥ 4 times, the percentage of children aged under five who receive complete basic immunization, the percentage of households consuming iodized salt, the percentage of households with decent source of drinking water and the real per capita expenditure. The analysis showed that LCR-GWR is able to produce a better model than the GWR model in overcoming local multicollinearity problems in stunting in East Nusa Tenggara Province, with lower RMSE value (0.0344) than the GWR RMSE model (3.8899).

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