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Disaggregating census data for population mapping using a bayesian additive regression tree model

APPLIED GEOGRAPHY 2024年 172卷

作者： Yankey, Ortis Utazi, Chigozie E. Nnanatu, Christopher C. Gadiaga, Assane N. Abbot, Thomas Lazar, Attila N. Tatem, Andrew J. Univ Southampton Worldpop Res Grp Southampton SO17 1BJ England

Population data is crucial for policy decisions, but fine-scale population numbers are often lacking due to the challenge of sharing sensitive data. Different approaches, such as the use of the Random Forest (RF) model, have been used to disaggregate census data from higher administrative units to small area scales. A major limitation of the RF model is its inability to quantify the uncertainties associated with the predicted populations, which can be important for policy decisions. In this study, we applied a bayesian additive regression tree (BART) model for population disaggregation and compared the result with a RF model using both simulated data and the 2021 census data for Ghana. The BART model consistently outperforms the RF model in out-of-sample predictions for all metrics, such as bias, mean squared error (MSE), and root mean squared error (RMSE). The BART model also addresses the limitations of the RF model by providing uncertainty estimates around the predicted population, which is often lacking with the RF model. Overall, the study demonstrates the superiority of the BART model over the RF model in disaggregating population data and highlights its potential for gridded population estimates.

关键词： Population modelling bayesian dasymetric population Ghana Population disaggregation bayesian additive regression tree Random forest WorldPop

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bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study

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BMC MEDICAL RESEARCH METHODOLOGY 2024年第1期24卷 262页

作者： Ahmadiankalati, Mojtaba Boury, Himani Subbarao, Padmaja Lou, Wendy Lu, Zihang Queens Univ Dept Publ Hlth Sci Kingston ON K7L 3N6 Canada Hosp Sick Children SickKids Res Inst Dept Pediat & Translat Med Toronto ON Canada Univ Toronto Dalla Lana Sch Publ Hlth Toronto ON Canada Queens Univ Dept Math & Stat Kingston ON Canada

BackgroundAsthma is a heterogeneous disease that affects millions of children and adults. There is a lack of objective gold standard diagnosis that spans the ages;instead, diagnoses are made by clinician assessment based on a cluster of signs, symptoms and objective tests dependent on age. Yet, there is a clear morbidity associated with chronic asthma symptoms. Machine learning has become a popular tool to improve asthma diagnosis and classification. There is a paucity of literature on the use of bayesian machine learning algorithms to predict asthma diagnosis in children. This paper develops a prediction model using the bayesian additive regression trees (BART) and compares its performance to various machine learning algorithms in predicting the diagnosis of childhood *** relevant variables collected at or before 3 years of age from 2794 participants in the CHILD Cohort Study were used to predict physician-diagnosed asthma at age 5. BART and six other commonly used machine learning algorithms, namely adaptive boosting, logistic regression, decision tree, neural network, random forest, and support vector machine were trained. Measures of performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were calculated. The confidence intervals were calculated using Bootstrapping samples. Important predictors and interaction effects associated with asthma were also identified using ***, logistic regression and random forest showed the highest area under the ROC curve compared to other machine learning algorithms. Based on BART, recurrent wheeze, respiratory infection and food sensitization at 3 years of age were the most important predictors. The three most important interaction effects were found to be interaction terms of respiratory infection at 3 years and recurrent wheezing at 3 years, maternal asthma and paternal asthma, and maternal wheezing and inhalant sensitization of child at

关键词： Asthma bayesian additive regression tree CHILD Cohort Study Machine learning Prediction

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Pedestrian crossing volume estimation at signalized intersections using bayesian additive regression trees

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JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2022年第5期26卷 557-571页

作者： Li, Xiaofeng Xu, Peipei Wu, Yao-Jan Univ Arizona Dept Civil & Architectural Engn & Mech 1209 E 2nd StRoom 324F Tucson AZ 85721 USA

Pedestrian crossing volume is one of the most important variables used to retime and optimize the signal timing plans for traffic delay migration and traffic safety improvement at signalized intersections. However, most of the existing studies only focus on long-term pedestrian volume estimation for planning purposes. To bridge the research gap, this study applied a bayesian additive regression trees (BART) model to estimate the short-term pedestrian crossing volume at signalized intersections equipped with pushbutton devices. Pedestrian-related traffic controller event-based data used as the time-dependent variables representing the temporal trend of pedestrian crossing volume in conjunction with point-of-interest (POI) data and transit trips are chosen as the inputs of the BART model. Seventy signalized intersections from the Pima County region are selected to collect data for calibrating and validating the developed model. When compared with ground-truth data, the proposed method has an R-squared of 0.83, 0.81, and 0.71 for 60 min, 30 min, and 15 min intervals of pedestrian crossing volume, respectively. To further evaluate the performance of the proposed method, the proposed method is used in comparison to two traditional methods (stepwise linear regression and Random Forest). The comparison results show that the BART model is superior to the other two models for hourly pedestrian crossing volume estimation. The evaluation results show that the proposed method can accurately estimate pedestrian crossing volume and provide valuable information for signal retiming.

关键词： bayesian additive regression tree pedestrian crossing volume pushbutton signalized intersection traffic controller event-based data

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Is information and communication technology satisfying educational needs at school?

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COMPUTERS & EDUCATION 2018年 122卷 194-204页

作者： Ferraro, Simona Tallinn Univ Technol Akad Tee 3 EE-12618 Tallinn Estonia

This paper assesses how the integration of ICT in education has affected the mathematics test scores for Italian students measured by the Programme for International Student Assessment 2012 data. The problem of endogeneity that affects survey data in this area, is addressed by applying the bayesian additive regression trees (BART) methodology as in Cabras & Tena Horrillo (2016). The BART methodology needs a prior and likelihood functions using the Markov Chain Monte Carlo (MCMC) algorithm to obtain the posterior distribution. Controlling for socioeconomic, demographic and school factors, the predicted posterior distribution implies an increase, on average, of 16 points in the test scores. The result indicates that the use of ICT at school has a positive and strong impact on mathematic test scores.

关键词： ICT bayesian additive regression tree Posterior distribution PISAJEL Classification I20 O33 C25

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A machine learning-based generalized approach for predicting unauthorized immigration flow considering dynamic border security nexus

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RISK ANALYSIS 2024年第6期44卷 1460-1481页

作者： Al Aziz, Ridwan Ahmed, Tanvir Zhuang, Jun Bangladesh Univ Engn & Technol Dept Ind & Prod Engn Dhaka Bangladesh Univ Buffalo Dept Ind & Syst Engn New York NY USA

Unauthorized immigration has been a long-standing and contentious challenge for developed and developing countries. Numerous continually evolving push and pull factors across international borders, such as economy, employment, population density, unrest, corruption, and climate have driven this migration. Large-scale pandemics such as COVID-19, causing further instability in countries' financial well-being, can initiate or alter emigration flow from different countries. In light of such a complex confluence of factors, climate change, and demographic shifts in migrant communities, it is high time to shift toward machine learning-reinforced generalized approaches from the traditional parametric approaches based on migrant community-specific localized surveys. To our best knowledge, no literature has explored the nonparametric approach and developed a comprehensive database independent of localized surveys to analyze unauthorized migration. This article fills this gap by deploying nine nonparametric machine learning algorithms for predicting unauthorized immigration flow considering the dynamic border security nexus. This framework considers the Seasonal Autoregressive Integrated Moving Average model as the null model. The proposed novel framework removes the dependency on localized survey-based studies and provides a more cost-effective, faster, and big data-friendly approach. This study finds the bayesian additive regression tree model as the best predictive model.

关键词： bayesian additive regression tree climate change cross-border illegal border crossing international migration

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Developing radiology diagnostic tools for pulmonary fibrosis using machine learning methods

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CLINICAL IMAGING 2024年 106卷 110047-110047页

作者： Fan, Weijia Chen, Qixuan Maccarrone, Valerie Luk, Lyndon Navot, Benjamin Salvatore, Mary Columbia Univ Mailman Sch Publ Hlth Dept Biostat 722st 168th St New York NY 10032 USA Columbia Univ Dept Radiol Irving Med Ctr 630 W 168th St New York NY 10032 USA

Background: Accurate and prompt diagnosis of the different patterns for pulmonary fibrosis is essential for patient management. However, accurate diagnosis of the specific pattern is challenging due to overlapping radiographic characteristics. Materials and methods: We conducted a retrospective chart review utilizing two machine learning methods, classification and regression tree and bayesian additive regression tree, to select the most important radiographic features for diagnosing the three most common fibrosis patterns and created an online diagnostic app for convenient implementation. Results: Four hundred patients (median age of 67 with inter quartile range 58-73;200 males) were included in the study. Peripheral distribution, homogeneity, lower lobe predominance and mosaic attenuation of fibrosis are the four most important features identified. bayesian additive regression tree demonstrates better performance than classification and regression tree in diagnosis prediction and provides the predicted probability of each diagnosis with uncertainty intervals for each combination of features. Conclusion: The model and app built with bayesian additive regression tree can be used as an effective tool in assisting radiologists in the diagnostic process of pulmonary fibrosis pattern recognition.

关键词： Pulmonary fibrosis Machine learning Classification and regression tree bayesian additive regression tree Diagnostic tool Online implementation tool

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Who contributes to disaster preparedness? Predicting decision making in social dilemmas pertaining to community resilience

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RISK ANALYSIS 2023年第12期43卷 2659-2670页

作者： Johnson, Paul M. M. Baroud, Hiba Brady, Corey E. E. Abkowitz, Mark Vanderbilt Univ Dept Civil & Environm Engn Nashville TN USA Vanderbilt Univ Dept Teaching & Learning Nashville TN USA Vanderbilt Univ Dept Civil & Environm Engn 2301 Vanderbilt PlPMB 35183 Nashville TN 37240 USA

Planning for community resilience through public infrastructure projects often engenders problems associated with social dilemmas, but little work has been done to understand how individuals respond when presented with opportunities to invest in such developments. Using statistical learning techniques trained on the results of a web-based common pool resource game, we analyze participants' decisions to invest in hypothetical public infrastructure projects that bolster their community's resilience to disasters. Given participants' dispositions and in-game circumstances, bayesian additive regression tree (BART) models are able to accurately predict deviations from players' decisions that would reasonably lead to Pareto-efficient outcomes for their communities. Participants tend to overcontribute relative to these Pareto-efficient strategies, indicating general risk aversion that is analogous to individuals purchasing disaster insurance even though it exceeds expected actuarial costs. However, higher trait Openness scores reflect an individual's tendency to follow a risk-neutral strategy, and fewer available resources predict lower perceived utilities derived from the infrastructure developments. In addition, several input variables have nonlinear effects on decisions, suggesting that it may be warranted to use more sophisticated statistical learning methods to reexamine results from previous studies that assume linear relationships between individuals' dispositions and responses in applications of game theory or decision theory.

关键词： bayesian additive regression tree big five personality community resilience decision analysis social dilemma

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Prediction of groundwater nitrate concentration in a semiarid region using hybrid bayesian artificial intelligence approaches

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第14期29卷 20421-20436页

作者： Alkindi, Khalifa M. Mukherjee, Kaustuv Pandey, Manish Arora, Aman Janizadeh, Saeid Quoc Bao Pham Duong Tran Anh Ahmadi, Kourosh Univ Nizwa UNESCO Chair Aflaj Studies Archaeohydrol Nizwa Oman Chandidas Mahavidyalaya Dept Geog Birbhum 731215 WB India Chandigarh Univ Univ Ctr Res & Dev UCRD Mohali 140413 Punjab India Chandigarh Univ Univ Inst Engn Dept Civil Engn Mohali 140413 Punjab India Jamia Millia Islamia Dept Geog Fac Nat Sci Delhi 10025 India Tarbiat Modares Univ Fac Nat Resources & Marine Sci Dept Watershed Management Engn & Sci Tehran 14115111 Iran Thu Dau Mot Univ Inst Appl Technol Thu Dau Mot Binh Duong Prov Vietnam Ho Chi Minh City Univ Technol HUTECH 475ADien Bien PhuWard 25 Ho Chi Minh City Vietnam Tarbiat Modares Univ Fac Nat Resources & Marine Sci Dept Forestry Tehran 14115111 Iran

Nitrate is a major pollutant in groundwater whose main source is municipal wastewater and agricultural activities. In the present study, bayesian approaches such as bayesian generalized linear model (BGLM), bayesian regularized neural network (BRNN), bayesian additive regression tree (BART), and bayesian ridge regression (BRR) were used to model groundwater nitrate contamination in a semiarid region Marvdasht watershed, Fars province, Iran. Eleven groundwater (GW) nitrate conditioning factors have been taken as input parameters for predictive modeling. The results showed that the bayesian models used in this study were all competent to model groundwater nitrate and the BART model with R-2 = 0.83 was more efficient than the other models. The result of variable importance showed that potassium (K) has the highest importance in the models followed by rainfall, altitude, groundwater depth, and distance from the residential area. The results of the study can support the decision-making process to control and reduce the sources of nitrate pollution.

关键词： bayesian approach Groundwater nitrate contamination bayesian additive regression tree Pollution monitoring

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Proactive Load Balancing Through Constrained Policy Optimization for Ultra-Dense Networks

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IEEE COMMUNICATIONS LETTERS 2022年第10期26卷 2415-2419页

作者： Huang, Miaona Chen, Jun Dongguan Univ Technol Sch Elect Engn & Intelligentizat Dongguan 523808 Guangdong Peoples R China Huawei Technol Shenzhen 518129 Guangdong Peoples R China

Designing an intelligent self-organizing network (SON) architecture is challenging for future wireless networks. To meet the needs of SON, the reactive self-organizing model of the traditional network needs to be transformed into an active and interactive one. Due to the user mobility and small coverage of cells in ultra-dense networks (UDNs), the network load usually becomes unbalanced, leading to deteriorated network performance, such as low throughput, radio link failure, and poor user experience. Therefore, the technique of mobility load balancing (MLB) is critical to ensuring a seamless user experience among cells. This letter proposes an active and interactive MLB strategy for UDNs, which transforms the original reactive MLB into a forward-aware and active one. In particular, user mobility is first predicted based on the bayesian additive regression tree (BART). Then, with the mobility predictions, the joint mobility robust optimization and MLB problem subject to users' rate constraint is solved via safe reinforcement learning. The pertaining simulation results show that the proposed method can improve the network performance and realize intelligent mobile management for future UDNs.

关键词： Handover Optimization Load management Reinforcement learning Quality of service Load modeling regression tree analysis bayesian additive regression tree constrained policy optimization mobility load balancing ultra-dense networks

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TARGETED SMOOTH bayesian CAUSAL FORESTS: AN ANALYSIS OF HETEROGENEOUS TREATMENT EFFECTS FOR SIMULTANEOUS VS. INTERVAL MEDICAL ABORTION REGIMENS OVER GESTATION

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ANNALS OF APPLIED STATISTICS 2021年第3期15卷 1194-1219页

作者： Starling, Jennifer E. Murray, Jared S. Lohr, Patricia A. Aiken, Abigail R. A. Carvalho, Carlos M. Scott, James G. Univ Texas Austin Dept Stat & Data Sci Austin TX 78712 USA Univ Texas Austin McCombs Sch Business Austin TX 78712 USA British Pregnancy Advisory Serv London England Univ Texas Austin Lyndon B Johnson Sch Publ Affairs Austin TX 78712 USA

We introduce Targeted Smooth bayesian Causal Forests (tsBCF), a non-parametric bayesian approach for estimating heterogeneous treatment effects which vary smoothly over a single covariate in the observational data setting. The tsBCF method induces smoothness by parameterizing terminal tree nodes with smooth functions and allows for separate regularization of treatment effects vs. prognostic effect of control covariates. Smoothing parameters for prognostic and treatment effects can be chosen to reflect prior knowledge or tuned in a data-dependent way. We use tsBCF to analyze a new clinical protocol for early medical abortion. Our aim is to assess the relative effectiveness of simultaneous vs. interval administration of mifepristone and misoprostol over the first nine weeks of gestation. Our analysis yields important clinical insights into how to best counsel patients seeking early medical abortion, where understanding even small differences in relative effectiveness can yield dramatic returns to public health. The model reflects our expectation that the treatment effect varies smoothly over gestation but not necessarily over other covariates. We demonstrate the performance of the tsBCF method on benchmarking experiments. Software for tsBCF is available at https://***/jestarling/tsbcf/ and in the Supplementary Material (Starling (2020)).

关键词： bayesian additive regression tree causal inference regularization Gaussian process heterogeneous treatment effects

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