scholarly journals Using Predictive Analytics for Public Policy: The Case for Lost Work due to the COVID-19

2021 ◽  
Author(s):  
Kent Jason Go Cheng
Keyword(s):  
Public Policy ◽  
Predictive Analytics ◽  
Regression Trees ◽  
Boosted Regression Trees ◽  
Means Testing ◽  
Research Article ◽  
Testing Data ◽  
Testing Error ◽  
Inability To Work ◽  
Societal Level

In this brief research article, I demonstrate how predictive analytics or machine learning can be used to predict outcomes that are of interest in public policy. I developed a predictive model that determined who were not able to work during the past four weeks because the COVID-19 pandemic led their employer to close or lose business. I used the Current Population Survey (CPS) collected from May to November 2020 (N=352,278). Predictive models considered were logistic regression and ensemble-based methods (bagging of regression trees, random forests, and boosted regression trees). Predictors included (1) individual-, (2) family-, (3) and community or societal- level factors. To validate the models, I used the random training test splits with equal allocation of samples for the training and testing data. The random forest with the full set of predictors and number of splits set to the square root of the number of predictors yielded the lowest testing error rate. Predictive analytics that seek to forecast the inability to work due to the pandemic can be used for automated means-testing to determine who gets aid like unemployment benefits or food stamps.

Sexuality

2020 ◽  
pp. 402-417
Author(s):  
John Danaher
Keyword(s):  
Sexual Identity ◽  
Predictive Analytics ◽  
Sexual Contact ◽  
Use Of Self ◽  
Use Of Technology ◽  
Societal Level ◽  
New Form

This chapter studies the opportunities and challenges posed by the use of AI in how humans express and enact their sexualities. It first considers the idea of digisexuality, which according to some commentators is the identity label that should be applied to those whose primary sexual identity comes through the use of technology, particularly through the use of robotics and AI. The chapter questions whether it is necessary or socially desirable to see this as a new form of sexual identity. It then looks at the role that AI can play in facilitating human-to-human sexual contact, focusing in particular on the use of self-tracking and predictive analytics in optimizing sexual and intimate behavior. There are already a number of apps and services that promise to use AI to do this, but they pose a range of ethical risks that need to be addressed at both an individual and societal level. Finally, the chapter considers the idea that a sophisticated form of AI could be an object of love.

scholarly journals Using Boosted Regression Trees and Remotely Sensed Data to Drive Decision-Making

2017 ◽  
Vol 07 (05) ◽  
pp. 859-875 ◽  
Author(s):  
Brigitte Colin ◽  
Samuel Clifford ◽  
Paul Wu ◽  
Samuel Rathmanner ◽  
Kerrie Mengersen
Keyword(s):  
Decision Making ◽  
Regression Trees ◽  
Remotely Sensed ◽  
Boosted Regression Trees ◽  
Remotely Sensed Data

scholarly journals Biologically informed ecological niche models for an example pelagic, highly mobile species

2017 ◽  
Vol 3 (1) ◽  
pp. 55-75 ◽  
Author(s):  
Kate Ingenloff
Keyword(s):  
Model Development ◽  
Regression Trees ◽  
Marine Conservation ◽  
Boosted Regression Trees ◽  
Minimum Volume ◽  
Population Declines ◽  
Ecological Niche Models ◽  
Mobile Species ◽  
Further Development ◽  
Niche Models

AbstractBackground: Although pelagic seabirds are broadly recognised as indicators of the health of marine systems, numerous gaps exist in knowledge of their at-sea distributions at the species level. These gaps have profound negative impacts on the robustness of marine conservation policies. Correlative modelling techniques have provided some information, but few studies have explored model development for non-breeding pelagic seabirds. Here, I present a first phase in developing robust niche models for highly mobile species as a baseline for further development. Methodology: Using observational data from a 12-year time period, 217 unique model parameterisations across three correlative modelling algorithms (boosted regression trees, Maxent and minimum volume ellipsoids) were tested in a time-averaged approach for their ability to recreate the at-sea distribution of non-breeding Wandering Albatrosses (Diomedea exulans) to provide a baseline for further development. Principle Findings/Results: Overall, minimum volume ellipsoids outperformed both boosted regression trees and Maxent. However, whilst the latter two algorithms generally overfit the data, minimum volume ellipsoids tended to underfit the data. Conclusions: The results of this exercise suggest a necessary evolution in how correlative modelling for highly mobile species such as pelagic seabirds should be approached. These insights are crucial for understanding seabird-environment interactions at macroscales, which can facilitate the ability to address population declines and inform effective marine conservation policy in the wake of rapid global change.

scholarly journals Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

2020 ◽  
Vol 12 (4) ◽  
pp. 1396
Author(s):  
Shufang Wang ◽  
Xiyun Jiao ◽  
Liping Wang ◽  
Aimin Gong ◽  
Honghui Sang ◽  
...  
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Markov Model ◽  
Land Use Changes ◽  
Regression Trees ◽  
Boosted Regression Trees ◽  
Utilization Rate ◽  
Protection Measures ◽  
Unused Land ◽  
The Government

The simulation and prediction of the land use changes is generally carried out by cellular automata—Markov (CA-Markov) model, and the generation of suitable maps collection is subjective in the simulation process. In this study, the CA-Markov model was improved by the Boosted Regression Trees (BRT) to simulate land use to make the model objectively. The weight of ten driving factors of the land use changes was analyzed in BRT, in order to produce the suitable maps collection. The accuracy of the model was verified. The outcomes represent a match of over 84% between simulated and actual land use in 2015, and the Kappa coefficient was 0.89, which was satisfactory to approve the calibration process. The land use of Hotan Oasis in 2025 and 2035 were predicted by means of this hybrid model. The area of farmland, built-up land and water body in Hotan Oasis showed an increasing trend, while the area of forestland, grassland and unused land continued to show a decreasing trend in 2025 and 2035. The government needs to formulate measures to improve the utilization rate of water resources to meet the growth of farmland, and need to increase ecological environment protection measures to curb the reduction of grass land and forest land for the ecological health.

scholarly journals Insights into the BRT (Boosted Regression Trees) Method in the Study of the Climate-Growth Relationship of Masson Pine in Subtropical China

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 228 ◽  
Author(s):  
Hongliang Gu ◽  
Jian Wang ◽  
Lijuan Ma ◽  
Zhiyuan Shang ◽  
Qipeng Zhang
Keyword(s):  
Tree Growth ◽  
General Circulation ◽  
Growth Response ◽  
Circulation Model ◽  
Regression Trees ◽  
Pinus Massoniana ◽  
Boosted Regression Trees ◽  
Climate Factors ◽  
Masson Pine ◽  
Hydroclimatic Variability

Dendroclimatology and dendroecology have entered mainstream dendrochronology research in subtropical and tropical areas. Our study focused on the use of the chronology series of Masson pine (Pinus massoniana Lamb.), the most widely distributed tree species in the subtropical wet monsoon climate regions in China, to understand the tree growth response to ecological and hydroclimatic variability. The boosted regression trees (BRT) model, a nonlinear machine learning method, was used to explore the complex relationship between tree-ring growth and climate factors on a larger spatial scale. The common pattern of an asymptotic growth response to the climate indicated that the climate-growth relationship may be linear until a certain threshold. Once beyond this threshold, tree growth will be insensitive to some climate factors, after which a nonlinear relationship may occur. Spring and autumn climate factors are important controls of tree growth in most study areas. General circulation model (GCM) projections of future climates suggest that warming climates, especially temperatures in excess of those of the optimum growth threshold (as estimated by BRT), will be particularly threatening to the adaptation of Masson pine.

scholarly journals Residuals in the modelling of pollution concentration depending on meteorological conditions and traffic flow, employing decision trees

2018 ◽  
Vol 23 ◽  
pp. 00016 ◽  
Author(s):  
Joanna A. Kamińska
Keyword(s):  
Random Forest ◽  
Traffic Flow ◽  
Goodness Of Fit ◽  
Regression Trees ◽  
Meteorological Conditions ◽  
Boosted Regression Trees ◽  
Significant Preference ◽  
Explanatory Variables ◽  
The City

Two data mining methods – a random forest and boosted regression trees – were used to model values of roadside air pollution depending on meteorological conditions and traffic flow, using the example of data obtained in the city of Wrocław in the years 2015–2016. Eight explanatory variables – five continuous and three categorical – were considered in the models. A comparison was made of the quality of the fit of the models to empirical data. Commonly used goodness-of-fit measures did not imply a significant preference for either of the methods. Residual analysis was also performed; this showed boosted regression trees to be a more effective method for predicting typical values in the modelling of NO2, NOx and PM2.5, while the random forest method leads to smaller errors when predicting peaks.

scholarly journals Evaluating the Influence of Rain Event Characteristics on Rainfall Interception by Urban Trees Using Multiple Correspondence Analysis

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2659
Author(s):  
Katarina Zabret ◽  
Mojca Šraj
Keyword(s):  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Regression Trees ◽  
Pinus Nigra ◽  
Rainfall Amount ◽  
Urban Trees ◽  
Boosted Regression Trees ◽  
Rain Event ◽  
Rainfall Interception ◽  
Pine Tree

Urban trees play an important role in the built environment, reducing the rainfall reaching the ground by rainfall interception. The amount of intercepted rainfall depends on the meteorological and vegetation characteristics. By applying the multiple correspondence analysis (MCA), we analysed the influence of rainfall amount, intensity and duration, the number of raindrops, the mean volume diameter (MVD), wind speed and direction on rainfall interception. The analysis was based on data from 176 events collected over more than three years of observations. Measurements were taken under birch (Betula pendula Roth.) and pine (Pinus nigra Arnold) trees located in an urban park in the city of Ljubljana, Slovenia. The results indicate that rainfall interception is influenced the most by rainfall amount and the number of raindrops. In general, the ratio of rainfall interception to gross rainfall decreases with longer and more intense rainfall events. The influence of the raindrop number depends also on their size (MVD), which is evident especially for the pine tree. For example, pine tree interception increases with smaller raindrops regardless of their number. In addition, MCA gives a new insight into the influence of wind characteristics, which was not visible using previous methods of data analysis (regression analysis, correlation matrices, regression trees, boosted regression trees). According to the nearby buildings, a wind corridor is sometimes created, decreasing rainfall interception by both tree species.

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