Accurate land use land cover (LULC) classification is vital for the sustainable management of natural resources and to learn how the landscape is changing due to climate. For accurate and efficient LULC classification, high-quality datasets and robust classification methods are required. With the increasing availability of satellite data, geospatial analysis tools, and classification methods, it is essential to systematically assess the performance of different combinations of satellite data and classification methods to help select the best approach for LULC classification. Therefore, this study aims to evaluate the LULC classification performance of two commonly used platforms (i.e., ArcGIS Pro and Google Earth Engine) with different satellite datasets (i.e., Landsat, Sentinel, and Planet) through a case study for the city of Charlottetown in Canada. Specifically, three classifiers in ArcGIS Pro, including support vector machine (SVM), maximum likelihood (ML), and random forest/random tree (RF/RT), are utilized to develop LULC maps over the period of 2017–2021. Whereas four classifiers in Google Earth Engine, including SVM, RF/RT, minimum distance (MD), and classification and regression tree (CART), are used to develop LULC maps for the same period. To identify the most efficient and accurate classifier, the overall accuracy and kappa coefficient for each classifier is calculated throughout the study period for all combinations of satellite data, classification platforms, and methods. Change detection is then conducted using the best classifier to quantify the LULC changes over the study period. Results show that the SVM classifier in both ArcGIS Pro and Google Earth Engine presents the best performance compared to other classifiers. In particular, the SVM in ArcGIS Pro shows an overall accuracy of 89% with Landsat, 91% with Sentinel, and 94% with Planet. Similarly, in Google Earth Engine, the SVM shows an accuracy of 87% with Landsat 8 and 92% with Sentinel 2. Furthermore, change detection results show that 13.80% and 14.10% of forest areas have been turned into bare land and urban class, respectively, and 3.90% of the land has been converted into the urban area from 2017 to 2021, suggesting the intensive urbanization. The results of this study will provide the scientific basis for selecting the remote sensing classifier and satellite imagery to develop accurate LULC maps.

A robust predictive model for crop yield is essential for designing a commercially viable index-based insurance policy. Index-based insurance for crops is still at a relatively infant stage, and more research and development is needed in order to address a main limitation, which is referred to as basis risk. Traditionally, ground weather station measurements have been the most common approach used in weather indices, and this approach has often led to high levels of basis risk. Recent advances in satellite-based remote sensing provide new opportunities to use publicly available and transparent “big data,” to potentially make index-based insurance policies more relevant by reducing basis risk. This is the first article to provide a comprehensive comparison of 13 pasture production indices (PPIs), including those developed based on satellite-derived vegetation and biophysical parameter indices using data products from the Moderate Resolution Imaging Spectroradiometer (MODIS). A validation protocol is established, and a unique dataset covering the period 2002 to 2016 from a network of pasture clip sites in the province of Alberta, Canada, is used to demonstrate new applications for insurance based on remote sensing–derived data. The results of the satellite-derived PPIs are compared to PPIs based on ground weather station data as benchmarks, which to date are the most common design for index-based insurance. Overall, the satellite-based indices report higher correlations with the ground truth forage yield data compared to the weather station PPIs. As an example, in 2015 and 2016 the best performing satellite-based PPIs produced correlations close to 90%. When considering the whole sample period, the highest overall average correlation is 62.0% for the biophysical parameter PPI based on FPAR 500-m MODIS, and the lowest overall average correlation is 43.8% for the vegetation PPI based on EVI 250-m MODIS. Comparatively, the highest correlation reported for the weather station indices is for precipitation, at 12.51%. Other predictive models based on principal component analysis and shrinkage methods (such as lasso, ridge regression, and elastic net) are considered to address the issues of variable selection and dimension reduction in insurance design. This research makes an important contribution to the field of actuarial science and insurance, because it highlights potential new opportunities for insurance design and predictive analytics using large and comprehensive satellite datasets that remain relatively unexplored to date in insurance practice. Though forage is used as the example here, the research could be extended to other crops, and other areas in the property and casualty sector, including for fire and flood.

The valuation of large variable annuity (VA) portfolios is an important problem of interest, not only because of its practical relevance but also because of its theoretical significance. This is prompted by the phenomenon that many existing sophisticated algorithms are typically efficient at valuing a single VA policy but they are not scalable to valuing large VA portfolios consisting of hundreds of thousands of policies. As a result, this sparks a new research direction exploiting machine learning methods (such as data clustering, nearest neighbor kriging, neural network) on providing more efficient algorithms to estimate the market values and sensitivities of large VA portfolios. The idea underlying these approximation methods is to first determine a set of VA policies that is “representative” of the entire large VA portfolio. Then the values from these representative VA policies are used to estimate the respective values of the entire large VA portfolio. A substantial reduction in computation time is possible because we only need to value the representative set of VA policies, which typically is a much smaller subset of the entire large VA portfolio. Ideally the large VA portfolio valuation method should adequately address issues such as (1) the complexity of the proposed algorithm; (2) the cost of finding representative VA policies; (3) the cost of the initial training set, if any; (4) the cost of estimating the entire large VA portfolio from the representative VA policies; (5) the computer memory constraint; and (6) the portability to other large VA portfolio valuation. Most of the existing large VA portfolio valuation methods do not necessary reflect all of these issues, particularly the property of portability, which ensures that we only need to incur the start-up time once and the same representative VA policies can be recycled to valuing other large portfolios of VA policies. Motivated by their limitations and by exploiting the greater uniformity of the randomized low discrepancy sequence and the Taylor expansion, we show that our proposed method, a green mesh method, addresses all of the above issues. The numerical experiment further highlights its simplicity, efficiency, portability, and, more important, its real-time valuation application.

Momentum as elaborated under a recent novel definition has been shown quantitatively to have a significant impact on basketball game outcomes. This paper makes two contributions to the analytical literature on sports momentum: (1) two aspects of the new definition are operationalized so that its practicality becomes evident; and (2) through a dimension-reduction technique (elastic net), key factors associated with momentum are identified. Both technical variables such as field goals, assists, rebounds, etc. and environmental variables such as the spectator attendance rate and player salary dispersion are considered, and the potential for useful real-time analyzes is illustrated.

Background: Aortic dissection (AD) is one of the most catastrophic aortic diseases associated with a high mortality rate. In contrast to the advances in most cardiovascular diseases, both the incidence and in-hospital mortality rate of AD have experienced deviant increases over the past 20 years, highlighting the need for fresh prospects on the prescreening and in-hospital treatment strategies. Methods: Through two cross-sectional studies, we adopt image recognition techniques to identify pre-disease aortic morphology for prior diagnoses; assuming that AD has occurred, we employ functional data analysis to determine the optimal timing for BP and HR interventions to offer the highest possible survival rate. Results: Compared with the healthy control group, the aortic centerline is significantly more slumped for the AD group. Further, controlling patients’ blood pressure and heart rate according to the likelihood of adverse events can offer the highest possible survival probability. Conclusions: The degree of slumpness is introduced to depict aortic morphological changes comprehensively. The morphology-based prediction model is associated with an improvement in the predictive accuracy of the prescreening of AD. The dynamic model reveals that blood pressure and heart rate variations have a strong predictive power for adverse events, confirming this model’s ability to improve AD management.