A comparison of statistical and machine-learning approaches for spatiotemporal modeling of nitrogen dioxide across Switzerland

Land use regression modeling has commonly been used to model ambient air pollutant concentrations in environmental epidemiological studies. Recently, other statistical and machine-learning methods have also been applied to model air pollution, but their relative strengths and limitations have not been extensively investigated. In this study, we developed and compared land-use statistical and machine-learning models at annual, monthly and daily scales estimating ground-level NO2 concentrations across Switzerland (at high spatial resolution 100 × 100 m). Our study showed that the best model type varies with context, particularly with temporal resolution and training data size. Linear-regression-based models were useful in predicting long-term (annual, monthly) spatial distribution of NO2 and outperformed machine-learning models. However, linear-regression-based models were limited in representing short-term temporal variation even when predictor variables with temporal variability were provided. Machine-learning models showed high capability in predicting short-term temporal variation and outperformed linear-regression-based models for modeling NO2 variation at high temporal resolution (daily). However, the best performing models, XGBoost and LightGBM, constantly overfit on training data and may result in erratic patterns in the model-estimated concentration surfaces. Therefore, the temporal and spatial scale of the study is an important factor on which the choice of the suitable model type should be based and validation is required whatever approach is used

Spatial econometrics and the Lasso estimator : theory and applications

This thesis links two topics of empirical economics: spatial econometrics and the Lasso estimator. Spatial econometrics is concerned with methods and models accounting for interaction effects between units. The Lasso estimator is a regularisation technique that allows for simultaneous variable selection and estimation in a high dimensional setting where the number of parameters may exceed the sample size. Three applied and theoretical articles are presented that demonstrate how spatial econometric research can benefit from high-dimensional methods and, specifically, the Lasso. The introduction in Chapter 1 presents a literature review of both fields and discusses the connections between the two topics. Chapter 2 examines the effect of economic growth on civil conflicts in Africa. The Lasso estimator is employed to generate instrumental variables, which account for non-linearity and spatial heterogeneity. The theoretical contribution in Chapter 3 proposes a two-step Lasso estimator that can consistently estimate the spatial weights matrix in a spatial autoregressive panel model. Chapter 4 is an application to the US housing market. A Lasso-based estimation method is considered that controls for spatial effects in a spatial error-correction model. Chapter 5 provides concluding remarks

A geographically weighted regression approach to understanding urbanization impacts on urban warming and cooling: a case study of Las Vegas

A surface urban heat island (SUHI) effect is one of the most significant consequences of urbanization. Great progress has been made in evaluating the SUHI with cross-sectional studies performed in a number of cities across the globe. Few studies; however, have focused on the spatiotemporal changes in an area over a long period of time. Using multi-temporal remote sensing data sets, this study examined the spatiotemporal changes of the SUHI intensity in Las Vegas, Nevada, over a 15-year period from 2001 to 2016. We applied the geographically weighted regression (GWR) and advanced statistical approaches to investigating the SUHI variation in relation to several important biophysical indicators in the region. The results show that (1) Las Vegas had experienced a significant increase in the SUHI over the 15 years, (2) Vegetation and large and small water bodies in the city can help mitigate the SUHI effect and the cooling effect of vegetation had increased continuously from 2001 to 2016, (3) An urban heat sink (UHS) was identified in developed areas with low to moderate intensity, and (4) Increased surface temperatures were mainly driven by the urbanization-induced land conversions occurred over the 15 years. Findings from this study will inspire thoughts on practical guidelines for SUHI mitigation in a fast-growing desert city

Sources of Atmospheric Fine Particles and Adsorbed Polycyclic Aromatic Hydrocarbons in Syracuse, New York

Land surface temperature (LST) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor have been widely utilized across scientific disciplines for a variety of purposes. The goal of this dissertation was to utilize MODIS LST for three spatial modeling applications within the conterminous United States (CONUS). These topics broadly encompassed agriculture and human health. The first manuscript compared the performance of all methods previously used to interpolate missing values in 8-day MODIS LST images. At low cloud cover (\u3c30%), the Spline spatial method outperformed all of the temporal and spatiotemporal methods by a wide margin, with median absolute errors (MAEs) ranging from 0.2°C-0.6°C. However, the Weiss spatiotemporal method generally performed best at greater cloud cover, with MAEs ranging from 0.3°C-1.2°C. Considering the distribution of cloud contamination and difficulty of implementing Weiss, using Spline under all conditions for simplicity would be sufficient. The second manuscript compared the corn yield predictive capability across the US Corn Belt of a novel killing degree day metric (LST KDD), computed with daily MODIS LST, and a traditional air temperature-based metric (Tair KDD). LST KDD was capable of predicting annual corn yield with considerably less error than Tair KDD (R2 /RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). The superior performance can be attributed to LST’s ability to better reflect evaporative cooling and water stress. Moreover, these findings suggest that long-term yield projections based on Tair and precipitation alone will contain error, especially for years of extreme drought. Finally, the third manuscript assessed the extent to which daily maximum heat index (HI) across the CONUS can be estimated by MODIS multispectral imagery in conjunction with land cover, topographic, and locational factors. The derived model was capable of estimating HI in 2012 with an acceptable level of error (R 2 = 0.83, RMSE = 4.4°F). LST and water vapor (WV) were, by far, the most important variables for estimation. Expanding this analytical framework to a more extensive study area (both temporally and spatially) would further validate these findings. Moreover, identifying an appropriate interpolation and downscaling approach for daily MODIS imagery would substantially increase the utility of the corn yield and HI models

Factors Affecting Domestic Water Consumption on the Spanish Mediterranean Coastline

During the last decades on the Spanish Mediterranean coastline there has been a great development of low-density urban areas, as well as a change in the sociodemographic structures, especially in the municipalities that have developed a residential tourism model. Likewise, urban and tourist development have stressed the balance between the availability of water resources and urban water demands, generating situations of scarcity that might be aggravated by climate change. This study identifies the determinants of water consumption on the Spanish Mediterranean coastline, focusing on the variables related to urban land uses and socioeconomic and sociodemographic variables at the municipal level using an ordinary least square (OLS) and a geographically weighted regression (GWR) model. The GWR model results substantially improved the results of the OLS model, explaining 88.27 percent of the variance in domestic water consumption and solving the spatial autocorrelation problem of some independent variables. The most influential variables include the percentage of second homes or the percentage of residential properties with swimming pools at the municipal level. These characteristics must be considered to develop demand management policies and an updated hydrological planning to ensure urban supply in a future with less available water resources.Durante las últimas décadas ha ocurrido gran desarrollo de áreas urbanas de baja densidad a lo largo de la costa mediterránea española, al propio tiempo que un cambio en las estructuras sociodemográficas, especialmente en las municipalidades que han impulsado un modelo de turismo residencial. Igualmente, los desarrollos urbano y turístico han presionado el balance entre la disponibilidad de recursos hídricos y las demandas urbanas de agua, generando situaciones de escasez que podrían agravarse con el cambio climático. Este estudio identifica los determinantes del consumo de agua en la costa mediterránea española, centrando la atención en las variables relacionadas con los usos del suelo urbano y las variables socioeconómicas y sociodemográficas a nivel municipal, usando cuadrados mínimos ordinarios (OLS) y un modelo de regresión geográficamente ponderada (GWR). Los resultados del modelo GWR mejoraron sustancialmente los resultados del modelo OLS, explicando el 88.27 por ciento de la varianza en el consumo doméstico de agua y solucionando el problema de autocorrelación espacial de algunas variables independientes. Las variables más influyentes incluyen el porcentaje de segundas residencias o el porcentaje de propiedades residenciales con piscinas a nivel municipal. Estas características deben tomarse en cuenta para desarrollar políticas del manejo de la demanda y una actualización de la planificación hidrológica para asegurar el suministro urbano en un futuro dotado de menos recursos hídricos.The results presented in this article are part of a research project entitled “Uses and Management of Non-conventional Water Resources on the Coast of the Regions of Valencia and Murcia as a Drought Adaptation Strategy” funded by the Spanish Ministry of Economy and Competitiveness under Grant Number CSO2015-65182-CS-2-P. This work is also a result of a predoctoral fellowship (FPU15/01144), granted by the Spanish Ministry of Education, Culture and Sport

Mapping regional land cover and land use change using MODIS time series

Coarse resolution satellite observations of the Earth provide critical data in support of land cover and land use monitoring at regional to global scales. This dissertation focuses on methodology and dataset development that exploit multi-temporal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve current information related to regional forest cover change and urban extent. In the first element of this dissertation, I develop a novel distance metric-based change detection method to map annual forest cover change at 500m spatial resolution. Evaluations based on a global network of test sites and two regional case studies in Brazil and the United States demonstrate the efficiency and effectiveness of this methodology, where estimated changes in forest cover are comparable to reference data derived from higher spatial resolution data sources. In the second element of this dissertation, I develop methods to estimate fractional urban cover for temperate and tropical regions of China at 250m spatial resolution by fusing MODIS data with nighttime lights using the Random Forest regression algorithm. Assessment of results for 9 cities in Eastern, Central, and Southern China show good agreement between the estimated urban percentages from MODIS and reference urban percentages derived from higher resolution Landsat data. In the final element of this dissertation, I assess the capability of a new nighttime lights dataset from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) for urban mapping applications. This dataset provides higher spatial resolution and improved radiometric quality in nighttime lights observations relative to previous datasets. Analyses for a study area in the Yangtze River Delta in China show that this new source of data significantly improves representation of urban areas, and that fractional urban estimation based on DNB can be further improved by fusion with MODIS data. Overall, the research in this dissertation contributes new methods and understanding for remote sensing-based change detection methodologies. The results suggest that land cover change products from coarse spatial resolution sensors such as MODIS and VIIRS can benefit from regional optimization, and that urban extent mapping from nighttime lights should exploit complementary information from conventional visible and near infrared observations