This dissertation contains three essays studying topics in applied microeconomics. The first chapter is a co-authored paper in which we use daytime satellite imagery and convolutional neural networks to model economic growth at the neighborhood level. In the second chapter, I use this model to examine the spatial distribution of residential impacts from fracking. The third chapter investigates methods of measuring skill distance between occupations and proposes a new method which matches patterns of observed occupational transition. Each chapter uses unconventional data sources and machine learning techniques to contribute to central questions in labor economics research and policy. In the first chapter we apply deep learning to daytime satellite imagery to predict changes in income and population at high spatial resolution in US data. Our model predictions achieve R2 values of and 0.32 to 0.46 in decadal changes, which have no counterpart in the literature and are 3-4 times larger than for commonly used nighttime lights. Our network has wide application for analyzing localized economic shocks. One such application is my second chapter, which studies changes in total neighborhood income and population in areas near fracking extraction and shale reserves. My microspatial approach identifies that fracking exposure as far as 20 miles away leads to a 2 percent decline in neighborhood income. The spatial gradient and associated mechanisms of this effect indicate that it is driven by local industrialization rather than direct environmental externalities. Examination reveals margins of policy and labor conditions which attenuate the observed impacts. In the third chapter I show that a regression framework generates a novel, empirical occupational skill distance norm which is disciplined by observed occupation switching patterns. This approach relieves key limitations of existing measures such as linearity and symmetry. It also allows for an analysis of which skill dimensions relate to the portability of human capital, and which do not. Implications for existing results on skill portability are discussed, along with immediate policy applications on employee adjustment costs.