The dissertation mainly focuses on the spatial imbalance of resources observed in the sharing economy. It consists of two main chapters, with the first chapter focusing on designing policies to solve this imbalance and the second chapter focusing on evaluating the performance of easy-to-implement policies on solving this imbalance of resources. In the first chapter, we consider the problem of managing resources in shared micromobility systems (bike-sharing and scooter-sharing). An important task in managing such systems is periodic repositioning/recharging/sourcing units to avoid stockouts or excess inventory at nodes with unbalanced flows. We consider a discrete-time model: each period begins with an initial inventory at each node in the network, and then customers (demand) materialize at the nodes. Each customer picks up a unit at the origin node and drops it off at a randomly sampled destination node with an origin-specific probability distribution. We model the above network inventory management problem as an infinite horizon discrete-time discounted Markov Decision Process and prove the asymptotic optimality of a novel mean-field approximation to the original MDP as the number of stations becomes large. To compute an approximately optimal policy for the mean-field dynamics, we provide an algorithm with a running time that is logarithmic in the desired optimality gap. Lastly, we compare the performance of our mean-field-based policy to state-of-the-art heuristics via numerical experiments, including experiments using Austin scooter-sharing data. The second chapter considers the joint optimization of rebalancing/sourcing inventory on a graph. We focus on the lost-sales setting with customer-induced relocations. Through a coupling analysis, we provide worst-case performance bounds, with tight instances, for policies commonly used in practice. We provide further insights into the performance of these policies and discuss cost regimes where they are effective.