Government welfare schemes such as the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) in India fund the creation of assets for natural resource management in rural villages to support farmers for their agricultural and livelihoods-based needs. With most agriculture in India being rain-fed, structures such as farm ponds, checkdams, trenches and bunds play a crucial role in supporting groundwater recharge and providing critical lifesaving irrigation in times of dry spells and droughts. In this study, we investigate the impact of farm ponds built under the MGNREGA scheme in Northern India as a source of protective irrigation for cropping areas in their immediate neighbourhood. We assess the impact of farm ponds on the following aspects: (i) we study their impact on agricultural productivity for up to five years since their construction, (ii) we separately study their impact in drought years during this period, (iii) we study the extent to which they are able to to reduce the sensitivity to droughts of sites having farm ponds. A causal analysis framework was designed by identifying control sites that did not have farm ponds, and the treatment effect of having farm ponds was computed using the difference-in-differences approach. Remote sensing data was processed to compute changes in vegetation indices around the treated and control locations before and after the construction of farm ponds. Our results indicate that farm ponds were instrumental in improving the agricultural productivity during the monsoon season in general. The impact during the monsoon season in drought years is also positive and significant. Furthermore, farm ponds also facilitated in reducing drought sensitivity during the monsoon season. The impact during the post-monsoon season was found to be lower, and the impact during the summer agricultural season was found to be the least.

The ubiquity of mobile internet has led to the success of Spatial Crowdsourcing platforms like real-time taxi-hailing services, online food ordering services, etc. A critical component of such services is the task assignment algorithm employed for assigning the tasks to the workers of the platform. Our study of the literature in this domain shows that most of the task assignment algorithms developed for spatial crowdsourcing platforms address the problem from a utilitarian perspective, i.e., they optimise for only kind of entity. In contrast, we address the task assignment problem in spatial crowdsourcing platforms from an egalitarian perspective. An egalitarian approach aims to optimise the expectation of all entities involved. Specifically, we aim to minimise the waiting time for the customers and workers, while maximising the profit earned by the platform. To the best of our knowledge, ours is the only study that achieves this objective in a fully-online setting, with deadlines for both customers and workers. We propose two heuristic algorithms to solve the problem, and evaluate our algorithms on a real taxi-trips records dataset. Our algorithms exhibit a superior performance than the state-of-the-art algorithm for the fully-online bottleneck matching problem with deadlines, in terms of solution quality, running time and response time.

Globally, women are cautious when planning their routine travel routes. In a recent survey on street harassment, 82% of international respondents reported taking a different route to their destination than the conventional route due to fear of harassment. Such studies indicate an increasing need for `Safe Routing', especially in developing nations where the lack of infrastructure such as street lights, may contribute to higher crime rates. However, to the best of our knowledge, no state-of-the-art navigation system provides the option of `Safe Routing'. In this work, we propose a novel system that recommends "Safe Routes". Routes recommended by our system balance the conflicting requirements of increasing the safety and constraining the total length of the path to be within a reasonable limit (as desired by the user). From a theoretical perspective, the problem of `Safe Routing' can be modeled as the Arc Orienteering Problem, which is a well-known NP-hard combinatorial optimization problem.