The unintended [or intended?] political consequences of electoral systems: The case of Bolivia

The present study was concerned with evaluating one basic institution in Bolivian democracy: its electoral system. The study evaluates the impact of electoral systems on the interaction between presidents and assemblies. It sought to determine whether it is possible to have electoral systems that favor multipartism but can also moderate the likelihood of executive-legislative confrontation by producing the necessary conditions for coalition building. ^ This dissertation utilized the case study method as a methodology. Using the case of Bolivia, the research project studied the variations in executive-legislative relations and political outcomes from 1985 to the present through a model of executive-legislative relations that provided a typology of presidents and assemblies based on the strategies available to them to bargain with each other for support. A complementary model that evaluated the state of their inter-institutional interaction was also employed. ^ Results indicated that executive-legislative relations are profoundly influenced by the choice of the electoral system. Similarly, the project showed that although the Bolivian mixed system for legislative elections, and executive formula favor multipartism, these electoral systems do not necessarily engender executive-legislative confrontation in Bolivia. This was mainly due to the congressional election of the president, and the formulas utilized to translate the popular vote into legislative seats. However, the study found that the electoral system has also allowed for anti-systemic forces to emerge and gain political space both within and outside of political institutions. ^ The study found that government coalitions in Bolivia that are promoted by the system of congressional election of the president and the D'Hondt system to allocate legislative seats have helped ameliorate one of the typical problems of presidential systems in Latin America: the presence of a minority government that is blocked in its capacity to govern. This study was limited to evaluating the impact of the electoral system, as the independent variable, on executive-legislative interaction. However, the project revealed a need for more theoretical and empirical work on executive-legislative bargaining models in order to understand how institutional reforms can have an impact on the incentives of presidents and legislators to form coherent coalitions. ^

Comparison of Belowground Biomass in C3- and C4-Dominated Mixed Communities in a Chesapeake Bay Brackish Marsh

Belowground biomass is a critical factor regulating ecosystem functions of coastal marshes, including soil organic matter (SOM) accumulation and the ability of these systems to keep pace with sea-level rise. Nevertheless, belowground biomass responses to environmental and vegetation changes have been given little emphasis marsh studies. Here we present a method using stable carbon isotopes and color to identify root and rhizomes of Schoenoplectus americanus (Pers.) Volk. ex Schinz and R. Keller (C3) and Spartina patens (Ait.) Muhl. (C4) occurring in C3− and C4-dominated communities in a Chesapeake Bay brackish marsh. The functional significance of the biomass classes we identified is underscored by differences in their chemistry, depth profiles, and variation in biomass and profiles relative to abiotic and biotic factors. C3 rhizomes had the lowest concentrations of cellulose (29.19%) and lignin (14.43%) and the lowest C:N (46.97) and lignin:N (0.16) ratios. We distinguished two types of C3 roots, and of these, the dark red C3 roots had anomalously high C:N (195.35) and lignin:N (1.14) ratios, compared with other root and rhizome classes examined here and with previously published values. The C4-dominated community had significantly greater belowground biomass (4119.1 g m−2) than the C3-dominated community (3256.9 g m−2), due to greater total root biomass and a 3.6-fold higher C3-root:rhizome ratio in the C4-dominated community. C3 rhizomes were distributed significantly shallower in the C4-dominated community, while C3 roots were significantly deeper. Variability in C3 rhizome depth distributions was explained primarily by C4 biomass, and C3 roots were explained primarily by water table height. Our results suggest that belowground biomass in this system is sensitive to slight variations in water table height (across an 8 cm range), and that the reduced overlap between C3 and C4 root profiles in the C4-dominated community may account for the greater total root biomass observed in that community. Given that future elevated atmospheric CO2 and accelerated sea-level rise are likely to increase C3 abundance in Atlantic and Gulf coast marshes, investigations that quantify how patterns of C3 and C4 belowground biomass respond to environmental and biological factors stand to improve our understanding of ecosystem-wide impacts of global changes on coastal wetlands.

Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Mixed Spatial and Nonspatial Problems in Location Based Services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.