A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters

Keyword-based web queries with local intent retrieve web content that is relevant to supplied keywords and that represent points of interest that are near the query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, namely the top-k spatial textual clusters (k-STC) query that returns the top-k clusters that (i) are located the closest to a given query location, (ii) contain the most relevant objects with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a basic algorithm that relies on on-line density-based clustering and exploits an early stop condition. To improve the response time, we design an advanced approach that includes three techniques: (i) an object skipping rule, (ii) spatially gridded posting lists, and (iii) a fast range query algorithm. An empirical study on real data demonstrates that the paper's proposals offer scalability and are capable of excellent performance

Geographica: A Benchmark for Geospatial RDF Stores

Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently been defined and corresponding geospatial RDF stores have been implemented. However, there is no widely used benchmark for evaluating geospatial RDF stores which takes into account recent advances to the state of the art in this area. In this paper, we develop a benchmark, called Geographica, which uses both real-world and synthetic data to test the offered functionality and the performance of some prominent geospatial RDF stores

Measuring and explaining cross-country immigration policies

The intensified international migration pressures of the recent decades prompted many developed countries to revise their immigration regulations and increase border controls. However, the development of these reforms as well as their effectiveness in actually managing new immigration flows remains poorly understood. The main reason is that migration regulations are hard to quantify, which has prevented the construction of a universal measure of migration policy. To fill this gap in the literature, we construct an indicator of the restrictiveness of immigration entry policy across countries as well as a more comprehensive indicator of migration policy that also accounts for staying requirements and regulations to foster integration. These indexes are then used to disentangle the factors determining the toughness of migration regulations. Our empirical framework combines elements from the median voter and interest group approach and accounts for cross-country correlation in migration policies. We find strong evidence of spatial correlation in particular in entry restrictiveness, while the impact of economic determinants of migration policy remains much more modest