Scaling geospatial searches in large spatial databases

Modern geographical databases, which are at the core of geographic information systems (GIS), store a rich set of aspatial attributes in addition to geographic data. Typically, aspatial information comes in textual and numeric format. Retrieving information constrained on spatial and aspatial data from geodatabases provides GIS users the ability to perform more interesting spatial analyses, and for applications to support composite location-aware searches; for example, in a real estate database: “Find the nearest homes for sale to my current location that have backyard and whose prices are between $50,000 and $80,000”. Efficient processing of such queries require combined indexing strategies of multiple types of data. Existing spatial query engines commonly apply a two-filter approach (spatial filter followed by nonspatial filter, or viceversa), which can incur large performance overheads. On the other hand, more recently, the amount of geolocation data has grown rapidly in databases due in part to advances in geolocation technologies (e.g., GPS-enabled smartphones) that allow users to associate location data to objects or events. The latter poses potential data ingestion challenges of large data volumes for practical GIS databases. In this dissertation, we first show how indexing spatial data with R-trees (a typical data pre-processing task) can be scaled in MapReduce—a widely-adopted parallel programming model for data intensive problems. The evaluation of our algorithms in a Hadoop cluster showed close to linear scalability in building R-tree indexes. Subsequently, we develop efficient algorithms for processing spatial queries with aspatial conditions. Novel techniques for simultaneously indexing spatial with textual and numeric data are developed to that end. Experimental evaluations with real-world, large spatial datasets measured query response times within the sub-second range for most cases, and up to a few seconds for a small number of cases, which is reasonable for interactive applications. Overall, the previous results show that the MapReduce parallel model is suitable for indexing tasks in spatial databases, and the adequate combination of spatial and aspatial attribute indexes can attain acceptable response times for interactive spatial queries with constraints on aspatial data.

Macroinvertebrate community response to eutrophication in an oligotrophic wetland: An in situ mesocosm experiment

Eutrophication from anthropogenic nutrient enrichment is a primary threat to the oligotrophic freshwater marshes of southern Florida. Macrophyte and periphyton response to increased phosphorus (P) has been well documented in both correlative and experimental studies, but the response of consumer communities remains poorly understood, especially in southern marl prairies. We conducted a P-loading experiment in in situ mesocosms in Taylor Slough, Everglades National Park, and examined the response of macroinvertebrate communities. Mesocosms at two sites were loaded weekly with P at four levels: control (0 g P/m2/yr), low (0.2 g P/m2/yr), intermediate (0.8 g P/m2/yr), and high (3.2 g P/m2/ yr). After ∼2 yrs of P-loading, macroinvertebrates were sampled using periphyton mat and benthic floc cores. Densities of macroinvertebrate taxa (no./g AFDM) were two to 16 times higher in periphyton mats than benthic floc. Periphyton biomass decreased with enrichment at one site, and periphyton was absent from many intermediate and all high P treatments at both sites. Total macroinvertebrate density in periphyton mats increased with intermediate P loads, driven primarily by chironomids and nematodes. Conversely, total macroinvertebrate density in benthic floc decreased with enrichment, driven primarily by loss of chironomids and ceratopogonids (Dasyhelea). This study suggests that macroinvertebrate density increases with enrichment until periphyton mats are lost, after which it decreases, and mat infauna fail to move into benthic substrates in response to mat loss. These results were noted at nutrient levels too low to yield anoxia, and we believe that the decrease of macroinvertebrate density resulted from a loss of habitat. This work illustrates the importance of periphyton mats as habitat for macroinvertebrates in the Everglades. This study also indicates that in this system, macroinvertebrate sampling should be designed to target periphyton mats or conducted with special attention to inclusion of substrates relative to their coverage.