Geographic Information System Query Research Articles

Fuzzy Logic Applied Spatial Queries in Geographic Information Systems

In the recent years, the usage of Geographic Information Systems (GIS) has been rapidly increasing and it became the main tool for analyzing spatial data in unprecedented number of fields of activities. The processing time is important and currently uses more advanced applied mathematical and computer science knowledge: One of these mathematical theories is fuzzy logic. The fuzzy logic theory gives the possibility of enhancing spatial data management with the modelling of uncertainty. In this paper, the applicability of fuzzy set theory has been presented to respond spatial queries and the comparison performance between normal and spatial queries.

Geographic information system query optimisation algorithm based on redundant data deletion and filtering technology

In order to reduce the interference of redundant information during geographic information system query and improve the accuracy of information query, a geographic information system query optimisation algorithm based on redundant data deletion and filtering technology is proposed. In this method, a distributed data query structure model of geographic information system is constructed; adaptive group matching of query intervals is conducted through the segment fusion method, and then key information points after group matching are located through the association rule feature information fusion method. Finally, the high-order cumulant feature extraction method is adopted to achieve accurate information query of geographic information system. Simulation results show that in geographic information system query under redundant data infection, this method can provide accuracy 8.75% higher than that provided by the FCM method and 12.98% higher than that provided by the support vector machine algorithm, which indicates that this method can provide strong anti-interference ability of query and cost less overhead.

Geographic information system query optimisation algorithm based on redundant data deletion and filtering technology

Geographic information system query optimisation algorithm based on redundant data deletion and filtering technology

Modelling the spatial distribution of Fasciola hepatica in bovines using decision tree, logistic regression and GIS query approaches for Brazil.

Fascioliasis is a condition caused by the trematode Fasciola hepatica. In this paper, the spatial distribution of F. hepatica in bovines in Brazil was modelled using a decision tree approach and a logistic regression, combined with a geographic information system (GIS) query. In the decision tree and the logistic model, isothermality had the strongest influence on disease prevalence. Also, the 50-year average precipitation in the warmest quarter of the year was included as a risk factor, having a negative influence on the parasite prevalence. The risk maps developed using both techniques, showed a predicted higher prevalence mainly in the South of Brazil. The prediction performance seemed to be high, but both techniques failed to reach a high accuracy in predicting the medium and high prevalence classes to the entire country. The GIS query map, based on the range of isothermality, minimum temperature of coldest month, precipitation of warmest quarter of the year, altitude and the average dailyland surface temperature, showed a possibility of presence of F. hepatica in a very large area. The risk maps produced using these methods can be used to focus activities of animal and public health programmes, even on non-evaluated F. hepatica areas.

GISQAF: MapReduce guided spatial query processing and analytics system

SummaryThe Global Database of Event, Language, and Tone (GDELT) is the only global political georeferenced event dataset with more than 250 million observations covering all countries in the world since January 1, 1979. TABARI and CAMEO are the tools that are used to collect and code events from all international news coverage. To query such big geospatial data, traditional RDBMS can no longer be used, and the need for parallel distributed solutions has become a necessity. MapReduce paradigm has proven to be a scalable platform to process and analyze Big Data in the cloud. Hadoop, as an implementation of MapReduce, is an open‐source application that has been widely used and accepted in academia and industry. However, when dealing with Spatial Data, Hadoop is not equipped well and does not perform efficiently. SpatialHadoop is an extension of Hadoop with the support of spatial data. In this paper, we present Geographic Information System Query and Analytics Framework (GISQAF), which has been built on top of SpatialHadoop. GISQAF focuses on two parts: query processing and data analytics. For the query processing part, we show how this solution outperforms Hadoop query processing by orders of magnitude when applying queries on the GDELT dataset with a size of 60 GB. We show the results for various types of queries. For the data analytics part, we present an approach for finding Spatial co‐occurring events. We show how GISQAF is suitable and efficient to handle data analytics techniques. Copyright © 2015 John Wiley & Sons, Ltd.

IRSJ: incremental refining spatial joins for interactive queries in GIS

An increasing number of emerging web database applications deal with large georeferenced data sets. However, exploring these large data sets through spatial queries can be very time and resource intensive. The need for interactive spatial queries has arisen in many applications such as Geographic Information Systems (GIS) for efficient decision-support. In this paper, we propose a new interactive spatial query processing technique for GIS. We present a family of the Incremental Refining Spatial Join (IRSJ) algorithms that can be used to report incrementally refined running estimates for aggregate queries while simultaneously displaying the actual query result tuples of the data sets sampled so far. Our goal is to minimize the time until an acceptably accurate estimate of the query result is available (to users) measured by a confidence interval. Our approach enables more interactive data exploration and analysis. While similar work has been done in relational databases, to the best of our knowledge, this is the first work using this approach in GIS. We investigate and evaluate different sampling methodologies through extensive experimental performance comparisons. Experiments on both real and synthetic data show an order of magnitude response time improvement relative to the final answer obtained when using a full R-tree join. We also show the impact of different index structures on the performance of our algorithms using three known sampling methods.

A national look at carbon capture and storage—National carbon sequestration database and geographical information system (NatCarb)

Abstract The US Department of Energy’s Regional Carbon Sequestration Partnerships (RCSPs) are responsible for generating geospatial data for the maps displayed in the Carbon Sequestration Atlas of the United States and Canada . Key geospatial data (carbon sources, potential storage sites, transportation, land use, etc.) are required for the Atlas, and for efficient implementation of carbon sequestration on a national and regional scale. The National Carbon Sequestration Database and Geographical Information System (NatCarb) is a relational database and geographic information system (GIS) that integrates carbon storage data generated and maintained by the RCSPs and various other sources. The purpose of NatCarb is to provide a national view of the carbon capture and storage potential in the U.S. and Canada. The digital spatial database allows users to estimate the amount of CO 2 emitted by sources (such as power plants, refineries and other fossil-fuel-consuming industries) in relation to geologic formations that can provide safe, secure storage sites over long periods of time. The NatCarb project is working to provide all stakeholders with improved online tools for the display and analysis of CO 2 carbon capture and storage data. NatCarb is organizing and enhancing the critical information about CO 2 sources and developing the technology needed to access, query, model, analyze, display, and distribute natural resource data related to carbon management. Data are generated, maintained and enhanced locally at the RCSP level, or at specialized data warehouses, and assembled, accessed, and analyzed in real-time through a single geoportal. NatCarb is a functional demonstration of distributed data-management systems that cross the boundaries between institutions and geographic areas. It forms the first step toward a functioning National Carbon Cyberinfrastructure (NCCI). NatCarb provides access to first-order information to evaluate the costs, economic potential and societal issues of CO 2 capture and storage, including public perception and regulatory aspects. NatCarb online access has been modified to address the broad needs of a spectrum of users. NatCarb includes not only GIS and database query tools for high-end user, but simplified display for the general public using readily available web tools such as Google Earth™and Google Maps™. Not only is NatCarb connected to all the RCSPs, but data are also pulled from public servers including the U.S. Geological Survey-EROS Data Center and from the Geography Network. Data for major CO 2 sources have been obtained from U.S. Environmental Protection Agency (EPA) databases, and data on major coal basins and coalbed methane wells were obtained from the Energy Information Administration (EIA).

Spatial targeting using queries in a 3-D GIS environment with application to mineral exploration

A query framework for spatial targeting within a 3-D geographic information system (GIS) software environment is introduced. Input to a query consists of parameters relevant to the query type together with a set of Common Earth Modelling objects represented as point sets, polygonal lines, surfaces, and grids or a region set (subset) thereof. The result of a 3-D GIS query is a region within each of the input objects that consists of nodes or grid cells where the query criteria was satisfied. We provide example scenarios, drawn from mineral exploration, where 3-D queries are used to guide spatial targeting within a near-mine or regional map scale setting. Query types supported are: proximity query (to a “probe” object), property query (numeric attribute), shell query (containment within a closed surface), meta-data query, feature query (dome, depression, curvature), trend query (dip plane, vector) and intersection query (with a “probe” object). Queries can be specific for a given object type but in general transcend object types. Standard set theoretical operations for a query results in newly defined regions and are supported within the Gocad © development environment. This development focuses on queries relevant in the 3-D data integration and interpretation stages of mature geological model development as well as early analysis, typically undertaken before a fully partitioned and attributed 3-D topological model is available.

Use of satellite remote sensing and geographic information systems to model the distribution and abundance of snail intermediate hosts in Africa: a preliminary model for Biomphalaria pfeifferi in Ethiopia

Geographic information system (GIS) risk models for the snail-borne diseases caused by Schistosoma spp. and Fasciola spp. have recently been developed based on climate and satellite-retrieved data on temperature and vegetation coverage. By using these models, it was possible to describe a relationship between vegetation index (Normalized Differences Vegetation Index (NDVI)), land surface temperature ( T max) and disease prevalence, but little reference was made to the distribution of the corresponding intermediate host snail. Presence of the intermediate host snail is a key factor determining distribution of the disease in sub-Saharan Africa and a good snail distribution mode would probably mirror the endemic area of schistosomiasis. In the present analysis, it was shown that snail distribution data corresponds with schistosomiasis prevalence data in relation to a forecast model based on NDVI and T max data derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration satellite series. The ‘best fit’ model included NDVI values from 125 to 145 and a T max data range of 10–32°C. This model included 92.3, 90.4 and 94.6% of the positive snail sample sites in GIS query overlay areas extracted from annual, dry season and wet season composite maps, respectively. For other sites in Africa, other NDVI and T max ranges may be more appropriate, depending on the species of snail present, a topic that will be examined in further studies.

ROADVEG: Utah Department of Transportation Roadside Vegetation Inventory and Geographic Information System

The Utah Department of Transportation (UDOT), in collaboration with the Utah State University Ecology Center, has created a geographic information system (GIS) of spatially referenced roadside and context landscape attributes pertinent to roadside vegetation management. Construction of the ROADVEG GIS involved the design of a relational database, the assignment of attributes to 2200 km (1,365 mi) of road segments through on-the-road field inventories, and the linkage of database attributes to spatial coverages for GIS presentation and query. The ROADVEG GIS offers UDOT personnel a new way to remotely assess existing roadside and context vegetation attributes along Utah’s roadways, as well as a potentially powerful method of performing multivariate spatial queries for long-range planning. Through queries of the ROADVEG inventory, road segments with specified multivariate conditions are readily identifiable. Visual depictions of multiple vegetation management scenarios with varying cost-benefit ratios are now possible.