Dataset Information

FESTIval: A versatile framework for conducting experimental evaluations of spatial indices.

ABSTRACT: The use of a spatial index is a common strategy to improve the performance of spatial queries in spatial database systems and Geographic Information Systems. Choosing the right spatial index to be employed in a given context requires a quantitative method to analyze the performance of spatial indices. This is done through extensive experimental evaluations. However, conducting these evaluations is an expensive, error-prone, and challenging task because (i) spatial objects are complex data to manage, (ii) spatial indices can apply different parameter values and thus assume distinct configurations, and (iii) there are indices specifically developed for different storage systems, such as disks and flash memories. In this article, we propose FESTIval, a versatile framework for conducting experimental evaluations of spatial indices. FESTIval has the following main advantages: •the support for different types of disk-based and flash-aware spatial indices;•the specification and execution of user-defined workloads;•the use of a data schema that stores index configurations and statistical data of executed workloads. Because of its characteristics, FESTIval allows users to reproduce executed experiments. Further, FESTIval provides an extensible environment, where any spatial dataset can be handled by spatial indices. FESTIval has been used to validate new proposals of flash-aware spatial indices, such as eFIND-based indices.

SUBMITTER: Carniel AC

PROVIDER: S-EPMC6994642 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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FESTIval: A versatile framework for conducting experimental evaluations of spatial indices.

Carniel Anderson C AC Ciferri Ricardo R RR Ciferri Cristina D A CDA

MethodsX 20191016

The use of a spatial index is a common strategy to improve the performance of spatial queries in spatial database systems and Geographic Information Systems. Choosing the right spatial index to be employed in a given context requires a quantitative method to analyze the performance of spatial indices. This is done through extensive experimental evaluations. However, conducting these evaluations is an expensive, error-prone, and challenging task because (i) spatial objects are complex data to man ...[more]

PMID: 32021811

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Project description:Vehicle emissions represent one of the most important air pollution sources in most urban areas, and elevated concentrations of pollutants found near major roads have been associated with many adverse health impacts. To understand these impacts, exposure estimates should reflect the spatial and temporal patterns observed for traffic-related air pollutants. This paper evaluates the spatial resolution and zonal systems required to estimate accurately intraurban and near-road exposures of traffic-related air pollutants. The analyses use the detailed information assembled for a large (800 km2) area centered on Detroit, Michigan, USA. Concentrations of nitrogen oxides (NOx) due to vehicle emissions were estimated using hourly traffic volumes and speeds on 9,700 links representing all but minor roads in the city, the MOVES2010 emission model, the RLINE dispersion model, local meteorological data, a temporal resolution of 1 hr, and spatial resolution as low as 10 m. Model estimates were joined with the corresponding shape files to estimate residential exposures for 700,000 individuals at property parcel, census block, census tract, and ZIP code levels. We evaluate joining methods, the spatial resolution needed to meet specific error criteria, and the extent of exposure misclassification. To portray traffic-related air pollutant exposure, raster or inverse distance-weighted interpolations are superior to nearest neighbor approaches, and interpolations between receptors and points of interest should not exceed about 40 m near major roads, and 100 m at larger distances. For census tracts and ZIP codes, average exposures are overestimated since few individuals live very near major roads, the range of concentrations is compressed, most exposures are misclassified, and high concentrations near roads are entirely omitted. While smaller zones improve performance considerably, even block-level data can misclassify many individuals. To estimate exposures and impacts of traffic-related pollutants accurately, data should be geocoded or estimated at the most-resolved spatial level; census tract and larger zones have little if any ability to represent intraurban variation in traffic-related air pollutant concentrations. These results are based on one of the most comprehensive intraurban modeling studies in the literature and results are robust. Recommendations address the value of dispersion models to portray spatial and temporal variation of air pollutants in epidemiology and other studies; techniques to improve accuracy and reduce the computational burden in urban scale modeling; the necessary spatial resolution for health surveillance, demographic, and pollution data; and the consequences of low resolution data in terms of exposure misclassification.

Dataset Information

FESTIval: A versatile framework for conducting experimental evaluations of spatial indices.

Publications

FESTIval: A versatile framework for conducting experimental evaluations of spatial indices.

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