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Abstract
Within a CyberGIS environment, the development of effective mechanisms to encode metadata for spatial analytical methods and to track the provenance of operations is a key requirement. Spatial weights are a fundamental element in a wide range of spatial analysis methods that deal with testing for and estimating models with spatial autocorrelation. They form the link between the data structure in a GIS and the spatial analysis methods. Over time, the number of formats for spatial weights implemented in software has proliferated, without any standard or easy interoperability. In this paper, we propose a flexible format that provides a way to ensure interoperability within a cyberinfrastructure environment. We illustrate the format with an application of a spatial weights web service, which is part of an evolving spatial analytical workbench. We describe an approach to embed provenance in spatial weights structures and illustrate the performance of the web service by means of a number of small experiments.
Acknowledgments
This research was supported in part by NSF Award OCI-1047916, SI2-SSI: CyberGIS Software Integration for Sustained Geospatial Innovation.
Notes
1. For a recent example of the implementation of parallelization in PySAL, see Rey et al. (Citation2013).
2. Our experimental system consists of a Mac Pro workstation with two 2.93 GHz Quad-Core Intel Xeon processors and 16 GB of 1066 MHz DDR3 ECC memory, running the Mac OS X Lion 10.7.4 operating system.
3. The back end consists of 132 Quad-core HPC nodes with over 1000 computing cores available. The front and back end of the cluster have access to 70 TB shared storage for data exchange.