Parallel optimal choropleth map classification in PySAL

Rey, Sergio J.; Anselin, Luc; Pahle, Robert; Kang, Xin; Stephens, Philip A.

doi:10.1080/13658816.2012.752094

Cited by 20 publications

(9 citation statements)

References 14 publications

(15 reference statements)

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“…In light of significant computational advancement, the low observation count suggested by Hartigan no longer holds. Having said that, the computational cost still scales quadratically with the number of observations (dropping the constant scalar k inline with standard notation), and long run times are observable with as few as 5,000 observations (Rey et al, ; Laura & Rey, ).…”

Section: Methodsmentioning

confidence: 99%

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An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings

Rey

Stephens

Laura

2016

Transactions in GIS

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Large data contexts present a number of challenges to optimal choropleth map classifiers. Application of optimal classifiers to a sample of the attribute space is one proposed solution. The properties of alternative sampling-based classification methods are examined through a series of Monte Carlo simulations. The impacts of spatial autocorrelation, number of desired classes, and form of sampling are shown to have significant impacts on the accuracy of map classifications. Tradeoffs between improved speed of the sampling approaches and loss of accuracy are also considered. The results suggest the possibility of guiding the choice of classification scheme as a function of the properties of large data sets.

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Section: Methodsmentioning

confidence: 99%

“…The utilization of parallel computing paradigms has significantly reduced the total compute time required to utilize an optimal map classification (Rey, Anselin, Pahle, Kang, & Stephens ; Laura & Rey, ). Unfortunately, these methods still scale quadratically in memory space (RAM), making them intractable in big data settings.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings

Rey

Stephens

Laura

2016

Transactions in GIS

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“…Tang (2013) investigated the use of graphics processing units (GPUs) to accelerate the construction of large circular cartograms. Rey et al (2013) implemented a parallel choropleth map classification algorithm using the Python Spatial Analysis Library (PySAL). Parallel map visualization belongs to typical embarrassing parallelism, because a visualization domain (i.e., a rectangular window for map drawing within the whole spatial domain of the data, also termed view area/window) can be completely divided into multiple sub-domains for parallel processing, and there is no need for communications between computing units during parallel computing.…”

Section: Parallel Vector Map Visualizationmentioning

confidence: 99%

A spatially adaptive decomposition approach for parallel vector data visualization of polylines and polygons

Guo

Guan

Xie

et al. 2015

International Journal of Geographical Information Science

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With the wide adoption of big spatial data and the emergence of CyberGIS, the nontrivial computational intensity introduced by massive amount of data poses great challenges to the performance of vector map visualization. The parallel computing technologies provide promising solutions to such problems. Evenly decomposing the visualization task into multiple subtasks is one of the key issues in parallel visualization of vector data. This study focuses on the decomposition of polyline and polygon data for parallel visualization. Two key factors impacting the computational intensity were identified: the number of features and the number of vertices of each feature. The computational intensity transform functions (CITFs) were constructed based on the linear relationships between the factors and the computing time. The computational intensity grid (CIG) can then be constructed using the CITFs to represent the spatial distribution of computational intensity. A noninterlaced continuous space-filling curve is used to group the lattices of CIG into multiple sub-domains such that each subdomain entails the same amount of computational intensity as others. The experiments demonstrated that the approach proposed in this paper was able to effectively estimate and spatially represent the computational intensity of visualizing polylines and polygons. Compared with the regular domain decomposition methods, the new approach generated much more balanced decomposition of computational intensity for parallel visualization and achieved near-linear speedups, especially when the data is greatly heterogeneously distributed in space.

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“…In this study, the local-scale clustering of heavy metal pollution in river sediments was analyzed using the ILINCS method, which incorporates a Monte Carlo simulation to assess the statistical significance of the detected clusters. The GeoDaNet toolbox [35] is applied to calculate the ILINCS in this research.…”

Section: Local Indicators Of Network-constrained Clusters Approachesmentioning

confidence: 99%

Measuring Spatial Distribution Characteristics of Heavy Metal Contaminations in a Network-Constrained Environment: A Case Study in River Network of Daye, China

Wang

Nie

2017

Sustainability

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Abstract:Measuring the spatial distribution of heavy metal contaminants is the basis of pollution evaluation and risk control. Considering the cost of soil sampling and analysis, spatial interpolation methods have been widely applied to estimate the heavy metal concentrations at unsampled locations. However, traditional spatial interpolation methods assume the sample sites can be located stochastically on a plane and the spatial association between sample locations is analyzed using Euclidean distances, which may lead to biased conclusions in some circumstances. This study aims to analyze the spatial distribution characteristics of copper and lead contamination in river sediments of Daye using network spatial analysis methods. The results demonstrate that network inverse distance weighted interpolation methods are more accurate than planar interpolation methods. Furthermore, the method named local indicators of network-constrained clusters based on local Moran' I statistic (ILINCS) is applied to explore the local spatial patterns of copper and lead pollution in river sediments, which is helpful for identifying the contaminated areas and assessing heavy metal pollution of Daye.

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Parallel optimal choropleth map classification in PySAL

Cited by 20 publications

References 14 publications

An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings

An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings

A spatially adaptive decomposition approach for parallel vector data visualization of polylines and polygons

Measuring Spatial Distribution Characteristics of Heavy Metal Contaminations in a Network-Constrained Environment: A Case Study in River Network of Daye, China

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