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Network-Based and Binless Frequency Analyses.

Derrible S, Ahmad N - PLoS ONE (2015)

Bottom Line: The methodology is validated by sampling 12 typical distributions, and it is applied to a number of real-world data sets with both spatial and temporal components.The methodology can be applied to any data set and provides a robust means to uncover meaningful patterns and trends.A free python script and a tutorial are also made available to facilitate the application of the method.

View Article: PubMed Central - PubMed

Affiliation: Complex and Sustainable Urban Networks (CSUN) Laboratory, University of Illinois at Chicago, Chicago, IL, United States of America.

ABSTRACT
We introduce and develop a new network-based and binless methodology to perform frequency analyses and produce histograms. In contrast with traditional frequency analysis techniques that use fixed intervals to bin values, we place a range ±ζ around each individual value in a data set and count the number of values within that range, which allows us to compare every single value of a data set with one another. In essence, the methodology is identical to the construction of a network, where two values are connected if they lie within a given a range (±ζ). The value with the highest degree (i.e., most connections) is therefore assimilated to the mode of the distribution. To select an optimal range, we look at the stability of the proportion of nodes in the largest cluster. The methodology is validated by sampling 12 typical distributions, and it is applied to a number of real-world data sets with both spatial and temporal components. The methodology can be applied to any data set and provides a robust means to uncover meaningful patterns and trends. A free python script and a tutorial are also made available to facilitate the application of the method.

No MeSH data available.


Application of Network-Based Methodology on Three Real-World Data Sets.(A) Chicago Metropolitan Statistical Area population density for 2,207 census tracts, where we used a log-scale because of the heterogeneity in the data (Source: 2010 US Census). (B) 2012 residential electricity use for the 50 US states and the District of Columbia (Source: EIA). (C) 2012 world life expectancy for 199 countries (Source: World Bank). See Section F in S1 File for details on each data set.
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pone.0142108.g003: Application of Network-Based Methodology on Three Real-World Data Sets.(A) Chicago Metropolitan Statistical Area population density for 2,207 census tracts, where we used a log-scale because of the heterogeneity in the data (Source: 2010 US Census). (B) 2012 residential electricity use for the 50 US states and the District of Columbia (Source: EIA). (C) 2012 world life expectancy for 199 countries (Source: World Bank). See Section F in S1 File for details on each data set.

Mentions: The NB histogram methodology can be applied to any data sets. To further illustrate its benefits, we apply it to three completely different data sets: (1) 2010 population density in Chicago, (2) 2012 residential electricity use in the United-States, (3) 2012 life expectancy in the world. The results for these three applications are shown in Fig 3 and tabulated in Table 2.


Network-Based and Binless Frequency Analyses.

Derrible S, Ahmad N - PLoS ONE (2015)

Application of Network-Based Methodology on Three Real-World Data Sets.(A) Chicago Metropolitan Statistical Area population density for 2,207 census tracts, where we used a log-scale because of the heterogeneity in the data (Source: 2010 US Census). (B) 2012 residential electricity use for the 50 US states and the District of Columbia (Source: EIA). (C) 2012 world life expectancy for 199 countries (Source: World Bank). See Section F in S1 File for details on each data set.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4631440&req=5

pone.0142108.g003: Application of Network-Based Methodology on Three Real-World Data Sets.(A) Chicago Metropolitan Statistical Area population density for 2,207 census tracts, where we used a log-scale because of the heterogeneity in the data (Source: 2010 US Census). (B) 2012 residential electricity use for the 50 US states and the District of Columbia (Source: EIA). (C) 2012 world life expectancy for 199 countries (Source: World Bank). See Section F in S1 File for details on each data set.
Mentions: The NB histogram methodology can be applied to any data sets. To further illustrate its benefits, we apply it to three completely different data sets: (1) 2010 population density in Chicago, (2) 2012 residential electricity use in the United-States, (3) 2012 life expectancy in the world. The results for these three applications are shown in Fig 3 and tabulated in Table 2.

Bottom Line: The methodology is validated by sampling 12 typical distributions, and it is applied to a number of real-world data sets with both spatial and temporal components.The methodology can be applied to any data set and provides a robust means to uncover meaningful patterns and trends.A free python script and a tutorial are also made available to facilitate the application of the method.

View Article: PubMed Central - PubMed

Affiliation: Complex and Sustainable Urban Networks (CSUN) Laboratory, University of Illinois at Chicago, Chicago, IL, United States of America.

ABSTRACT
We introduce and develop a new network-based and binless methodology to perform frequency analyses and produce histograms. In contrast with traditional frequency analysis techniques that use fixed intervals to bin values, we place a range ±ζ around each individual value in a data set and count the number of values within that range, which allows us to compare every single value of a data set with one another. In essence, the methodology is identical to the construction of a network, where two values are connected if they lie within a given a range (±ζ). The value with the highest degree (i.e., most connections) is therefore assimilated to the mode of the distribution. To select an optimal range, we look at the stability of the proportion of nodes in the largest cluster. The methodology is validated by sampling 12 typical distributions, and it is applied to a number of real-world data sets with both spatial and temporal components. The methodology can be applied to any data set and provides a robust means to uncover meaningful patterns and trends. A free python script and a tutorial are also made available to facilitate the application of the method.

No MeSH data available.