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Shen C.-H.,Nanjing Normal University | Shen C.-H.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource | Shen C.-H.,Key Laboratory of Virtual Geographic Environment of Ministry of Education
Physics Letters, Section A: General, Atomic and Solid State Physics | Year: 2015

To analyze the unique contribution of meteorological factors to the air pollution index (API), a new method, the detrended semipartial cross-correlation analysis (DSPCCA), is proposed. Based on both a detrended cross-correlation analysis and a DFA-based multivariate-linear-regression (DMLR), this method is improved by including a semipartial correlation technique, which is used to indicate the unique contribution of an explanatory variable to multiple correlation coefficients. The advantages of this method in handling nonstationary time series are illustrated by numerical tests. To further demonstrate the utility of this method in environmental systems, new evidence of the primary contribution of meteorological factors to API is provided through DMLR. Results show that the most important meteorological factors affecting API are wind speed and diurnal temperature range, and the explanatory ability of meteorological factors to API gradually strengthens with increasing time scales. The results suggest that DSPCCA is a useful method for addressing environmental systems. © 2015 Elsevier B.V. Source


Shen C.,Nanjing Normal University | Shen C.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource | Shen C.,Key Laboratory of Virtual Geographic Environment of Ministry of Education
Physics Letters, Section A: General, Atomic and Solid State Physics | Year: 2015

A time-lagged DCCA cross-correlation coefficient is proposed with objective of quantifying the level of time-lagged cross-correlation between two nonstationary time series at time scales. This coefficient, ρ(n,τ,R,R′), is defined based on a DCCA cross-correlation coefficient ρDCCA(n). The implementation of this coefficient will be illustrated through selected time series of wind speed and air pollution index (API). The results indicate that both time scales and time lags are very small, ρ(n,τ,R,R′) is attributed to a time-lagged effect; while when time lags are comparatively large, ρDCCA(n) contributes partially to ρ(n,τ,R,R′). This partial contribution is greater when τn. ρ(n,τ,R,R′) is applied in meteorology. It is found that the method is reasonable and reliable. Therefore, the detrended time-lagged cross-correlation analysis can be useful to deepen and broaden our understanding of cross-correlations between nonstationary time series. © 2014 Elsevier B.V. All rights reserved. Source


Shen C.-H.,Nanjing Normal University | Shen C.-H.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource | Shen C.-H.,Key Laboratory of Virtual Geographic Environment of Ministry of Education | Li C.-L.,Nanjing Normal University
Physica A: Statistical Mechanics and its Applications | Year: 2016

In order to reveal the intrinsic cross-correlations between air pollution index (API) records and synchronously meteorological elements data, the detrended partial cross-correlation (DPCC) coefficients are analyzed using a detrended partial cross-correlation analysis (DPCCA). DPCC coefficients for different spatial locations and seasons are calculated and compared. The results show that DPCCA can uncover intrinsic cross-correlations between API and meteorological elements, and most of their interactional mechanisms can be explained. DPCC coefficients are either positive or negative, and vary with spatial locations and seasons, with consistently interactional mechanisms. More remarkable, we find that detrended cross-correlation analysis can present the cross-correlations between the fluctuations in two nonstationary time series, but this cross-correlation does not always fully reflect the interactional mechanism for the original time series. Despite this, DPCCA is recommended as a comparatively reliable method for revealing intrinsic cross-correlations between API and meteorological elements, and it can also be useful for our understanding of their interactional mechanisms. © 2015 Elsevier B.V. All rights reserved. Source


Shen C.-H.,Nanjing Normal University | Shen C.-H.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource | Shen C.-H.,Key Laboratory of Virtual Geographic Environment of Ministry of Education | Huang Y.,Nanjing Normal University | Yan Y.-N.,Nanjing Normal University
Physica A: Statistical Mechanics and its Applications | Year: 2016

This paper describes multifractal characteristics of daily air pollution index (API) records in Nanjing from 2001 to 2012. The entire daily API time series is first divided into 12 parts that serve as research objects, and the generalized Hurst exponent is calculated for each series. And then, the multifractal sources are analyzed and singularity spectra are shown. Next, based on a singularity spectrum, the multifractal-characteristics parameters (maximum exponent α0, spectrum width Δα, and asymmetry Δαas) are introduced. The results show that the fractality of daily API for each year is multifractal. The multifractal sources originate from both a broad probability density function and different long-range correlations with small and large fluctuations. The strength of the distribution multifractality is stronger than that of the correlation multifractality. The variation in the structure of API time series with increasing years is mainly related to long-range correlations. The structure of API time series in some years is richer. These findings can provide a scientific basis for further probing into the complexity of API. © 2016 Elsevier B.V.All rights reserved. Source


Hu Z.,Key Laboratory of Virtual Geographic Environment of Ministry of Education | Hu Z.,Engineering Technical Laboratory of Digital Protection of Traditional Village of Hunan Province | Tang G.,Key Laboratory of Virtual Geographic Environment of Ministry of Education | Lu G.,Key Laboratory of Virtual Geographic Environment of Ministry of Education
Journal of Geographical Sciences | Year: 2014

Language plays a vital role in the communication, sharing and transmission of information among human beings. Geographical languages are essential for understanding, investigating, representing and propagating geo-spatial information. Geographical languages have developed and evolved gradually with improvements in science, technology and cognitive levels. Concerning the theoretical progress from geographical information ontology, epistemology and linguistic theory, this paper firstly puts forward the concept of a GIS language and discusses its basic characteristics according to changes in the structures, functions and characteristics of geographical languages. This GIS language can be regarded as a system of synthetic digital symbols. It is a comprehensive representation of geographical objects, phenomena and their spatial distributions and dynamic processes. This representation helps us generate a universal perception of geographical space using geographical scenarios or symbols with geometry, statuses, processes, spatio-temporal relationships, semantics and attributes. Furthermore, this paper states that the GIS language represents a new generation of geographical language due to its intrinsic characteristics, structures, functions and systematic content. Based on the aforementioned theoretical foundation, this paper illustrates the pivotal status and contributions of the GIS language from the perspective of geographical researchers. The language of GIS is a new geographical language designed for the current era, with features including spatio-temporal multi-dimension representation, interactive visualization, virtual geographical scenarios, multi-sensor perception and expedient broadcasting via the web. The GIS language is the highest-level geographical language developed to date, integrating semantic definitions, feature extraction, geographical dynamic representation and spatio-temporal factors and unifying the computation of geographical phenomena and objects. The GIS language possesses five important characteristics: abstraction, systematicness, strictness, precision and hierarchy. In summary, the GIS language provides a new means for people to recognize, understand and simulate entire geo-environments. Therefore, exploration of the GIS language's functions in contemporary geographical developments is becoming increasingly important. Similarly, construction of the conceptual model and scientific systems of the GIS language will promote the development of the disciplines of geography and geographical information sciences. Therefore, this paper investigates the prospects of the GIS language from the perspectives of digital technology, geographical norms, geographical modeling and the disciplinary development of geography. © 2014 Science Press and Springer-Verlag Berlin Heidelberg. Source

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