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Data Analytics on Sustainable, Resilient and Just Communities

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 30128

Special Issue Editors


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Guest Editor
Department of Geography, University at Buffalo, 105 Wilkeson, Buffalo, NY 14261, USA
Interests: agent-based modeling; computational social science (CSS); geocomputation; geographic information science (GIS); urban systems; spatial analysis; social networks; volunteered geographic information (VGI)
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Guest Editor
Institute for Geospatial Research and Education, Eastern Michigan University, Ypsilanti, MI 48197, USA
Interests: geographic information science; spatial modelling; remote sensing theory and methodology; spatiotemporal modelling of urban growth; grassland ecosystem; coupled impacts of human dynamics and environmental change on resource management and ecosystem recovery; land-use and land-cover changes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Rapid urbanization is resulting in more economic and social human activities affecting natural ecosystems. It also means more natural land is being converted into urban land, with consequential detrimental impacts on the processes and functions of ecosystems. Developing countries’ economic growth is prioritized ahead of environmental conservation, but the unprecedented urban growth we are now witnessing has triggered drastic land use conversion, either replacing natural landscapes with semi-natural mixtures or resulting in complete urban development. Inevitably, areas converted into urban land use have altered the structure, pattern, and functionality of natural ecosystems. More research is needed on sustainable urban development; an approach that harmonizes the demand for urban encroachment with the preservation of delicate ecosystems is needed as well.

This special issue plans to focus on Data Analytics on Sustainable, Resilient, and Just Communities. It is a topic with immense collaborative potential and interdisciplinary challenges for environmental scientists, ecologists, economists, and policy-makers. Building on a series of five successful annual conferences in the USA and China, this special issue will bring together leading scholars in related disciplines to share their research on the challenges and solutions regarding Methodological Advances in Sustainable, Resilient, and Just Communities Research. This special issue will be open to the submission of manuscripts from outside the conference as well, provided that they fit within the scope of the Special Issue. Submitted manuscripts will need to be full-length papers that have not been previously published in a substantially-similar format. In addition, all manuscripts will need to be reviewed through electronic an Manuscript Tracking System and accord to the same editorial guidelines as all other submitted manuscripts.

Appropriate topics include, but are not limited to, the following:

  • Advanced geo-computational ecosystems modelling
  • Creation of new visualization products that increase the understanding of large and diverse forms of ecosystems information
  • Discovery of patterns in large volumes of ecosystems data through analytic techniques such as data mining and predictive analytics in applications
  • Ecological, environmental, and socioeconomic modeling and coupling
  • Land use and land cover (LULC) change
  • Smart city and geo-design
  • Technological advances in hardware, storage, data management, networking, and computing models such as visualization and cloud computing for Ecosystems applications

GSES-2018 is the sixth annual academic conference in a series held between China and the USA sponsored by an agreement between multiple universities from these countries. The conference attracts participants in a diverse range of fields, including geographic information science, geo-design, geo-sustainability studies, ecological and environmental sciences, resource management and policy, and sustainable urban and regional development and modeling. GSES-2018 continues the conference series’ unique blend of topics focusing on advances in earth observation, geo-spatial analysis, and technologies and their applications in natural resource management and sustainable society.

Prof. Dr. Xinyue Ye
Prof. Dr. Andrew T Crooks
Prof. Dr. Yichun Xie
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Research

29 pages, 3429 KiB  
Article
Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities
by Sana Mujeeb, Nadeem Javaid, Manzoor Ilahi, Zahid Wadud, Farruh Ishmanov and Muhammad Khalil Afzal
Sustainability 2019, 11(4), 987; https://doi.org/10.3390/su11040987 - 14 Feb 2019
Cited by 83 | Viewed by 7957
Abstract
This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights [...] Read more.
This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting. Full article
(This article belongs to the Special Issue Data Analytics on Sustainable, Resilient and Just Communities)
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22 pages, 3893 KiB  
Article
Impacts of Climate on Spatiotemporal Variations in Vegetation NDVI from 1982–2015 in Inner Mongolia, China
by Xinxia Liu, Zhixiu Tian, Anbing Zhang, Anzhou Zhao and Haixin Liu
Sustainability 2019, 11(3), 768; https://doi.org/10.3390/su11030768 - 01 Feb 2019
Cited by 29 | Viewed by 3771
Abstract
By using the Global Inventory Modeling and Mapping Studies (GIMMS) third-generation normalized difference vegetation index (NDVI3g) data, this paper explores the spatiotemporal variations in vegetation and their relationship with temperature and precipitation between 1982 and 2015 in the Inner Mongolia region of China. [...] Read more.
By using the Global Inventory Modeling and Mapping Studies (GIMMS) third-generation normalized difference vegetation index (NDVI3g) data, this paper explores the spatiotemporal variations in vegetation and their relationship with temperature and precipitation between 1982 and 2015 in the Inner Mongolia region of China. Based on yearly scale data, the vegetation changes in Inner Mongolia have experienced three stages from 1982 to 2015: the vegetation activity kept a continuous improvement from 1982–1999, then downward between 1999–2009, and upward from 2009 to 2015. On the whole, the general trend is increasing. Several areas even witnessed significant vegetation increases: in the east and south of Tongliao and Chifeng, north of Xing’anmeng, north and west of Hulunbir, and in the west of Inner Mongolia. Based on monthly scale data, one-year and half-year cycles exist in normalized difference vegetation index (NDVI) and temperature but only a one-year cycle in precipitation. Finally, based on the one-year cycle, the relationship between NDVI and climatic were studied; NDVI has a significant positive correlation with temperature and precipitation, and temperature has a greater effect in promoting vegetation growth than precipitation. Moreover, based on a half-year changing period, NDVI is only affected by temperature in the study region. Those findings can serve as a critical reference for grassland managers or policy makers to make informed decisions on grassland management. Full article
(This article belongs to the Special Issue Data Analytics on Sustainable, Resilient and Just Communities)
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20 pages, 6790 KiB  
Article
Spatial Responses of Net Ecosystem Productivity of the Yellow River Basin under Diurnal Asymmetric Warming
by Jianjian He, Pengyan Zhang, Wenlong Jing and Yuhang Yan
Sustainability 2018, 10(10), 3646; https://doi.org/10.3390/su10103646 - 11 Oct 2018
Cited by 9 | Viewed by 2836
Abstract
The net ecosystem productivity (NEP) of drainage basins plays an important role in maintaining the carbon balance of those ecosystems. In this study, the modified CASA (Carnegie Ames Stanford Approach) model and a soil microbial respiration model were used to estimate [...] Read more.
The net ecosystem productivity (NEP) of drainage basins plays an important role in maintaining the carbon balance of those ecosystems. In this study, the modified CASA (Carnegie Ames Stanford Approach) model and a soil microbial respiration model were used to estimate net primary productivity (NPP) and NEP of the Yellow River Basin’s (YRB) vegetation in the terrestrial ecosystem (excluding rivers, floodplain lakes and other freshwater ecosystems) from 1982 to 2015. After analyzing the spatiotemporal variations in the NEP using slope analysis, the coefficient of variation, and the Hurst exponent, precipitation was identified as the main factor limiting vegetation growth in the YRB. Hence, precipitation was treated as the control variable and a second-order partial correlation method was used to determine the correlation between diurnal asymmetric warming and the YRB’s NEP. The results indicate that: (i) diurnal asymmetric warming occurred in the YRB from 1982 to 2015, with nighttime warming (Tmin) being 1.50 times that of daytime warming (Tmax). There is a significant correlation between variations in NPP and diurnal warming; (ii) the YRB’s NEP are characterized by upward fluctuations in terms of temporal variations, large differences between the various vegetation types, high values in the western and southeastern regions but low values in the northern region in terms of spatial distribution, overall relative stability in the YRB’s vegetation cover, and changes in the same direction being more dominant than those in the opposite direction (although the former is not sustained); and (iii) positive correlations between the NEP and nighttime and daytime warming are approximately 48.37% and 67.51% for the YRB, respectively, with variations in nighttime temperatures having more extensive impacts on vegetation cover. Full article
(This article belongs to the Special Issue Data Analytics on Sustainable, Resilient and Just Communities)
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24 pages, 4696 KiB  
Article
A Regionalized Study on the Spatial-Temporal Changes of Grassland Cover in the Three-River Headwaters Region from 2000 to 2016
by Naijing Liu, Yaping Yang, Ling Yao and Xiafang Yue
Sustainability 2018, 10(10), 3539; https://doi.org/10.3390/su10103539 - 01 Oct 2018
Cited by 5 | Viewed by 2281
Abstract
The Three-River Headwaters Region (TRHR) is located in the interior of the Qinghai-Tibetan Plateau, which is a typical research area in East Asia and is of fragile environment. This paper studied the characteristics of grassland cover changes in the TRHR between 2000 and [...] Read more.
The Three-River Headwaters Region (TRHR) is located in the interior of the Qinghai-Tibetan Plateau, which is a typical research area in East Asia and is of fragile environment. This paper studied the characteristics of grassland cover changes in the TRHR between 2000 and 2016 using methods of area division (AD) based on natural conditions and tabulate area (TA) dependent on Moderate-resolution Imaging Spectroradiometer (MODIS) 44B product. Further investigations were conducted on some of the typical areas to determine the characteristics of the changes and discuss the driving factors behind these changes. Classification and Regression Trees (CART), Random Forest (RF), Bayesian (BAYE), and Support Vector Machine (SVM) Machine Learning (ML) methods were employed to evaluate the correlation between grassland cover changes and corresponding variables. The overall trend for grassland cover in the TRHR towards recovery that rose 0.91% during the 17-year study period. The results showed that: (1) The change in grassland cover was more divisive in similar elevation and temperature conditions when the precipitation was stronger. The higher the temperature was, the more significant the rise of grassland cover was in comparable elevation and precipitation conditions. (2) There was a distinct decline and high change standard deviation of grassland cover in some divided areas, and strong correlations were found between grassland cover change and aspect, slope, or elevation in these areas. (3) The study methods of AD and TA achieved enhancing performance in interpretation of grassland cover changes in the broad and high elevation variation areas. (4) RF and CART methods showed higher stability and accuracy in application of grassland cover change study in TRHR among the four ML methods utilized in this study. Full article
(This article belongs to the Special Issue Data Analytics on Sustainable, Resilient and Just Communities)
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21 pages, 6468 KiB  
Article
Impacts of Climate Change and Human Activities on the Surface Runoff in the Wuhua River Basin
by Zhengdong Zhang, Luwen Wan, Caiwen Dong, Yichun Xie, Chuanxun Yang, Ji Yang and Yong Li
Sustainability 2018, 10(10), 3405; https://doi.org/10.3390/su10103405 - 25 Sep 2018
Cited by 10 | Viewed by 3520
Abstract
The impacts of climate change and human activities on the surface runoff in the Wuhua River Basin (hereinafter referred to as the river basin) are explored using the Mann–Kendall trend test, wavelet analysis, and double-mass curve. In this study, all the temperature and [...] Read more.
The impacts of climate change and human activities on the surface runoff in the Wuhua River Basin (hereinafter referred to as the river basin) are explored using the Mann–Kendall trend test, wavelet analysis, and double-mass curve. In this study, all the temperature and precipitation data from two meteorological stations, namely, Wuhua and Longchuan, the measured monthly runoff data in Hezikou Hydrological Station from 1961 to 2013, and the land-cover type data in 1990 and 2013 are used. This study yields valuable results. First, over the past 53 years, the temperature in the river basin rose substantially, without obvious changes in the average annual precipitation. From 1981 to 2013, the annual runoff fluctuated and declined, and this result is essentially in agreement with the time-series characteristics of precipitation. Second, both temperature and precipitation had evidently regular changes on the 28a scale, and the annual runoff changed on the 19a scale. Third, forestland was the predominant land use type in the Wuhua river basin, followed by cultivated land. Major transitions mainly occurred in both land-use types, which were partially transformed into grassland and construction land. From 1990 to 2013, cultivated land was the most active land-use type in the transitions, and construction land was the most stable type. Finally, human activities had always been a decisive factor on the runoff reduction in the river basin, accounting for 85.8%. The runoff in the river basin suffered most heavily from human activities in the 1980s and 1990s, but thereafter, the impact of these activities diminished to a certain extent. This may be because of the implementation of water loss and soil erosion control policies. Full article
(This article belongs to the Special Issue Data Analytics on Sustainable, Resilient and Just Communities)
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18 pages, 10073 KiB  
Article
MCR-Modified CA–Markov Model for the Simulation of Urban Expansion
by Xiuquan Li, Meizhen Wang, Xuejun Liu, Zhuan Chen, Xiaojian Wei and Weitao Che
Sustainability 2018, 10(9), 3116; https://doi.org/10.3390/su10093116 - 31 Aug 2018
Cited by 20 | Viewed by 4215
Abstract
Ecosystem balance is an important factor that affects healthy and sustainable urban development. The traditional cellular automata (CA) model considers only a few ecological factors, however, the MCR model can account for ecological factors. In previous studies, few ecological factors were added to [...] Read more.
Ecosystem balance is an important factor that affects healthy and sustainable urban development. The traditional cellular automata (CA) model considers only a few ecological factors, however, the MCR model can account for ecological factors. In previous studies, few ecological factors were added to the CA model. Thus, the minimal cumulative resistance (MCR) model is combined with the CA and Markov models for the simulation of urban expansion. To verify the reliability of the method, the Wuhan metropolitan area was selected as a representative urban area, and its expansion in the past and future was simulated. Firstly, seven influential factors were selected from the perspective of location theory. The transformation rules of the comprehensive resistance surface followed by the modified CA–Markov model were constructed on the basis of the MCR model. The expansion of the Wuhan metropolitan area in 2013 was simulated on the basis of the 1996 and 2006 maps of land-use status, and the kappa coefficient was used as an index to evaluate the accuracy of the proposed method. Then, the expansion of the Wuhan metropolitan area in 2020 was simulated. Finally, the simulation results obtained with and without the MCR model were compared and analysed from the macro- and micro levels. Results show that the prediction accuracy of the two models differed for ecological regions, such as woodlands and water bodies. The similarities between the regions that were overestimated and underestimated by the MCR-modified CA–Markov model and non-MCR model may be attributed to solution of the land-use transfer matrix with the Markov model. The accuracy of the MCR-modified CA–Markov model for predicting forests, water and other ecological regions was higher than that of the Markov model. Therefore, the proposed MCR-modified CA–Markov model has potential applications in environmentally-conscious urban expansion. Full article
(This article belongs to the Special Issue Data Analytics on Sustainable, Resilient and Just Communities)
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17 pages, 3437 KiB  
Article
Assessment of Carbon Storage and Its Influencing Factors in Qinghai-Tibet Plateau
by Zhonghe Zhao, Gaohuan Liu, Naixia Mou, Yichun Xie, Zengrang Xu and Yong Li
Sustainability 2018, 10(6), 1864; https://doi.org/10.3390/su10061864 - 04 Jun 2018
Cited by 33 | Viewed by 4828
Abstract
Land use/cover change (LUCC) is one of the major factors influencing the storage of ecosystem carbon. The carbon storage in Qinghai-Tibet Plateau, the world’s highest plateau, is affected by a combination of many factors. Using MCD12Q1 land classification data, aboveground biomass, belowground biomass, [...] Read more.
Land use/cover change (LUCC) is one of the major factors influencing the storage of ecosystem carbon. The carbon storage in Qinghai-Tibet Plateau, the world’s highest plateau, is affected by a combination of many factors. Using MCD12Q1 land classification data, aboveground biomass, belowground biomass, soil carbon and humus carbon data, as well as field sampling data for parameters verification, we applied the InVEST model to simulate the ecosystem carbon storage and the impacts of driving factors. The field survey samples were used to test the regression accuracy, and the results confirmed that the model performance was reasonable and acceptable. The main conclusions of this study are as follows: From 2001 to 2010, carbon storage in the Qinghai-Tibet Plateau increased by 10.39 billion t when assuming that the carbon density in each land cover type was constant. Changes of the land cover types caused carbon storage to increase by 116 million t, which contributed 13.82% of the dynamic carbon storage. Consequently, changes in carbon density accounted for 86.18% of the carbon storage change. In addition, we investigated the soil organic matter and aboveground biomass characteristics between 2012 and 2014 and found that the influences of fencing and dung on carbon storage were positive. Full article
(This article belongs to the Special Issue Data Analytics on Sustainable, Resilient and Just Communities)
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