Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.3
Journals
Sustainability
Special Issues
Spatial Statistics and Ecological and Environmental Sustainability
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Spatial Statistics and Ecological and Environmental Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: closed (1 September 2019) | Viewed by 7754

Special Issue Editors

Dr. Paul Harris

E-Mail Website
Guest Editor
Sustainable Agriculture Sciences,Rothamsted Research, North Wyke,Okehampton, Devon EX20 2SB, UK
Interests: environmental and agricultural statistics; analyses of remote sensing; crowd-sourced; land cover and land use data; hybridisation of agri-process-based models with statistical models; data mining methods for quality control of large; multivariate; spatio-temporal environmental data sets collected through IoT-based sensor systems; visualisation
Prof. Dr. Alexis Comber

E-Mail Website
Guest Editor
School of Geography, University of Leeds, Leeds LS2 9JT, UK
Interests: spatial analysis; geocomputation; GIS; land cover; land use; spatial data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue on “Spatial Statistics for Ecological and Environmental Sustainability” aims to gather papers that study how techniques in spatial and spatiotemporal statistics can be formulated and applied to the diverse challenges of sustainability. Spatially indexed terrestrial, aquatic/marine, and atmospheric data are routinely collected in many ecological and environmental disciplines, including those in pollution and ecosystem degradation, agriculture, and agroecology. Commonly, these data link with other data describing processes related to urbanization, population change and migration, land use, transportation, energy resources, poverty, wealth, and health, so that sustainability metrics and associated trade-offs can be reliably calculated to quantify vulnerabilities to future developments and climate change, for different regions, and at different times. Increasingly, such data sets are massive due to enhanced sensing of our world (e.g., through remote sensing, citizen science, sensor networks), and it is vital that spatial data are reliably and robustly harmonized, integrated and transformed into information addressing key issues of sustainability. Spatial statistics provides a rich toolkit to achieve this, where the discipline itself continues to evolve not only to address big data and computational issues, but also the application and adaptation of existing techniques to new disciplines. From this viewpoint, authors are invited to submit their research describing innovative methodologies and models for understanding, maintaining. and enhancing sustainable systems.

Dr. Paul Harris
Prof. Alexis Comber
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.

Keywords

  • spatial statistics
  • spatial analysis
  • spatial ecology
  • quantitative geography
  • dynamic spatial processes
  • geostatistics
  • spatial data science
  • space–time statistics
  • geocomputation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 1613 KiB  
Article
Spatial Correlation and Convergence Analysis of Eco-Efficiency in China
by Defeng Zheng, Shuai Hao, Caizhi Sun and Leting Lyu
Sustainability 2019, 11(9), 2490; https://doi.org/10.3390/su11092490 - 28 Apr 2019
Cited by 20 | Viewed by 3214
Abstract
In this paper, we first measured the eco-efficiency of 31 provinces in China during 2000–2015 using the SBM (Slack-Based Measure) model, and the spatial character of eco-efficiency was identified based on symmetrical spatial weight matrix. We then proposed a new asymmetrical spatial weight [...] Read more.
In this paper, we first measured the eco-efficiency of 31 provinces in China during 2000–2015 using the SBM (Slack-Based Measure) model, and the spatial character of eco-efficiency was identified based on symmetrical spatial weight matrix. We then proposed a new asymmetrical spatial weight matrix based on the eco-economic transformation index (EETI)-distance reciprocal principle to assess the spatial character of eco-efficiency. Finally, we analyzed the convergence of eco-efficiency’s total factor productivity (EETFPs) in mainland China and in three major regions based on the results of EETFP. The study revealed the following findings: (1) There were some limitations to the spatial autocorrelation of eco-efficiency in mainland China by the symmetrical spatial weight methods based on the spatial proximity principle or spatial distance principle. However, the new spatial weight scheme improved the reliability of the accounting results of the spatial autocorrelation. (2) The clustering effect of eco-efficiency exhibited a downward trend in mainland China during the study period; meanwhile, the significant high-high and low-high clustering areas were located in the eastern, the central, and the western regions. (3) The study of convergence showed that there was a club-convergence phenomenon in mainland China, and except for the western region, all the regions expressed conditional convergence. The results provide a significant reference for ecological-economy management and sustainable development in China. Full article
(This article belongs to the Special Issue Spatial Statistics and Ecological and Environmental Sustainability)
►▼ Show Figures

Figure 1

19 pages, 14951 KiB  
Article
Measuring the Spatial Allocation Rationality of Service Facilities of Residential Areas Based on Internet Map and Location-Based Service Data
by Xinxin Zhou, Yuan Ding, Changbin Wu, Jing Huang and Chendi Hu
Sustainability 2019, 11(5), 1337; https://doi.org/10.3390/su11051337 - 4 Mar 2019
Cited by 11 | Viewed by 3665
Abstract
The spatial allocation rationality of the service facilities of residential areas, which is affected by the scope of the population and the capacity of service facilities, is meaningful for harmonious urban development. The growth of the internet, especially Internet map and location-based service [...] Read more.
The spatial allocation rationality of the service facilities of residential areas, which is affected by the scope of the population and the capacity of service facilities, is meaningful for harmonious urban development. The growth of the internet, especially Internet map and location-based service (LBS) data, provides micro-scale knowledge about residential areas. The purpose is to characterize the spatial allocation rationality of the service facilities of residential areas from Internet map and LBS data. An Internet map provides exact geographical data (e.g., points of interests (POI)) and stronger route planning analysis capability through an application programming interface (API) (e.g., route planning API). Meanwhile, LBS data collected from mobile equipment afford detailed population distribution values. Firstly, we defined the category system of service facilities and calculated the available service facilities capacity of residential areas (ASFC-RA) through a scrappy algorithm integrated with the modified cumulative opportunity measure model. Secondly, we used Thiessen polygon spatial subdivision to gain the population distribution capacity of residential areas (PDC-RA) from Tencent LBS data at the representative moment. Thirdly, we measured the spatial allocation rationality of service facilities of residential areas (SARSF-RA) by combining ASFC-RA and PDC-RA. In this case, a trial strip census, consisting of serval urban residential areas from Wuxi City, Jiangsu Province, is selected as research area. Residential areas have been grouped within several ranges according to their SARSF-RA values. Different residential areas belong to different groups, even if they are spatially contiguous. Spatial locations and other investigation information coordinate with these differences. Those results show that the method that we proposed can express the micro-spatial allocation rationality of different residential areas dramatically, which provide a new data lens for various researchers and applications, such as urban residential areas planning and service facilities allocation. Full article
(This article belongs to the Special Issue Spatial Statistics and Ecological and Environmental Sustainability)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop