Big Data in Geocomputation for the Built Environment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 5898

Special Issue Editors

Department of Industrial and Information Engineering and Economics, University of L'Aquila, 67100 L'Aquila, Italy
Interests: software engineering; model-driven engineering; automatic code generation; quality metrics; metadata repository; reuse of UML artifacts
Special Issues, Collections and Topics in MDPI journals
School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: spatial planning; spatial simulation; geodemographics; urban modelling; geocomputation; urban simulation models; planning environmental studies on climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the past few decades, the main problem in geographical analysis was the lack of spatial data availability. The growth of availability of spatiotemporal data in urban environment produced renewed approaches to traditional geocomputational models and methods. Geocomputational models and methods are very promising to define a road map towards sustainable, and hence safer, built environments, because they support the complex decision-making process on which the solution of most real-life problems is based. This Special Issue encourages the submission of papers reporting about the adoption of geocomputational models and methods to solve real-life problems. The contributions must put a special emphasis on the interactions between people and the sustainable, and hence safer, development of the built environments.

Welcome papers should provide, through real-life case studies, clear evidence of the benefits arising from the adoption of geocomputational models and methods. The papers must cover both the theoretical and the experimental aspects.

Contributions on the following topics are particularly welcome:

  • Citizen sensing and open data;
  • Crowdsourcing and citizen science data;
  • Geospatial uncertainty;
  • Spatial databases;
  • Spatial data mining;
  • GISs technology;
  • Open geospatial science;
  • Agent-based spatial modeling;
  • Cellular automata spatial modeling;
  • Spatial statistical models;
  • Environmental modeling;
  • Urban modeling;
  • Land use dynamics;
  • Geographic visualization and visual analytics;
  • Complex systems analysis;
  • Machine learning methods for environmental planning;
  • Built environment;
  • Environmental protection;
  • Case studies.

You are cordially invited to contribute to the Special Issue. There is no restriction on the length of papers; this ensures that sufficient information is provided for the results to be reproduced.

The deadline for manuscripts submission is 30 September 2020.

Prof. Dr. Paolino Di Felice
Prof. Dr. Beniamino Murgante
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. Applied Sciences 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 (2 papers)

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Research

13 pages, 3406 KiB  
Article
World Ocean Thermocline Weakening and Isothermal Layer Warming
by Peter C. Chu and Chenwu Fan
Appl. Sci. 2020, 10(22), 8185; https://0-doi-org.brum.beds.ac.uk/10.3390/app10228185 - 19 Nov 2020
Cited by 1 | Viewed by 2635
Abstract
This paper identifies world thermocline weakening and provides an improved estimate of upper ocean warming through replacement of the upper layer with the fixed depth range by the isothermal layer, because the upper ocean isothermal layer (as a whole) exchanges heat with the [...] Read more.
This paper identifies world thermocline weakening and provides an improved estimate of upper ocean warming through replacement of the upper layer with the fixed depth range by the isothermal layer, because the upper ocean isothermal layer (as a whole) exchanges heat with the atmosphere and the deep layer. Thermocline gradient, heat flux across the air–ocean interface, and horizontal heat advection determine the heat stored in the isothermal layer. Among the three processes, the effect of the thermocline gradient clearly shows up when we use the isothermal layer heat content, but it is otherwise when we use the heat content with the fixed depth ranges such as 0–300 m, 0–400 m, 0–700 m, 0–750 m, and 0–2000 m. A strong thermocline gradient exhibits the downward heat transfer from the isothermal layer (non-polar regions), makes the isothermal layer thin, and causes less heat to be stored in it. On the other hand, a weak thermocline gradient makes the isothermal layer thick, and causes more heat to be stored in it. In addition, the uncertainty in estimating upper ocean heat content and warming trends using uncertain fixed depth ranges (0–300 m, 0–400 m, 0–700 m, 0–750 m, or 0–2000 m) will be eliminated by using the isothermal layer. The isothermal layer heat content with the monthly climatology removed (i.e., relative isothermal layer heat content) is calculated for an individual observed temperature profile from three open datasets. The calculated 1,111,647 pairs of (thermocline gradient, relative isothermal layer heat content) worldwide show long-term decreasing of the thermocline gradient and increasing of isothermal layer heat content in the global as well as regional oceans. The global ocean thermocline weakening rate is (−2.11 ± 0.31) × 10−3 (°C m−1 yr−1) and isothermal layer warming rate is (0.142 ± 0.014) (W m−2). Full article
(This article belongs to the Special Issue Big Data in Geocomputation for the Built Environment)
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23 pages, 4246 KiB  
Article
Urban Ecosystem Services (UES) Assessment within a 3D Virtual Environment: A Methodological Approach for the Larger Urban Zones (LUZ) of Naples, Italy
by Maria Cerreta, Roberta Mele and Giuliano Poli
Appl. Sci. 2020, 10(18), 6205; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186205 - 07 Sep 2020
Cited by 11 | Viewed by 2715
Abstract
The complexity of the urban spatial configuration, which affects human wellbeing and landscape functioning, necessitates data acquisition and three-dimensional (3D) visualisation to support effective decision-making processes. One of the main challenges in sustainability research is to conceive spatial models adapting to changes in [...] Read more.
The complexity of the urban spatial configuration, which affects human wellbeing and landscape functioning, necessitates data acquisition and three-dimensional (3D) visualisation to support effective decision-making processes. One of the main challenges in sustainability research is to conceive spatial models adapting to changes in scale and recalibrate the related indicators, depending on scale and data availability. From this perspective, the inclusion of the third dimension in the Urban Ecosystem Services (UES) identification and assessment can enhance the detail in which urban structure–function relationships can be studied. Moreover, improving the modelling and visualisation of 3D UES indicators can aid decision-makers in localising, analysing, assessing, and managing urban development strategies. The main goal of the proposed framework is concerned with evaluating, planning, and monitoring UES within a 3D virtual environment, in order to improve the visualisation of spatial relationships among services and to support site-specific planning choices. Full article
(This article belongs to the Special Issue Big Data in Geocomputation for the Built Environment)
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