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Geospatial Technologies and the 4th Industrial Revolution for Sustainable Urban Environment

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 31597

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Special Issue Editors

Dr. Byungyun Yang

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Guest Editor

Department of Geography Education, Dongguk University, Jung-gu, Seoul 04620, South Korea
Interests: disaster and coastal management; applications of remote sensing; community-driven research; geographic information science; big data and text mining

Prof. Chul Sue Hwang

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Guest Editor

Department of Geography, Kyung Hee University, Seoul 02447, South Korea
Interests: big data; IoT; smart cities; spatial cognition

Prof. Jeong Chang Seong

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Guest Editor

Department of Geosciences, GeorgiaView, University of West Georgia, 1601 Maple St., Carrollton, GA 30118, USA
Interests: drone imaging; solar energy potential; Image Atlas; LiDAR

Special Issue Information

Dear Colleagues

In urban settings, sustainability is important to increase the quality of life. For example, it enables us to stay in walkable places or green living locations such as eco-friendly cities. As such, a sustainable urban environment requires an integrated system or process with consideration of social, economic, and environment impacts. As technology evolved over the past decade, debates about the emerging approaches of geospatial technology for a sustainable urban environment have been increasingly influenced by the discussion of smart, ubiquitous, digital, and innovation cities. As a result, devices are becoming part of the urban environment in the 4th Industrial Revolution. Geospatial technologies have acted as vital elements to build sustainable smart cities. However, research on sustainable urban environments requires collaborative, synergetic, and community-driven approaches. It is necessary to study conceptual explorations and new approaches of geospatial technologies for a sustainable urban environment: for example, applications of the Internet of Things, big data analysis, and data science.

Thus, this Special Issue seeks the latest high-quality interdisciplinary, theoretical, and empirical research associated with the urban environment. In particular, we welcome the submission of contributions relating to any of the following perspectives, but not limited to:

Conceptual explorations of sustainable urban development;
Community-driven research;
Urban applications of geographic information systems and science;
Environmental justice and interaction between environmental and human aspects;
Urban domestic violence and/or social inequalities;
Policy and governance perspectives;
Studies related to smart or green cities;
Data science, big data, IoT, living labs, or text mining.

Submissions to this Special Issue will be selected via a rigorous peer-review procedure.

Assist. Prof. Byungyun Yang
Prof. Chul Sue Hwang
Prof. Jeong Chang Seong
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

conceptual explorations of sustainable urban development
community-driven research
urban applications of geographic information systems and science
environmental justice and interaction between environmental and human aspects
urban domestic violence and/or social inequalities
policy and governance perspectives
studies related to smart or green cities
data science, big data, IoT, living labs, or text mining

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Published Papers (6 papers)

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Research

14 pages, 1452 KiB

Open AccessArticle

Investigating Sustainable Commuting Patterns by Socio-Economic Factors

by Woo Jang, Fei Yuan and Jose Javier Lopez

Sustainability 2021, 13(4), 2180; https://doi.org/10.3390/su13042180 - 18 Feb 2021

Cited by 5 | Viewed by 2691

Abstract

This research aims to analyze how modes of transportation differ according to socio-economic factors in an urban space. The study area is Ramsey County, the most densely populated county in Minnesota. The primary data used were from the recent 2012–2016 Census Transportation Planning Products (CTPP). We performed regression models to identify the relationship between mode of transport and socio-economic variables, and further analyzed disaggregate trip data to provide a more realistic evaluation of commuting patterns by use of multiple variables in combination. The research found that sustainable commuting patterns correlated significantly with both poverty and minority group status, but bore no significant relationship to older workers. Additionally, there was a significant correlation between commuting alone by car with both minority group status and older workers, but not with poverty. This research also confirmed that the sustainable commuting patterns of the working poor were mostly located in the downtown area, while causes of low-income workers driving alone typically involved much longer commutes to and from points throughout the study area, suggesting that more efficient commutes are a significant quality of life factor for the urban poor when evaluating residential and employment opportunities in the central city. Full article

(This article belongs to the Special Issue Geospatial Technologies and the 4th Industrial Revolution for Sustainable Urban Environment)

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21 pages, 9141 KiB

Open AccessArticle

GIS-Enabled Digital Twin System for Sustainable Evaluation of Carbon Emissions: A Case Study of Jeonju City, South Korea

by Jiman Park and Byungyun Yang

Sustainability 2020, 12(21), 9186; https://doi.org/10.3390/su12219186 - 4 Nov 2020

Cited by 35 | Viewed by 8163

Abstract

Despite the growing interest in digital twins (DTs) in geospatial technology, the scientific literature is still at the early stage, and concepts of DTs vary. In common perspectives, the primary goals of DTs are to reduce the uncertainty of the physical systems in real-world projects to reduce cost. Thus, this study is aimed at developing a structural schematic of a geographic information system (GIS)-enabled DT system and exploring geospatial technologies that can aid in deploying a DT system for a real-world project—in particular, for the sustainable evaluation of carbon emissions. The schematic includes three major phases: (1) data collection and visualization, (2) analytics, and (3) deployment. Three steps are designed to propose an optimal strategy to reduce carbon emissions in an urban area. In the analytics phase, mapping, machine learning algorithms, and spatial statistics are applied, mapping an ideal counterpart to physical assets. Furthermore, not only are GIS maps able to analyze geographic data that represent the counterparts of physical assets but can also display and analyze spatial relationships between physical assets. In the first step of the analytics phase, a GIS map spatially represented the most vulnerable area based on the values of carbon emissions computed according to the Intergovernmental Panel on Climate Change (IPCC) guidelines. Next, the radial basis function (RBF) kernel algorithm, a machine learning technique, was used to forecast spatial trends of carbon emissions. A backpropagation neural network (BPNN) was used to quantitatively determine which factor was the most influential among the four data sources: electricity, city gas, household waste, and vehicle. Then, a hot spot analysis was used to assess where high values of carbon emissions clustered in the study area. This study on the development of DTs contributes the following. First, with DTs, sustainable urban management systems will be improved and new insights developed more publicly. Ultimately, such improvements can reduce the failures of projects associated with urban planning and management. Second, the structural schematic proposed here is a data-driven approach; consequently, its outputs are more reliable and feasible. Ultimately, innovative approaches become available and services are transformed. Consequently, urban planners or policy makers can apply the system to scenario-based approaches. Full article

(This article belongs to the Special Issue Geospatial Technologies and the 4th Industrial Revolution for Sustainable Urban Environment)

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21 pages, 16772 KiB

Open AccessArticle

Spatializing an Artist-Resident Community Area at a Building-Level: A Case Study of Garosu-Gil, South Korea

by Jiman Park, Jihang Kim and Byungyun Yang

Sustainability 2020, 12(15), 6116; https://doi.org/10.3390/su12156116 - 29 Jul 2020

Cited by 3 | Viewed by 2630

Abstract

This study integrated a focus on geographical, physical, and commercial characteristics to explore the commercial gentrification phenomenon and its related statistical summaries in the area of Garosu-gil in Seoul’s Sinsa-dong ward. In particular, this study first collected parcel and building data and corresponding attribute information and mapped the resulting datasets in a geographic information system (GIS) environment. We then examined gentrification issues per building and conducted statistical analyses to investigate spatial patterns of commercial gentrification, which were used to develop criteria for determining degrees of gentrification. Third, this study conducted correlation and regression analyses to quantify the strength of the linear relationship between pairs of variables associated with primary factors contributing to commercial gentrification, and used a geographically weighted regression model (GWR) to help understand and predict spatial relationships between significant variables. The results showed positive correlations between several variables and commercial gentrification in the study area, namely neighborhood-convenience facilities, building ages, store rents, new franchise and restaurant businesses, distance to subways, and the presence of multiple roads. Based on its finding, there are key contributions of this study as follows. The first significant contribution of this study is developing measurement of gentrification levels that can be used by policy makers at each of four stages of the gentrification process. Furthermore, this paper develops a comprehensive approach for spatially identifying gentrifying neighborhoods across multiple time periods in 2- and 3-dimensions. It eventually helps urban planners implement preventative or supportive programs to protect lower-income residents and small businesses and thereby engender more sustainable community development. Full article

(This article belongs to the Special Issue Geospatial Technologies and the 4th Industrial Revolution for Sustainable Urban Environment)

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21 pages, 5912 KiB

Open AccessArticle

Strengthening the Statistical Summaries of Economic Output Areas for Urban Planning Support Systems

by Chul Sue Hwang, Seong-Yun Hong, TaeKeon Hwang and Byungyun Yang

Sustainability 2020, 12(14), 5640; https://doi.org/10.3390/su12145640 - 13 Jul 2020

Cited by 4 | Viewed by 2144

Abstract

Despite efforts to research the transformation of urban structures, difficulties remain in estimating credible statistical information in the existing census output areas. This research proposes two alternatives to construct new economic output areas by considering the socioeconomic homogeneities where economic activities occur. In particular, we developed an algorithm to aggregate new economic zones into the existing census output areas. For this purpose, we utilized matrix systems that consider population sizes, the number of workers and workplaces, and a combination of these factors in the two alternatives. Urban planners need to provide credible statistical summaries at the census output areas. Our findings contribute to this research by suggesting that it is essential to consider the population and the number of workplaces with socioeconomic homogeneity. These findings will also help other researchers who study the transformation of urban structures because they can use more reliable statistical information for their simulation model that predicts an urban structure. Furthermore, it will help improve the national statistics office’s roles for public and urban planners and provide an important source for the national statistical geographic information services. Full article

(This article belongs to the Special Issue Geospatial Technologies and the 4th Industrial Revolution for Sustainable Urban Environment)

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25 pages, 12624 KiB

Open AccessArticle

GIS Crime Mapping to Support Evidence-Based Solutions Provided by Community-Based Organizations

by Byungyun Yang

Sustainability 2019, 11(18), 4889; https://doi.org/10.3390/su11184889 - 6 Sep 2019

Cited by 12 | Viewed by 8693

Abstract

This study explored geospatial technologies to support efforts of community organizations and of the Chicago Gun Violence Research Collaboration to reduce gun-related crimes. It entailed (1) identification of spatial trends in gun-related crimes during 2012 to 2017 in each of the following four areas: Austin, East Garfield, North Lawndale, and Englewood; (2) investigation of changes in crime patterns near safe school zones in the areas before and after the establishment of the city’s safe passage routes in 2009 to protect the youth from street violence when traveling to school; and (3) development of a web-enabled mobile application to provide researchers and residents with spatial information on local crime incidents and to enable community members to collect and share their information in a GIS environment. The results of this research revealed that, although the number of safe passage routes has increased in these areas over the past several years, hotspot trends for gun-related crimes have intensified in most of the communities in these areas, which include school zones and safe passage routes. Accordingly, it turned out that GIS can serve as an ideal platform supporting collaborative efforts between communities and researchers. Full article

(This article belongs to the Special Issue Geospatial Technologies and the 4th Industrial Revolution for Sustainable Urban Environment)

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14 pages, 7211 KiB

Open AccessArticle

Developing a Mobile Mapping System for 3D GIS and Smart City Planning

by Byungyun Yang

Sustainability 2019, 11(13), 3713; https://doi.org/10.3390/su11133713 - 7 Jul 2019

Cited by 31 | Viewed by 6329

Abstract

The creation of augmented reality-related geographic information system (GIS) mapping applications has witnessed considerable advances in the technology of urban modeling; however, there are limitations to the technology that is currently used to create similar resources. The cost of the creation of the vehicle is an obstacle, and the rendering of textures of buildings is often lacking because of the distortion caused by the types of lenses that have been used. Generally, mobile mapping systems (MMSs) can extract detailed three-dimensional (3D) data with high quality texture information of the 3D building model. However, mapping urban areas by MMSs is expensive and requires advanced mathematical approaches with complicated steps. In particular, commercial MMS, which generally includes two GPS receivers, is an expensive device, costing ~$1 million. Thus, this research is aimed at developing a new MMS that semi-automatically produces high-quality texture information of 3D building models proposes a 3D urban model by hybrid approaches. Eventually, this study can support urban planners and people to improve their spatial perception and awareness of urban area for Smart City Planning. Full article

(This article belongs to the Special Issue Geospatial Technologies and the 4th Industrial Revolution for Sustainable Urban Environment)

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