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Geographic Big Data for Sustainable City
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Geographic Big Data for Sustainable City

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 8147

Special Issue Editors

Dr. Chunxiao Zhang

E-Mail Website
Guest Editor
Department of Geographic Information Science, School of Information Engineering, China University of Geosciences, Beijing, China
Interests: knowledge- and data-driven geographic simulation; virtual geographic environments; urban thermal environment simulation; urban flood control
Special Issues, Collections and Topics in MDPI journals
Dr. Fan Zhang

E-Mail Website
Guest Editor
Senseable City Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
Interests: GIScience; urban informatics; social sensing; GeoAI; urban visual intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xinqi Zheng

E-Mail Website
Guest Editor
School of Information Engineering, China University of Geosciences in Beijing, Beijing 100083, China
Interests: urban simulation and geo-spatial modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous improvement of the level of science and technology, the current data stock are growing exponentially, and the types of data are constantly enriched. Mankind has entered the era of big data. At present, geographic big data represented by remote sensing products, network big data, etc., have played an important role in urban development.

The city is the symbol of human civilization and the main carrier and population center of social and economic development. The development of a city determines whether human beings can successfully embark on the road of sustainable development. The United Nations Economic and Social Council (Asia) also pointed out in a report, "Sustainable urbanization is a dynamic process that creates sustainable conditions for generations by solving environmental, economic, social and governance problems". Under the framework of this definition of sustainable city, many distinctive sustainable urban construction actions and projects are developed. For example, the Basque declaration proposed by ICLEL city of Aalborg and the sustainable cities platform in Europe. It is proposed that sustainable cities are dynamic, resilient, livable, and inclusive. The project is based on building a cooperative development platform to help European cities set their own sustainable development goals and paths.

With the support of geographic big data, it has further promoted the sustainable development of cities. For example, the application of geographic big data can be seen in the fields of transportation planning, agricultural monitoring, air quality evaluation, disaster response, crime investigation, people’s livelihood improvement, forest management, environmental governance, interregional development, hydrological assessment, and so on. Its wide use promotes the sharing of spatial information and cross application in many fields. It serves geographical evaluation, prediction, analysis, and decision-making. Through its mining of spatial laws, it provides new support for urban planning transformation and sustainable development.

This Special Issue aims to show the relevant theory, methods, and technology of geospatial big data in solving urban problems, and explore more applications and possibilities of big data in urban development. The achievement of this Special Issue is helpful to understand and guide urban development in a more open and creative way, promote urban system optimization, provide solutions to urban problems, and promote urban sustainable development.

Dr. Chunxiao Zhang
Dr. Fan Zhang
Prof. Dr. Xinqi Zheng
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

  • geographic big data
  • city management
  • sustainable city

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Published Papers (4 papers)

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Research

27 pages, 8019 KiB  
Article
Neighborhood Identity Formation and the Changes in an Urban Regeneration Neighborhood in Gwangju, Korea
by Hae Young Yun and Hyun-ah Kwon
Sustainability 2023, 15(15), 11792; https://doi.org/10.3390/su151511792 - 31 Jul 2023
Cited by 1 | Viewed by 1730
Abstract
Since the Urban Regeneration Act in 2013, central and local Korean governments have endeavored to regenerate deprived urban neighborhoods. This study analyzed how these efforts have changed the nature of neighborhood identity in Yanglim, Gwangju, Korea. The authors analyzed 62,386 Naver blog posts [...] Read more.
Since the Urban Regeneration Act in 2013, central and local Korean governments have endeavored to regenerate deprived urban neighborhoods. This study analyzed how these efforts have changed the nature of neighborhood identity in Yanglim, Gwangju, Korea. The authors analyzed 62,386 Naver blog posts from 2013 to 2022, utilizing an Artificial Intelligence (AI) technique, Topic Modeling (i.e., Latent Dirichlet Allocation). Using trend analysis by topic, three phases were identified: (1) Phase 1: Flourishment (January 2013 to October 2016); (2) Phase 2: Maturation (November 2016 to February 2020); and (3) Phase 3: COVID-19 (March 2020 to October 2022). In the first phase, the collective actions between the local government and citizens to improve the declined neighborhood formed the Yanglim area’s reputation as the “History and Cultural Village” and as “Penguin Village”. The unique identity of the area in the second phase, along with gentrification issues, created a hot spot (e.g., cafés and restaurants), drawing the attention of tourists and locals. More recently, the Yanglim area has become a place for locals’ daily activities with their loved ones, as tourist traffic greatly dropped off due to the COVID-19 outbreak. Until now, the Yanglim area has experienced a process of successful urban regeneration from flourishment to degentrification. AI techniques represent a novel application that can support policy makers and stakeholders in understanding citizens and taking further actions to create economically and socially sustainable neighborhoods. Full article
(This article belongs to the Special Issue Geographic Big Data for Sustainable City)
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13 pages, 2948 KiB  
Article
Classifying Urban Functional Zones Based on Modeling POIs by Deepwalk
by Xin Yang, Shuaishuai Bo and Zhaojie Zhang
Sustainability 2023, 15(10), 7995; https://doi.org/10.3390/su15107995 - 13 May 2023
Cited by 6 | Viewed by 1877
Abstract
Developing urban functional zone classification method to study urban spatial structure is a hotspot in current research. Using the word embedding model to excavate spatial relationship of the geographic elements in urban functional zones is an important way to develop urban functional zone [...] Read more.
Developing urban functional zone classification method to study urban spatial structure is a hotspot in current research. Using the word embedding model to excavate spatial relationship of the geographic elements in urban functional zones is an important way to develop urban functional zone classification method. However, in these studies, the spatial relationship of geographic elements was regarded as their homogeneity, while the structural similarity of geographical elements was ignored, which inevitably reduces the classification accuracy of urban functional zone classification method. This paper proposes to develop an urban functional zone classification method based on Deepwalk model, which could extract homogeneity and structural similarity of nodes in graph. The proposed method uses POI data to represent geographical elements, organizes POIs into graphs, and uses Deepwalk to embedding POIs for urban functional zone classification. It was applied to classify the urban functional zones of Chaoyang district in Beijing; and the classification results were compared with those of two baseline method based on Word2vec model and Place2vec model. The experimental results show that considering both the homogeneity and structural similarity of geographical elements, the proposed model has higher accuracy than the models only considering the homogeneity of geographical elements. Full article
(This article belongs to the Special Issue Geographic Big Data for Sustainable City)
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15 pages, 6075 KiB  
Article
Evaluation of Urban Intensive Land Use Degree with GEE Support: A Case Study in the Pearl River Delta Region, China
by Yiqun Shang, Dongya Liu and Yi Chen
Sustainability 2022, 14(20), 13284; https://doi.org/10.3390/su142013284 - 16 Oct 2022
Cited by 1 | Viewed by 2097
Abstract
Evaluation of intensive land use (ILU) over long time series is essential for the rational use of land and urban development. We propose a novel framework for analyzing ILU in the Pearl River Delta (PRD) region of China. First, we used Google Earth [...] Read more.
Evaluation of intensive land use (ILU) over long time series is essential for the rational use of land and urban development. We propose a novel framework for analyzing ILU in the Pearl River Delta (PRD) region of China. First, we used Google Earth Engine (GEE) to obtain cities’ built-up land information. Second, we calculated the ILU degree and constructed an evaluation index system based on the Pressure–State–Response (PSR) theoretical framework. Third, we employed Geodetector to determine the dominant influencing factors on ILU. The findings are as follows: (1) It is accurate and effective to extract land use data using GEE. From 2000 to 2020, all cities’ built-up areas increased, but the increases differed by city. (2) While the ILU level in all cities has increased over the past 20 years, the ILU level in each city varies. Specifically, Shenzhen had the highest ILU degree in 2020, followed by core cities such as Guangzhou, Dongguan, and Zhuhai, while cities on the PRD region’s periphery, such as Zhaoqing and Jiangmen, had relatively low ILU levels. (3) In terms of time, the dominant factors influencing ILU in the PRD region have shifted over the past two decades. During this period, however, two factors (economic density and disposable income per capita) have always played a dominant role. This suggests that improving economic output efficiency and the city’s economic strength is a feasible way to raise the ILU level at this time. Full article
(This article belongs to the Special Issue Geographic Big Data for Sustainable City)
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14 pages, 6899 KiB  
Article
Relationships between Springtime PM2.5, PM10, and O3 Pollution and the Boundary Layer Structure in Beijing, China
by Qing Zhou, Lei Cheng, Yong Zhang, Zhe Wang and Shili Yang
Sustainability 2022, 14(15), 9041; https://doi.org/10.3390/su14159041 - 23 Jul 2022
Cited by 5 | Viewed by 1700
Abstract
Complex pollution with high aerosol and ozone concentrations has recently been occurring in several densely populated cities in China, raising concerns about the influence of meteorological factors, including synoptic circulation and local conditions. In this study, comprehensive analyses on the associations between PM [...] Read more.
Complex pollution with high aerosol and ozone concentrations has recently been occurring in several densely populated cities in China, raising concerns about the influence of meteorological factors, including synoptic circulation and local conditions. In this study, comprehensive analyses on the associations between PM2.5, PM10, and O3 and meteorological conditions were conducted based on observations from radar wind profiler, microwave radiometer, automatic weather station, and air quality monitoring sites in Beijing during the spring of 2019. The results showed that the boundary layer height and temperature inversion were negatively (positively) correlated with PM (O3) concentrations, modulating the degree of air pollution. Five identified synoptic patterns were derived using geopotential height data of the ERA5 reanalysis, among which Type 1, characterised by south-westerly prevailing winds with high pressure to the south, was considered to be associated with severe PM and O3 contamination. This indicates that air pollutants originating from southern regions exert a major influence on Beijing through the transportation effect. In addition, high temperature, relative humidity, and low wind velocity exacerbate pollution. Overall, this study provides significant information for understanding the vital roles played by meteorological elements at both the regional and local scales in regulating air contamination during spring in Beijing. Full article
(This article belongs to the Special Issue Geographic Big Data for Sustainable City)
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