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Geographical Data and Analysis for Sustainable Urban Studies
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Geographical Data and Analysis for Sustainable Urban Studies

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 (31 January 2024) | Viewed by 7922

Special Issue Editor

Dr. Kamyar Hasanzadeh

E-Mail Website
Guest Editor
Department of Built Environment, Aalto University, 02150 Espoo, Finland
Interests: GIS; PPGIS; spatial analysis; spatial modeling; urban studies; physical activity; walkability; data anonymization

Special Issue Information

Dear Colleagues,

“No science achieves maturity without data” and urban studies are no exception to this well-known fact. As our cities grow bigger and more complex, we encounter new challenges in keeping them sustainable. New challenges require new solutions and that often necessitates the collection and use of new data and analysis. In recent decades, new data sources have emerged, many of which come with geographical components enabling the spatial analysis of urban areas from various aspects. Public participation GIS data (PPGIS), volunteered geographic information (VGI), big data, GPS and mobile phone tracking data are a few examples of such new data frontiers.

Sustainability in an urban area has many layers and the studies can widely range from social and health aspects of urban life all the way to the environmental issues and challenges. However, what brings us together as urban researchers in this Special Issue, is the use of novel geographical data and analysis for tackling these urban sustainability challenges.

In this Special Issue, we welcome both methodological and empirical studies with a strong spatial analytical component. We are especially interested in studies that use novel geographical data and analytical and visualization methods to address various urban topics. Relevant research themes may include, but are not limited to:

  • Livability;
  • Environmental health promotion;
  • Physical activity;
  • Air quality and GHG emissions;
  • Environmental studies;
  • Transportation;
  • Mobility studies;
  • Activity spaces;
  • Public participatory mapping.

This Special Issue will contribute to the current body of literature by demonstrating high-quality urban research making innovative use of geographical data and/or applying novel spatial analytical methods. This will help promote the use of spatial data and analysis in urban research and showcase some of the novel practices in adopting such data as well as analyzing and visualizing them.

Dr. Kamyar Hasanzadeh
Guest Editor

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

  • GIS
  • geographical data
  • spatial analysis
  • visualization
  • urban
  • sustainability
  • participation

Published Papers (5 papers)

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Research

14 pages, 13741 KiB  
Article
Spatial-Performance Evaluation of Primary Health Care Facilities: Evidence from Xi’an, China
by Dan Zhao, Liu Shao, Jianwei Li and Lina Shen
Sustainability 2024, 16(7), 2838; https://doi.org/10.3390/su16072838 - 28 Mar 2024
Viewed by 397
Abstract
Primary health care (PHC) facilities play a significant role in constructing a “people-oriented city” to promote sustainable urban development. However, existing studies exhibit gaps in the spatial-performance evaluation of PHC facilities at the block scale and in identifying spatial association types between facilities [...] Read more.
Primary health care (PHC) facilities play a significant role in constructing a “people-oriented city” to promote sustainable urban development. However, existing studies exhibit gaps in the spatial-performance evaluation of PHC facilities at the block scale and in identifying spatial association types between facilities and the population. Therefore, we examined the elderly population, who rely heavily on PHC facilities, and developed a spatial-performance evaluation model for PHC facilities at the block scale using the Ga2SFCA method and the bivariate spatial autocorrelation method. The results revealed an evident concentric pattern and spatial mismatch between the accessibility of facilities and the elderly population. Facilities in the central area were inadequate due to the excessive density of the elderly population, whereas medical services in suburban areas were unsustainable due to poor accessibility. From a spatial-justice perspective, the spatial-performance evaluation at the block scale can identify spatial correlation types and distribution characteristics between PHC facilities and the elderly population. Full article
(This article belongs to the Special Issue Geographical Data and Analysis for Sustainable Urban Studies)
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30 pages, 8476 KiB  
Article
A Spatial Analysis of Urban Streets under Deep Learning Based on Street View Imagery: Quantifying Perceptual and Elemental Perceptual Relationships
by Haozun Sun, Hong Xu, Hao He, Quanfeng Wei, Yuelin Yan, Zheng Chen, Xuanhe Li, Jialun Zheng and Tianyue Li
Sustainability 2023, 15(20), 14798; https://doi.org/10.3390/su152014798 - 12 Oct 2023
Cited by 3 | Viewed by 1558
Abstract
Measuring the human perception of urban street space and exploring the street space elements that influence this perception have always interested geographic information and urban planning fields. However, most traditional efforts to investigate urban street perception are based on manual, usually time-consuming, inefficient, [...] Read more.
Measuring the human perception of urban street space and exploring the street space elements that influence this perception have always interested geographic information and urban planning fields. However, most traditional efforts to investigate urban street perception are based on manual, usually time-consuming, inefficient, and subjective judgments. This shortcoming has a crucial impact on large-scale street spatial analyses. Fortunately, in recent years, deep learning models have gained robust element extraction capabilities for images and achieved very competitive results in semantic segmentation. In this paper, we propose a Street View imagery (SVI)-driven deep learning approach to automatically measure six perceptions of large-scale urban areas, including “safety”, “lively”, “beautiful”, “wealthy”, “depressing”, and “boring”. The model was trained on millions of people’s ratings of SVIs with a high accuracy. First, this paper maps the distribution of the six human perceptions of urban street spaces within the third ring road of Wuhan (appearing as Wuhan later). Secondly, we constructed a multiple linear regression model of “street constituents–human perception” by segmenting the common urban constituents from the SVIs. Finally, we analyzed various objects positively or negatively correlated with the six perceptual indicators based on the multiple linear regression model. The experiments elucidated the subtle weighting relationships between elements in different street spaces and the perceptual dimensions they affect, helping to identify the visual factors that may cause perceptions of an area to be involved. The findings suggested that motorized vehicles such as “cars” and “trucks” can negatively affect people’s perceptions of “safety”, which is different from previous studies. We also examined the influence of the relationships between perceptions, such as “safety” and “wealthy”. Finally, we discussed the “perceptual bias” issue in cities. The findings enhance the understanding of researchers and city managers of the psychological and cognitive processes behind human–street interactions. Full article
(This article belongs to the Special Issue Geographical Data and Analysis for Sustainable Urban Studies)
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30 pages, 14185 KiB  
Article
A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes
by Mahsa Farahani, Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki and Soo-Mi Choi
Sustainability 2023, 15(13), 10403; https://doi.org/10.3390/su151310403 - 01 Jul 2023
Cited by 2 | Viewed by 1265
Abstract
The visual qualities of the urban environment influence people’s perception and reaction to their surroundings; hence the visual quality of the urban environment affects people’s mental states and can have detrimental societal effects. Therefore, people’s perception and understanding of the urban environment are [...] Read more.
The visual qualities of the urban environment influence people’s perception and reaction to their surroundings; hence the visual quality of the urban environment affects people’s mental states and can have detrimental societal effects. Therefore, people’s perception and understanding of the urban environment are necessary. This study used a deep learning-based approach to address the relationship between effective spatial criteria and people’s visual perception, as well as spatial modeling and preparing a potential map of people’s visual perception in urban environments. Dependent data on people’s visual perception of Tehran, Iran, was gathered through a questionnaire that contained information about 663 people, 517 pleasant places, and 146 unpleasant places. The independent data consisted of distances to industrial areas, public transport stations, recreational attractions, primary streets, secondary streets, local passages, billboards, restaurants, shopping malls, dilapidated areas, cemeteries, religious places, traffic volume, population density, night light, air quality index (AQI), and normalized difference vegetation index (NDVI). The convolutional neural network (CNN) algorithm created the potential map. The potential visual perception map was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC), with the estimates of AUC of 0.877 and 0.823 for pleasant and unpleasant visuals, respectively. The maps obtained using the CNN algorithm showed that northern, northwest, central, eastern, and some southern areas of the city are potent in pleasant sight, and southeast, some central, and southern regions had unpleasant sight potential. The OneR method results demonstrated that distance to local passages, population density, and traffic volume is most important for pleasant and unpleasant sights. Full article
(This article belongs to the Special Issue Geographical Data and Analysis for Sustainable Urban Studies)
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28 pages, 14499 KiB  
Article
Research on Regional Architectural Design Method Based on GIS
by Ren Zhou and Weimin Guo
Sustainability 2023, 15(12), 9291; https://doi.org/10.3390/su15129291 - 08 Jun 2023
Viewed by 2135
Abstract
Urbanization and continued population growth have had a major impact on the urban environment. Many buildings lack close regional and biomimetic characteristics during the rapid generation process, making it impossible to achieve ecological sustainability. This article took the rehabilitation center of Jinghong Dai [...] Read more.
Urbanization and continued population growth have had a major impact on the urban environment. Many buildings lack close regional and biomimetic characteristics during the rapid generation process, making it impossible to achieve ecological sustainability. This article took the rehabilitation center of Jinghong Dai Cultural Park in Yunnan as an example for research, intending to highlight regional characteristics and change the current severely homogenized urban style. Visual on-site environmental analysis was performed through Arc GIS, CFD-phoenics, and sketchup, and the site form planning of ecological buildings from the perspective of “regional characteristics” was explored. Morphological data of local buildings and regional plants were collected, plant growth patterns were analyzed in Grasshopper software, and skins were generated from the perspective of “biomimetic properties”, and we combined the planning form with the skin to form the overall regional architectural design. The design approach of multi-parameter software combinations brings a richer expression morphologically and distinguishes the more homogeneous stereotypical image of the city, which is of great significance and value in expanding the form of architectural design. The spatial form of the site layout and the phenology of the native plants respect the original natural environment and create a symbiosis between man and nature. Full article
(This article belongs to the Special Issue Geographical Data and Analysis for Sustainable Urban Studies)
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14 pages, 2245 KiB  
Article
A Study on the Impact of Built Environment Elements on Satisfaction with Residency Whilst Considering Spatial Heterogeneity
by Qi Chen, Yibo Yan, Xu Zhang and Jian Chen
Sustainability 2022, 14(22), 15011; https://doi.org/10.3390/su142215011 - 13 Nov 2022
Cited by 7 | Viewed by 1729
Abstract
The built environment, as perceived and felt by human beings, can shape and affect residential satisfaction. From the perspective of municipal administrators, understanding the building environment and its relationship with people’s residential satisfaction is crucial to improving people’s living environment. This study examines [...] Read more.
The built environment, as perceived and felt by human beings, can shape and affect residential satisfaction. From the perspective of municipal administrators, understanding the building environment and its relationship with people’s residential satisfaction is crucial to improving people’s living environment. This study examines the correlation between built environment elements and residential satisfaction using the consideration of spatial heterogeneity of such a correlation. Machine vision technology is introduced to quantify the design dimension of the built environment. The method of multiscale geographically weighted regression is used to evaluate the relationship between built environment and residential satisfaction and to analyze the spatial heterogeneity in the influencing effects. This empirical study draws on 399 collected samples from the residents of Zhengzhou, China. The results show that elements of the built environment, including street space design features, have a significant effect on people’s residential satisfaction in Zhengzhou City. The factors of functional diversity and distance to the city center show spatial heterogeneity in influencing effects on residential satisfaction. The results of this study could help municipal managers to improve people’s residential satisfaction in Zhengzhou City through the development of urban renewal policies. Full article
(This article belongs to the Special Issue Geographical Data and Analysis for Sustainable Urban Studies)
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