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Land
Special Issues
Recent Progress in RS&GIS-Based Urban Planning
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Recent Progress in RS&GIS-Based Urban Planning

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Planning and Landscape Architecture".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 4477

Special Issue Editors

Dr. Jing Xie

E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Interests: natural and urban environmental gradients; urban local climate; urban analytics; environmental factors' modeling; phenology and productivity of vegetation; land use land cover; climate change
Dr. Yeran Sun

E-Mail Website
Co-Guest Editor
Department of Geography, University of Lincoln, Lincoln LN6 7TS, UK
Interests: big data; GeoAI; spatial modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Xiao Liu

E-Mail Website
Co-Guest Editor
School of Architecture, South China University of Technology, Guangzhou 510641, China
Interests: green building; green campus; carbon-neutral building; healthy building; urban heat mitigation and adaptation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Within the context of the mobility of populations and urban development, feasible and accessible urban planning has become increasingly important, as well as for achieving the 2030 Agenda for Sustainable Development Goals (SDGs). The current problem of urban planning is that planners rely extensively on their subjective and historic experience, which makes urban planning inefficient, chaotic in zoning, poor in practicality, and unsustainable.

As an emerging information technology science in recent years, Remote Sensing (RS) and Geographic Information System (GIS), through the database platform, information service, and 3D modelling and monitoring, provides more scientific and quantitative analysis methods for urban planning. The Special Issue aims to combine geographic information systems and geospatial data, using technologies such as remote sensing, satellite imagery, and airborne lidar to build geographic information analysis models, and to provide decision-making tools for the players involved in urban planning.

We encourage researchers to submit their original research papers as well as technical or review articles to this Special Issue, focusing on the application and prospects of RS and GIS in urban planning, such as:

  • Application of geographic information systems (GIS);
  • Big data analytics for urban planning;
  • Green spaces and sustainable urban planning;
  • Urban planning with remote sensing;
  • Landscape and urban planning;
  • Urban planning and smart city;
  • Decision support tools in urban planning;
  • Urban planning and land management;
  • Sustainable urban-planning techniques.

We look forward to receiving your original research articles and reviews.

Dr. Jing Xie
Guest Editor

Dr. Yeran Sun
Dr. Xiao Liu
Co-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. Land is an international peer-reviewed open access monthly 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 2600 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

  • urban planning
  • geographic information systems
  • remote sensing
  • big data
  • land management
  • smart city
  • green spaces

Published Papers (4 papers)

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Research

25 pages, 6483 KiB  
Article
Assessment Methodology for Physical Vulnerability of Vernacular Architecture in Areas Affected by Depopulation: The Case of Comunidad Valenciana, Spain
by Eva Tortajada Montalvá, Camilla Mileto and Fernando Vegas López-Manzanares
Land 2024, 13(5), 695; https://doi.org/10.3390/land13050695 - 15 May 2024
Viewed by 366
Abstract
The intensity with which the phenomenon of depopulation has affected rural municipalities in Spain between 1950 and 2022 has led to a loss in the intergenerational transmission of traditional knowledge, values and customs. Sociocultural loss entails associated physical risks: the abandonment, demolition, and [...] Read more.
The intensity with which the phenomenon of depopulation has affected rural municipalities in Spain between 1950 and 2022 has led to a loss in the intergenerational transmission of traditional knowledge, values and customs. Sociocultural loss entails associated physical risks: the abandonment, demolition, and loss of vernacular architecture. This research analyzes the evolution of this type of architecture in a period of acute depopulation and its current state of conservation. A total of 180 case studies in the region of Comunidad Valenciana are analyzed through four factors affecting the physical vulnerability of dwellings: year of construction, state of conservation, type of use, and a combination of all three. Data management software is used to combine all the information and produce the results in a tabular and graphical format, while the Geographic Information System is used to draw up risk maps showing the results. These results are then divided into analysis groups created according to the degree of depopulation observed in the years mentioned. This made it possible to identify the relationship between depopulation and the conservation of vernacular architecture, showing the risk level for each case study, and thus creating an analysis methodology applicable in other territories affected by depopulation at a national and international level. Full article
(This article belongs to the Special Issue Recent Progress in RS&GIS-Based Urban Planning)
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19 pages, 21675 KiB  
Article
Unraveling the Dynamic Relationship between Neighborhood Deprivation and Walkability over Time: A Machine Learning Approach
by Qian Wang, Guie Li and Min Weng
Land 2024, 13(5), 667; https://doi.org/10.3390/land13050667 - 12 May 2024
Viewed by 397
Abstract
Creating a walkable environment is an essential step toward the 2030 Sustainable Development Goals. Nevertheless, not all people can enjoy a walkable environment, and neighborhoods with different socioeconomic status are found to vary greatly with walkability. Former studies have typically unraveled the relationship [...] Read more.
Creating a walkable environment is an essential step toward the 2030 Sustainable Development Goals. Nevertheless, not all people can enjoy a walkable environment, and neighborhoods with different socioeconomic status are found to vary greatly with walkability. Former studies have typically unraveled the relationship between neighborhood deprivation and walkability from a temporally static perspective and the produced estimations to a point-in-time snapshot were believed to incorporate great uncertainties. The ways in which neighborhood walkability changes over time in association with deprivation remain unclear. Using the case of the Hangzhou metropolitan area, we first measured the neighborhood walkability from 2016 to 2018 by calculating a set of revised walk scores. Further, we applied a machine learning algorithm, the kernel-based regularized least squares regression in particular, to unravel how neighborhood walkability changes in relation to deprivation over time. The results not only capture the nonlinearity in the relationship between neighborhood deprivation and walkability over time, but also highlight the marginal effects of each neighborhood deprivation indicator. Additionally, comparisons of the outputs between the machine learning algorithm and OLS regression illustrated that the machine learning approach did tell a different story and should contribute to remedying the contradictory conclusions in earlier studies. This paper is believed to renew the understanding of social inequalities in walkability by bringing the significance of temporal dynamics and structural interdependences to the fore. Full article
(This article belongs to the Special Issue Recent Progress in RS&GIS-Based Urban Planning)
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24 pages, 7024 KiB  
Article
Researching Tourism Space in China’s Great Bay Area: Spatial Pattern, Driving Forces and Its Coupling with Economy and Population
by Lingfeng Li and Quan Gao
Land 2023, 12(10), 1878; https://doi.org/10.3390/land12101878 - 6 Oct 2023
Cited by 2 | Viewed by 969
Abstract
Analysis of the spatial patterns and dynamics of tourism services and facilities is crucial for tourism and land use planning. However, most studies in the spatial analysis of tourism rely on the city- or regional-level data; limited research has used POI (point of [...] Read more.
Analysis of the spatial patterns and dynamics of tourism services and facilities is crucial for tourism and land use planning. However, most studies in the spatial analysis of tourism rely on the city- or regional-level data; limited research has used POI (point of interest) data to accurately uncover the spatial distribution of tourism, especially its interactive and coupling relationship with the local economy and population. Based on POI data, this paper, therefore, investigates the spatial patterns and driving forces of tourism services distribution and how tourism space is coupled with the local economy and population in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. The results show the following: (1) Different categories of tourism services (catering, shopping, scenic spots, leisure, and sports) exhibit diverse spatial patterns and agglomerations, but they tend to align with the variables of economic level and population in a grid of 1 km2. (2) The spatial econometric models further reveal that population density, transportation, and hospitality facilities are positively correlated with the spatial distribution of tourism services, but GDP in a grid of 1 km2 shows a weak negative correlation with the POI of tourism services, which may be attributed to the incoordination between GDP and tourism in some areas. (3) The analysis of coupling degree further identifies the areas where tourism services have good interaction/coupling with the local GDP and population density, such that these areas can be viewed as hotspots suitable for tourism promotion. This paper thus offers meaningful policy implications by calling for an optimization of the coupling of tourism services with local social–economic factors in the GBA. Full article
(This article belongs to the Special Issue Recent Progress in RS&GIS-Based Urban Planning)
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20 pages, 17527 KiB  
Article
Evaluation of Urban Green Space Supply and Demand Based on Mobile Signal Data: Taking the Central Area of Shenyang City as an Example
by Yukuan Dong, Xi Chen, Dongyang Lv and Qiushi Wang
Land 2023, 12(9), 1742; https://doi.org/10.3390/land12091742 - 7 Sep 2023
Cited by 3 | Viewed by 1761
Abstract
The degree of coordination between the supply and demand for urban green spaces serves as a vital metric for evaluating urban ecological development and the well-being of residents. An essential principle in assessing this coordination is the precise quantification of both the demand [...] Read more.
The degree of coordination between the supply and demand for urban green spaces serves as a vital metric for evaluating urban ecological development and the well-being of residents. An essential principle in assessing this coordination is the precise quantification of both the demand and supply of green spaces, as well as the differential representation of their spatiotemporal structures. This study utilizes the entropy weight method (EWM) and principal component analysis (PCA) to comprehensively measure supply indicators for green space quantity and quality in the central urban area of Shenyang, China. To establish reliable and quantifiable demand indicators, mobile signaling spatial-temporal data are corrected by incorporating static population cross-sectional data. The Gaussian two-step floating catchment area method (Ga2SFCA) is employed to calculate the accessibility of green spaces in each community with ArcGIS 10.2 software, while the Gini coefficient is utilized to assess the equity of green space distribution within the study area. This study employs location entropy to determine the levels of supply and demand for green spaces in each subdistrict. Furthermore, the priority of community-scale green space regulation is accurately determined by balancing vulnerable areas of green space supply and replenishing green space resources for the ageing population. The findings suggest a Gini coefficient of 0.58 for the supply and demand of green spaces in Shenyang’s central metropolitan region, indicating a relatively low level of equalization in overall green space allocation. Based on location entropy, the classification of supply and demand at the street level yields the following outcomes: balanced areas comprise 21.98%, imbalanced areas account for 26.37%, and highly imbalanced regions represent 51.65%. After eliminating the balanced regions, the distribution of the elderly population is factored in, highlighting the spatial distribution and proportions of communities with distinct regulatory priorities: Level 1 (S1) constitutes 7.4%, Level 2 (S2) accounts for 60.9%, and Level 3 (S3) represents 31.7%. Notably, the communities in the S1 category exhibit spatial distribution characteristics of aggregation within the inner ring and the northern parts of the third ring. This precise identification of areas requiring urgent regulation and the spatial distribution of typical communities can provide reliable suggestions for prioritizing green space planning in an age-friendly city. Full article
(This article belongs to the Special Issue Recent Progress in RS&GIS-Based Urban Planning)
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