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Land
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Big Data Analytics, Spatial Optimization for Land Use Planning
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Big Data Analytics, Spatial Optimization for Land Use 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: closed (2 June 2023) | Viewed by 20056

Special Issue Editors

Prof. Dr. Kai Cao

E-Mail Website
Guest Editor
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Interests: GIScience; spatial optimization; spatial big data analytics; spatially integrated social science
Dr. Wenting Zhang

E-Mail Website
Guest Editor
College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
Interests: spatial and temporal simulation and optimization of land use

Special Issue Information

Dear Colleagues,

Land use planning is a process of resource allocation, by which different land uses or activities are allocated to specific units of land area, usually within a city or a region. Spatial optimization, a powerful spatial analysis technique that can be used to identify optimal solution(s) and generate a large number of alternatives, is able to support the land use planning process effectively. There have been many successful relevant studies focusing on developing spatial optimization models to support the land use planning practices, which have promoted the advancement of the field in recent decades. Meanwhile, emerging big datasets have also started to play a more and more important role in land use management and planning. The big data revolution is significantly changing the way of how land use planning could be conducted and how spatial optimization techniques could be utilized.

In order to capture the latest advancement and encourage more efforts in this field of the integration of big data and spatial optimization for better supporting the practices of land use planning, this Special Issue with Land aims to develop a viable research agenda in this area of research.

We are seeking original unpublished papers on the following topics including, but not limited to:

  • Big data acquisition and analytics techniques for spatial optimization and land use planning;
  • Quantitative modelling of objectives and constraints with the support of big data analytics for land use planning;
  • Design of novel optimization algorithms for effective and efficient spatial optimization in supporting land use planning;
  • Spatio-temporal optimization for land use planning;
  • Integration of high-performance computing, cloud computing, GPU-based parallel computing in spatial optimization and land use planning;
  • Land use planning (decision making) support system.

Prof. Dr. Kai Cao
Dr. Wenting Zhang
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

  • spatial optimization
  • big data analytics
  • land use planning
  • spatio-temporal optimization
  • land use planning support

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Published Papers (7 papers)

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Research

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17 pages, 10673 KiB  
Article
Collaborative Optimal Allocation of Urban Land Guide by Land Ecological Suitability: A Case Study of Guangdong–Hong Kong–Macao Greater Bay Area
by Tingting Pan, Yu Zhang, Fengqin Yan and Fenzhen Su
Land 2023, 12(4), 754; https://doi.org/10.3390/land12040754 - 27 Mar 2023
Cited by 2 | Viewed by 1649
Abstract
Urban land optimization in urban agglomerations plays an important role in promoting territorial spatial planning to achieve high-quality development, land ecological suitability (LES) is one of the important variables influencing its urbanization and needs to be considered in urban growth simulation and modeling. [...] Read more.
Urban land optimization in urban agglomerations plays an important role in promoting territorial spatial planning to achieve high-quality development, land ecological suitability (LES) is one of the important variables influencing its urbanization and needs to be considered in urban growth simulation and modeling. This research proposed a multi-objective urban land optimization (MULO) model based on the non-dominated sorting genetic algorithm II (NSGA-II) which integrates the LES assessment. MULO starts with LES analysis based on a fuzzy analytical hierarchy process (AHP) and a minimum cumulative resistance (MCR) model. Then, two-step linear regression is used to optimize the quantity structure of built-up land. Finally, suitability and compactness are assigned to NSGA-II as objectives to obtain optimal spatial patterns. Taking the example of the Guangdong–Hong Kong–Macao Greater Bay Area, we found that all the newly added built-up land in 2030 is distributed in peri-urban areas around the original settlements, with approximate clustering in the northern part of Guangzhou and the southern part of Foshan under a balanced development scenario. This study highlights the importance of LES in urban growth modeling, and MULO can provide effective support for the spatial planning of urban agglomerations. Full article
(This article belongs to the Special Issue Big Data Analytics, Spatial Optimization for Land Use Planning)
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16 pages, 1915 KiB  
Article
Industrial Spatio-Temporal Distribution of High-Speed Rail Station Area from the Accommodation Facilities Perspective: A Multi-City Comparison
by Bingjie Niu, Ping Yin and Pengxia Shen
Land 2023, 12(2), 332; https://doi.org/10.3390/land12020332 - 26 Jan 2023
Cited by 1 | Viewed by 1544
Abstract
As a new engine of urban development, the high-speed rail (HSR) station area is an emerging location where the service industry is concentrated. This study aims to reflect the development of accommodation facilities in transport hub areas through the spatial distribution and agglomeration [...] Read more.
As a new engine of urban development, the high-speed rail (HSR) station area is an emerging location where the service industry is concentrated. This study aims to reflect the development of accommodation facilities in transport hub areas through the spatial distribution and agglomeration characteristics of the lodging industry in HSR station areas. HSR stations in Beijing, Tianjin, Nanjing, Jinan, Kunshan, and Xuzhou are selected. The Geodetector model is applied to analyze the pertinent driving factors. The findings indicate that: (1) The smaller the population size of the city, the closer the high agglomeration area of the accommodation industry in the HSR station area is to the HSR station. (2) The longer the HSR station is open, the stronger the agglomeration intensity of the accommodation industry is. (3) At HSR stations in various cities, the driving factors affecting the accommodation industry are heterogeneous. The interaction between the factors has a synergistic enhancement effect. Full article
(This article belongs to the Special Issue Big Data Analytics, Spatial Optimization for Land Use Planning)
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13 pages, 4272 KiB  
Article
More Urban Elderly Care Facilities Should Be Placed in Densely Populated Areas for an Aging Wuhan of China
by Zhenwei Wang, Xiaochun Wang, Zijin Dong, Lisan Li, Wangjun Li and Shicheng Li
Land 2023, 12(1), 220; https://doi.org/10.3390/land12010220 - 10 Jan 2023
Cited by 4 | Viewed by 2239
Abstract
Global aging is getting worse, especially in China, a country with a large population. It is urgently needed to plan the site of new urban elderly care facilities for an aging society. Based on point of interest data and machine learning algorithms, we [...] Read more.
Global aging is getting worse, especially in China, a country with a large population. It is urgently needed to plan the site of new urban elderly care facilities for an aging society. Based on point of interest data and machine learning algorithms, we established a site selection model of urban elderly care facilities for Wuhan in China and selected potential optimal sites for new urban elderly care facilities. We found that 2059 of the 31,390 grids with a resolution of 500 m × 500 m of Wuhan are priority layout grids for new urban elderly care facilities. A total of 635 priority grids were further selected based on the agglomeration degree of the aging population in each street. They are mainly distributed in the areas with a concentrated aging population within the Second Ring Road around the urban centers. Additionally, some outer suburban streets with a relatively high aging degree also require immediate facility construction. The point of interest data and machine learning algorithms to select the location of urban elderly care facilities can optimize their overall configuration and avoid the subjectivity of site selection to some degree, provide empirical support for how to achieve a good configuration of “population–facilities” in space, and continuously improve the science of the spatial allocation of elderly care facilities. Full article
(This article belongs to the Special Issue Big Data Analytics, Spatial Optimization for Land Use Planning)
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27 pages, 112997 KiB  
Article
Research on the Establishment of Provincial Characteristic Scenic Lines Based on GIS
by Zhixian Zhu, Linjia Wu, Wenyuan Jiang, Weijue Wang and Qibing Chen
Land 2022, 11(11), 1998; https://doi.org/10.3390/land11111998 - 7 Nov 2022
Cited by 2 | Viewed by 1680
Abstract
China is entering a stage of rapid development. To ensure strategic development, more regions have begun to integrate and reconstruct regional spaces by strengthening their regional cooperation and focusing on top-level design. The scenic line is a physical space that integrates ecological landscape [...] Read more.
China is entering a stage of rapid development. To ensure strategic development, more regions have begun to integrate and reconstruct regional spaces by strengthening their regional cooperation and focusing on top-level design. The scenic line is a physical space that integrates ecological landscape resources, cultural carriers, and industrial foundations into regional spaces. Its construction is of considerable importance for aggregating functions and supporting regional integration. Sichuan, China, has some of the most abundant bamboo resources worldwide, and the bamboo scenic line has Chinese characteristics. This study takes 12 areas suitable for bamboo growth in Sichuan Province as research objects using GIS technology combined with methods such as suitability evaluation. An ecological base layer, landscape pattern layer, facility foundation layer, and industrial development layer were developed as the four element layers, along with their influencing factors. The weights of the factors were assigned using the entropy method, and the cost path was analyzed for the resistance side, branch point, and context. A suitability evaluation system was constructed for the scenic line, and a provincial organic development pattern of “one point, two axes and three belts” was formed for the bamboo scenic line, which can provide guidance for planning and design. Full article
(This article belongs to the Special Issue Big Data Analytics, Spatial Optimization for Land Use Planning)
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22 pages, 4830 KiB  
Article
Demography-Oriented Urban Spatial Matching of Service Facilities: Case Study of Changchun, China
by Yingzi Chen, Yaqi Hu and Lina Lai
Land 2022, 11(10), 1660; https://doi.org/10.3390/land11101660 - 26 Sep 2022
Cited by 10 | Viewed by 2251
Abstract
People-oriented urban planning requires that service facilities should efficiently meet individual and community activity needs across the demographic landscape that defines a city. To develop a conceptual basis for urban spatial infrastructure optimization, we empirically studied existing population activities and service facilities in [...] Read more.
People-oriented urban planning requires that service facilities should efficiently meet individual and community activity needs across the demographic landscape that defines a city. To develop a conceptual basis for urban spatial infrastructure optimization, we empirically studied existing population activities and service facilities in Changchun, China, using kernel density estimation, bivariate spatial autocorrelation analysis, and other models. The spatial relationships we derived from multiple sources of big data such as mobile phone signaling and POI data indicated that the intensity of population activity has obvious temporal regularity, and its spatial distribution is “center-periphery.” Service facilities display a “One main and two subs” distribution with no obvious spatial dependence between the core’s density and diversity. Population activities and service facility diversity show a high-high spatial correlation and multiple matching patterns. At the same time, a certain degree of spatial mismatch between different age groups and service facilities was also observed. Our research suggests several urban renewal actions to rectify this mismatch, such as: decentralizing the core area medical service facilities; reducing the attractiveness of the core area and its traffic pressure; and renewing and renovating old facilities to reduce construction costs. At a government planning level, construction along the periphery of the urban can enrich the diversity of its service facilities to improve the efficiency of spatial allocation. Full article
(This article belongs to the Special Issue Big Data Analytics, Spatial Optimization for Land Use Planning)
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20 pages, 5008 KiB  
Article
Research on the Equity of Urban Green Park Space Layout Based on Ga2SFCA Optimization Method—Taking the Core Area of Beijing as an Example
by Yilun Cao, Yuhan Guo and Mingjuan Zhang
Land 2022, 11(8), 1323; https://doi.org/10.3390/land11081323 - 16 Aug 2022
Cited by 10 | Viewed by 3141
Abstract
(1) Background: The issue of equity in the layout of urban green park spaces is an essential dimension of urban public resource allocation. (2) Objective: To analyze the equity of the distribution of parkland in the core area of Beijing from a quantitative [...] Read more.
(1) Background: The issue of equity in the layout of urban green park spaces is an essential dimension of urban public resource allocation. (2) Objective: To analyze the equity of the distribution of parkland in the core area of Beijing from a quantitative and spatial perspective. By measuring both vehicular and pedestrian transport modes, the study identifies areas with low levels of green space provision and provides strategies for optimization. It is hoped that this study can provide a basis for future green space construction in the core area of Beijing. (3) Methods: In this paper, the Gauss Two-step Floating Catchment Area Method (Ga2SFCA) is used to study the green park space layout in the core area of Beijing. The two modes of 30min-walk and 10min-car-journey were used to measure the fair values of the residential unit scale, the street district scale, and the overall scale, respectively. (4) Results: The study results show that the fair values based on the 30-min walk and the 10-min car journey differ significantly. For the 30-min walk-based travel mode, the proportion of fair (Class IV) and fairer (Class V) areas is approximately 20%, while for the 10 min car-based travel mode, the corresponding class is over 90%. (5) Conclusions: The overall equity of urban parkland in Beijing core area is better for car-based travel modes, while for walking modes, the supply is still insufficient, and the distribution of parkland is polarized. Full article
(This article belongs to the Special Issue Big Data Analytics, Spatial Optimization for Land Use Planning)
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Review

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19 pages, 1955 KiB  
Review
Cellular Automata in Modeling and Predicting Urban Densification: Revisiting the Literature since 1971
by Anasua Chakraborty, Sujit Sikder, Hichem Omrani and Jacques Teller
Land 2022, 11(7), 1113; https://doi.org/10.3390/land11071113 - 20 Jul 2022
Cited by 14 | Viewed by 5460
Abstract
The creation of an accurate simulation of future urban growth is considered to be one of the most important challenges of the last five decades that involves spatial modeling within a GIS environment. Even though built-up densification processes, or transitions from low to [...] Read more.
The creation of an accurate simulation of future urban growth is considered to be one of the most important challenges of the last five decades that involves spatial modeling within a GIS environment. Even though built-up densification processes, or transitions from low to high density, are critical for policymakers concerned with limiting sprawl, the literature on models for urban study reveals that most of them focus solely on the expansion process. Although the majority of these models have similar goals, they differ in terms of implementation and theoretical assumptions. Cellular automata (CA) models have been proven to be successful at simulating urban growth dynamics and projecting future scenarios at multiple scales. This paper aims to revisit urban CA models to determine the various approaches for a realistic simulation and prediction of urban densification. The general characteristics of CA models are described with respect to analysis of various driving factors that influence urban scenarios. This paper also critically analyzes various hybrid models based on CA such as the Markov chain, artificial neural network (ANN), and logistic regression (LR). Limitation and uncertainties of CA models, namely, neighborhood cell size, may be minimized when integrated with empirical and statistical models. The result of this review suggests that it is useful to use CA models with multinomial logistic regression (MLR) in order to analyze and model the effects of various driving factors related to urban densification. Realistic simulations can be achieved when multidensity class labels are integrated in the modeling process. Full article
(This article belongs to the Special Issue Big Data Analytics, Spatial Optimization for Land Use Planning)
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