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Applications of Machine Learning in National Territory Spatial Planning

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 14043

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Special Issue Editors

Prof. Dr. Jun Yang

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Guest Editor

Jangho Architecture College, Northeastern University, Shenyang 110169, China
Interests: spatial analysis; land use modelling; land use change; urban climatology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bing Xue

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Guest Editor

Institute of Applied Ecology, Chinese Academy of Sciences, No. 72 Wenhua Road, Shenyang 110016, China
Interests: human-natural system analysis; policy & human dimensions in sustainability; industrial ecology and circular economy
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Prof. Dr. Xiangming Xiao

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Guest Editor

Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, 101 David L. Boren Blvd., Norman, OK 73019-5300, USA
Interests: applications of remote sensing and GIS in ecosystems science and natural resources; land use and cover changes; ecosystem service assessment; biogeochemistry of terrestrial ecosystems; ecosystem modeling at large spatial scales; integrated impact assessment of climate change; ecology and epidemiology of infectious diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

National territory spatial planning guides national spatial development and the spatial blueprint of sustainable development. It is the basis for all kinds of development, protection, and construction activities. It is of great significance to solve problems such as the prominent human–land conflict, the lack of spatial resources, and the imbalance of regional development in the process of rapid urbanization, industrialization, and modernization. National territory spatial planning integrates multiple spatial plans, including main functional area planning as well as land use planning and urban and rural planning, and opens a new era of integrated territorial governance. Integrated territorial governance is the theoretical interpretation of the direction of national territory spatial governance. Eradicating the conflicts among multiple spatial plans is the basic premise of the reconstruction of a national territory spatial planning system. Supporting the construction of ecological civilization is an important mission of national territory spatial governance for the current age.

Machine learning technology is multi-disciplinary field, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in determining how computers simulate or realize human learning behavior in order to acquire new knowledge or skills, and reorganizing existing knowledge structures to continuously improve its own performance.

This Special Issue solicits the latest application achievements and advanced technologies of machine learning in the theory and practice of national territory spatial planning. We expect these selected academic papers to systematically summarize and sort out the methods of national territory spatial planning, determine the technical problems existing in national territory spatial planning as it currently exists, and to provide reference technical guidance for theoretical research and practice of national territory spatial planning in the future. We sincerely request the technological applications of machine learning in the following topics, but works on other related aspects are welcome.

Assessment of the current situation of national territory spatial development and the protection and judgment of future risks.
Optimization of national territory spatial pattern in response to global climate change.
National territory spatial development patterns under the background of new globalization.
Strategy and system of main functional areas in the era of ecological civilization.
Safety and sustainable guarantee of water, soil, energy, and mineral resources.
National ecological security pattern.
National food security and agricultural spatial pattern.
National rural revitalization and urban–rural integrated development.
Population and urbanization trends and national urban development distribution.
Spatiotemporal patterns of population migration and mobility trends.
Optimization of national industrial spatial distribution.
Improving the quality of human settlements and community life cycles.
Supply of national territory spatial development serving high quality of life.
Development, protection and utilization of marine space and steadfastly promoting land and marine development in a coordinated way.
Coordinated development of comprehensive transportation systems and national territory spatial development patterns.
New infrastructure construction.
Coordinated protection, development and mechanism innovation of important economic zones and watersheds.
Knowledge graph mapping of national territory spatial planning.
Zoning guidance and transmission mechanisms of national territory spatial development.
Guarantee mechanism and system innovation for the implementation of national territory spatial development.
Big data applications and platforms supporting the preparation of national territory spatial development.
Practical cases of local territory spatial development innovation.

Prof. Dr. Jun Yang
Prof. Dr. Bing Xue
Prof. Dr. Xiangming Xiao
Guest Editors

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Keywords

national territory spatial planning
national territory spatial governance
ecological civilization
machine learning
big data
technological innovation
ecological security and ecological risk
food safety
global climate change
low-carbon spatial planning
sustainable development
knowledge graph
spatio-temporal network model

Published Papers (7 papers)

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Research

20 pages, 7867 KiB

Open AccessArticle

Georeferenced Analysis of Urban Nightlife and Noise Based on Mobile Phone Data

by Luís B. Elvas, Miguel Nunes, Joao C. Ferreira, Bruno Francisco and Jose A. Afonso

Appl. Sci. 2024, 14(1), 362; https://0-doi-org.brum.beds.ac.uk/10.3390/app14010362 - 30 Dec 2023

Viewed by 636

Abstract

Urban environments are characterized by a complex soundscape that varies across different periods and geographical zones. This paper presents a novel approach for analyzing nocturnal urban noise patterns and identifying distinct zones using mobile phone data. Traditional noise-monitoring methods often require specialized equipment and are limited in scope. Our methodology involves gathering audio recordings from city sensors and localization data from mobile phones placed in urban areas over extended periods with a focus on nighttime, when noise profiles shift significantly. By leveraging machine learning techniques, the developed system processes the audio data to extract noise features indicative of different sound sources and intensities. These features are correlated with geographic location data to create comprehensive city noise maps during nighttime hours. Furthermore, this work employs clustering algorithms to identify distinct noise zones within the urban landscape, characterized by their unique noise signatures, reflecting the mix of anthropogenic and environmental noise sources. Our results demonstrate the effectiveness of using mobile phone data for nocturnal noise analysis and zone identification. The derived noise maps and zones identification provide insights into noise pollution patterns and offer valuable information for policymakers, urban planners, and public health officials to make informed decisions about noise mitigation efforts and urban development. Full article

(This article belongs to the Special Issue Applications of Machine Learning in National Territory Spatial Planning)

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20 pages, 1127 KiB

Open AccessArticle

Impact of “Non-Grain” in Cultivated Land on Agricultural Development Resilience: A Case Study from the Major Grain-Producing Area of Northeast China

by Gaofeng Ren, Ge Song, Quanxi Wang and Hongjun Sui

Appl. Sci. 2023, 13(6), 3814; https://0-doi-org.brum.beds.ac.uk/10.3390/app13063814 - 16 Mar 2023

Cited by 5 | Viewed by 1438

Abstract

Previous studies focused on the status and driving factors of non-grain cultivated land (NGCL), but lacked research on the impact mechanism of NGCL on sustainable agricultural development from the perspective of farmers’ household livelihoods and agricultural production factor allocation. Therefore, the concept of resilience was introduced. According to official statistics of China from 2010 to 2021, such as the local statistical yearbooks, the impact of NGCL on agricultural development resilience (ADR) in the main grain-producing area of Northeast China was explored based on the threshold effect model and the spatial lag model. The results indicate that: (1) the overall level of NGCI in the study area from 2011 to 2020 show an upward followed by a downward trend; (2) the size of agricultural labor force and average area per labor constrain the impact of NGCI on ADR, and the change can be characterized by negative to positive, increasing and then decreasing respectively, and the former is more constrained than the latter; (3) a negative effect of the NGCI trend on ADR exists without spatial spillover effect. The expansion of food production exacerbates the risk of factor mismatch, which is accentuated by the governance environment that pursues food production excessively. Establishing the NFP governance standards should consider the transformation of farmers’ livelihoods and the optimization of production factor allocation. Constructing a resilient risk management mechanism, promoting moderate scale operation and optimizing agricultural labor scale are specific paths for improving the governance mechanisms of NGCI. This study provides a theoretical reference for the development of policies and governance strategies for NGCI in underdeveloped areas. Full article

(This article belongs to the Special Issue Applications of Machine Learning in National Territory Spatial Planning)

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15 pages, 2899 KiB

Open AccessArticle

Multi-Dimensional Evaluation of Land Comprehensive Carrying Capacity Based on a Normal Cloud Model and Its Interactions: A Case Study of Liaoning Province

by Huisheng Yu, Xinyue Zhang, Wenbo Yu, Yanpeng Gao, Yuyu Xue, Wei Sun and Dongqi Sun

Appl. Sci. 2023, 13(5), 3336; https://0-doi-org.brum.beds.ac.uk/10.3390/app13053336 - 06 Mar 2023

Cited by 2 | Viewed by 1061

Abstract

Studying land comprehensive carrying capacity (LCCC) is the foundational and key requirement for determining land development planning and urban spatial development patterns of a region. However, the traditional evaluation method discounts the fuzziness and randomness of the evaluation index and its results. The cloud model combines randomness and fuzziness to reveal the correlation between randomness and fuzziness using numerical feature entropy and is used to represent the granularity of a qualitative concept. This study used the Liaoning Province as the study area, and developed a multi-dimensional evaluation index system for LCCC using a normal cloud model. Based on this, the relationship between the different elements of geological condition, resources and environment, economic scale and urban construction were studied using the coupling coordination degree model that reflected not only the system interactions but also the strengths of its degree of coordination. Our results were as follows: (1) numerical feature entropy were evaluated to determine the carrying capacity level of the land, and comprehensive land carrying capacity evaluations were conducted in terms of both quantitative results and the reliability of the results, promoting the scientific application of uncertainty theory in the field of comprehensive land evaluation as well as carrying capacity. (2) Liaoning Province’s prefecture-level cities had distinctly different LCCC, demonstrating “low in the west and high in the east” spatial distribution characteristics. Cities with established economies and relatively strong infrastructures had larger comprehensive land carrying capacities. Overall, there was considerable consistency across the region, though the “low in the west and high in the east” spatial distribution characteristics affected the degree of coordination. Full article

(This article belongs to the Special Issue Applications of Machine Learning in National Territory Spatial Planning)

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26 pages, 3357 KiB

Open AccessArticle

Machine Learning and Food Security: Insights for Agricultural Spatial Planning in the Context of Agriculture 4.0

by Vítor João Pereira Domingues Martinho, Carlos Augusto da Silva Cunha, Maria Lúcia Pato, Paulo Jorge Lourenço Costa, María Carmen Sánchez-Carreira, Nikolaos Georgantzís, Raimundo Nonato Rodrigues and Freddy Coronado

Appl. Sci. 2022, 12(22), 11828; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211828 - 21 Nov 2022

Cited by 9 | Viewed by 3202

Abstract

Climate change and global warming interconnected with the new contexts created by the COVID-19 pandemic and the Russia-Ukraine conflict have brought serious challenges to national and international organizations, especially in terms of food security and agricultural planning. These circumstances are of particular concern due to the impacts on food chains and the resulting disruptions in supply and price changes. The digital agricultural transition in Era 4.0 can play a decisive role in dealing with these new agendas, where drones and sensors, big data, the internet of things and machine learning all have their inputs. In this context, the main objective of this study is to highlight insights from the literature on the relationships between machine learning and food security and their contributions to agricultural planning in the context of Agriculture 4.0. For this, a systematic review was carried out based on information from text and bibliographic data. The proposed objectives and methodologies represent an innovative approach, namely, the consideration of bibliometric evaluation as a support for a focused literature review related to the topics addressed here. The results of this research show the importance of the digital transition in agriculture to support better policy and planning design and address imbalances in food chains and agricultural markets. New technologies in Era 4.0 and their application through Climate-Smart Agriculture approaches are crucial for sustainable businesses (economically, socially and environmentally) and the food supply. Furthermore, for the interrelationships between machine learning and food security, the literature highlights the relevance of platforms and methods, such as, for example, Google Earth Engine and Random Forest. These and other approaches have been considered to predict crop yield (wheat, barley, rice, maize and soybean), abiotic stress, field biomass and crop mapping with high accuracy (R2 ≈ 0.99 and RMSE ≈ 1%). Full article

(This article belongs to the Special Issue Applications of Machine Learning in National Territory Spatial Planning)

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17 pages, 2462 KiB

Open AccessArticle

Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal

by Michael McCord, Daniel Lo, Peadar Davis, John McCord, Luc Hermans and Paul Bidanset

Appl. Sci. 2022, 12(20), 10660; https://0-doi-org.brum.beds.ac.uk/10.3390/app122010660 - 21 Oct 2022

Cited by 4 | Viewed by 1495

Abstract

Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches and machine learning (ML) algorithms have been shown to help improve house price prediction and mass appraisal assessment. Nonetheless, the adoption a of ML within mass appraisal has been protracted and subject to scrutiny by assessment jurisdictions due to their failure to account for spatial autocorrelation and limited practicality in terms of value significant estimates needed for tribunal defense and explainability. Existing research comparing traditional regression approaches has tended to examine unsupervised ML methods such as Random Forest (RF) models which remain more esoteric and less transparent in producing value significant estimates necessary for mass appraisal explainability and defense. Therefore, the purpose of this study is to apply the supervised Regularized regression technique which offers a more transparent alternative, and integrate this with a more nuanced geo-statistical technique, the Eigenvector Spatial Filter (ESF) approach, to more accurately account for spatial autocorrelation and enhance prediction accuracy whilst improving explainability needed for mass appraisal exercises. By undertaking such an approach, the research demonstrates the application of this method can be easily adopted for property tax jurisdictions in a framework which is more interpretable, transparent and useable within mass appraisal given its simple and appealing approach. The findings reveal that the integration of the ESFs improves model explainability, prediction accuracy and spatial residual error compared to baseline classical regression and Elastic-net regularized regression architectures, whilst offering the necessary ‘front-facing’ and flexible structure for in-sample and out-of-sample assessment needed by the assessment community for valuing the unsold housing stock. In terms of policy and practice, the study demonstrates some important considerations for mass appraisal tax assessment and for the improvement of taxation assessment and the alleviation of horizontal and vertical inequity. Full article

(This article belongs to the Special Issue Applications of Machine Learning in National Territory Spatial Planning)

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16 pages, 3746 KiB

Open AccessArticle

Discretization of the Urban and Non-Urban Shape: Unsupervised Machine Learning Techniques for Territorial Planning

by Lorena Fiorini, Federico Falasca, Alessandro Marucci and Lucia Saganeiti

Appl. Sci. 2022, 12(20), 10439; https://0-doi-org.brum.beds.ac.uk/10.3390/app122010439 - 16 Oct 2022

Cited by 7 | Viewed by 1684

Abstract

One of the goals of the scientific community is to equip the discipline of spatial planning with efficient tools to handle huge amounts of data. In this sense, unsupervised machine learning techniques (UMLT) can help overcome this obstacle to further the study of spatial dynamics. New machine-learning-based technologies make it possible to simulate the development of urban spatial dynamics and how they may interact with ecosystem services provided by nature. Modeling information derived from various land cover datasets, satellite earth observation and open resources such as Volunteered Geographic Information (VGI) represent a key structural step for geospatial support for land use planning. Sustainability is certainly one of the paradigms on which planning and the study of past, present and future spatial dynamics must be based. Topics such as Urban Ecosystem Services have assumed such importance that they have become a prerogative on which to guide the administration in the difficult process of transformation, taking place not only in the urban context, but also in the peri-urban one. In this paper, we present an approach aimed at analyzing the performance of clustering methods to define a standardized system for spatial planning analysis and the study of associated dynamics. The methodology built ad hoc in this research was tested in the spatial context of the city of L’Aquila (Abruzzo, Italy) to identify the urbanized and non-urbanized area with a standardized and automatic method. Full article

(This article belongs to the Special Issue Applications of Machine Learning in National Territory Spatial Planning)

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20 pages, 4402 KiB

Open AccessArticle

Big Data-Driven Measurement of the Service Capacity of Public Toilet Facilities in China

by Bo Fu, Xiao Xiao and Jingzhong Li

Appl. Sci. 2022, 12(9), 4659; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094659 - 06 May 2022

Cited by 8 | Viewed by 2457

Abstract

Public health facility planning is one of the important contents of national land planning, which needs to balance geospatial equity and service capacity. However, assessment models and data acquisition methods based on a geosystemic analysis perspective have been lacking for a long time. By focusing on urban public toilets and taking the highly urbanized city of Shenyang, China as the study area, this study developed a new data strategy for urban public facilities with points of interests (POI) big data as the main data source, and subsequently corrected the POI data and analyzed the errors through a field survey, and conducted an empirical assessment oriented toward spatial equity and service capacity to discover the development dynamics of urban facilities over the past ten years and the impacting factors. We found that the integrated population and spatial elements could more accurately evaluate the service capacity of public toilets. Meanwhile, POI data have value in the research of public health facilities, but there are some errors in data quality and data access. The study empirically explores the geographic analysis methods of field research data (small data) and POI data (big data) with empirical contributions. Full article

(This article belongs to the Special Issue Applications of Machine Learning in National Territory Spatial Planning)

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