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Land
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Big Data in Urban Land Use Planning
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Big Data in Urban Land Use Planning

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Urban Contexts and Urban-Rural Interactions".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5769

Special Issue Editors

Prof. Dr. Thomas W. Sanchez

E-Mail Website
Guest Editor
Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77840, USA
Interests: urban planning; technology; social equity; scholarly impact
Special Issues, Collections and Topics in MDPI journals
Dr. Soheil Sabri

E-Mail Website
Guest Editor
Urban Digital Twin Lab, School of Modeling Simulation and Training, University of Central Florida, 3100 Technology Parkway, Orlando, FL 32826, USA
Interests: urban analytics; sustainable cities and communities; spatial statistics; spatial analysis and modelling; geodesign; planning support systems; urban digital twin
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to urbanization and population growth, the problem of human–land conflict has become increasingly serious, and it seriously restricts the healthy and sustainable development of urban areas. Since the urban land use planning process is complex, involving social, economic, environmental, and political systems, the knowledge of how these systems interact is the domain of professional planners. Advances in artificial intelligence (AI) and big data technology present planners with a ripe opportunity to critically assess their approaches and explore how new data collection, analysis, and methods can augment the understanding of places as they seek to anticipate a future with an improved quality of life. Artificial intelligence (AI) and big data technology can offer access to more and better information about travel patterns, energy consumption, land utilization, and environmental impacts, while also helping to better integrate the various involved systems.

For this Special Issue, we invite you to submit original research articles and reviews to provide insights on big data and urban land use planning. Research areas may include (but are not limited to) the following:

  • Application of artificial intelligence (AI) and big data technology in urban land management;
  • Urban land use and transportation;
  • Multi-source heterogeneous spatiotemporal data fusion of urban land;
  • Geographic big data mining;
  • Urban land use change detection;
  • Modeling and simulation of urban land use changes;
  • Big data and multi-dimensional (above- and under-ground) urban land analysis.

We look forward to receiving your contributions.

Prof. Dr. Thomas W. Sanchez
Dr. Soheil Sabri
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

  • artificial intelligence (AI)
  • big data
  • technology
  • urban land
  • land use planning
  • data fusion

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

17 pages, 7105 KiB  
Article
Research on the Method of Artificial Intelligence for Identifying Urban Land-Use Types Based on Areas of Interest (AOI) and Multi-Source Data
by Miaoyi Li and Ningrui Zhu
Land 2024, 13(12), 2040; https://doi.org/10.3390/land13122040 - 28 Nov 2024
Viewed by 133
Abstract
Urban land-use types, a fundamental aspect of urban planning, land management, and the effective utilization of spatial resources, are exhibiting increasing complexity. Efficient and scientific identification of large-scale urban land-use types has become a major challenge in urban research. To address this, the [...] Read more.
Urban land-use types, a fundamental aspect of urban planning, land management, and the effective utilization of spatial resources, are exhibiting increasing complexity. Efficient and scientific identification of large-scale urban land-use types has become a major challenge in urban research. To address this, the present study adopts a functional structure-based perspective and integrates commercial AOI data, POI data, nighttime light data, and population distribution data to classify land use. Departing from existing data weighting algorithms, this research applies artificial intelligence techniques, utilizing the categorical information of AOI data as labels. Through supervised deep learning, urban land-use types are refined into nine major categories and 21 subcategories across cities of different scales and locations. Compared to SVM, RF, and MLP models, the XGBoost model achieved the highest accuracy in classifying urban construction land (weighted avg F1 score = 0.87). Furthermore, by comparing the AOI data with real-world test datasets, the accuracy and granularity of land-use classification were significantly enhanced. Finally, this AI model, combined with remote sensing imagery and transportation network data, was used to generate a land-use map for the target city, offering insights into the generalizability of AI models in urban land-use classification. Full article
(This article belongs to the Special Issue Big Data in Urban Land Use Planning)
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Figure 1

Figure 1
<p>(<b>a</b>) Differences in the building form of industrial land; (<b>b</b>) differences in the color of buildings on industrial land.</p> Full article ">Figure 2
<p>AOI data transformation rules.</p> Full article ">Figure 3
<p>Research framework.</p> Full article ">Figure 4
<p>Location and extent of the study area.</p> Full article ">Figure 5
<p>Comparison of accuracy scores for different models.</p> Full article ">Figure 6
<p>Confusion matrix for XGBoost model.</p> Full article ">Figure 7
<p>Feature importance of XGBoost model.</p> Full article ">Figure 8
<p>(<b>a</b>) Mean SHAP value contribution (population distribution data). (<b>b</b>) Mean SHAP value contribution (nighttime light data).</p> Full article ">Figure 9
<p>Feature dependency graph of population distribution data (feature 129) vs. nighttime light data (feature 130).</p> Full article ">Figure 10
<p>(<b>a</b>–<b>c</b>) Land-use map of Jinjiang City (partial). (<b>d</b>) Land-use map color legend.</p> Full article ">
18 pages, 7825 KiB  
Article
Spatiotemporal Analysis of High-Quality Development and Coordination in Cities Along the Lower Yellow River
by Ge Zhai, Maoxin Zhang, Tingting He and Peng Ren
Land 2024, 13(11), 1863; https://doi.org/10.3390/land13111863 - 7 Nov 2024
Viewed by 493
Abstract
The current urban development in cities along the Lower Yellow River is in tension regarding human–land relations. To achieve the goals of ecological protection and high-quality development (HQD), it is urgent to scientifically measure and analyse the region’s function development and development coordination [...] Read more.
The current urban development in cities along the Lower Yellow River is in tension regarding human–land relations. To achieve the goals of ecological protection and high-quality development (HQD), it is urgent to scientifically measure and analyse the region’s function development and development coordination (DC). This study focuses on cities along the Lower Yellow River, constructs a three-dimensional HQD assessment framework based on urban functions through multiple remote sensing data, and evaluates DCs by feature classification. The results show the following: (1) The HQD of the study area shows a trend of decreasing and then increasing during 2000–2020 and reaches its highest level at the end. HQD shows a spatial trend of decreasing from south to north and from east to west. (2) The overall agricultural function of the study area declined slightly; the ecological function declined first and then increased, with the highest value occurring in 2000; and the urban function increased steadily and improved significantly after 2015. (3) DCs under different administrative levels are polarised, with high-level DCs exhibiting a spatial leader effect. (4) Urban development preferences in the study area are divergent, and the functional type with the highest share under different administrative scales is agro-ecological, which is mainly influenced by differences in natural base. This study reveals the characteristics of HQD and functional changes in cities along the Lower Yellow River, combined with a hierarchical classification of DCs and the types of development preferences, providing a reference for the formulation of spatial governance strategies. Full article
(This article belongs to the Special Issue Big Data in Urban Land Use Planning)
►▼ Show Figures

Figure 1

Figure 1
<p>The location of the study area (<b>a</b>) and cities along the riverbank (<b>b</b>).</p> Full article ">Figure 2
<p>Interpretations of (<b>a</b>) the ternary diagram, (<b>b</b>) the determination of the degree of “Agro–Urban–Eco” development coordination, and (<b>c</b>) the six different development preference categories in the case of Anyang.</p> Full article ">Figure 3
<p>Distribution of integrated development effectiveness levels of towns and cities in lower reaches of Yellow River in different periods.</p> Full article ">Figure 4
<p>Levels of ecological–cropland–urban development in cities of Lower Yellow River in different periods.</p> Full article ">Figure 5
<p>Levels of “Agro–Urban–Eco” development at different times.</p> Full article ">Figure 6
<p>Ranks of DC at municipal (<b>a</b>) and county (<b>b</b>) scales and their spatial distribution (<b>c</b>) in study area.</p> Full article ">Figure 7
<p>HQD and DC levels for each city in 2020 (<b>a</b>), and bar charts showing the number of different functional types within county-level (<b>b</b>).</p> Full article ">
18 pages, 4048 KiB  
Article
Evaluating the Quality of Children’s Active School Travel Spaces and the Mechanisms of School District Friendliness Impact Based on Multi-Source Big Data
by Chenyu Lu, Changbin Yu and Xiaowan Liu
Land 2024, 13(8), 1319; https://doi.org/10.3390/land13081319 - 21 Aug 2024
Viewed by 1120
Abstract
With the advancement of child-friendly urban planning initiatives, the significance of Active School Travel Spaces (ASTSs) in shaping urban development and promoting the physical and mental well-being of children has become increasingly apparent. This research focuses on 151 public primary schools in the [...] Read more.
With the advancement of child-friendly urban planning initiatives, the significance of Active School Travel Spaces (ASTSs) in shaping urban development and promoting the physical and mental well-being of children has become increasingly apparent. This research focuses on 151 public primary schools in the central urban area of Lanzhou City. Utilizing the Amap pedestrian route planning API, we establish a walking route network, evaluate the paths using spatial syntax and street view recognition methods, and analyze their influencing factors using a Geographic Detector model. The results show the following: ① The overall friendliness of ASTSs in Lanzhou City is moderate, with 44% of school districts exhibiting low friendliness. ② The distribution of child friendliness in ASTS exhibits a “core-periphery” pattern. Anning District demonstrates higher friendliness compared to Chengguan District and Qilihe District, while Xigu District exhibits the lowest level of friendliness. ③ Different levels of friendliness have different tendencies for access, safety, and comfort. A high degree of friendliness favors comfort. Low friendliness has the lowest requirements for safety and comfort. ④ Population density and transportation convenience exert a significant positive impact on friendliness, while the size of the school district and the centrality of schools have a negative impact. The synergistic effects among these influencing factors notably enhance the explanatory power of friendliness. Full article
(This article belongs to the Special Issue Big Data in Urban Land Use Planning)
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Figure 1

Figure 1
<p>Location and extent of the study area.</p> Full article ">Figure 2
<p>(<b>a</b>) Walking routes to and from school in surveyed elementary schools; (<b>b</b>) walking route network for elementary school students in the central urban area of Lanzhou city.</p> Full article ">Figure 3
<p>Measurement method of school pre-space quality at microscale.</p> Full article ">Figure 4
<p>Linear spatial evaluation calculation results.</p> Full article ">Figure 5
<p>Node spatial evaluation calculation results.</p> Full article ">Figure 6
<p>(<b>a</b>) Number and percentage of different levels of friendliness in the four administrative districts; (<b>b</b>) Spatial distribution of different levels of friendliness.</p> Full article ">Figure 7
<p>Distribution of child-friendliness ratings.</p> Full article ">Figure 8
<p>(<b>a</b>) Explanatory power of factors influencing friendliness; (<b>b</b>) explanatory power of friendliness under the synergy of two-factor interactions.</p> Full article ">
32 pages, 23148 KiB  
Article
Form-Based Code Revisited: Leveraging Geographic Information Systems (GIS) and Spatial Optimization to Chart Commuting Efficiency Landscapes under Alternative City Planning Frameworks
by Reza Mortaheb, Piotr Jankowski, Alan Murray and Marcos Bastian
Land 2024, 13(8), 1190; https://doi.org/10.3390/land13081190 - 1 Aug 2024
Viewed by 1389
Abstract
The core promise of land use and zoning reforms is to metamorphose the car-dominated urban spatial structure—which is the legacy of use-based, modernist land use and transportation planning of the past century—into human-centered forms of urbanism characterized by walkable, accessible, transit-friendly, ecologically sustainable, [...] Read more.
The core promise of land use and zoning reforms is to metamorphose the car-dominated urban spatial structure—which is the legacy of use-based, modernist land use and transportation planning of the past century—into human-centered forms of urbanism characterized by walkable, accessible, transit-friendly, ecologically sustainable, equitable and resilient urban fabrics. This empirical study aims to measure the effectiveness of a reformed city planning framework, known as the form-based code (FBC), in terms of optimizing journey-to-work trips. To this end, the study integrates geographic information systems (GIS) and spatial analysis techniques with linear programming, including a variant of the transportation problem, to evaluate aggregated and disaggregated commuting efficiency metrics. Utilizing the zonal data (ZDATA) for the Orlando metropolitan region, the proposed models account for the commuting terrains associated with three major workforce cohorts, segmented along key industry sectors, within the context of three urban growth scenarios. The findings suggest that the FBC system holds the potential to enhance commuting patterns through various place-based strategies, including juxtaposing, densifying, and diversifying employment and residential activities at the local level. At the regional level, however, the resultant urban form falls short of an ideal jobs–housing arrangement across major industry sectors. Full article
(This article belongs to the Special Issue Big Data in Urban Land Use Planning)
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Figure 1

Figure 1
<p>Schematic representation of urban commuting spectrum.</p> Full article ">Figure 2
<p>Geographical location of Orange County within the Orlando Metropolitan Area, Central Florida.</p> Full article ">Figure 3
<p>Variation in basic commuting efficiency metrics within the first work trip cohort, categorized by industry sector, level of analysis, and urban growth scenario.</p> Full article ">Figure 4
<p>Variation in basic commuting efficiency metrics within the third work trip cohort, categorized by industry sector, level of analysis, and urban growth scenario.</p> Full article ">Figure 5
<p>Estimated interzonal peak-hour home-based work trips (inclusive of all workforce groups) originating from and confined to the boundaries of the study area in the baseline scenario (Year 2020).</p> Full article ">Figure 6
<p>Optimal (minimized) work trips exclusively internal to the boundaries of the study area under the baseline scenario (2020). (<b>Upper Panel</b>): Commercial Workforce Flow; (<b>Middle Panel</b>): Service Workforce Flow; (<b>Lower Panel</b>): Industrial Workforce Flow (single dots represent intra-TAZ commutes).</p> Full article ">Figure 6 Cont.
<p>Optimal (minimized) work trips exclusively internal to the boundaries of the study area under the baseline scenario (2020). (<b>Upper Panel</b>): Commercial Workforce Flow; (<b>Middle Panel</b>): Service Workforce Flow; (<b>Lower Panel</b>): Industrial Workforce Flow (single dots represent intra-TAZ commutes).</p> Full article ">Figure 6 Cont.
<p>Optimal (minimized) work trips exclusively internal to the boundaries of the study area under the baseline scenario (2020). (<b>Upper Panel</b>): Commercial Workforce Flow; (<b>Middle Panel</b>): Service Workforce Flow; (<b>Lower Panel</b>): Industrial Workforce Flow (single dots represent intra-TAZ commutes).</p> Full article ">Figure 7
<p>Distribution of basic commuting efficiency metrics inclusive of all work trip cohorts, categorized by industry sector, level of analysis, and urban growth scenario.</p> Full article ">Figure 8
<p>Distribution of basic commuting efficiency metrics inclusive of all industry sectors, categorized by work trip cohort, level of analysis, metric type, and urban growth scenario.</p> Full article ">Figure 9
<p>Distribution of the total workforce-level commuting efficiency metrics (<b>left column</b>) and industry-sector-level commuting efficiency metrics (<b>right column</b>) for all work trip cohorts and industry sectors, categorized by metric type, level of analysis, and urban growth scenario.</p> Full article ">Figure 10
<p>Optimal workforce commuting patterns exclusively internal to the boundaries of the study area in the planning horizon year 2045 under the status quo growth scenario. (<b>Upper Panel</b>): Commercial Workforce Flow; (<b>Middle Panel</b>): Service Workforce Flow; (<b>Lower Panel</b>): Industrial Workforce Flow (single dots represent intra-TAZ commutes).</p> Full article ">Figure 10 Cont.
<p>Optimal workforce commuting patterns exclusively internal to the boundaries of the study area in the planning horizon year 2045 under the status quo growth scenario. (<b>Upper Panel</b>): Commercial Workforce Flow; (<b>Middle Panel</b>): Service Workforce Flow; (<b>Lower Panel</b>): Industrial Workforce Flow (single dots represent intra-TAZ commutes).</p> Full article ">Figure 10 Cont.
<p>Optimal workforce commuting patterns exclusively internal to the boundaries of the study area in the planning horizon year 2045 under the status quo growth scenario. (<b>Upper Panel</b>): Commercial Workforce Flow; (<b>Middle Panel</b>): Service Workforce Flow; (<b>Lower Panel</b>): Industrial Workforce Flow (single dots represent intra-TAZ commutes).</p> Full article ">Figure 11
<p>Optimal work trips internal to the boundaries of the study area in the planning horizon year 2045 under the FBC growth scenario. (<b>Upper Panel</b>): Commercial Workforce Flow; (<b>Middle Panel</b>): Service Workforce Flow; (<b>Lower Panel</b>): Industrial Workforce Flow (single dots represent intra-TAZ commutes).</p> Full article ">Figure 11 Cont.
<p>Optimal work trips internal to the boundaries of the study area in the planning horizon year 2045 under the FBC growth scenario. (<b>Upper Panel</b>): Commercial Workforce Flow; (<b>Middle Panel</b>): Service Workforce Flow; (<b>Lower Panel</b>): Industrial Workforce Flow (single dots represent intra-TAZ commutes).</p> Full article ">Figure 11 Cont.
<p>Optimal work trips internal to the boundaries of the study area in the planning horizon year 2045 under the FBC growth scenario. (<b>Upper Panel</b>): Commercial Workforce Flow; (<b>Middle Panel</b>): Service Workforce Flow; (<b>Lower Panel</b>): Industrial Workforce Flow (single dots represent intra-TAZ commutes).</p> Full article ">
45 pages, 43391 KiB  
Article
Urban Big Data Analytics: A Novel Approach for Tracking Urbanization Trends in Sri Lanka
by Nimesh Akalanka, Nayomi Kankanamge, Jagath Munasinghe and Tan Yigitcanlar
Land 2024, 13(6), 888; https://doi.org/10.3390/land13060888 - 19 Jun 2024
Viewed by 1083
Abstract
The dynamic nature of urbanization calls for more frequently updated and more reliable datasets than conventional methods, in order to comprehend it for planning purposes. The current widely used methods to study urbanization heavily depend on shifts in residential populations and building densities, [...] Read more.
The dynamic nature of urbanization calls for more frequently updated and more reliable datasets than conventional methods, in order to comprehend it for planning purposes. The current widely used methods to study urbanization heavily depend on shifts in residential populations and building densities, the data of which are static and do not necessarily capture the dynamic nature of urbanization. This is a particularly the case with low- and middle-income nations, where, according to the United Nations, urbanization is mostly being experienced in this century. This study aims to develop a more effective approach to comprehending urbanization patterns through big data fusion, using multiple data sources that provide more reliable information on urban activities. The study uses five open data sources: national polar-orbiting partnership/visible infrared imaging radiometer suite night-time light images; point of interest data; mobile network coverage data; road network coverage data; normalized difference vegetation index data; and the Python programming language. The findings challenge the currently dominant census data and statistics-based understanding of Sri Lanka’s urbanization patterns that are either underestimated or overestimated. The proposed approach offers a more reliable and accurate alternative for authorities and planners in determining urbanization patterns and urban footprints. Full article
(This article belongs to the Special Issue Big Data in Urban Land Use Planning)
►▼ Show Figures

Figure 1

Figure 1
<p>Officially identified urban local government areas in Sri Lanka.</p> Full article ">Figure 2
<p>Methodological framework.</p> Full article ">Figure 3
<p>Images of Sri Lanka for 2013, 2017, and 2021.</p> Full article ">Figure 4
<p>Spatial distribution of the POI data for 2013, 2017, and 2021.</p> Full article ">Figure 5
<p>Mobile network coverage map for Sri Lanka for 2013, 2017, and 2021.</p> Full article ">Figure 6
<p>NDVI maps for 2013, 2017, and 2021.</p> Full article ">Figure 7
<p>Road network maps of Sri Lanka for 2013, 2017, and 2021.</p> Full article ">Figure 8
<p>NDBI maps for 2013, 2017, and 2021.</p> Full article ">Figure 9
<p>Population density distribution for 2013, 2017, and 2021.</p> Full article ">Figure 10
<p>Identified 60 UPs in Sri Lanka.</p> Full article ">Figure 11
<p>Chart of rate of urban growth (RUG) and the urban area extent.</p> Full article ">Figure 12
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 12 Cont.
<p>Maps of identified 60 UPs in Sri Lanka with UGs and RUGs.</p> Full article ">Figure 13
<p>UPs identified as large towns in Sri Lanka.</p> Full article ">Figure 14
<p>UPs identified as medium-sized towns in Sri Lanka.</p> Full article ">Figure 15
<p>UPs identified as small towns in Sri Lanka.</p> Full article ">Figure 15 Cont.
<p>UPs identified as small towns in Sri Lanka.</p> Full article ">Figure 16
<p>Colombo and Kandy urban areas in 2021.</p> Full article ">Figure 17
<p>Colombo and Kandy urban areas in 2021.</p> Full article ">Figure 18
<p>Comparison of fusion results with the real ground realities—cases of Batticaloa and Kaththankudy.</p> Full article ">
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