Assessment of coastal land use structure and efficiency based on multi-source data: From the perspective of sea-land gradient.

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Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Mar 3;16:11876. doi: 10.1038/s41598-026-40256-5 Search in PMC Search in PubMed View in NLM Catalog Add to search Assessment of coastal land use structure and efficiency based on multi-source data: From the perspective of sea-land gradient Yifei Pei Yifei Pei 1 School of Geography, Liaoning Normal University, Dalian, Liaoning, China Find articles by Yifei Pei 1 , Jianfeng Zhu Jianfeng Zhu 1 School of Geography, Liaoning Normal University, Dalian, Liaoning, China 2 Physical Geography and Geomatics, Liaoning Key Laboratory, DalianLiaoning, 116029 China 3 Key Research Base of Humanities and Social Sciences of Ministry of Education, Institute of Marine Sustainable Development, Liaoning Normal University, DalianLiaoning, 116029 China Find articles by Jianfeng Zhu 1, 2, 3, ✉ , Jiake Zhou Jiake Zhou 1 School of Geography, Liaoning Normal University, Dalian, Liaoning, China Find articles by Jiake Zhou 1 , Guangshun Sun Guangshun Sun 1 School of Geography, Liaoning Normal University, Dalian, Liaoning, China Find articles by Guangshun Sun 1 , Shiru Tang Shiru Tang 1 School of Geography, Liaoning Normal University, Dalian, Liaoning, China Find articles by Shiru Tang 1 Author information Article notes Copyright and License information 1 School of Geography, Liaoning Normal University, Dalian, Liaoning, China 2 Physical Geography and Geomatics, Liaoning Key Laboratory, DalianLiaoning, 116029 China 3 Key Research Base of Humanities and Social Sciences of Ministry of Education, Institute of Marine Sustainable Development, Liaoning Normal University, DalianLiaoning, 116029 China ✉ Corresponding author. Received 2025 Oct 30; Accepted 2026 Feb 11; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13065806  PMID: 41776229 Abstract The economic evaluation of urban land depends critically on two aspects: land use structure and land use efficiency(LUE). Understanding how land use structure and efficiency change in response to urban development is critical. Geographically, coastal regions have higher population densities. However, it is unclear from the current study how changes in land use efficiency and structure relate to distance from the coast. Thus, the land use structure of Jinpu New Area from 2015 to 2020 is evaluated in this paper using the location entropy, Lorenz curve, and Gini coefficient methods. The land use efficiency is assessed using the comprehensive index method of multi-source data fusion, and the coupling analysis is carried out in conjunction with the sea-land gradient to thoroughly examine the space–time variation law of the land use structure and efficiency. The results demonstrate that: (1) The land use structure in Jinpu New Area exhibits distinct gradient differentiation. Within the [0,2] km range, land use types are evenly distributed, with an average Gini coefficient of 0.088. The [14,max] km range shows significant disparities in land use distribution, most notably in railway land (locational entropy > 11) and urban land (Gini coefficient > 0.7); (2) Based on land use intensity and land use efficiency, it is concluded that the coupling efficiency of land use in 2015 and 2020 both showed an obvious land-sea gradient: coastal regions had a higher share of inefficient land use with an average coupling efficiency of 0.170 (CI > 0); inland areas had a larger proportion of overloaded land use with an average coupling efficiency of -1.04 (CI < 0); and the land-sea transition zones demonstrated favorable coupling efficiency, with the coupling index being approximately zero. (3) Land use structure correlates with land use efficiency. Land use efficiency exhibits a significant positive correlation with urban land use(p < 0.05) and a significant negative correlation with railway land use(p < 0.1). In contrast, inland regions are often underdeveloped in terms of land use intensity, and railway land use may serve as a useful indicator of potential for future development. Keywords: Land use structure, Land use efficiency, Sea-land gradient, Lorenz curve, Gini coefficient, Multi-source data fusion, Jinpu new area Subject terms: Environmental sciences, Environmental social sciences, Geography, Geography Introduction As the fundamental resource that human society depends on for existence and advancement, land has always held a crucial and irreplaceable place in the history of humanity. Numerous issues, including the depletion of natural resources, the degradation of the environment, and threats to food security, have gained international attention in recent years 1 – 3 , which has increased the urgency of thoroughly investigating land-related subjects and highlighted the importance of thorough and in-depth land research. Scholars from both domestic and foreign institutions have mostly examined land issues in recent years from a variety of angles, including national land use structure, policy evaluation, ecological impacts, land use change, and technology innovation. In their systematic analysis of the socioeconomic and natural drivers of Land Use and Land Cover Change worldwide, Lambin et al. focused on area variability and identified the primary drivers as urbanization, agricultural growth, and policy interventions 4 . In their appeal for sustainable land management, Foley et al. noted that changes in land use have an impact on biodiversity, the carbon cycle, and other aspects of global climate change 5 . The efficiency of China’s ecological protection red line policy was evaluated by Bryan et al., who came to the conclusion that while it reduces ecological degradation, cross-area cooperation needs to be improved 6 . To measure changes in forests around the world and help land regulation with data, Hansen et al. employed remote sensing technology 7 . The demand for territorial spatial functions has a goal-oriented effect on the evolution and optimization of national land use structure, according to Qu Yanbo et al. 8 . They also found an interacting relationship between territorial spatial functions and national land use structure. In order to reveal changes in land use spatial patterns, research on urban land issues primarily takes two forms: first, it focuses on the spatial evolution of urban sprawl, such us patch diffusion, fragmentation, and aggregation processes 9 ; Second, it emphasizes joint analysis with density enhancement and uses sprawl indicators such as patch count and maximum patch index to characterize the spatial structure of land transformation, with density calculations reflecting the core driving forces of the transformation 10 . Together, these two methods support one another and provide a thorough analytical framework for studies on land transformation. In terms of urban sprawl and spatial form evolution, the issue of illogical land use structure is becoming more and more noticeable as urbanization and industrialization pick up speed 11 . It may logically distribute different kinds of land and improve the overall efficiency of land use through the optimization research of land use structure. Land use transformation, spatial pattern optimization, ecological effect evaluation, policy regulation, etc., have been the main topics of land use structure study in recent years. In the research of Long et al., industrial upgrading and urban–rural population mobility are the main drivers of China’s land use structure’s move from agricultural production dominance to multifunctional integration 12 . According to Newbold et al.’s analysis of global statistics, the growth of agricultural area resulted in a 13.6% decrease in biodiversity 13 . A strategy of optimizing land use allocation in mountainous areas based on the synchronization of resource development and ecological conservation was proposed by Liu Yansui et al. after they analyzed the features of the land structure pattern in these locations 14 . Based on multi-source remote sensing data, Zhao Hengqian et al. examined land use changes and their ecological and environmental effects in Beijing’s Tongzhou District between 2006 and 2016. They discovered that the ecological and environmental quality showed a trend of decline followed by an increase as the amount of cultivated land and built-up land decreased. They attributed the changes to the combined effects of policy events, population, and economic development 15 . Morteza Shabani, Shadman Darvishi et al. examined the impact of LULC changes and urban expansion in Sanandaj city from 1989 to 2019 on geographically oriented land degradation. They validated the effectiveness of three models—ANN-CA, LR-CA, and WOE-CA—and projected that by 2034, the city will continue expanding southward, with further increases in agricultural and built-up areas. Planning measures are needed to prevent excessive destruction of vegetation and wasteland 16 . The integrated remote sensing and Geographic Information System approach, statistical modeling and machine learning, field survey and long-term assessment forecast 17 , land system modeling and scenario simulation, and other academic research techniques are currently available for land use structure 18 . Because of their straightforward and precise features, the Lorenz curve and Gini coefficient approaches in economics have been widely applied in recent years to the spatial characterization of economic and social activities. These applications include the study of the law of dynamic change and the spatial distribution of land use 19 . In terms of growing land density, as urbanization and population expansion progress, land resources become more scarce, and instances of irrational land usage become more prevalent, resulting in inefficient land utilization 20 , 21 . The key to attaining sustainable land resource utilization, encouraging green economic development, and preserving ecological balance is carrying out an in-depth study on land use structure and efficiency. Land use efficiency has been primarily examined in recent years regarding urban and agricultural land use efficiency, as well as the effects of policies and methodologies for measuring land use efficiency. According to Li et al., who assessed China’s ecoefficiency of arable land use between 2000 and 2020, there is "high in the east and low in the west" variability in both space and time 22 . Hu et al. assessed the land use efficiency of 767 resource cities in China and discovered that there is a great deal of regional variation in land use efficiency. They also discovered that environmental regulations and modernizing industrial structures are important factors in increasing efficiency 23 . Zhang et al. studied the land use efficiency of cities in Jiangsu province, found that the efficiency differences between cities are significant and characterized by spatial agglomeration, and pointed out that innovation investment and industrial structure upgrading are the key driving factors to improve efficiency 24 . In the Shandong Peninsula urban agglomeration, Cui Xuegang et al. examined the spatial link between the degree of high-speed traffic dominance and land use efficiency. They found a strong positive spatial coupling feature between the two 25 . Based on their analysis of the Yangtze River Delta urban agglomeration, Linlin R et al. discovered and made public a technique for assessing the resource mismatch of building land by utilizing the coupling index of land use efficiency and land use intensity, thereby identifying the actual inefficient land 26 . Data envelopment analysis (DEA), the comprehensive assessment approach, and the single indicator method are the three primary methodologies used to assess land use efficiency. Despite being straightforward to understand, the single indicator approach has several drawbacks and finds it challenging to accurately capture the complexity and multifaceted characteristics of land use. In contrast, DEA is more widely used in land use efficiency evaluation. However, it is aimed at evaluating relative efficiency, based on multi-indicator inputs and multi-indicator outputs, to carry out effectiveness evaluation of decision-making units 27 . Nevertheless, the method primarily uses administrative districts as the research boundary and collects the research data; it does not analyze spatial measurement, which makes it impossible to identify the spatial pattern and change process of land use efficiency; the method is influenced by the data, primarily choosing to analyze the macroscale, such as the country, urban agglomeration, and province, and lacks the analysis of the microscale such as development zones. This paper adopts the comprehensive evaluation method of integrating multi-source data to measure land use efficiency and performs a comparative analysis of 2015 and 2020 in a long time series, even though there are fewer studies on this approach. This method overcomes the limitations of panel data. There is a close interaction between urban sprawl and increased land density. While an efficient use will optimize the land use structure, a reasonable land use structure can greatly increase land use efficiency. Research on land use structure and land use efficiency has been separately matured both domestically and internationally; however, the coupling relationship between the two remains relatively understudied. Existing coupling studies predominantly focus on outcome-oriented policy proposals. For instance, Guo Shihong evaluated the urban land use structure and land use efficiency of the Haixi Urban Agglomeration, but did not propose corresponding land use and structural adjustment strategies based on the analysis results of urban land use structure and efficiency 28 . Similarly, Chen Zhangxi and Wu Zhenbang’s evaluation of land use structure and efficiency in the Guangdong-Hong Kong-Macao Greater Bay Area urban cluster failed to identify the relationship between the two, and merely formulated differentiated land use policies for the Greater Bay Area based on these analysis results 29 . Cheng Zhenni identified significant interactive effects between land use structure and efficiency in the Chengdu Metropolitan Area, with land use structure exerting a stronger influence on efficiency. Due to variations in development models and stages across regions within the Chengdu Metropolitan Area, differences exist in land use structure, efficiency, and their interactive effects 30 ; yet, quantitative characterization of their relationship remains insufficient. Only a few studies have attempted to quantify the association between individual land use types and efficiency, failing to cover the comprehensive impact of the entire land use structure on efficiency. Lu Youpeng, He Tingting et al. employed Pearson’s correlation coefficient to examine the relationship between arable land structure and land use efficiency, concluding a negative correlation: a higher proportion of arable land in urban fringe areas significantly reduces urban land use efficiency (ULUE) 31 . Research on the spatial differentiation of land use structure and efficiency along the coastal-inland gradient remains limited. Most studies adopt multiple cities as their spatial research areas, comparing land use structure and efficiency of cities. The coastal zone is an area where the Earth’s lithosphere, hydrosphere, atmosphere, and biosphere interact and various factors come into play. It is characterized by high productivity and high economic value, which contributes to the needs of regional economic development. Furthermore, due to the combined effects of climate change and human activities, coastal ecosystems and the natural environment are extremely fragile, exhibiting a high degree of dynamism, complexity, and diversity in this area 32 . As a result, studying coastal zones is crucial for analyzing land use structure and efficiency. This research combines land use structure analysis, which captures spatial differentiation through sprawl, with land use efficiency assessment, which reflects the functional implications of density enhancement. It seeks to uncover the complex features and underlying logic of land alteration in coastal areas by integrating and expanding these two conventional methods. To achieve this goal, the study draws on data from the Jinpu New Area for the years 2015 and 2020. The Location Entropy, Lorenz Curve, and Gini Coefficient were adopted to evaluate the land use structure of the study area, while data processing and spatial analysis were implemented with the aid of tools including Google Earth Engine (GEE) and ArcGIS. A three-tier analytical framework, namely Land Use Intensity (LUI) – Land Use Efficiency (LUE)– Coupling Index (CI), was constructed. Specifically, LUI was derived from the weighted integration of horizontal layout and vertical height. LUE was quantified by integrating five indicators (nighttime light intensity(NTL), land surface temperature(LST), population density(PD), points of interest(POI), and Gross Domestic Product (GDP)) based on the entropy weight method. CI was employed to measure the mismatch degree between LUI and LUE, thereby determining the coupling efficiency of the two dimensions. On this basis, inefficient land use and overloaded land use within the study area were identified, and a correlation analysis was conducted between these two land use types and the land use structure. The findings were further applied to provide an analysis and evaluation of the planning of Jinpu New Area. Finally, we performed a sea-land gradient analysis based on the above findings, aiming to uncover the spatial heterogeneity of land use and its dynamic changes along the coastal zone. Study area and data preparation Study area Research on land use structure and efficiency is crucial because Jinpu New Area, a state-level new area, is a key platform for the growth and opening up of the Liaoning coastal economic belt. Jinpu New Area is engaged in development activities, including urbanization and industrial upgrading. However, the supply and demand of land resources are in conflict, and the issue of ecological land being squeezed and structural imbalance impacts the area’s high quality development 33 . Liaoning’s development strategy of "one circle, one belt, and two zones" also calls for increasing the efficiency of land use, while the national "14th Five-Year Plan" places a strong emphasis on optimizing the spatial layout of the country’s territory and encouraging the intensive and efficient use of resources. Additionally, the macro spatial scale is the primary focus of land use efficiency research, with the micro spatial scale receiving less attention. As a result, using Jinpu New Area as the study area and examining its land use structure and coupling efficiency can help meet the demands of regional and national strategic development, optimize the land use layout, enhance resource utilization, and foster the synergy between ecological protection and economic development. Jinpu New Area, belonging to Dalian City, Liaoning Province, covers all the administrative area of Jinzhou District and part of Pulandian District, Dalian City 34 , with geographic coordinates of 38°56′-39°23′N latitude and 121°26′-122°19′E longitude as of June 2020, Jinpu New Area is located in the south of Liaodong Peninsula and northeast of Dalian city, with a total area of about 2,299 square kilometers 35 . The topography of Jinpu New Area slopes down from north to south, with the mountain range acting as the axis on both sides. It is surrounded by the sea on both sides, with the Yellow Sea to the east and the Bohai Sea to the west. It has winding coasts, connected harbors and bays, and a terraced topography that is low in the two wings and high in the middle, with low mountainous and hilly areas as well as alluvial coastal plains, etc. This topography makes its sea-land transition zone have rich topographic changes, from mountains to plains to the sea, and provides a diverse basis for the study of the sea-land gradient. This topography makes its land-sea transition zone rich in topographic changes, from mountains to plains to the sea, providing a diverse topographic basis for the study of the sea-land gradient. Jinpu New Area has 25 streets(Fig. 1 ), including: Congzheng Street, Youyi Street, Maqiaozi Street, etc 36 . Fig. 1. Open in a new tab Geographic location of jinpu new area. Data sources In this paper, the second national land survey data and the third national land survey data of Jinpu New Area are selected to analyze the spatial distribution characteristics of the land use structure in Jinpu New Area by choosing a total of eight indicators, including Urban Land, Township Construction Land, Port and Wharf Land, Highway Land, Railway Land, Hydraulic Construction Land and Mining Land. In the calculation of LUE, panel data pertaining to economic, social, and environmental dimensions are typically employed. Accordingly, this study also selects spatial data relevant to these three dimensions for the quantification of LUE. NTL data typically reflects the intensity of economic activity in a region. The higher the population density, the larger the built-up area per capita, which may result in brighter nighttime lights and greater energy consumption. LST data are closely linked to vegetation coverage and are also influenced by population concentration, land use types, and the size of built-up areas, which may lead to specific trends in land surface temperature. PD data is associated with economic development levels; generally, regions with higher economic development levels tend to have higher population densities. The distribution and quantity of POI may influence regional infrastructure distribution and the intensity of commercial activities in the area. GDP data is closely linked to economic development levels and is associated with labor density and capital density. Generally, regions with higher labor density may have higher GDP and stronger economic development levels. As depicted in Fig. 2 , these five spatial data types exhibit close associations with human activities and economic output 37 – 39 . Consequently, this study integrates NTL, LST, PD, POI, and GDP data to quantify LUE 26 . Fig. 2. Open in a new tab Relationship between remote sensing data and panel data (bold factors: frequently used factors). The nighttime light data were obtained from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite (sensor ID: VIIRS (Suomi NPP)), released by the National Oceanic and Atmospheric Administration (NOAA, https://www.noaa.gov/ ). The core VIIRS nighttime light products used to generate the annual composites are the VNP46A1 (daily level-3 product) and VNP46A3 (monthly level-3 product) datasets, which are official NOAA VIIRS nighttime light products. The dataset has a spatial resolution of approximately 500 m (0.004° at the equator) 40 . The land surface temperature data is based on the Terra platform with the MODIS instrument; its spatial extent covers the globe from 90°N to 90°S and 180°E to 180°W, with a spatial resolution of 1000 meters × 1000 meters, adopting the MODIS Sinusoidal Tiling System as the spatial reference system, the CARTESIAN coordinate system, and GEODETIC granule spatial representation. The temporal extent spans from February 18, 2000, to the present with an 8-day temporal resolution; its Concept ID is C2269056084-LPCLOUD, there are 375,846 files/granules, the processing level is 3, and both the publication and update dates are February 4, 2021 41 . PD data source for the National Aeronautics and Space Administration (NASA) socioeconomic data and application center developed by the World Version 4.11 grid population 42 . The POI data for 2020 were collected by crawling the Gaode map, and the number of POIs in each grid was calculated as the attribute value. The Resource and Environmental Science Data Platform of the Chinese Academy of Sciences (RESDC) released a gridded dataset of the spatial distribution of China’s GDP with a resolution of 1 km, which provided GDP products for seven periods in 1995, 2000, 2005, 2010, 2015, 2019 and 2020 43 , and the 2015 and 2020 GDP data were selected for the study. The measurement of land use intensity (LUI) integrates land cover data and building height data 44 . The land cover data were based on the China Land Cover Dataset (CLCD) developed by Professors Yang Jie and Huang Xin of Wuhan University using Landsat imagery 45 . This dataset provides an annual land cover product of China with a resolution of 30 m from 1990 to 2023, with an overall accuracy of 79.31%. The product contains eight land cover types: cropland, woodland, shrubland, grassland, water bodies, snow and ice, bare ground, impervious surface, and wetland. This study selected impervious surfaces as construction land. Vertical height and horizontal layout jointly characterize the three-dimensional morphology of construction land. Combined with the Building Height Dataset of China (BHDC) based on Sentinel-1 SAR imagery, this study provides building height data with a resolution of 1 km for China in 2017 46 . The overall R2 was 0.81, and the root mean square error was 4.22 m. The GC3S team of the School of Life Sciences, Fudan University, produced and released a selection of building height data with a resolution of 10 m as a supplement 47 . The dataset is based on all weather earth observation (radar, optical and night light images), and adopts a multi window statistical method to comprehensively consider the influence of shadows and other factors on the estimation of building heights, and utilizes the random forest model to construct and optimize the estimation model to adapt to the complex urban structure of the national scale in China, and the comparative validation yields that the computational simulation results have a very strong correlation with the actual building observation data (the root mean square error is 6.1 m, mean error = 5.2 m, R = 0.77). The administrative data come from the Earth Resources Data Cloud Platform ( www.gis5g.com ); the township and street point data come from the 2023 national statistical zoning code and urban/rural division code published by the National Bureau of Statistics. Based on the national township street names therein and using the address to coordinate tool, the latitude and longitude of the national township streets can be obtained. The DEM topographic data were obtained from the NASA Earth Science Data website ( https://nasadaacs.eos.nasa.gov/ ), which is high-precision topographic data acquired by ALOS satellites with a resolution of 12.5 m. To ensure spatial consistency, this study reprojected all data to the WGS-84 geographic coordinate system. The data sources are shown in Table 1 . Table 1. Data sources. Data type Source Year Resolution The Second National Land Survey and The Third National Land Survey National land survey results sharing and application platform 2015–2020 / NTL National oceanic and atmospheric administration(NOAA) 2015\2020 500 m LST Resource and environmental science data registration and publishing System 2015\2020 1 km PD WorldPop 2015\2020 1 km POI Amap 2015\2020 1 km GDP Resource and environmental science data platform 2015\2020 1 km Land Cover Data China land cover dataset 2015\2020 30 m Building heigh China building height dataset zenodo platform 2017\2020 1 km\10 m Open in a new tab To attain a more detailed analysis, the study region was separated into several regular grids, akin to making grid cells, because of its complexity. This can help to get specific information about each grid cell, limit the overall study area, prevent data generalization and inaccuracies produced by an excessively vast area, and make the data statistics and analysis more precise and targeted. This approach overcomes the drawback of employing just administrative divisions as the study unit and makes it easier to collect pertinent data effectively 48 . Furthermore, the grid cell may compute each grid by setting up an assessment system after processing each grid as a separate unit. A 300×300 m grid cell was built for this study. When calculating the land use intensity, based on the China land cover dataset, by counting the impervious surface area in each grid cell and calculating the ratio between it and the area of the net, it was used as an indicator of the horizontal layout of the plot. When calculating building height data, values in the range of 0–1 were obtained by calculating the ratio of the average building height in a grid cell to the highest value of building height in all grid cells. Grid cell statistics were also used while working with data linked to land use efficiency (such as GDP, POI, etc.), and the values in each grid cell were standardized to provide a distribution of values between 0 and 1. This paper introduces the concept of sea-land gradient, which is used to quantify the temporal and spatial changes of coastal land use with spatial distance from the shoreline. In landscape ecology, the idea of gradient analysis—such as the urban–rural gradient—is widely applied. The various changes from the sea to the land area are represented by the sea-land gradient. Figure 1 illustrates how the buffer analysis function was used to set eight buffer intervals—[0,2] km, [2, 4]km, [4, 6] km, [6, 8] km, [8, 10] km, [12, 14] km, and [14, max] km—to the inland area. The first gradient among them is [0,2]km, and then, in descending sequence, eight more gradients. We examine the land use structure and efficiency at various periods and gradients by combining various gradients with land use location entropy at multiple times. Research methods Methods for measuring land use structure To facilitate the analysis of gradients in land use structure and efficiency, this paper employs location entropy, Lorenz curves, and Gini coefficients to examine the land use structure. It adopts the entropy weight method for multi-source data fusion to quantify the matching degree between land use intensity and efficiency, thereby deriving the true land use efficiency. Furthermore, an analytical framework is constructed to investigate the land use structure and efficiency across different gradients in Jinpu New Area (Fig. 3 ). Fig. 3. Open in a new tab Methodology framework. Location entropy In location analysis, location entropy—also referred to as specialization rate—was initially introduced by P. Haggett 49 . The percentage of a given factor in a given location is compared to the percentage of that factor in a higher-level area (such as the entire country or province), which is determined using the following formula to determine the degree of factor concentration in that area: 1 where: is the locational entropy; is the area of the ith site type in the jth gradient; is the total area of the site in the jth gradient. is the total area of the site type; is the total area of the site. When the location entropy , it means that a particular land use type is characteristic or advantageous in the area, has a high degree of specialization, is relatively concentrated in the area, has a comparative advantage throughout the area, and contributes significantly to the area’s social and economic development. When , it indicates that the distribution of a certain land use type is comparable to the area’s average level, that its level of specialization is typical, and that there are no clear benefits or drawbacks to the area. It may be necessary to further optimize the layout of a particular land use type or increase its utilization efficiency when . This indicates that the land use type is relatively dispersed in the area, has a low degree of specialization, and is at a relative disadvantage in the area 50 . Lorentz curve The distribution of resources (like wealth, income, or land) among various groups is depicted graphically by the Lorenz curve. Economist Max Lorenz created it to compare the difference between a perfectly equal distribution and the actual distribution 51 . The Lorenz curve would be a diagonal line that runs from (0,0) to (1,1) if resources were distributed exactly equally. The actual Lorenz curve typically sits below this diagonal, and the more space there is between the diagonal and the curve, the more unequal the distribution of resources. In the analysis of land use structure, if the curve of a particular land category approaches the uniform line more closely, it indicates a more balanced spatial distribution of that land type within the study area. Conversely, a greater deviation from the uniform line reflects a more uneven distribution. The plotting methodology is described as follows. Sort by size from lowest to highest. Calculate the cumulative percentage of the total area of a certain type of land use and the cumulative percentage of the area of a certain type of land use in each interval. Plot a Lorenz curve with the cumulative percentage of the total area of a certain type of land use as the x-axis and the cumulative percentage of the area of a certain type of land use in each interval as the y-axis 52 . Gini coefficient The Gini coefficient, proposed by the Italian statistician Corrado Gini 53 , is a statistical measure quantifying the inequality in the distribution of income, wealth, or other resources within a region. Derived from the Lorenz curve, it is defined as the ratio of the area between the Lorenz curve and the line of perfect equality to the total area under the line of perfect equality. The coefficient ranges from 0 (perfect equality) to 1 (perfect inequality), with higher values indicating greater disparity 54 . Conventionally, a Gini coefficient below 0.2 reflects near absolute equality; 0.2–0.3 suggests relative equality; 0.3–0.4 denotes moderate inequality; 0.4–0.5 signifies high inequality; and values exceeding 0.5 represent severe inequality 55 . Mathematically, the Gini coefficient is expressed as: 2 where represents the number of groups after sorting the various land types in descending order of locational entropy; is a counting variable in the summation formula used to traverse the groups from 1 to , representing the ordinal number of the subgroups; represents the cumulative percentage of the land area of group about the total land area, and usually represents the cumulative percentage of the area of a particular type of land use from group 1 to group . Methods for measuring land use efficiency To measure the land use efficiency of Jinpu New Area, LUI was calculated by integrating data at both horizontal and vertical scales. LUE was calculated by combining NTL, LST, PD, POI, and GDP data. The coupling index between LUI and LUE was calculated to assess the land imbalance and thereby calculate the true land use efficiency. Finally, spatial analyses of each gradient zone of LUI, LUE, and CI were conducted to reveal the unbalanced, insufficient, and uncoordinated spatial pattern of land use in Jinpu New Area. The detailed method is shown in Fig. 4 . Fig. 4. Open in a new tab The calculation method of LUE. Methods for measuring land use intensity The land use intensity index is regarded as a reflection of the physical conditions of land use within the study area, consisting of horizontal layout and vertical height. The formula for the land use intensity index is described as follows: 3 4 5 is the building area index, representing the proportion of building land within a 300m×300m pixel. IAS is the impervious area within a pixel, TA is the total area of a pixel, is the building height index, BUH is the average building height within a pixel, and is the maximum building height in Jinpu New Area. Methodology for measuring land use efficiency Although remote sensing-based assessments of land use efficiency remain limited in the literature, normalized weighted synthesis calculations have been widely employed in land use intensity evaluations. Following this established approach, the entropy weight method was adopted in the present study to ensure objective determination of weighting coefficients. Using the entropy weight method, dispersion among the indices can be characterized: a smaller entropy value indicates greater dispersion among indices and higher corresponding weights 56 . Based on these derived weights, a normalized weighted composite formula for calculating the LUE index was established, as follows: 6 Among them: is the nighttime light index, is the land surface temperature index, is the population density index, is the POI density index, and is the GDP index. As the raw temperature data contains substantial climate background signals influenced by nonanthropogenic factors, preprocessing of thermal datasets is required. This study aims to assess built-up land use efficiency by establishing correlations between human activity intensity and land output efficiency. The urban heat island (UHI) effect, a characteristic consequence of urbanization, fundamentally results from differential heat absorption and release properties when artificial surfaces (e.g., impervious structures and pavement) replace natural vegetation cover. The temperature differential method enables isolation of climate background signals (e.g., regional climatic variations), thereby revealing the intrinsic urban heat island intensity of built-up areas. This approach facilitates a more precise correlation between anthropogenic activity intensity and land use efficiency. When integrated with complementary indicators—including nighttime light emissions (reflecting economic activity) and population density (indicating residential concentration)—it supports the development of a comprehensive, multidimensional LUE assessment framework. Consequently, temperature data were processed through the following steps: The UHI intensity was quantified by computing the difference between the LST of built-up areas and the mean LST of all non-urban land cover types within Jinpu New Area, as expressed in the following equation: 7 where LSTbuilt-up is the built-up area image element temperature, and LSTnon-built-up is the average temperature of the non-built-up area image elements. Calculate NTLindex, LSTindex, PDindex, POIindex, and GDPindex using the following formulas: 8 9 10 11 12 Among these, for each pixel, NTL, LST, PD, POI, and GDP values are represented by NTL, LST, PD, POI, and GDP, respectively. The maximum values of these variables across all pixels within Jinpu New Area are denoted as , , , and , while their minimum values are designated as , , , and respectively. Use the entropy weight method to calculate the weights , , , and . The calculation steps are as follows: (1) Determine the data and indicator direction. Since the data sample covers many years, the average values of each indicator from 2015 to 2020 are used as data. Among the five indicators set, some indicators are positively correlated with land use efficiency, and these indicators are positive, while the opposite are negative indicators 57 . (2) Perform standardization processing. Since the selection methods, dimensions, and characteristics of each indicator differ, to enable comparison and calculation of the indicator data, the data must first be standardized. The processing methods for positive and negative indicators differ. For positive indicators, the standardization formula is: 13 The standardized calculation formula for negative indicators is: 14 where: represents the year ( ), represents the jth indicator ( ), and represents the statistical value of the jth indicator in year i. an represent the minimum and maximum values of the jth indicator in year i, respectively. (3) Calculate the weight of each indicator. Calculate the weight of the jth indicator for the ith year using the following formula: 15 (4) Calculate the information entropy : 16 where: is a constant, n is the number of years; . When the values of a certain indicator tend to be consistent across years, it indicates that the indicator has little fluctuation and tends toward 1; the larger the value of , the smaller the weight of the indicator in the comprehensive evaluation. (5) Calculate the information entropy redundancy : 17 (6) Calculate the weight of each evaluation indicator: 18 (7) Based on the assessment weights, this study constructed a normalized weighted comprehensive calculation formula for land use efficiency, which is ultimately expressed as follows: 19 Methods for measuring land use coupling efficiency A low land use efficiency does not equate to inefficient land use in a given area; true inefficient land use is characterized by a mismatch between LUE and LUI, coupled with resource waste22. Typically, such inefficient land demonstrates elevated BUI coupled with depressed LUE. To systematically identify these mismatches, a coupling model integrating land use efficiency and intensity was developed. This study quantifies the mismatch degree through the difference between LUI and LUE, with the CI formulated as follows: 20 value close to 0 indicates an ideal state, where land use efficiency and land use intensity are well-coupled, and the more developed the construction land, the more efficient the region. If , then LUI is greater than LUE, and the construction land may be inefficient; if , then LUE is greater than LUI, and the construction land may be overloaded. Robustness test of coupling index method In this study, land use coupling efficiency values were computed via the entropy weight method. To verify the robustness of the coupling index under different weight configurations, the Analytic Hierarchy Process (AHP) was adopted for comparative verification. Furthermore, the robustness of the coupling index was validated via Pearson and Spearman correlation analyses. The AHP framework, with “land use coupling efficiency” as the objective layer, encompasses two criterion-level dimensions—“land use intensity” and “land use efficiency”—and includes seven core evaluation indicators. Land use intensity comprises two indicators: land use type and building height. Land use efficiency comprises five indicators: NTL, LTS, POI, PD, and GDP. Corresponding judgment matrices were constructed accordingly. Finally, the maximum eigenvalue of each judgment matrix was computed and substituted into the consistency test formula to derive the CI value. The indicator-specific weight of each indicator was multiplied by the weight of its respective criterion layer to obtain the final composite weight of each indicator relative to the overall objective, as presented in the Table 2 . Table 2. Comparison of AHP and entropy weight method weights. Criterion Layer Indicator Layer AHP Global Weight Entropy Weight Method Weight Weight Difference (AHP-Entropy Weight Method) B1(Land Use Intensity) C1(Land Use Type) 0.125 0.5 -0.375 C2(Building Height) 0.125 0.5 -0.375 B2(Land Use Efficiency) C3(NTL) 0.137 0.220 -0.084 C4(LST) 0.081 0.192 -0.11 C5(POI) 0.050 0.194 -0.145 C6(PD) 0.031 0.206 -0.175 C7(GDP) 0.451 0.188 0.264 Open in a new tab The coupling indices obtained under the entropy weight method (Table 5 ) and the AHP (Table 3 ) weighting schemes for 2015 and 2020 show Pearson correlation coefficients of 0.954 and 0.964, respectively, and Spearman rank correlation coefficients (ρ) of 0.976 and 1.000, respectively, as indicated by the AHP method calculation results in Table 7. Different weight configurations have little effect on the coupling index calculation results, as both correlation coefficients are at a very high level. This confirms the coupling index’s excellent robustness and stability in this study and the accuracy of its computation findings for further relevant analyses. Table 5. Average land use coupling efficiency values for each gradient in Jinpu New Area in 2015 and 2020 (based on the Entropy Weight Method). Year [0,2]km [2,4]km [4,6]km [6,8]km [8,10]km [10,12]km [12,14]km [14, max]km 2015 0.245 0.157 0.096 -0.024 -0.053 -0.063 -0.072 -0.104 2020 0.170 0.109 0.047 0.037 -0.016 -0.044 -0.061 -0.093 Open in a new tab Table 3. Average land use coupling efficiency values for each gradient in Jinpu New Area in 2015 and 2020 (based on the AHP method). Year [0,2]km [2,4]km [4,6]km [6,8]km [8,10]km [10,12]km [12,14]km [14, max]km 2015 0.148 0.161 0.126 0.099 0.092 0.091 0.089 0.077 2020 0.131 0.129 0.088 0.062 0.048 0.044 0.040 0.028 Open in a new tab Results Spatial and temporal effects of land use structure sea-land gradient Location Entropy Figure 5 presents the locational entropy distribution across sea-land gradient zones in Jinpu New Area for 2015 and 2020, stratified by land use type. Fig. 5. Open in a new tab The location entropy of various land use types in each gradient of Jinpu New Area in 2015 and 2020. ( a ) [0,2]km, ( b ) [2,4]km, ( c ) [4,6]km, ( d ) [6,8]km, ( e )[8,10]km, ( f ) [10,12]km, ( g ) [12,14]km, ( h ) [14, max]km (Land 1: Mining Land; Land 2: Urban land; Land 3: Scenic and Special-Use land; Land 4: Port and Wharf Land; Land 5: Highway Land; Land 6: Township Construction Land; Land 7: Hydraulic Construction Land; Land 8: Railway Land). Between 2015 and 2020, the general pattern stayed consistent and showed clear gradient differentiation: Port and terminal land use was heavily concentrated at the center of the nearshore zone [0,2]km. Town-level administrative areas, hydraulic engineering structures, and scenic and special-purpose land increasingly replaced the major land uses as the distance from the coast rose. Railway land usage dominated the offshore zone [12,max]km with very high concentration levels. The [0,2]km area has the highest location entropy of Port and Wharf Land use, suggesting that Port and Wharf Land use is highly concentrated and specialized in this area; the location entropy of Urban Land and Mining Land is greater than 1, suggesting a high degree of specialization, which is above average and not significantly different from the level of the entire Jinpu New Area. In contrast to the first gradient, the location entropy of Port and Wharf Land rapidly decreases in the [2,4]km area, while the location entropy of Scenic and Special-Use Land increases. Even now, the ratio of Urban Land to Mining Land is greater than 1, suggesting above-average levels of specialization. In the [4,6]km range, the locational entropy of Urban Land and Mining Land starts to be less than 1, whereas it starts to be greater than 1 for Township Construction Land, Highway Land, Hydraulic Construction Land, and Railroad Land. Over time, Scenic and Special-Use Land transform into valuable land types. By 2020, there will be a significant decrease in the level of specialization of Hydraulic Construction Land compared to 2015. [6,8]km of Hydraulic Construction Land becomes a dominant land type, while Township Construction Land begins to develop rapidly. Scenic and Special-Use Land begins to decline, and other land types remain largely unchanged. Within the [8,10]km buffer zone, Hydraulic Construction Land and Township Land emerged as the dominant land-use types. Meanwhile, Railway Land initiated development, exhibiting increased specialization. Highway Land, Township Land, Hydraulic Construction Land, and Railway Land remained dominant, whereas other land-use types showed minimal changes overall. Location entropy analysis of the [10,12]km area reveals that Township Construction Land has become the dominant type. Railway Land has started to develop, with a heightened degree of specialization. Highway Land, Railway Land, and Township Construction Land act as the dominant types. Scenic and Special-Use Land has reached average levels, and other land-use types have remained essentially unchanged. In the [12,14]km area, Railway Land has become the dominant type. Other land-use types are relatively balanced, with no significant alterations. Similar to the situation in 2015, the location entropy of Railway Land decreased in 2020, while other land-use types underwent little change. Within the [14, max]km buffer zone, Railway Land use serves as the dominant land-use type, with its location entropy reaching 11.54. Railway Land use is highly concentrated, while other land-use types are relatively balanced and show no major changes. Comparable to 2015, in 2020, the location entropy of Railway Land increased to 11.98. Hydraulic Construction Land use declined from 1.74 to 0.57, and other land use types remained largely unchanged. In summary, the land use location entropy in Jinpu New Area in 2015 and 2020 indicates that railway land within the ranges of [12,14]km and [14,max]km stands out as the most advantageous land use. Lorentz curve The degree of difference between the land type’s uniform distribution throughout the region and its actual distribution is indicated by the curve’s deviation from the absolute uniform line. The land type is more evenly distributed throughout the area, the closer the curve is to the absolute uniform line; on the other hand, the distribution is more unevenly distributed throughout the area, i.e., it is relatively dispersed, the farther the curve is from the absolute uniform line 58 . Figure 6 shows the Lorenz curve for each land use type in the Jinpu New Area. Fig. 6. Open in a new tab Lorenz curve diagram of various land use types in Jinpu New Area. ( a ) Urban Land, ( b ) Township Construction Land, ( c ) Port and Wharf Land, ( d )Highway Land, ( e ) Railway Land, ( f ) Hydraulic Construction Land, ( g ) Mining Land, ( h )Scenic and Special-Use Land, (n = 8). Significant gradient difference was seen in Jinpu New Area’s land use distribution uniformity across coastal gradients between 2015 and 2020: overall uniformity was constant across all land use categories within the nearshore zone [0,6] km. Urban land and town-level settlement land usage in the offshore zone [14,max] km showed the least consistency, which progressively declined with increasing distance from the coast. Within the [0,2]km buffer zone, the Lorenz curves of all land use types closely approximate the absolute uniformity line, indicating that land use is evenly distributed. Within the [2,4]km buffer zone, the Lorenz curves of all land use types except Port and Wharf Land closely approximate the absolute uniformity line, indicating an even distribution pattern. Within the [4,6]km buffer zone, land-use distribution across all types is relatively uniform. Within the [6,8]km buffer zone, Urban Land and Mining Land exhibit slight uneven distribution, while other land use types maintain a uniform distribution. Within the [8,10]km buffer zone, Mining Land and Scenic and Special-Use Land show the lowest uniformity among the eight buffer zones. Urban Land distribution is relatively uneven, whereas other land use types remain relatively uniform. Within the [10,12]km buffer zone, Urban Land, Scenic, and Special-Use Land exhibit uneven distribution, while other land use types are relatively uniform. Within the [12,14]km buffer zone, Urban Land, Highway Land, and Hydraulic Construction Land display relatively uneven distribution, while other land use types remain relatively uniform. Within the [14, max]km buffer zone, Urban Land, Township Construction Land, and Highway Land show the most pronounced uneven distribution among the eight buffer zones. Except for Railway Land, the uniformity of other land use types is also poor. Port and wharf land is only present within the [0,2]km and [2,4]km buffer zones; no other zones contain this land-use type, so uniformity analysis for it is not applicable in other zones. In summary, within the [14,max] km gradient range of Jinpu New Area, the uniformity of distribution between urban land and town-level administrative land exhibits the lowest level across all gradients. Gini coefficient The Gini coefficients for different regions within Jinpu New Area from 2015 to 2020 were calculated using the formula (Table 4 ). Table 4. Gini coefficient of urban land use types in Jinpu New Area from 2015 to 2020. Buffer Zone Urban Land Township Construction Land Port and Wharf Land Highway Land Railway Land Hydraulic Construction Land Mining Land Scenic and Special-Use Land [0,2]km 0.044 0.122 0.007 0.056 0.127 0.240 0.067 0.040 [2,4]km 0.069 0.153 0.329 0.088 0.065 0.389 0.079 0.116 [4,6]km 0.084 0.036 / 0.014 0.097 0.318 0.205 0.076 [6,8]km 0.336 0.215 / 0.038 0.152 0.275 0.349 0.050 [8,10]km 0.702 0.202 / 0.065 0.029 0.336 0.658 0.449 [10,12]km 0.707 0.228 / 0.161 0.033 0.264 0.289 0.342 [12,14]km 0.750 0.157 / 0.210 0.076 0.375 0.233 0.121 [14,max]km 0.757 0.550 / 0.455 0.020 0.283 0.326 0.180 Open in a new tab The Gini coefficients for land use across marine and terrestrial gradients in Jinpu New Area showed clear gradient differences between 2015 and 2020: the total Gini coefficient progressively rose from sea to inland locations. The distribution of Urban land varied significantly between [8, max]km (Gini coefficient ≥ 0.7), the distribution of Mining land was uneven between [8, 10] km (Gini coefficient ≥ 0.6), and the distribution of Township Construction Land and Highway Land was relatively uneven between [14, max] km (Gini coefficient ≥ 0.4). The distribution was comparatively uniform in other locations. Within the [0,2]km buffer zone, the Gini coefficients of all land use types except Hydraulic Construction Land are uniformly below 0.2, indicating a relatively even distribution. Hydraulic Construction Land also exhibits a relatively uniform distribution pattern. Within the [2,4]km buffer zone, the Gini coefficients for Port and Wharf Land and Hydraulic Construction Land fall between 0.3 and 0.4, suggesting a relatively rational distribution. Other land use types show a uniformly distributed pattern. Within the [4,6]km section, the Gini coefficient for Hydraulic Construction Land ranges from 0.3 to 0.4, denoting a relatively rational distribution. The Gini coefficient for Mining Land lies between 0.2 and 0.3, indicating a relatively uniform distribution. Absolute uniformity is exhibited by other land use types. Within the [6,8]km interval, the Gini coefficients for Urban Land and Mining Land are between 0.3 and 0.4, signifying a relatively rational distribution. For Township Construction Land and Hydraulic Construction Land, the coefficients range from 0.2 to 0.3, indicating a relatively uniform distribution. Absolute uniformity characterizes other land use types. Within the [8,10]km interval, the Gini coefficients for Urban Land and Mining Land exceed 0.6, indicating significant disparities in their distribution. The coefficient for Scenic and Special-Use Land ranges from 0.4 to 0.5, suggesting considerable distribution differences. For Hydraulic Construction Land, the coefficient spans 0.3-0.4, representing a relatively rational distribution. The coefficient for Township Construction Land lies between 0.2 and 0.3, implying a relatively even distribution. Other land uses are uniform. Within the [10,12]km interval, the Gini coefficient for Urban Land exceeds 0.6, with significant distribution disparities observed. For Scenic and Special-Use Land, the Gini coefficient is in the range of 0.3-0.4, indicating a relatively rational distribution. The Gini coefficients for Township Construction Land, Hydraulic Construction Land, and Mining Land fall between 0.2 and 0.3, representing a relatively average distribution. Absolute uniformity is exhibited by other land use types. Within the [12,14]km interval, the Gini coefficient for Urban Land exceeds 0.6, indicating significant distribution disparities. The Gini coefficient for Hydraulic Construction Land (0.3-0.4) is relatively rational. For Mining Land and Highway Land, a relatively uniform distribution is observed, with Gini coefficients ranging from 0.2 to 0.3. Absolute uniformity characterizes other land use types. Within the [14, max]km interval, Urban Land shows significant distribution disparities, with a Gini coefficient exceeding 0.6. Township Construction Land and Highway Land exhibit relatively large distribution disparities, with Gini coefficients ranging from 0.4 to 0.5. Mining Land has a relatively rational distribution, with a Gini coefficient of 0.3 to 0.4. Hydraulic Construction Land shows a relatively uniform distribution, with a Gini coefficient of 0.2 to 0.3. Absolute uniformity is exhibited by other land use types. In summary, the Jinpu New Area exhibits the most pronounced differences in urban land distribution within the [8, max] km range across all elevation gradients. Spatial and temporal effects of land use efficiency on the sea-land gradient Land use intensity Land use intensity typically reflects the degree of land development and utilization, with higher values indicating more intensive land use. Using horizontal layout and vertical height data for Jinpu New Area, land use intensity in 2015 and 2020 was analyzed, and corresponding intensity maps were generated (Fig. 7 ). There was little difference in land use intensity between 2015 and 2020. The distribution of land use intensity values was relatively scattered, with patches of different colors interspersed, and the distribution of high and low efficiency values showed little change. In 2015, the mean, minimum, and maximum LUI values for land use intensity in Jinpu New Area were 0.198, 0, and 0.988, respectively. By 2020, these values shifted to 0.247, 0, and 0.985, respectively. Overall, land use intensity increased between 2015 and 2020, characterized by a slight decrease in the maximum value and a notable increase in the mean value. High-intensity areas predominantly concentrated in southeastern coastal regions within the [0,2]km, [2,4]km, and [4,6]km gradients, forming contiguous block-like distributions. These areas encompass urban neighborhoods such as Maqiaozi Street, Wanli Street, and Guangming Street—key drivers of urban development characterized by convenient transportation and rapid growth. Medium-intensity areas are primarily distributed adjacent to high-intensity zones within the [4,6]km and [6,8]km gradients. A significant expansion of medium-intensity land use was observed in 2020, with the majority of this expansion occurring within the [6,8]km gradient. LUI values in most parts of the [6,8]km, [8,10]km, [10,12]km, [12,14]km, and [14, max]km gradients fall within the lower range, indicating relatively low land use intensity in these regions. These areas are dominated by agricultural land, forests, and other underdeveloped land types. Fig. 7. Open in a new tab Land use intensity maps of Jinpu New Area in 2015 and 2020. Land use efficiency Land use efficiency in Jinpu New Area from 2015 to 2020 was determined through the integration of multi-source spatial data(Fig. 8 ). The efficiency of land use did not significantly change between 2015 and 2020. There was minimal variation in the distribution of high and low efficiency values, and the land use efficiency values were rather scattered, with patches of various colors blending. Land use efficiency in the Jinpu New Area was 0.195 on average, 0.048 on average, and 0.650 on average in 2015. The land use efficiency of Jinpu New Area in 2020 was 0.202 on average, 0.091 on average, and 0.682 on average. The number of areas with medium to high efficiency increased, land use efficiency improved, and the average, minimum, and maximum values all rose between 2015 and 2020. The southeast coastal regions of the three gradients [0,2]km, [2,4]km, and [4,6]km are primarily home to high land use efficiency areas (red areas), which are dispersed in blocks, primarily around Maqiaozi Street, Wanli Street, and Guangming Street. Because of their quick development and easy access to transportation, these areas are urban development engines. With medium-to-high efficiency zones situated in urban areas with dense populations, easy access to transportation, and a wealth of medical and educational services, the medium-efficiency zone is mostly centered around the high-efficiency zone. Inland regions with low efficiency (yellow areas) are widely dispersed throughout the map, primarily within a [6,8]km gradient. These regions, which include Daheishan and Xiaheishan, are primarily mountainous and ecologically protected areas. These regions are remote from major cities, have few settlements, few highways, and are inefficient. Due to industrial upgrading and investments in infrastructure development and construction, the land use efficiency near Liangjia Dian Street at [10,12]km and [12,14]km has rapidly improved and altered. Fig. 8. Open in a new tab Land use efficiency in Jinpu New Area in 2015 and 2020. Land use coupling efficiency The high-efficiency and high-intensity zones, as well as the low-efficiency and low-intensity zones, were found to significantly overlap when land use efficiency and land use intensity were compared. The central region contains a concentration of areas with high efficiency and high intensity, suggesting that the development surrounding the old urban area started earlier, is more comprehensive, and has better urbanization. The central northern region’s inland areas are largely home to low-efficiency and low-intensity zones. We coupled the land use efficiency and land use intensity of 2015 and 2020 and carried out a graded research and analysis to comprehend the inefficient, well-coupled, and overloaded areas of Jinpu New Area. Coupling of land use efficiency and land use intensity in 2015 and 2020 (Fig. 9 and Fig. 10 ). According to this formula, the closer the CI value is to 0, the better the coupling effect, indicating that land development in the region is more consistent with its utilization. If CI > 0, it indicates that the region is inefficient; if CI < 0, it indicates that the region is overloaded. Fig. 9. Open in a new tab Map of land use coupling efficiency and maps of coupling efficiency for each gradient in jinpu new area, 2015. ( a ) [0,2]km, ( b ) [2,4]km, ( c ) [4,6]km, ( d ) [6,8]km, ( e )[8,10]km, ( f ) [10,12]km, ( g ) [12,14]km, ( h ) [14, max]km. Fig. 10. Open in a new tab Map of land use coupling efficiency and maps of coupling efficiency for each gradient in jinpu new area, 2020. ( a ) [0,2]km, ( b ) [2,4]km, ( c ) [4,6]km, ( d ) [6,8]km, ( e )[8,10]km, ( f ) [10,12]km, ( g ) [12,14]km, ( h ) [14, max]km. The mean, minimum, and maximum values of the CI between land use efficiency and land use intensity in the Jinpu New Area were -0.004, -0.284, and 0.960 in 2015; the mean, minimum, and maximum values of the coupling CI between land use efficiency and land use intensity in the Jinpu New Area were 0.019, -0.231, and 0.957 in 2020; the minimum and average values of the coupling CI between land use efficiency and land use intensity in the Jinpu New Area increased between 2015 and 2020, while the maximum value decreased, and the difference between the maximum and minimum values of coupling efficiency decreased. This suggests that between 2015 and 2020, extremely inefficient and extremely overloaded areas were adjusted, resulting in the clustering of land use coupling efficiency in areas with good coupling. Table 5 summarizes the average values of each gradient to make it easier to see the overall changes in coupling efficiency between gradients. Without the influence of extreme values, the data will be dispersed around the mean because the values computed in the table are averages, reflecting the average trend of the data set. Consequently, it is reasonable to assume that numerous regions are extremely close to 0 when the mean itself is near 0. In order to compare the spatial distribution characteristics of "well-coupled areas" under various thresholds, thresholds were established at 0.05, 0.1, and 0.15, respectively. Sensitivity study revealed that the spatial distribution of well-coupled areas at a threshold of 0.1 closely matched the Jinpu New Area’s coastal-to-inland growth gradient. This value was chosen as a result. The coupling between land use intensity and land use efficiency in Jinpu New Area is relatively strong, indicating that land use, socioeconomic benefits, and urbanization are generally well aligned. Proximity to the coast correlates with higher coupled index values and a greater distribution of low-efficiency areas; conversely, increasing distance inland corresponds to lower coupled index values and more overloaded areas. In the transitional zone between coastal and inland regions, the coupling situation is favorable. In 2015, five gradients exhibited good coupling: [4,14]km. By 2020, this number increased to six, [14, max]km added as new gradients with good coupling. Over the five years, the average coupled index values for the [0,2]km and [6,8]km gradients showed significant variation. The [0,2]km gradient, being close to the ocean, experienced rapid improvements in land use efficiency. For the [6,8]km gradient, the primary driver of increased land use intensity was the expansion of urban construction and development of built-up areas over the five years 59 . As illustrated in Fig. 9 , Fig. 10 , and Table 3 , within the [0,2]km, [2,4]km, and [4,6]km gradients in 2015, red low-efficiency zones and blue overloaded zones were distributed at an approximate ratio of 1:1, with their average values exceeding 0. Yellow well-coupled zones were relatively sparse. Red low-efficiency areas were predominantly concentrated in the central-southern development zone, indicating high land use intensity. From the [6,8]km gradient onward, blue overloaded areas became dominant, with average values below 0—this pattern is attributed to low land use intensity, influenced by terrain and other developmental constraints. In 2020, low-efficiency areas were additionally distributed within the southeastern built-up regions of the [0,2]km, [2,4]km, and [4,6]km buffer zone. The natural breakpoint method was used to separate the built-up area’s coupling efficiency into three categories: high-intensity, medium-intensity, and low-intensity areas. This was done to shed light on the particular circumstances of low coupling efficiency in the Jinpu New District’s built-up area. Figure 11 displays the findings. Fig. 11. Open in a new tab Land use coupling index map of the built-up area of Jinpu New Area in 2015 and 2020. The following were the findings of the coupling index study conducted in 2015 for Jinpu New Area’s built-up area: (1) Built-up areas with a CI value less than 0.028, which covered an area of 73.98 square kilometers, were classified as low-intensity zones and made up 13.45% of the total area of Jinpu New Area’s central urban area; (2) built-up areas with a CI value greater than 0.028 but less than 0.334, which covered an area of 224.28 square kilometers, were classified as medium-intensity areas and made up 40.77% of the region’s central urban area; (3) built-up areas with a CI value greater than 0.034, which covered an area of 251.82 square kilometers, were classified as high-intensity areas and made up 45.78% of the central urban area of Jinpu New Area. Likewise, the 2020 CI values of Jinpu New Area are separated into three groups based on the natural breakpoint method: (1) Low-intensity areas, comprising 70.56 square kilometers, comprise 13.30 percent of Jinpu New Area’s central urban area; (2) medium-intensity areas, comprising 205.47 square kilometers and 38.72% of the central urban area, are comprised of built-up areas with CI values greater than 0.028 but less than 0.334; (3) high-intensity areas, comprising 254.61 square kilometers and 47.98% of the central urban area, are comprised of built-up areas with CI values greater than 0.034. The findings indicate that: In the Jinpu New Area’s built-up areas, land use intensity typically outpaces land use efficiency. The distribution of high, medium, and low-intensity areas changed little between 2015 and 2020; high-intensity areas increased while medium and low-intensity areas decreased. This suggests that the Jinpu New Area is still experiencing rapid urbanization. The majority of high-intensity regions are located in [0,2]km, [2,4]km, and [4,6]km. These are high-intensity locations. Land use efficiency still needs to be improved because of high urbanization, increased construction investment, and increased land use intensity. As a result, land use efficiency now falls short of development intensity, which is extremely inefficient and necessitates urgent intervention in the area. The [6,8]km and [8,10]km gradients and the vicinity of the red areas in the [0,2]km, [2,4]km, and [4,6]km intervals are the primary locations for medium intensity areas. These are medium intensity areas, and the disparity between land input and efficiency is evident as land use intensity continues to increase. The [8,10]km, [12,14]km, and [14, max]km buffer zones exhibit the greatest distribution of low-intensity areas. The possible risk of inefficiency should be taken into consideration because these areas have low land use intensity and are near a well-coupled state. Discussion Coupling of land use structure and efficiency: mechanisms and policy implications Optimizing land use structure is based on land use coupling efficiency, and maximizing land use coupling efficiency is made easier by a sensible land use structure. Each of the two elements influences and interacts with the other 30 . Pearson’s correlation coefficient approach was used to investigate the relationship between efficiency and land use structure in more detail. To ascertain the relationship between different land structure types and efficiency, the average values of land use locational entropy and land use coupling efficiency throughout each gradient were included in the computations. Land use efficiency and urban land usage showed a highly substantial positive association in both 2015 and 2020, as Table 6 illustrates. Efficiency and railway land utilization showed a strong inverse relationship in 2020. Table 6. Pearson correlation between land use structure and efficiency in jinpu new area (2015 and 2020). Year Urban Land Township Construction Land Port and Wharf Land Highway Land Railway Land Hydraulic Construction Land Mining Land Scenic and Special-Use Land 2015 0.946** -0.486 0.706 -0.556 -0.701 -0.393 0.676 -0.126 2020 0.880** -0.310 0.683 -0.422 -0.799* -0.196 0.575 -0.215 Open in a new tab Note: * p < 0.1, ** p < 0.05. Planning recommendations are put forth for the identified inefficient and overloaded land within the study area based on the correlation analysis between efficiency and land use structure, as well as the significant spatial patterns found in the land use structure and coupling efficiency across various gradients within the Jinpu New Area. These suggestions make planning guidelines clearer and make it easier to rationally create land use regulations and urban development plans. Overloaded Areas: Where railway land use predominates, these zones mostly cover the inland areas between [12, 14] km and [14, max] km. To reduce detrimental effects on efficiency, railway land must be carefully planned. The planning logic for railway land allocation needed to be reevaluated in 2020 due to a considerable negative association between efficiency and railway land utilization. Reduce the detrimental effects on efficiency of excessive railway land occupation or planning that is out of step with industrial and urban expansion by coordinating regional industrial layouts and transportation demands. Encourage the development of railway land for mixed uses: Integrate commercial, logistical, and industrial operations by implementing TOD (Transit-Oriented Development) models around railway hubs. To counteract its negative link with efficiency, convert the railway land’s transportation characteristics into economic efficiency. Inefficient Areas: These are mostly found in the [0,2]km and [2,4]km zones close to the coast, where port terminal and urban land predominate. Except for hydraulic engineering land, all of their Gini values are less than 0.2, showing significant specialization and comparatively balanced resource allocation. Although land use intensity and efficiency are both high, there are many inefficient zones because development intensity greatly outpaces efficiency advances. The highly significant positive association between urban land use and land use efficiency highlights the critical role that sensible urban land allocation plays in improving efficiency. Planning should encourage the regeneration of existing land and tightly regulate the growth of urban land area. To improve the input–output efficiency of urban land, strategies like mixed-use development and three-dimensional space use should be used. Anchor the urban land’s functional layout: To ensure that high-efficiency industries are compatible with urban land, optimize the land allocation ratio of core urban functional areas (such as commercial districts and industrial parks) with an eye on efficiency enhancement. Steer clear of inefficient urban land use expansion or functional mismatches. Well-coupled Areas: The transitional zones of the land-sea transition belt, particularly the [4,6]km and [6,8]km ranges, contain a number of well-coupled zones. Where there is a comparatively fair distribution of land use groups, including built-up communities, highways, and hydraulic engineering structures. A balanced land use structure is demonstrated by the fact that no single land use category has a notable advantage. The area’s ecological and economic worth as a "functional transition buffer zone" is validated by the prevalence of well-coupled zones, which guarantee that land use efficiency corresponds with intensity. To prevent indiscriminate investment, still, land use types such as Township Construction Land and Hydraulic Construction Land should be rationally considered. Demand should determine how land is distributed; it is not necessary to intentionally increase or decrease the amount of such land for the sake of efficiency. Rather, fundamental goals such as resource endowment, industrial demand, and ecological conservation should be used to establish realistic bounds. Investigate indirect efficiency pathways concurrently. Indirect empowerment can be accomplished through policy coordination even in the absence of direct linear connections (e.g., ecological restoration of Mining Land linked with cultural tourism, or extending logistics supply chains from Port and Wharf Land). Gains in overall efficiency can be achieved by utilizing supporting industries. Advantages and Limitations The fundamental ideas of distance decay theory, this study embeds the Coupling Index framework within the research context of land transformation and densification to establish the CI model. It is grounded in the core theories of distance decay, coastal growth poles, and multifunctional land use. When examining land-sea gradients, distance decay theory offers the premise that "land use efficiency diminishes with increasing distance from the coast." The theory of multifunctional land use supports the selection of dimensions for the "intensity-efficiency" coupling index. Coastal growth pole theory asserts that coastal regions become economic growth poles because of transportation advantages, with land use intensity and efficiency exceeding inland areas. Perspectives from new economic geography and core-periphery theory are used for further developments. According to the core-periphery hypothesis, inland areas function as reliant peripheries and coastal areas frequently act as core zones for high-value activity agglomeration; this dynamic is represented in higher coastal land use efficiency. According to new economic geography theory, industrial agglomeration fueled by cheap maritime transportation increases returns to scale, which improves the efficiency and intensity of coastal land use. Furthermore, the gradient link between “structural equilibrium” and “efficiency matching” in coastal areas is first revealed by this work. It supports Lambin et al.’s theoretical framework 60 on "land use structure influencing efficiency" by showing how distributive equilibrium and structural specialization work together to increase efficiency. In terms of modeling methodology, this study introduces a land-sea gradient perspective, marking the first analysis of the relationship between land use structure and land use efficiency from this angle. It accurately depicts the geographical differential patterns of land use structure and efficiency from coastal to inland areas by defining buffer zones at varied distances from the coastline. It provides focused evidence for distinct land management options by clearly identifying the most common land use structures and efficiency traits across various gradient zones. Additionally, it provides a visual representation of how land-sea interactions affect land use, guaranteeing that study findings are in line with the real-world development requirements of coastal areas. In order to solve practical issues like excessive coastal development intensity and inadequate inland coordination, this offers empirical support for integrated land-sea development in coastal zones. In order to measure the degree of mismatch between the two, the Coupling Index was created by methodically examining and utilizing urban sprawl indicator systems. The compatibility between "spatial form and utilization quality" during urbanization is examined by this index and urban form sprawl indicators, although they have different primary focuses. (1) The former focuses on the "quality-efficiency" of land use and highlights the balance between "intensity and efficiency." The latter focuses on the "quantity-efficiency" of spatial growth by emphasizing the morphological features of spatial expansion such us diffusion patterns 61 and fragmentation levels 62 ; coastal-terrestrial gradient characteristics, which add adaptability to the distinct geographic environment of coastal zones to accurately identify specific mismatch patterns in maritime areas; (2) Different application contexts: While traditional sprawl indicators are usually appropriate for plains cities or inland areas, this study’s coupling index incorporates coastal-terrestrial gradient characteristics, adding flexibility to the particular geographic environment of coastal zones to accurately identify specific mismatch patterns in maritime areas; (3) Different compositional elements: The coupling index integrates multi-source data, such as building height, GDP, and nighttime lights. It overcomes the drawbacks of conventional sprawl indicators that only consider spatial features while ignoring usage efficiency by incorporating both spatial form information and socioeconomic output dimensions 63 . When combined, these methods enhance the quantitative assessment aspects of land transformation.Meanwhile,The traditional methodology of classic methodologies, such as DEA and SBM, which usually treat administrative areas as the unit of analysis, has been abandoned by research on land use efficiency. The "Land Use Intensity—Land Use Efficiency—Coupling Index" three-tiered analytical framework has been devised. This method finds "intensity-efficiency" mismatches in developed land by combining quantitative calculations with multi-source data. The direction and severity of these mismatches are directly shown by the ensuing difference values, which produce simply comprehensible and useful results that quickly identify high/low-efficiency zones. This approach, which is applicable at the municipal, county, and provincial levels, offers quantitative underpinnings for compact city design and directly promotes land use optimization. In terms of outcomes, the South Bengal 21 and the Bule Hora region of Ethiopia 64 have seen particular patterns of urban sprawl and fragmentation as built-up areas have grown at the expense of other land types. While low-density built-up areas frequently have worse living conditions, high-density built-up areas usually have better household environmental conditions. In China, transportation hubs greatly improve urban economic resilience; in cities with comparatively low levels of urban sprawl, this effect is more noticeable 65 . The spatial heterogeneity of land use efficiency is further supported by the study’s positive correlation between land use efficiency and urban land area and its negative correlation with railway land area. These analogies bolster the framework’s broad applicability and are consistent with Seto et al.’s analysis of the ecological effects of urban growth 63 . Existing theoretical research has found that imbalances in urban spatial structures inhibit land use efficiency 66 . The inhibiting effect of spatial structural imbalances increases with the size of cities. This confirms my results that inland areas with structural imbalances show comparatively poorer efficiency, whereas coastal areas with reasonably balanced structures show higher efficiency. Xia Liao, Chuanglin Fang, and others 67 investigated 280 Chinese cities and discovered that monocentric urban structures generally had a good impact. Urban structure agglomeration simultaneously has a two-sided effect: the inhibiting influence increases while the driving effect decreases. From the viewpoints of agglomeration levels and polycentricity within urban clusters, this study investigated the relationship between urban land use structure and efficiency. Both studies seek to answer the fundamental question of "how structure influences efficiency," however, they differ from my research in that they concentrate on land use structure. Their findings complement and corroborate one another. It is assumed that Jinpu New Area, a polycentric city with notable agglomeration, is less efficient than monocentric cities that are optimally clustered. It fails to achieve efficient land use due to the inhibiting effects of significant urbanization. In order to improve land use efficiency, it is very important to modify the land use structure. Using data from China’s Second National Land Survey, Shuyi Ren, Sijing Ye, and colleagues 68 came to the conclusion that standard land consolidation measures could be ineffective, underscoring the need for region-specific strategies. Land consolidation can increase productivity in flatland areas, while resilient land management techniques and better infrastructure are more important in dispersed mountainous locations. The main findings of this study are strongly supported by this research, which validates the important role that zoned governance models play in improving land use efficiency. The key to resolving the regional heterogeneity of land use efficiency, according to both studies, is "differentiated zoned regulation," but distinct from this study’s zoning strategy, which is predicated on particular sea-land gradient differentiation traits. While ignoring gradient differences in the intensity-efficiency relationship, research by Youpeng Lu, Tingting He, et al 31 . shows that "higher farmland ratios correlate with lower urban land use efficiency," highlighting the limitations that farmland protection places on urban expansion patterns and efficiency. The percentage of industrial land within construction land shows a positive U-shaped relationship with building land efficiency, whereas the percentage of public facility land within construction land has a positive correlation with building land efficiency, according to Nie Lei, Guo Zhongxing, et al 69 . My research conclusion that "land use efficiency is positively correlated with urban land and negatively correlated with railway land" is logically supported by the results of these two studies. The spatial pattern of “functional zoning” is reflected in all conclusions, which essentially highlight "the degree of alignment between land use structure and regional development needs." Land use efficiency is high, construction land prices are high, and urban and public facility land make up a large portion of core areas (urban centers). Arable land makes up a large portion of peripheral areas (urban–rural transition zones, isolated inland regions), while railway land is dispersed and typically inefficient. There is a corresponding relationship between these two areas’ spatial distribution patterns.The "spatial tension between farmland conservation and urban expansion" is the primary conflict in urban–rural transition zones or areas with strict farmland protection, according to the findings of Youpeng Lu, Tingting He, and others. The study’s findings, on the other hand, are applicable to coastal areas, where the "tension between land use structure and land use efficiency" is the main source of conflict. It is important to note that there are still issues with data resolution matching in current research. Aggregation issues may result from the substantial difference in resolution between built-up area data (30 m) and building height data (1 km). In order to improve accuracy, future research may use more sophisticated data processing techniques to unify the resolution of both datasets based on the 30 m data. Furthermore, lengthier time-series data are required to evaluate the evolutionary paths between land use structure and efficiency, showing the processes by which policies affect land use efficiency. Dynamic evaluation models can be created by combining machine learning algorithms with multi-source geographical data, such as socioeconomic statistics and remote sensing monitoring data. This method will allow for a more accurate quantitative examination of the intricate mechanisms that drive the differentiation of the land-sea gradient, which are influenced by both urbanization and natural geography. Conclusion This study first evaluates the land use structure of Jinpu New Area from 2015 to 2020 using location entropy, Lorenz curves, and Gini coefficients to characterize the current state of urban land use. Second, a comprehensive index method integrating multi-source data is employed to assess land use efficiency and intensity in 2015 and 2020, with land use efficiency coupled with intensity to derive the coupled index. To further elucidate the influence of the sea-land gradient in coastal areas, Jinpu New Area was divided into gradient zones, and the aforementioned analyses were conducted by gradient. The conclusions are as follows: The Port and Wharf Land in Jinpu New Area exhibits considerable spatial differentiation, with its distribution concentrated in the [0,2]km gradient zone. In contrast, Hydraulic Construction Land shows an uneven distribution pattern, mainly clustered in the [6,8]km and [8,10]km gradient zones. The distribution of Township Construction Land and Railway Land is relatively rational across the eight gradient zones, while other land use types are uniformly distributed. In Jinpu New Area, high-efficiency zones are highly overlapped with high-intensity zones, and low-efficiency zones coincide with low-intensity zones. The land use coupling efficiency presents a distinct gradient pattern characterized by low efficiency in coastal areas, good coupling in intermediate zones, and overloading in inland areas. The overall coupling status is favorable, and the area with good land use coupling efficiency expanded over the five-year study period. Jinpu New Area is still in a phase of rapid development. The area with good coupling status in built-up zones accounts for a relatively small proportion, and inefficient land use dominates the region. Given the growing expansion of high-intensity zones in the [0,2]km, [2,4]km, and [4,6]km gradient zones, these areas should be prioritized for targeted intervention. In Jinpu New Area, land use efficiency and land use structure are significantly correlated. In particular, there is a substantial negative association between land use efficiency and Railway land and a significant positive correlation with Urban land. Author contributions Conceptualization, YFP, Jianfeng Zhu, and Jiake Zhou; Formal analysis, Jianfeng Zhu; Funding acquisition, Jianfeng Zhu; Investigation, SRT, Jiake Zhou, and GSS; Methodology, YFP, Jiake Zhou, and GSS; Project administration, Jianfeng Zhu; Software, YFP; Visualization, YFP and SRT; Writing original draft, YFP; Writing review & editing, Jianfeng Zhu. The authors applied the SDC approach for the sequence of authors. All authors have read and agreed to the published version of the manuscript. Funding This research has been supported by the National Natural Science Foundation of China, Grant Number 42101257. Data availability Data will be made available on request. Declarations Competing interests The authors declare no competing interests. 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