Evolution Of Spatial Patterns Research Articles

Impact of Digital Inclusive Finance and Environmental Regulation on Eco‐Efficiency of Farmland Utilization in China

ABSTRACTThe farmland pollution has threatened the lifeblood of people's livelihoods through the soil‐to‐crop system, and the resulting food safety risks are becoming more and more pronounced. Digital inclusive finance (DIF) can provide convenient financial services for the eco‐transformation of farmland utilization. This study analyzed the spatial evolution pattern of eco‐efficiency of farmland utilization (EEFU) and DIF, empirically tested the impact of DIF on EEFU, and discussed the impact of DIF on EEFU with changes in the level of environmental regulatory intensity based on panel data from 2011 to 2022 in China. The results found that the development of EEFU and DIF is increasing, and the polarization phenomenon is constantly easing, but the absolute differences between different provinces in China have an expanding trend. DIF can improve the EEFU, and this promotion effect is more pronounced in eastern and central China, non‐grain‐producing areas, and areas with low levels of financial development. DIF will not be able to improve EEFU until environmental regulation has reached a certain level. China should fully leverage the positive role of DIF and environmental regulations in promoting the EEFU, provide more differentiated financial products, financial tools, and financial models for the eco‐transformation of farmland utilization, and explore a path for eco‐transformation of farmland utilization that complements environmental regulations and development of market‐inclusive finance.

A Coupled CA Urban Development Model (UDM) and Urban Fabric Generator (UFG) for the Assessment of Climate Hazards on Future Urban Development

Abstract. Cellular Automata (CA) spatial models have become a de facto means by which to simulate future scenarios of land use activity, particularly urban development. Increasingly, such models are being employed within large scale climate impact, adaption and resilience studies to provide future scenarios of land use change and urban development, facilitating the calculation of the economic impact of future climate hazards and development land use adaption options. However, many spatial CA models only provide information on whether land has undergone a transition from one use/activity to a new one. In the case of urban development, they are unable to provide information on the spatial pattern of new development at an intra-cell level of spatial fidelity. However, in many cases such information is important as the spatial impacts of hazards and the adaption and resilience of new urban development will be dependent on the precise spatial configuration of buildings, roads and urban green space. In this paper an Urban Fabric Generator (UFG) is presented that takes the outputs of an Urban Development Model (UDM) and simulates plausible spatial configurations of buildings, roads and urban green space. The utility of the UFG is demonstrated via two flooding case studies, where generated spatial patterns of urban form are used to parameterise a hydrodynamic pluvial flood model that evaluates the impact of future climate driven rainfall on property damage and the utility of infrastructure investment with regards to improving surface water flood resilience.

Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

Analysis of the spatial distribution patterns of education infrastructure development (A case study of 33 regencies/city in North Sumatra Province)

This study explores the spatial distribution pattern of educational infrastructure development across districts and cities in North Sumatra, identifying significant disparities between urban and rural areas. The study aims to: (1) determine the distribution of educational development across districts and cities, (2) analyze global spatial autocorrelation, and (3) identify priority locations for educational development policies in North Sumatra Province. The methodology includes quantile analysis, Moran’s Global Index, and Local Indicators of Spatial Autocorrelation (LISA) using GeoDa software to address spatial autocorrelation. The results indicate that there are nine areas with a low School Participation Rate Index (SPRI), eleven areas with a low School Facilities and Infrastructure Index (SFII), and eleven areas with a low Regional Education Index (REI). Spatial autocorrelation analysis reveals that SFII shows positive spatial autocorrelation, while SPRI and REI exhibit negative spatial autocorrelation, indicating a high level of inequality between regions. Labuhan Batu Selatan and Labuhan Batu are identified as priorities for the provincial government in overseeing educational development policies.

Detecting Driving Factors from Spatial Spectrums Perspective: A Multilevel Analysis of Homestay Industry in Heterogeneous Heritage Destinations

ABSTRACT Homestay represents a tourism product constrained by space and dependent on regional context. While academic attention has grown toward studying homestay distribution patterns in urban areas, heritage destinations have received limited focus. This study establishes a two-scale analytical framework to explore the spatial evolution patterns and formation mechanism of the homestay industry in diverse heritage destinations, identifying the main factors influencing spatial heterogeneity through geographic detectors. Our findings elucidate several key insights: (a) a consistent annual growth trend, alongside a distinct clustering and spatial autocorrelation in the distribution of homestays across the three types of heritage destinations – natural, cultural, and mixed World Heritage sites; (b) the predominant influence of topography, tourism development levels, and transportation infrastructure in shaping the spatial differentiation of homestays; and (c) socioeconomic and tourism development factors emerging as the primary drivers of this differentiation, while the transportation factor, particularly road mileage, exhibits a heightened significance in natural heritage sites. This study offers insights for planning, marketing, and managing tourism heritage destinations.

Spatial pattern and coupling characteristics analysis of maritime traffic and economic development based on shipping big data

The rapid development of maritime transport and the gradual increase in the number of ports, ships and shipping routes can produce direct economic benefits for regional development, and a profound grasp of the actual situation of maritime transport makes it possible to make practical plans for economic development and thus rationally develop and use marine resources. However, there is a lack of research on maritime transport, so this study is based on the AIS, waterways, routes and ports data in the near-shore sea area of Guangdong Province. Using GIS spatial analysis technology and various mathematical models, we refer to the evaluation method of road traffic dominance. It analyses and proposes the evaluation indexes of maritime traffic advantages, such as the density of maritime traffic network and the proximity of ports. Based on the actual situation of the study regions, the indicators were quantitatively evaluated and analyzed. The economic development level of each region was also quantitatively evaluated using principal component analysis, and the study regions were classified based on the coupling-coordination model. The results show that the coastal area of Guangdong Province is divided into four types of zones according to the coupling-coordination type of zoning criteria. Corresponding development suggestions are put forward for different zones, and the research results provide certain practical guidelines for promoting the benign cycle development of maritime traffic and economy, and have important guiding significance and application value for the organization and safety of maritime traffic.

Chile Niño/Niña in the coupled model intercomparison project phases 5 and 6

The north and central coast of Chile is influenced by El Niño-Southern Oscillation (ENSO) through oceanic and atmospheric teleconnections. However, it also experiences episodic oceanic warmings off central Chile (30°S) lasting a few months that are not necessarily associated with ENSO. These episodes, called “Chile Niño” events, besides their ecological and socio-economical impacts, have also the potential to influence tropical Pacific variability. Here, we investigate how realistically the models in the Coupled Model Intercomparison Project (CMIP, Phases 5 and 6) simulate Chile Niño/Niña (CN) events, and quantify their changes under anthropogenic forcing. Despite limitations of the global models in simulating realistically coastal upwelling dynamics, we show that they simulate reasonably well the observed spatial pattern, amplitude and seasonal evolution of CN events. They however fail to properly represent the positive skewness from observations. The analysis of a sub-group of models (36) that simulate ENSO realistically reveals that CN events increase in amplitude and variance in the future climate with no changes in their frequency of occurence. This is interpreted as resulting from compensating effects amongst changes in remote drivers and local feedbacks. In particular, ENSO variance increases while that of the South Pacific Oscillation decreases. Conversely, we found that while the Wind-Evaporation-SST feedback tends to increase and the coupling between mixed-layer depth and SST weakens, favoring the development of CN events, the thermocline and wind-SST feedbacks decrease. However, only the change in the thermocline feedback is correlated to changes in CN variance amongst the models, suggesting a dominant role of local oceanic stratification changes in constraining the sensitivity of CN to global warming.

A Physics-Informed Auto-Learning Framework for Developing Stochastic Conceptual Models for ENSO Diversity

Abstract Understanding ENSO dynamics has tremendously improved over past decades. The ENSO diversity in spatial pattern, peak intensity, and temporal evolution is however still poorly represented in conceptual ENSO models. In this paper, a physics-informed auto-learning framework is applied to derive ENSO stochastic conceptual models with varying degrees of freedom. The framework is computationally efficient and easy to apply. Once the state vector of the target model is set, causal inference is exploited to build the right-hand side of the equations based on a mathematical function library. Fundamentally different from standard nonlinear regression, the auto-learning framework provides a parsimonious model by retaining only terms that improve the dynamical consistency with observations. It can also identify crucial latent variables and provide physical explanations. This methodology successfully reconstructs the equations of a realistic six-dimensional reference ENSO model based on the recharge oscillator theory from its data. A hierarchy of lower-dimensional models is derived and their representation of ENSO (including its diversity) systematically assessed. The minimum model that represents ENSO diversity is four-dimensional, with three interannual variables describing the western Pacific thermocline depth, the eastern and central Pacific sea surface temperatures (SSTs), and one intraseasonal variable for westerly wind events. Without the intraseasonal variable, the resulting three-dimensional model underestimates extreme events and is too regular. The limited number of weak nonlinearities in the model are essential in reproducing the observed extreme El Niño events and the observed nonlinear relationship between eastern and western Pacific SSTs.

Population Distribution in Guizhou’s Mountainous Cities: Evolution of Spatial Pattern and Driving Factors

Guizhou is a typical mountainous province and is also one of the lowland regions in China that has attracted a population influx. Here, using population density data from 2000 to 2020 as the basic dataset and the coefficient of variation method and standard deviation ellipse analysis, we investigated the spatial characteristics across different years. The results show: Firstly, Guizhou’s population has a distinct spatial distribution, characterized by a lower population density in the southeast and a higher density in the northwest as well as an increasing polarization of population concentration toward the centers of prefecture-level cities and provincial capitals. Fluctuations in population density resemble a central siphon effect, which is particularly pronounced in the provincial capital and show a significant gravitational pull. Secondly, the coefficient of variation in population density across Guizhou’s counties is spatially divided by Guiyang, showing higher values in the east and lower values in the west. Furthermore, the ellipse of the standard deviation of population density is gradually shrinking, indicating an increasingly concentrated population distribution. Thirdly, the explanatory power of the population and socio-economic systems on the population distribution in Guizhou is significantly greater than that of the natural systems. Population distribution and migration patterns have shifted from purely “economic driven” to coexisting with “economic and comfort-oriented” trends, and there is an urgent need to improve the comfort level of public services as a typical supply, in order to boost Guizhou’s population attraction.

Study on the influencing factors of the evolution of space pattern based on principal component analysis in Duolun County

In order to explore the influencing factors of spatial and temporal evolution of production-living-ecological space in Beijing Tianjin sandstorm source area, the remote sensing images, natural environment and socio-economic data of Duolun County in Inner Mongolia from 2000 to 2020 were selected, and the spatial auto-correlation model and principal component analysis model were used to analyze the spatial pattern evolution and influencing factors of production-living-ecological space. The results show that: (1) the function of production space decreases slightly, and the degree of spatial agglomeration decreases; (2) The function of living space rose slightly, and its spatial agglomeration degree showed an upward trend; (3) The ecological spatial function showed a slow upward trend, and its spatial agglomeration degree increased; (4) The spatial pattern of production-living-ecological space is characterized by “high in the southwest and low in the northeast”; (5) Precipitation has the greatest impact on the spatial evolution of the production-living-ecological space. The distance from the main residential areas, per capita GDP, the distance from the main roads and the distance from the main waters have strong explanatory power on the spatial evolution of the production-living-ecological space.

Characteristics of Spatial–Temporal Evolution of Carbon Emissions from Land Use and Analysis of Influencing Factors in Hubao-Eyu Urban Agglomerations, China

Exploring the dynamic relationship between land use change and carbon emissions is of great significance in promoting regional low-carbon sustainable development and “dual-carbon”. We reveal the characteristics of the evolution of spatial temporal patterns of land use carbon emissions at the county scale in resource-based urban agglomerations over the past 20 years and the analysis of influencing factors. The research results show that: (1) In terms of spatial and temporal characteristics, from 2000 to 2020, net carbon emissions from land use showed an overall upward trend, with construction land being the main source of increased carbon emissions; the spatial distribution pattern of carbon emissions shows a trend of further clustering of centers in the northeast-southwest direction, which mainly occurs in areas rich in coal resources; the economy-contributive coefficient is increasing, but ecological support coefficients are decreasing; (2) In the analysis of influencing factors, land use structure is the most significant factor contributing to the increase of carbon emissions, followed by economic level, while land use intensity per unit of GDP is the most significant factor inhibiting the increase of carbon emissions. The results of the study provide a useful reference for resource-based urban agglomerations to formulate regionally appropriate emission reduction strategies and realize low-carbon sustainable development.

Progress in Remote Sensing and GIS-Based FDI Research Based on Quantitative and Qualitative Analysis

Foreign direct investment (FDI) by transnational companies (TNCs) is the primary indicator of urban globalization. The initial publication on the topic of remote sensing and geographic information system-based urban globalization research was published in 1981. However, the number of publications on this topic remains relatively limited. Despite some advances in the field in recent decades, there is currently no comprehensive review of related research, and it is not clear how the different perspectives and views have been developed. Furthermore, previous literature reviews on the utilization of remote sensing and GIS technology in urban development have predominantly employed quantitative methodologies, which has resulted in a paucity of qualitative analysis. In order to address these shortcomings, this paper employs a mixed-methods approach, integrating quantitative and qualitative analyses. This entails the utilization of a combination of the scientometric method and a qualitative literature review method. The findings are as follows: (1) The number of publications is still relatively limited, and research in this area is still in its infancy. (2) Some of the articles are evidently interdisciplinary in nature. (3) Progress has been made in terms of geographic visualization of FDI, macro-environmental research at different scales, global value chains, the micro-geography of TNCs, and globalization of the geo-information industry. (4) The spatial and temporal development pattern, location, and accessibility of FDI have constituted a significant area of research interest in the past. Similarly, the relationships between FDI and regional development, urban growth, land use, and environmental change have emerged as prominent research directions. China’s Belt and Road Initiative is an emerging popular topic. (5) In recent years, there has been a notable increase in the number of papers employing multi-source data and multi-method approaches. (6) The extent of research collaborations between countries is relatively limited, with the majority of such collaborations occurring within the past five years. Finally, based on these research findings, this paper suggests future research directions.

Quantifying the influencing factors of the thermal state of permafrost in Northeast China

In Northeast China, permafrost is controlled by a combination of biotic, climatic, physiographic, and anthropogenic factors. Due to the complexity of these governing or influencing factors, it is challenging to exactly describe the features of the Xing’an permafrost in Northeast China. By integrating remote sensing (RS) and geographic information system (GIS) technologies, we have quantified these influencing factors of permafrost changes as an important approach to understanding the nature of latitudinal and mountain permafrost in Northeast China at the mid-latitudes in the Northern Hemisphere. In this study, we combine Geographical Detector (Geodetector) model, trend analysis, and multi-source RS data to quantify the controlling or influencing factors of permafrost thermal state and of permafrost changes, and explain the interactions among permafrost, environment, and climate. The results indicate that, at the regional scale, changes in the thermal state of permafrost are primarily governed or influenced by mean annual land surface temperature (MALST), precipitation, and snow cover duration (SCD). Topographic factors also affect the spatial patterns of permafrost development. Additionally, in the context of climate warming, the insulation effect of snow cover on the permafrost is weakened, or has been weakening. Moreover, the interactive effects among various factors significantly enhance their explanatory power for changes in the thermal state of permafrost. The study emphasizes the complexity of the interactions among permafrost, climate, and the environment, and highlights the significance of understanding these interactions for regional socio-economic development, ecological management, carbon pool stabilization, and research on future climate change in Northeast China.

The Spatial Pattern Evolution of Urban Innovation Actors and the Planning Response to Path Dependency: A Case Study of Guangzhou City, China

The capacity for urban innovation is a significant symbol of contemporary urban development. In order to promote sustainable urban innovation, it is crucial to match and optimize innovation spaces, actors, and their behavioral needs. Based on the data from patent inventions, which are commonly used to represent urban innovation, in this study, we investigated the formation mechanism of Guangzhou’s innovation pattern and its characteristics from 1990 to 2020 using Geographic Information System (GIS) technology. The results indicated that Guangzhou’s innovation spaces developed a center-radiation structure of “two districts and seven cores”. We investigated the path dependence of spaces, actors, and behavioral needs by examining the interaction between the innovation space layout and behavioral needs. The findings provide theoretical support for the city’s sustainable development in terms of innovation in the future.

Spatial development of agricultural production in Russia: The comparative advantages approach

The territorial planning approach to allocating productive forces is based on the fact that territories have competitive advantages in producing specific products. However, in agriculture, the advantages principle cannot be used to shape the allocation patterns, due to a variety of intervening factors, such as the climatic and environmental conditions for agricultural production and the quality of land and availability of water. In the case of Russia, one of the most diverse countries in terms of the territorial disparities in agricultural production, this study examines the location and development patterns of the agricultural sector. The study identifies the competitive advantages of territories by comparing localization of agricultural production, production costs, performance, and profitability of agricultural producers, as well as prices of agricultural products in 78 different administrative regions in Russia. The study reveals which regions have more advantageous conditions for over-concentrating energy capacities, labor resources, fixed capital, and investments. However, at a certain point, over-concentrated production forces can lead to a deterioration in the performance of farmers due to an increase in capital intensity. Therefore, countries with significant regional differences in agricultural production should adjust their spatial development patterns according to the parameters of territories’ comparative advantages.

Spatio-Temporal Differentiation of Non-Grain Production of Cropland and Its Influencing Factors: Evidence from the Yangtze River Economic Belt, China

Food security is important to guarantee national security and people’s livelihoods, but the increasingly serious problem of non-grain production (NGP) on croplands has exacerbated the risk of food security and directly affected the sustainable development of the national economy and society. This study adopted 130 cities (states) in the Yangtze River Economic Belt as the research units and used ArcGIS10.8, GeoDA1.22, and Origin2022 software and spatial autocorrelation, standard deviational ellipse, and GeoDetector methods to conduct analyses. This study explored the spatial evolution patterns and factors influencing cropland NGP in the Yangtze River Economic Belt. The results show, firstly, that the NGP rate of cropland in the Yangtze River Economic Belt increased from 35.85% in 2006 to 38.62% in 2022. The number of cities (states) with mild and moderate NGP decreased, while the number of cities (states) with severe NGP increased significantly. Secondly, the spatial distribution of the rate of cropland NGP in the Yangtze River Economic Belt had a strong positive correlation, with “high–high agglomeration” tending to be dispersed, “low–low agglomeration” tending to be concentrated, and the overall trajectory of the center of gravity migrating from the northeast to the southwest. Thirdly, the single-factor detection found that the per capita food possession, slope, elevation, and average annual precipitation had strong explanatory power regarding the spatial difference in cropland NGP in the Yangtze River Economic Belt, and the interaction of any two influencing factors showed nonlinear enhancement. The results of this study can help to precisely identify the spatial and temporal evolution characteristics of cropland NGP in the Yangtze River Economic Belt, which is of great significance for supporting the country in controlling the risk of NGP cultivation, promoting the sustainable development of the Yangtze River Economic Belt, and guaranteeing food security.

Analysis of Urbanization-Induced Land Subsidence in the City of Recife (Brazil) Using Persistent Scatterer SAR Interferometry

The article addresses anthropogenic and geological conditions related to the development of soil subsidence in the western zone of Recife (Brazil). Over the past 50 years, human activity has intensified in areas previously affected by soft soils (clay, silt, and sandstone) resulting in subsidence due to additional loads (landfills and constructions). The duration of the settlement process can be significantly influenced by the specific characteristics of the soil composition and geological conditions of the location. This work presents, for the first time, accurate InSAR time series maps that describe the spatial pattern and temporal evolution of the settlement, as well as the correlation with the geological profile, and validation with Global Navigation Satellite System (GNSS) data. Persistent Scatterer Interferometry (PS-InSAR) was employed in the analysis of Single Look Complex (SLC) images generated by 100 ascending COSMO-SkyMed (CSK) and 65 PAZ (32 ascending, and 33 descending) from the X-band, along with 135 descending Sentinel-1 (S1) acquisitions from the C-band. These data were acquired over the period from 2011 to 2023. The occurrence of subsidence was identified in several locations within the western region, with the most significant displacement rates observed in the northern, central, and southern areas. In the northern region, the displacement rates were estimated to be approximately −20 mm/year, with the Várzea and Caxangá neighborhoods exhibiting the highest rates. In the central region, the displacement rates were estimated to be approximately −15 mm/year, with the Engenho do Meio, Cordeiro, Torrões, and San Martin neighborhoods exhibiting the highest rates. Finally, in the southern region, the displacement rates were estimated to be up to −25 mm/year, with the Caçote, Ibura, and Ipsep neighborhoods exhibiting the highest rates. Additionally, east–west movements were observed, with velocities reaching up to −7 mm/year toward the west. These movements are related to the lowering of the land. The study highlights that anthropogenic effects in the western zone of Recife contribute to the region’s vulnerability to soil subsidence.

Heterogeneous changes in global glacial lakes under coupled climate warming and glacier thinning

Global glacial lakes have expanded markedly in recent decades, accompanied by many catastrophic glacial lake outburst floods. However, our understanding of glacial lake evolution and connections with global warming and glacier thinning remains insufficient due to fragmented studies and a lack of standardized mapping. Here we evaluated the glacial lake changes and their drivers, taking care to accurately map glacial lakes while excluding non-glacier fed lakes in the same geographic area. In 2020, a total of 71,508 glacial lakes were identified worldwide, covering an area of 21,770.9±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document}544.2 km2. The total volume of 1280.6±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document}354.1 km3 is equivalent to a sea-level rise of 3.45 mm. Since the 1990s, glacial lake numbers, areas, and volumes increased by 54%, 11%, and 9%, respectively. The spatial evolution patterns align well with resolved large-scale, decadal glacier mass changes. Glacier-fed lakes extensively expanded due to glacier retreat and melting, while ice-dammed lakes were primarily affected by thinning ice dams, resulting in heterogeneous changes and underestimated outburst potential. Under the sustained glacier degradation scenario, the results of this study have implications for further evaluation of glacial lake water resources and outburst risks on a global scale.

Исследование пространственно-временных закономерностей развития сейсмичности в подработанной толще массива на Расвумчоррском руднике

Studies of the spatial and temporal patterns of seismic activity development in the undermined rock mass have been performed at the Rasvumchorr Mine. The main cluster associated with generation of rock caving in the overhang part has been identified. It is established that caving was developing gradually from the depth of the rock mass towards the surface. The results are presented of estimating the quantitative parameters of the seismic process in the undermined rock stratum, i.e. the energy index and the cumulative apparent volume, which reflect the stress changes in the rock mass. Ranges of the stable state of the rock mass, when the overhang rocks do not cave, as well as the ranges of active fracturing and caving of the overhang rocks have been identified. Changes in these parameters for the studied area of the deposit reflect the loading and strength degradation stages in the rock mass. The results of this study correlate well with the data on actual caving of the undermined rock strata. This work is a continuation of the authors' research into the problem of identifying spatial and temporal patterns in the development of undermined rock strata cavings in the tectonically stressed Khibiny massif.

Land use evaluation and capacity assessment for sustainable urban physical development: case of study Ahvaz city

Land use evaluation and capacity assessment for sustainable urban development in Ahvaz city has become crucial due to rapid urban expansion negatively impacting current and future development. Urban population growth and resource demands have led to a decline in environmental quality. Over the past decades, Ahvaz has identified potential areas for future expansion until 2030. This study assesses the city's physical expansion and spatial development patterns, aiming to identify optimistic future development scenarios. Emphasizing balanced and equitable development, the study focuses on suitable areas, environmental preservation, pollution reduction, and mitigating urban hazards . Various factors, including land use, soil type, vegetation density, landform, groundwater level, geology, and distances from railways, industries, roads, hinterland cities, water networks, faults, and the city itself, are considered. Three decision-making methods—logic, Analytical Hierarchy Process (AHP), and integrated Fuzzy-AHP—are applied to map urban physical development. Fuzzy logic, AHP models, Expert Choice, Arc GIS software, and a land fit zoning map are used for data analysis. The AHP assesses the factors' relative importance, followed by standardization based on fuzzy set theory. Criteria factors are then combined using AHP and fuzzy logic to generate a land suitability map. The study identifies five zones for Ahvaz's future development—very high, high, medium, low, and very low—based on their physical expansion potential. The third scenario, supported by maps, indicates favorable conditions for future development in the northwest and southwest zones, considering environmental characteristics, infrastructure, and land continuity.