Cellular automata for simulating land use changes based on support vector machines

The extent of anthropogenic land use in watersheds determines the amount of pollutants discharged to streams. This indirectly and directly affects stream water quality and biological health. Most studies have therefore focused on ways to reduce non-point pollution sources to streams from the surrounding land use in watersheds. However, the mechanistic pathways between land use and the deterioration of stream water quality and biological assemblages remain unclear. This study estimated a structural equation model (SEM) representing the impact of agricultural and urban land use on water quality and the benthic macroinvertebrate index (BMI) using IBM AMOS in the Nam-Han river systems, South Korea. The estimated SEM showed that the percent of urban and agricultural land in the watersheds significantly affected both the water quality and the BMI of the streams. Specifically, a higher percent of urban land use had directly increased the biochemical oxygen demand (BOD) and total phosphorus (TP), and deteriorated the BMI of streams. Similarly, higher proportions of agricultural land use had also directly increased the BOD, total nitrogen (TN), and total phosphorus (TP) concentrations, and lowered the BMI of streams. In addition, it was observed that the percent of urban and agricultural land use had indirectly deteriorated the BMI through increased BOD. However, we were not able to observe any significant indirect effect of the percent of urban and agricultural land use through increased nutrients including TN and TP. These results indicate that increased urban and agricultural land use in the watersheds had directly and indirectly affected the physicochemical characteristics and benthic macroinvertebrate communities in streams. Our findings emphasize the need to develop more elaborate environmental management and restoration strategies to improve the water quality and biological status of streams.

The accessibility provided by the transportation system plays an essential role in driving urban growth and urban functional land use changes. Conventional studies on land use simulation usually simplified the accessibility as proximities and adopted the grid-based simulation strategy, leading to the insufficiencies of characterizing spatial geometry of land parcels and simulating subtle land use changes among urban functional types. To overcome these limitations, an Accessibility-interacted Vector-based Cellular Automata (A-VCA) model was proposed for the better simulation of realistic land use change among different urban functional types. The accessibility at both local and zonal scales derived from actual travel time data was considered as a key driver of fine-scale urban land use changes and was integrated into the vector-based CA simulation process. The proposed A-VCA model was tested through the simulation of urban land use changes in the City of Toronto, Canada, during 2012–2016. A vector-based CA without considering the driving factor of accessibility (VCA) and a popular grid-based CA model (Future Land Use Simulation, FLUS) were also implemented for comparisons. The simulation results reveal that the proposed A-VCA model is capable of simulating fine-scale urban land use changes with satisfactory accuracy and good morphological feature (kappa = 0.907, figure of merit = 0.283, and cumulative producer’s accuracy = 72.83% ± 1.535%). The comparison also shows significant outperformance of the A-VCA model against the VCA and FLUS models, suggesting the effectiveness of the accessibility-interactive mechanism and vector-based simulation strategy. The proposed model provides new tools for a better simulation of fine-scale land use changes and can be used in assisting the formulation of urban and transportation planning.

We explored land use, fish assemblage structure, and stream habitat associations in 20 catchments in Opequon Creek watershed, West Virginia. The purpose was to determine the relative importance of urban and agriculture land use on stream biotic integrity, and to evaluate the spatial scale (i.e., whole-catchment vs riparian buffer) at which land use effects were most pronounced. We found that index of biological integrity (IBI) scores were strongly associated with extent of urban land use in individual catchments. Sites that received ratings of poor or very poor based on IBI scores had > 7% of urban land use in their respective catchments. Habitat correlations suggested that urban land use disrupted flow regime, reduced water quality, and altered stream channels. In contrast, we found no meaningful relationship between agricultural land use and IBI at either whole-catchment or riparian scales despite strong correlations between percent agriculture and several important stream habitat measures, including nitrate concentrations, proportion of fine sediments in riffles, and the abundance of fish cover. We also found that variation in gradient (channel slope) influenced responses of fish assemblages to land use. Urban land use was more disruptive to biological integrity in catchments with steeper channel slopes. Based on comparisons of our results in the topographically diverse Opequon Creek watershed with results from watersheds in flatter terrains, we hypothesize that the potential for riparian forests to mitigate effects of deleterious land uses in upland portions of the watershed is inversely related to gradient.

Though the urban land use spatial dynamic simulation and forecasting based on cellular automata (CA) model have achieved remarkable progress, the CA model still has some problems and drawbacks in forecasting urban land use changes. In view of the deficiencies of traditional urban CA, an improved CA model based on spatial dynamic data mining and random forecast is proposed in this paper, which establishes an operable CA method to forecast and simulate the discrete status attribute. This improved CA model is examined in analyzing the urban land use structure changes in Jinan 2002-2006 and testified both feasible and effective. Based on the remote sensing images in Jinan 2002 and 2006, the urban land use spatial structures are classified into five types, commercial land, residential land, education facility, industrial land and the other. With the improved CA model, the urban land use framework in Jinan in 2010 was calculated, the result of which can be used as a reliable reference information for the following urban land use planning.

Revealing the spatial–temporal pattern and convergence characteristics of urban land use efficiency has important guiding significance for adjusting and optimizing the regional urban land use structure. Taking the provincial units in China as the research object, the urban land use efficiency evaluation system considering the unexpected output was constructed, and the slack-based measure (SBA) model was used to quantitatively measure the provincial urban land use efficiency from 2000 to 2020. The exploratory spatial data analysis (ESDA) model and spatial convergence index were combined to reveal the spatial–temporal pattern and convergence characteristics of provincial urban land use efficiency. The results showed that the provincial urban land use efficiency has been continuously improving, with regional differences as shown in eastern region > northeast region > central region > western region. Moran’s I of provincial urban land use efficiency was greater than 0, there was a positive spatial correlation, and the clustering feature became increasingly significant. The spatial form of LISA was characterized by “small agglomeration and large dispersion”; the H(High)-H(High) type was clustered in the Yangtze River Delta and Beijing–Tianjin–Hebei, while the L(Low)-L(Low) type was clustered in Xizang, Xinjiang and Qinghai. There was a σ convergence in provincial urban land use efficiency, and there was significant absolute β convergence and conditional β convergence of provincial urban land use efficiency. The results showed that the differences in provincial urban land use efficiency were shrinking, showing a “catch-up effect”, and converging to their respective stable states over time. Based on the analysis of the spatial–temporal pattern and convergence characteristics of provincial urban land use efficiency in China, we could provide a direction for the optimization of the urban land use structure and efficiency improvement in China, in order to narrow the differences in urban land use efficiency in China’s four major regions.

This study analyzes the spatial and temporal characteristics of urban land use structure of more than 18,000 small towns in China by using the National Urban Land Use Survey Data between 2009 and 2013. It finds that the urban land area of small towns expanded rapidly during the research period. The spatial pattern of urban land use structure in small towns exhibits significant regional differences. Small towns in developed coastal regions, particularly those located in global urban areas, have a higher urban land use scale relative to small towns in central and western regions. However, the urban land use scale of small towns located in less developed inland areas has grown faster. The spatial distribution of urban stock land in small towns is mainly influenced by demographic and socio-economic factors. However, the spatial distribution of new urban land is primarily influenced by the land supply policy, which is tilted toward the central and western regions. In terms of structure, the proportion of production and living land in small towns is high, while the proportion of public services and facilities land is low. This trend is being strengthened, with commercial service land and industrial, mining, and storage land becoming the major drivers for the rapid rise of urban land in small towns. The allocation of construction land quotas should be consistent with the direction of population movement and the demands of socioeconomic development.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

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Exploring the impact of urban form on urban land use efficiency under low-carbon emission constraints: A case study in China's Yellow River Basin

Rapid urbanization processes have been primarily attributed to tremendous changes in the exploitation manner and intensity of urban land use. The limited and nonrenewable characteristics of urban land resources prompted to develop and utilize urban land use rationally, which would push the sustainable development of urban land use. The evaluation of urban land use suitability contributed to identify suitable sites and better develop as well as make full use of urban land resources, consequently, which drived the urban land use structure to achieve the optimal allocation. In this paper, taking Jinan City as an example, which introduced Analytical hierarchical analysis (AHP) and moving windows in combination with conversion matrices based on SPOT and ALOS high resolution remote sensing data, supported by ArcGIS integrated with Fragstats software platform. The research method embedded landscape metrics into AHP as assessment factors. Additonally, the weights of all evaluation factors were determined by Analytical hierarchical analysis. Ultimately, single factor assessment on all urban land use types was carried out, which made up of suitability layers to weighted overlay using GIS technology. At the same time, the paper made suitability evaluation and rating for urban land use status quo and analyzed the variable changes in urban land use suitability between different years combined with natural constrains and economic regulation elements, during the years from 1996 to 2009 in Jinan City. The results will be valuable to urban and regional planners to evaluate the existing urban policies and consequently develop appropriate responses or strategies for the urban sustainable development, and at the same time provide significant reference opinions about thoughtful arrangement of urban land sources.

Different conflicting objectives are used in urban land use optimization problems. The maximization of social benefit is one of the important objectives in urban land use optimization problems. Many researchers have used different methods to measure social benefits in land use optimization. Studies show that there is no established method to measure social benefit in the urban land use allocation game. Against this background, this study aims to (a) identify the appropriate indicators as a measure of social benefit, and (b) propose a composite index to measure social benefit in urban land use optimization problems. Based on the literature review and expert opinion, this study identifies four indicators as a measure of social benefit. These are spatial compactness, land use compatibility, land use mix, and evenness of population distribution. Using the weighted sum approach, this study proposes a composite social benefit index (SBI) to measure social benefit in urban land use allocation/optimization problems and planning. The study suggests that spatial compactness is the most influential indicator to the SBI, but the most critical indicator is compatibility, whose 11.60% value reduction from 0.5 alters the decision of choice. Finally, the proposed method was applied in Rajshahi city in Bangladesh. The result suggests the potential of using SBI in the land use allocation problem. It is expected that the proposed social benefit index (SBI) will help the land use optimization and planning and will be helpful for decision makers.

Urban expansion and Land Use Land Cover (LULC) change pose critical challenges for sustainable urban planning and risks to food security. This study analyzes multi-temporal Landsat imagery from 1990 to 2020 for five major cities, Islamabad, Karachi, Lahore, Peshawar, and Quetta in Pakistan using the Smile Random Forest (SRF) algorithm within the Google Earth Engine (GEE) platform. Classification accuracies ranged from 86-90%, with Cohen's Kappa coefficients between 0.86 and 0.92, demonstrating substantial to almost perfect agreement. The results reveal significant increases in urban areas: Karachi expanded from 12.4% in 1990 to 41.3% in 2020, Lahore from 15.2% to 39.8%, and Islamabad from 9.1% to 28.6%, primarily at the expense of vegetation and barren land. Elevation also influenced LULC dynamics, with higher-altitude cities like Quetta exhibiting slower but more resource-constrained urban development. A change matrix quantified class transitions, showing that urban land predominantly expanded into agricultural and vegetative land areas, raising concerns about long-term food security. Future projections using the MOLUSCE-ANN model indicate continued urban expansion by 2030, particularly in Karachi and Lahore, where built-up areas are projected to exceed 45% of total land cover. Compared with previous studies that employed Classification and Regression Trees (CART), Support Vector Machine (SVM), and CA-Markov models in single-city or short-term contexts, this study provides a multi-decadal, multi-city analysis with predictive capacity and robust validation, offering novel insights into Pakistan's urbanization trajectory. By linking LULC change to the implications for natural resources and food security, the study contributes actionable evidence to support actions against disaster risk reduction, sustainable development and SDGs-aligned with urban policies.

Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America

Urban Land use changes, measurements, and the analysis of rate trends of growth would help in resources management and planning, etc. In this study, we analyze the urban change dynamics using a support vector machine model. This method derives the urban and rural land-use change and various components, such as population growth, built-up areas, and other utilities. Urban growth increases rapidly due to exponential growth of population, industrial growth, etc. The population growth also affects the availability of various purposes in its spatial distribution. In this present study, we carried out using multi-temporal satellite remote sensing data Landsat MSS (Multispectral scanner), ETM+ (Enhanced thematic mapper), OLI (Operational land imager) for the analysis of urban change dynamics between years 1980-1990, 1990-2003, 2012-2020 in Kanpur Nagar city in the state of Uttar Pradesh in India. In our study, we used SVM (Support Vector Machine) Model to analyze the urban change dynamics. A support vector machine classification technique was applied to generate the LULC maps using Landsat images of the years 1980, 1990, 2003, and 2020. Envi and ArcGIS software had used to identify the land cover changes and the applying urban simulation model (CA- Markov model) in Idrisi selva edition 17.0 software. The LULC maps of 2003 and 2020 were used to simulate the LULC projected map for 2050 using (Cellular automata) CA- Markov based simulation model.

In urban land use optimization problems, different conflicting objectives are applied. One of the most significant goals in urban land use optimization problems is to maximize environmental benefits. To quantify environmental benefits in land use optimization, many researchers have employed a variety of methodologies. According to previous studies, there is no standard approach for calculating environmental benefits in urban land use allocation problems. Against this background, this study aims to (a) identify indicators of environmental benefits and (b) propose a novel composite index to measure environmental benefits in urban land use optimization problems. This study identified four indicators as a measure of environmental benefits based on a literature assessment and expert opinion. These are spatial compactness, land surface temperature, carbon storage, and ecosystem service value. In this work, we proposed a novel composite environmental benefits index (EBI) to quantify environmental benefits in urban land use allocation problems using an ordered weighted averaging (OWA) method. The study results showed that land surface temperature (LST) is the most influential indicator of environmental benefit while carbon storage is the least important factor. Finally, the proposed method was applied in Rajshahi city in Bangladesh. This study identified that, in an average-risk decision, most of the land (64.55%) of the study area falls within the low-environmental-benefit zone due to a lack of vegetated land cover. The result suggests the potential of using EBI in the land use allocation problem to ensure environmental benefits.