Prediction Of Urban Expansion Research Articles

Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development

Urban expansion, a defining feature of the contemporary era, presents both challenges and opportunities for sustainable development [...]

Sustainable agricultural lands management by analyzing and predicting urban growth—a case study of Mitidja Plain, Algeria

The suitability of agricultural lands is basic data for both sustainable agricultural planning and optimal orientation of future urbanization projects. Indeed, unplanned urban expansion leads to many social and ecological problems. The prediction of urban expansion will strongly help the decision makers and local authorities to create a suitable future urban planning while preserving the regional ecosystem and especially the agricultural lands. Using multitemporal satellite images acquired in 1997, 2001, and 2013 over the Mitidja plain in Algeria, the aim of this study is the analysis of the land use and land cover change (LULCC) spatio-temporal distributions from 1997 to 2013 to model and then predict the future LULCC in 2025. The cellular automata Markov chain model (CA-Markov) that we developed is used for LULCC prediction with a focus on urban sprawl and agricultural lands degradation over the Mitidja plain. Once calibrated and validated, the CA-Markov model generates land suitability maps that indicate the best locations for urban expansion while safekeeping the agricultural lands. The results are interesting and present valuable tools for urban planners and policymakers to ensure the sustainability management of agricultural areas over the study region.

Assessment and Prediction of Urban Expansion Using CA-Based SLEUTH Urban Growth Model: A Case Study of Kolkata Metropolitan Area (KMA), West Bengal, India

Assessment and Prediction of Urban Expansion Using CA-Based SLEUTH Urban Growth Model: A Case Study of Kolkata Metropolitan Area (KMA), West Bengal, India

Prediction of urban expansion by using land cover change detection approach

Bangladesh has been experiencing rapid urban expansion over the last few decades, contributing much to the region's land cover transition into the urban area. The study aims to employ geospatial modeling techniques to investigate land cover scenarios in the Pabna municipality of Bangladesh. Therefore, the research examined Cellular Automata Markov and Multi-Layer Perceptron Markov models to detect land cover for 2023 and 2028. The study selected the Multi-Layer Perceptron Markov as the best fit model over Cellular Automata Markov based on the highest kappa value. The result reveals that urban area has increased from 3.39 to 8.79 km2 over 1998–2018. Urban expansion and its surrounding area are primarily occurring towards the northeast directions. However, the extent of urban build-up land will grow from 3.39 km2 in 1998 to 11.01 km2 in 2023 and 12.44 km2 in 2028. Moreover, the future land cover map delineated that the urban growth will expand in the northeast part of the study area. The scenario shown in this paper would assist urban planners in quantifying the urban growth under different land cover features and thus preparing proper strategic measures.

Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China

Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from 1992 to 2018, and evaluated the spatial and temporal characteristics of the built-up regions in terms of the area, expansion speed, and main expansion direction. The backpropagation (BP) neural network and autoregressive integrated moving average (ARIMA) model were used to predict the area of the built-up regions from 2019 to 2026. The model predictions were based on the GDP, ratio of the secondary industry output to the GDP, ratio of the tertiary industry output to the GDP, year-end urban population, and urban road area. The results demonstrated that the built-up area and expansion speed of the central cities in the eastern part of the Hunan province were significantly higher than those in the western part. The main expansion directions of the 14 central cities were east and south. The urban road area, year-end urban population, and GDP were the main driving factors of the expansion. The urban expansion model based on the BP neural network provided a high prediction accuracy (R = 0.966). It was estimated that the total area of urban built-up regions in the Hunan province will reach 2463.80 km2 by 2026. These findings provide a new perspective for predicting urban areas rapidly and simply, and it also provides a useful reference for studying the spatial expansion characteristics of central cities and formulating a sustainable urban development strategy during the 14th Five-Year Plan of China.

A novel CNN-LSTM-based approach to predict urban expansion

Time-series remote sensing data offer a rich source of information that can be used in a wide range of applications, from monitoring changes in land cover to surveillance of crops, coastal changes, flood risk assessment, and urban sprawl. In this paper, time-series satellite images are used to predict urban expansion. As the ground truth is not available in time-series satellite images, an unsupervised image segmentation method based on deep learning is used to generate the ground truth for training and validation. The automated annotated images are then manually validated using Google Maps to generate the ground truth. The remaining data were then manually annotated. Prediction of urban expansion is achieved by using a ConvLSTM network, which can learn the global spatio-temporal information without shrinking the size of spatial feature maps. The ConvLSTM based model is applied on the time-series satellite images and the results of prediction are compared with Pix2pix and Dual GAN networks. In this paper, experimental results are conducted using several multi-date satellite images representing the three largest cities in Saudi Arabia, namely: Riyadh, Jeddah, and Dammam. The evaluation results show that the proposed ConvLSTM based model produced better prediction results in terms of Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy as compared to Pix2pix and Dual GAN. Moreover, the training time of the proposed architecture is less than the Dual GAN architecture.

ESTIMATING BUILT-UP AREA CHANGE IN ULAANBAATAR CITY, MONGOLIA

Abstract. Long-term urban built-up area changes of the Ulaanbaatar city has accelerated since the 1950s and due to rapid urbanization most of the Mongolian population, or about 68%, live in urban areas. The systematic understanding of urban land expansion is a crucial clue for urban land use planning and sustainable land development. Therefore, in this paper, we used a Markov chain model and cellular automata (CA) to simulate and predict current and future built-up areas expansion is Ulaanbaatar. Landsat imageries (Landsat TM 5, Landsat ETM 7 and Landsat OLI 8) of 1988, 1998, 2008, and 2017 were used to derive main land use classes. Clark Lab’s (Clark University) Geospatial Monitoring and Model software had been used for the urban expansion prediction. The results are innovated to comparable to validate with other study results by using a different kind of methods. Built-up area expansion modeled and predicted 2028’s trends based on a historical expansion of the Ulaanbaatar city between 1988 and 2017, which are prepared according to input model requirements. The built-up area was 7282 hectares (ha) in 1988 and has expanded to 31144 ha in 2017. The built-up area growth of the Ulaanbaatar city has reached 4.3 times over the past 30 years, and from 2017 to 2028 the expansion of the built-up area will be 1.5 times. A comparison of urban expansion from 1988 to 2017 has revealed a rapid built-up invasion to the previous areas of agriculture, grassland, and forest. Simulation performance of Markov chain with the cellular automata model can be used for an improvement in the understanding of the urban expansion processes while allowing helpful for better planning of Ulaanbaatar city, as well as for other rapidly developing towns of Mongolia.

REMOTE SENSING AND MULTIVARIATE LOGISTIC REGRESSION MODEL FOR THE ESTIMATION OF URBAN EXPANSION (CASE OF DARKHAN CITY, MONGOLIA)

Abstract. Urban expansion and land use and land cover change (LUCC) studies are a key knowledge of efficient local governance and urban planning and a lot contributing to the future sustainable development of the city. The main goal of the paper is to model a future urban spatial expansion by 2029 and 2039 of Darkhan city using Landsat TM satellite imagery (land use and cover change map of 1999, 2009, and 2019) and multivariate logistic regression model. Clark Lab’s (Clark University) IDRISI & TerrSet software applied for the urban expansion prediction and the correlation between expansion and driving factors. On account of multivariate logistics regression modelling, eight physical factors influencing urban expansion identified to predict urban expansion based on USGS Landsat TM imageries (Landsat Multispectral Scanner with 60 m resolution). The regression statistic accounted for the probability of future urban expansion was positive. Overall, the LUCC has estimated the transition of natural cover to the impervious surface in Darkhan city. Our result estimates an increase in the built-up area and slum area during the period 1999–2009 and 2009–2019, represents LUCC was characterized by an external transformation from natural to urban area. According to the future urban growth prediction, the urban area would be significantly spread into the open space and natural vegetation area. The main findings stated here are that Darkhan city is expanding in an unsystematic way, even though the urban growth has not been analysed in detail and has a bad case of urban unregulated sprawl.

Research on dynamic simulation and prediction of urban expansion based on SLEUTH model

Research on dynamic simulation and prediction of urban expansion based on SLEUTH model

Research on dynamic simulation and prediction of urban expansion based on SLEUTH model

In order to improve the accuracy of traditional prediction methods in the analysis of urban expansion, the method for dynamic simulation of urban expansion based on SLEUTH (slope, land use, urban area, traffic, exclusion, hillshead) model is proposed. The method uses processed urban spatial data to establish a SLEUTH model of dynamic urban expansion, and it analyses the driving factors of urban expansion that are used as the influencing factor of urban expansion simulation. Through dynamic simulation, the law of urban expansion is derived and input SLEUTH model as the law of free expansion. The influencing factors are input into the model, and the prediction results are obtained with the law of free expansion. The experimental result shows that the new method has a prediction error rate of only 3.5% with the low delay and the strong reliability, compared with the traditional urban expansion prediction method.

공간 DBMS 기반의 국토환경성평가지도를 활용한 도시 확장 예측

공간 DBMS 기반의 국토환경성평가지도를 활용한 도시 확장 예측

Urban expansion modeling using an enhanced decision tree algorithm

Decision tree (DT) algorithms have been applied for classification and change detection in various geospatial studies and more recently, for urban expansion and land use/land cover (LULC) change modeling. However, these studies have not elaborated on specification of DT algorithms regarding data sampling, predictor variables, model configuration, and model evaluation. The focus of this study is to explore several balanced and unbalanced sampling methods, various predictor variables, different configurations of stopping rules, and reliable evaluation metrics to enhance the performance of classification and regression tree (CART), one of the most efficacious DT algorithms, for urban expansion modeling. The implementation of the model in the Triangle Region, North Carolina (NC) State, over the period of 2001 to 2011 demonstrates a striking performance with the training accuracy of 97%, the testing accuracy of 94%, and the Kappa value of 0.80. This performance was achieved using a training dataset containing all changed land cells and three times of that randomly selected from unchanged land cells and regulating the minimum number of records in a leaf node equal to 1, the minimum number of records in a parent node equal to 2, and the value of 10,000 for the maximum number of splits. The CART DT algorithm indicates that proximity to built areas, proximity to highways, current LULC type, elevation, and distance to water bodies are the most significant predictor variables for the urban expansion prediction in the study area.

An enhanced support vector machine model for urban expansion prediction

Given the dynamic and convoluted nature of urban expansion process and the necessity of handling continuous and categorical variables, non-normal distributed data, and non-linear relationships, urban expansion modeling is challenging. To handle these issues effectively and enhance the quality of urban expansion prediction, the capabilities of support vector machine (SVM) technique are explored in this study. A binary SVM model is developed using three different data sampling methods and nineteen predictor variables, four of which are first introduced in this study. The model is configured by regulating the penalty parameter, selecting the most appropriate kernel function, and setting the best value for the kernel function's parameter. A novel combination of goodness-of-fit metrics is used to more realistically evaluate the model accuracy to predict built and unbuilt land cells as well as changed and unchanged land cells in the whole study area. The implementation of the developed model in Guilford County, NC, over the period of 2001–2011, as a case study, demonstrated highly accurate and reliable results. The best performance of the model with the training accuracy of 98% and the testing accuracy of 85% was achieved using a balanced sampling method, fourteen predictor variables, the penalty parameter equal to 1, the radial basis function (RBF) kernel, and the value of 2 for the kernel's parameter. The urban expansion model based on SVM method can substantially improve the prediction accuracy and would be helpful for making appropriate plans and policies to mitigate the adverse impacts of urban expansion.

Monitoring spatial LULC changes and its growth prediction based on statistical models and earth observation datasets of Gautam Budh Nagar, Uttar Pradesh, India

It is well known and witnessed the fact that in recent years the growth of urbanization and increasing urban population in the cities, particularly in developing countries, are the primary concern for urban planners and other environmental professionals. The present study deals with multi-temporal satellite data along with statistical models to map and monitor the LULC change patterns and prediction of urban expansion in the upcoming years for one of the important cities of Ganga alluvial Plain. With the help of our study, we also tried to portray the impact of urban sprawl on the natural environment. The long-term LULC and urban spatial change modelling was carried out using Landsat satellite data from 2001 to 2016. The assessment of the outcome showed that increase in urban built-up areas favoured a substantial decline in the agricultural land and rural built-up areas, from 2001 to 2016. Shannon’s entropy index was also used to measure the spatial growth patterns over the period of time in the study area based on the land-use change statistics. Prediction of the future land-use growth of the study area for 2019, 2022 and 2031 was carried out using artificial neural network method through Quantum GIS software. Results of the simulation model revealed that 14.7% of urban built-up areas will increase by 2019, 15.7% by 2022 and 18.68% by 2031. The observation received from the present study based on the long-term classification of satellite data, statistical methods and field survey indicates that the predicted LULC map of the area will be precious information for policy and decision-makers for sustainable urban development and natural resource management in the area for food and water security.

Land Cover Change Simulations in Yangon Under Several Scenarios of Flood and Earthquake Vulnerabilities with Master Plan

Yangon is the largest city and major economic area in Myanmar. However, it is considered to have a high risk of floods and earthquakes. In order to mitigate future flood and earthquake damage in Yangon, land cover change simulations considering flood and earthquake vulnerabilities are needed to support urban planning and management. This paper proposes land cover change simulations in Yangon from 2020 to 2040 under various scenarios of flood and earthquake vulnerabilities with a master plan. In our methodology, we used a dynamic statistical model to predict urban expansion in Yangon from 2020 to 2040. We employed a master plan as the future dataset to enhance the prediction of urban expansion. We applied flood and earthquake vulnerabilities based on multi-criteria analysis as the areas vulnerable to disaster. We simulated land cover changes from 2020 to 2040 considering the vulnerable areas with a master plan for multiple scenarios. The experiments indicated that by using a master plan, some of the predicted urban areas are still located in areas highly vulnerable to floods and earthquakes. By integrating the prediction of urban expansion with flood and earthquake vulnerabilities, the predicted urban areas can effectively avoid areas highly vulnerable to floods and earthquakes.

GeoAI 2017 workshop report: the 1st ACM SIGSPATIAL International Workshop on GeoAI: @AI and Deep Learning for Geographic Knowledge Discovery

Deep Learning and Artificial Intelligence (AI) techniques are transforming a range of sectors from computer vision and natural language processing to autonomous driving and healthcare. In particular, deep learning methods achieve great success in many computer vision problems, such as image classification and object detection. Deep neural networks are very powerful to capture the hierarchical representation of features in massive and complex data by adopting multiple layers of non-linear information processing. Due to the availability of vast and high-resolution geospatial data and efficient high-performance computing architectures, deep learning techniques empower the geospatial system to provide fast and near-human level perception. For example, recent studies have shown deep learning techniques coupled with volunteered geographic information (such as OpenStreetMap data) can accurately extract buildings from satellite imagery for humanitarian mapping in rural African areas. Also, deep learning helps assimilate autonomous vehicles and intelligent transport system by incorporating a great amount of information gathered by traffic cameras and sensors. Moreover, deep learning technology facilitates the discovery of geographic information within unstructured text data across different languages. There are also many other applications of deep learning in the domain of GIS, such as the prediction for spatial diffusion patterns in epidemiology, urban expansion prediction, and hyperspectral image analysis. The 1st GeoAI workshop aims to bring geoscientists, computer scientists, engineers, entrepreneurs, and decision makers from academia, industry, and government to discuss the latest trends, successes, challenges, and opportunities in the field of deep learning for geographical data mining and knowledge discovery.

Modeling the dynamics of urban and ecological binary space for regional coordination: A case of Fuzhou coastal areas in Southeast China

The linkage of urban planning and the dynamics of regional ecosystem services value (ESV) allows sustainable aims more accessible, which is increasingly interested by researchers. This paper combines urban expansion and eco-compensation to construct the urban-ecological coordinated development model (UECDM) which aims to find a new urban expansion mode basing on the balance of regional ESV. UECDM is composed of three modules: prediction of urban expansion, eco-compensation and spatial simulation. With this model, the urban expansion and eco-compensation of Fuzhou areas was simulated. The result shows: urban land has expanded 12,805 ha, with a total ESV loss of 688 million yuan. In order to obtain the balance of regional ESV, 1,142 ha of cultivated land and 3,316 ha of other land should be converted into forestland; 2,588 ha of cultivated land into tidal flats; 2,027 ha of other land into water areas. This model could produce quantitative results for decision makers during the rapid urbanization for sustainable development.

Modeling urban expansion in Yangon, Myanmar using Landsat time-series and stereo GeoEye Images

This research proposed a methodology to model the urban expansion based dynamic statistical model using Landsat and GeoEye Images. Landsat Time-Series from 1978 to 2010 have been applied to extract land covers from the past to the present. Stereo GeoEye Images have been employed to obtain the height of the building. The class translation was obtained by observing land cover from the past to the present. The height of the building can be used to detect the center of the urban area (mainly commercial area). It was assumed that the class translation and the distance of multi-centers of the urban area also the distance of the roads affect the urban growth. The urban expansion model based on the dynamic statistical model was defined to refer to three factors; (1) the class translation, (2) the distance of the multicenters of the urban areas, and (3) the distance from the roads. Estimation and prediction of urban expansion by using our model were formulated and expressed in this research. The experimental area was set up in Yangon, Myanmar. Since it is the major of country's economic with more than five million population and the urban areas have rapidly increased. The experimental results indicated that our model of urban expansion estimated urban growth in both estimation and prediction steps in efficiency.

Spatio-temporal Prediction of Urban Expansion Using Bivariate Statistical Models: Assessment of the Efficacy of Evidential Belief Functions and Frequency Ratio Models

The urban development process is a continuous and dynamic spatio-temporal phenomenon associated with economic developments and growing populations. Understanding urban expansion processes require models capable of simulating, monitoring, and predicting both urban growth and urban sprawl. In this research, probability-based Evidential Belief Functions (EBF) and Frequency Ratio (FR) models were employed to simulate and to predict the urban expansion probability map of the metropolitan area in Tripoli, Libya. These methods have not been used before in the urban development simulations of cities. By using the geographic information system (GIS), three satellite imageries obtained from 1996, 2002, and 2010 were employed to extract seven urban-deriving factors for the study area. The urban factors are slope, distance to active economic center, distance to central business district (CBD), distance to roads, distance to built-up areas, distance to educational area, and distance to coastal areas. For model calibration, both the EBF and FR models were applied to simulate urban expansion from 1996 to 2002. Data from 2002 to 2010 were used for models validation. Consequently, future suitability maps of urban growth were produced. The validation results indicated 83 % prediction accuracy for the EBF model and 84 % for the FR model. The outcomes established that the models could be employed in the urban expansion modeling of metropolises. The applied models, however, have dynamic and temporal limitations that should be considered in urban growth analysis.

Spatial-temporal Characteristics and Future Prediction of Urban Expansion in Shanghai

Spatial-temporal Characteristics and Future Prediction of Urban Expansion in Shanghai