Urban Land Use Patterns Research Articles

Monitoring dynamic land-use change in rural–urban transition: a case study from Hathazari Upazila, Bangladesh

ABSTRACTThis study evaluates land-use/cover change (LULCC) and urban expansion in Hathazari Upazila, Chittagong, between 1977 and 2017 using satellite images. Spatial and temporal dynamics of LULCC was quantified using three Landsat images, a supervised classification algorithm, and the post-classification change detection technique through geographic information system. The overall supervised classification accuracy of the Landsat-derived land-use/cover maps ranged from 74% to 91%. The analysis revealed substantial growth of settlement (146%) and agricultural land (124%) in Hathazari Upazila, Chittagong, over the study period which resulted in significant decrease in the area of water bodies (68.9%), homestead (54.4%), and hill forest (43.3%). The research quantified the patterns of LULCC for the last 40 years for Hathazari Upazila, Chittagong, that might contribute to both development of sustainable urban land-use planning decisions and probable growth patterns.

An agent-based procedure with an embedded agent learning model for residential land growth simulation: The case study of Nanjing, China

The agent-based modelling (ABM) is commonly used to simulate urban land growth. A key challenge of ABM for the simulation of urban land-use dynamics in support of sustainable urban management is to understand and model how human individuals make and develop their location decisions that then shape urban land-use patterns. To investigate this issue, we focus on modelling the agent learning process in residential location decision-making process, to represent individuals' personal and interpersonal experience learning during their decision-making. We have constructed an extended reinforcement learning model to represent the human agents' learning when they make location decisions. Consequently, we propose and have developed a new agent-based procedure for residential land growth simulation that incorporates an agent learning model, an agent decision-making model, a land use conversion model, and the impacts of urban land zoning and the developers' desires. The proposed procedure was first tested by using hypothetical data. Then the model was used for a simulation of the urban residential land growth in the city of Nanjing, China. By validating the model against empirical data, the results showed that adding agent learning model contributed to the representation of the agent's adaptive location decision-making and the improvement of the model's simulation power to a certain extent. The agent-based procedure with the agent learning model embedded is applicable to studying the formulation of urban development policies and testing the responses of individuals to these policies.

Rediscovering Chinese cities through the lens of land-use patterns

Urbanization is a complex spatial phenomenon involving significant compositions and interactions in land use. Yet, only few studies have quantitatively examined multidimensional urban land-use patterns with insights into land use policy, particularly in the context of China’s rapid urbanization process. This paper aims to investigate the urban land-use patterns in China by employing multiple measurements with multi-sourced data, including Spatial Entropy and Dissimilarity Index, and a combination of cellular-automata (CA) modeling and Structural Equation Modeling (SEM). The results show that land-use patterns in China are characterized from more mixed (Beijing, Shanghai) to less segregated (Xiangyang, Tangshan, and Guiyang), and the most segregated (Chongqing), which can be categorized into three typical types: economically led, government led, and geographically constrained. The findings also indicate that residential sector has correlation with GDP and urban built-up area; public sector is driven by GDP, urban built-up area, and paved road area; and commercial sector is related to GDP and paved road area. Furthermore, land-use patterns are not only determined by economic forces, but also subject to China’s land policies that formulated based on its unique social and political characteristics. It reveals the complex spatial characterization of urbanization in China, where government still plays an important role in facilitating the land use allocation. The research sheds light on understanding land use policy for land-use patterns reconfiguration in the context of New-Type Urbanization towards better planning and governance.

Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques

ABSTRACTAlong with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, economic and urban issues. Previous studies have generally integrated traditional machine learning with cellular automaton (CA) models to simulate urban development. Nevertheless, difficulties still exist in the process of obtaining more accurate results with CA models; such difficulties are mainly due to the insufficient consideration of neighborhood effects during urban transition rule mining. In this paper, we used an effective deep learning method, named convolution neural network for united mining (UMCNN), to solve the problem. UMCNN has substantial potential to get neighborhood information from its receptive field. Thus, a novel CA model coupled with UMCNN and Markov chain was designed to improve the performance of simulating urban expansion processes. Choosing the Pearl River Delta of China as the study area, we excavate the driving factors and the transformational relations revealed by the urban land-use patterns in 2000, 2005 and 2010 and further simulate the urban expansion status in 2020 and 2030. Additionally, three traditional machine-learning-based CA models (LR, ANN and RFA) are built to attest the practicality of the proposed model. In the comparison, the proposed method reaches the highest simulation accuracy and landscape index similarity. The predicted urban expansion results reveal that the economy will continue to be the primary factor in the study area from 2010 to 2030. The proposed model can serve as guidance in urban planning and government decision-making.

Spatiotemporal patterns of urban land use change in the rapidly growing city of Lusaka, Zambia: Implications for sustainable urban development

This study examines the spatiotemporal patterns of urban land use (urban-LU) change in the rapidly urbanising city of Lusaka, Zambia, during the 1990-2000 and 2000-2010 periods, using geospatial tools and techniques. The results show that the city experienced rapid urban growth, with about a 233% increase in the total urban-LU area from 1990 − 2010. The results also show that urban-LU expansion was more intense during the 2000s than during the 1990s. Spatially, the city displays a disordered pattern of urban-LU associated with the pattern of major roads and the city centre, which draws its legacies from the colonial era. Lusaka has emerged as a highly unplanned city with approximately 40% of the city representing unplanned residential land use dominated by informal settlements (30%). The growth of commercial and industrial land use has also been consistent with high density residential land uses. The growth of planned residential, and public institutions and service land uses has been slow. The results further reveal spatial dependency of informal settlements on commercial and industrial, and planned medium to high density residential land uses. The study discusses and offers vital insights for strategic urban planning that can control the observed unplanned urban growth and stimulate sustainable urban development.

Estimation of built-up and green volume using geospatial techniques: A case study of Surabaya, Indonesia

Urban indicators are important for communicating relevant information to land planners. Traditionally, the intensity and spatial pattern of urban land use are analyzed laterally using the two-dimensional approach. The availability of geospatial data such as those from remote sensing satellite imageries and digital surface models introduces a third dimension in urban analysis, namely urban volume. Thus, an urban volume indicator is applied to quantify built-up volume and green volume of a big Indonesian city using geospatial techniques, including remote sensing and Geographic Information System (GIS). Surabaya is selected as a case study since the city has grown significantly in terms of both population and capital investment in this decade. We examined the clustering pattern of low and high green volume by performing hot and cold spot analysis. The results indicate that the average built-up volume reaches 173.05 m3/pixel, represented by a residential single house of less than 7 m in height. The average green volume is 14.74 m3/pixel, characterized by vegetation of 0.6–1 m in height, mostly found in the backyards of houses. The ratio of green volume to built-up volume is small (8.52%, or 0.085 m3 green volume per 1 m3 built-up volume). The Kebun Binatang Surabaya Park has been classified as a hot spot area for green volume (highly clustered pixels with high green volume). The areas classified as cold spots are mostly with low green volume and a scattered/low-clustering pattern. There is a huge imbalance between built-up volume and green volume, the latter being much lower, indicating that there is a need for more green spaces and higher green volume. Overall, this study can be used as a reference for future development initiatives that could help improve living conditions in the city through greening programs in the context of sustainable urban development.

Urban Mobility Analysis on Efficiency and Sustainability by Means of Transportation

Patterns of urban land use are inherently linked to the predominantly used means of transportation, both generating and being generated themselves. While each mode of transportation shapes a different development typology a clear understanding of their interrelations and dependencies is needed in order to create a comprehensive mobility strategy. The study proposes a 15-criteria analysis framework developed to identify and quantify the main modes of transportation’s key aspects. The analysis framework was applied to a yearlong research on Timisoara, Romania, comprising hard, quantitative data, digital simulations and mobility pattern analysis and soft data, quality assessment and perceived needs and satisfaction levels. The research was carried out in clear opposition to the national trend of official mobility strategies focusing on accommodating increased levels of car traffic on the underdeveloped existing roads infrastructure. By analysing the efficiency and sustainability of all four main modes of transportation the results offer a holistic comprehensive view. While, despite current practices, no mobility strategy can focus on a single means of transportation, the article will only present in detail the research on cycling, infrastructure and use, as it is the most underdeveloped and least discussed at the national level and proven through our study to be the most efficient for a city of Timisoara’s size and characteristics. By identifying a clear link between urban land use patterns, infrastructure quality and perceptions and the most efficient means of transportation for each particular city type mobility strategies could shift the trend of urban development towards a more sustainable one.

Urban development and landuse change patterns in Muscat city, Oman

Urban sprawl is a remarkable characteristic of cities of the Arabian Gulf region, including Muscat city, the capital of the Sultanate of Oman. The city has grown from discrete isolated settlements to a compact, dense urban metropolitan region across narrow coastal alluvial plain. This paper examines the specific implications and trends of urban landuse dynamic in Muscat city in the last five decades. Urban landuse patterns were traced and analyzed from different sources, including: Landsat, IRS, IKONOS, QuickBird, GeoEye, Aerial photos and Printed map. RS and GIS change detection techniques using Normalized Difference Building Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were used. The categorization of landused classes was mainly based on high resolution images like IKONOS, QuickBird and GeoEye. The results indicated that, residential, commercial and industrial landuses have shown large increase, basically at the expense of vacant and agricultural areas. The trend of urban landuse patterns was largely filling the gap and empty areas, creating an urban structure of high density. The actual range of urban expansion might have been small, and the degree of change for the whole city region, might have been not remarkable because of the relatively new expansions of urbanization and industrial development in Muscat. However, the negative impacts of these changes might escalate in the future if a plan to regulate land development is not developed and implemented.

SENSING URBAN LAND-USE PATTERNS BY INTEGRATING GOOGLE TENSORFLOW AND SCENE-CLASSIFICATION MODELS

Abstract. With the rapid progress of China’s urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model’s ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

The influence of landscape pattern on the risk of urban water-logging and flood disaster

Most Chinese cities face a series of ‘urban diseases’ or problems during rapid urbanization, and of which urban precipitation and subsequent water-logging is a severe hotspot. A better understanding of the influence of urban land-use patterns on water-logging will be helpful for enhancing urban flood resistance. The relationship between the characteristics of land-use patterns and the risk of urban water-logging and flood disaster (UWLFD) was analyzed in this paper, using landscape pattern indicators to describe the land-use structure. These included patch density (PD), edge density (ED), landscape division index (DIVISION), contagion (CONTAG), Shannon’s evenness index (SHEI), and Shannon’s diversity index (SHDI). The risk of UWLFD was measured using the topographic slope and urban surface cover. Based on GIS and remote sensing data in 2003 and 2013, a case study of the metropolitan region of Kunming City showed that the risk of UWLFD had a positive correlation with landscape fragmentation (e.g., PD and ED), and strong negative correlation with landscape contagion (e.g., CONTAG) during the study period. This implied that landscape fragmentation aggravated the risk of UWLFD while landscape connectivity reduced the risk. Measures such as unified and long-term urban planning and design and the construction of green infrastructure will reduce the risk of UWLFD. The relationship between the landscape pattern and the risk of UWLFD provides a new perspective for systematic urban planning.

An urban sprawl index based on multivariate and Bayesian factor analysis with application at the municipality level in Valencia

Urban sprawl is now a common and threatening phenomenon in Europe, severely affecting environmental and economic sustainability. An analytical characterization and measurement of urban sprawl are required to gain a better understanding of the phenomenon and to propose the possible solutions. Traditional factor analysis techniques, especially Principal Component Analysis and Factor Analysis, have been commonly used. In this paper, we additionally test Independent Component Analysis with the aim to obtain a multidimensional characterization of the sprawl phenomenon. We also use Bayesian Factor Analysis to obtain a single (unidimensional) measuring index of sprawl, which also allows us to obtain the uncertainty of the inferred index, in contrast to traditional approaches. All these techniques have been applied to study the phenomenon of urban sprawl at the municipality level in Valencia, Spain using a wide set of variables related to the characteristics and patterns of urban land use.

Integrated research on land-use changes in the face of urban transformations – An analytic framework for further studies

We state that land use is under constant pressure, no matter if land-use changes take place in growing or shrinking cities. Land-use changes witness multiple interdependencies between environmental, social and economic factors. In urban areas land is highly exposed to accelerating consumption which makes it a scarce and precious resource. Therefore we need transformative processes to deal with land use in a more sensible and responsible way. We define urban transformations as fundamental, multi-dimensional changes in urban land-use patterns, population developments, infrastructures, governance regimes as well as established values, norms and behaviours. A central focus is on land use and its changes to supply ecosystem services as key driver for quality of life. Most important for us is that transformative processes are highly dynamic and non-linear, thus affecting functions of urban land uses in different ways and with varying consequences. In this sense, governance research is a decisive component to implement our research in practice. With this viewpoint we want to contribute to a debate on land-use changes in alternating growing and shrinking cities to foster appropriate development responses for urban transformations towards sustainability.

Urban spatial structure and commute duration: An empirical study of China

ABSTRACTUrban traffic is embedded in and fundamentally shaped by the spatial pattern of urban land use, such as city size, density, extent of polycentricity, and the relationship between employment and residential locations. Previous evidence, mainly from European and American cities, suggests that the duration of commute trips increases with city size and the spatial separation between jobs and housing. On the other hand, the influences of density and polycentricity are less clear. Using data from 164 cities in China, this study empirically analyzes the relationship between city average commute duration and multiple dimensions of urban spatial structure. Controlling for economic, demographic, and infrastructure characteristics, we find that commute duration correlates positively with city size and jobs–housing separation but negatively with density and polycentricity. As one of the earliest studies on commute cost in the rapidly urbanizing and motorizing Chinese cities, this study can help Chinese decision makers improve urban economic and environmental efficiency through spatial planning and policy making. Specifically, compact, mixed-use, and polycentric spatial development may ease the burden of commute, and thus substitute for unnecessary infrastructure investment and energy consumption during a period of rapid urban expansion in China.

Quantifying Circular Urban Expansion Patterns of Compact Chinese Cities: The Case of Yangtze River Delta, China

Accelerating urbanization all over the world has renewed interdisciplinary interest and public concerns over sustainable urban forms. Despite abundant literature on the concentric model and its application to urban land-use patterns, a rigorous and straightforward methodology to characterize the model quantitatively in space is especially needed. In this paper we present a diagramatic method and refine five indicators to measure and clarify the circular pattern of urban expansion. With remote sensing data, the approach has been examined for the sample set of fifty-one cities in the Yangtze River Delta (YRD) region of China. The five indicators adopted illustrated that most of YRD cities display a compact centralized form with a relatively small matured core area and are undergoing fast expansion. Furthermore, we have found distinctive local models of density gradient in built-up areas, shaped by urban population size and local institutional structures. The preliminary results related to the methodology are inspiring, which should be useful for elucidating urban growth trends and for decision making.

Creating a three level building classification using topographic and address-based data for Manchester

Abstract. Buildings, the basic unit of an urban landscape, host most of its socio-economic activities and play an important role in the creation of urban land-use patterns. The spatial arrangement of different building types creates varied urban land-use clusters which can provide an insight to understand the relationships between social, economic, and living spaces. The classification of such urban clusters can help in policy-making and resource management. In many countries including the UK no national-level cadastral database containing information on individual building types exists in public domain. In this paper, we present a framework for inferring functional types of buildings based on the analysis of their form (e.g. geometrical properties, such as area and perimeter, layout) and spatial relationship from large topographic and address-based GIS database. Machine learning algorithms along with exploratory spatial analysis techniques are used to create the classification rules. The classification is extended to two further levels based on the functions (use) of buildings derived from address-based data. The developed methodology was applied to the Manchester metropolitan area using the Ordnance Survey‟s MasterMap®, a large-scale topographic and address-based data available for the UK.

Energy Conservation in Urban Transport

The intensity of travel being more in urban areas, it is better that the energy-saving measures are focused on urban transportation. The energy requirement of motorized private transport modes is much more compared to the energy requirement by public transport modes. Urban land-use pattern is also a factor influencing the intensity of travel in a city. Hence, orientation of the urban transportation planning process to facilitate use of public transport modes and non-motorized modes of transport with simultaneous restructuring of the urban land-use pattern will help energy saving.

Urban Land use Characteristics and Trip Generation Patterns in a Developing Economy: A Case Study of Ilesa, Osun State, Nigeria

There have been many studies on intra city movement in Nigeria but they have not examined the distribution pattern of land use which is central to urban mobility in any country of the world. It is against this background that the study is designed to describe land use characteristics and examine their correlations with household trip flow in a medium sized city of Ilesa, Nigeria. Primary and secondary sources of data were used for this research. Descriptive and inferential statistics were used to analyze the data. The findings reveal that urban land use in Ilesa does not comply with the planning regulations. Further analyses reveal that the distribution patterns of urban land use correlate with household travel patterns in the city. The study concludes that there is need to re- design the master plan of Ilesa and ensure strict compliance with the planning regulations in the city and many other cities in Nigeria as a whole. DOI: 10.5901/ajis.2014.v3n4p311

Examining the Satellite-Detected Urban Land Use Spatial Patterns Using Multidimensional Fractal Dimension Indices

Understanding the spatial patterns of urban land use at both the macro and the micro levels is a central issue in global change studies. Due to the nonlinear features associated with land use spatial patterns, it is currently necessary to provide some distinct analysis methods to analyze them across a range of remote sensing imagery resolutions. The objective of our study is to quantify urban land use patterns from various perspectives using multidimensional fractal methods. Three commonly used fractal dimensions, i.e., the boundary dimension, the radius dimension, and the information entropy dimension, are introduced as the typical indices to examine the complexity, centrality and balance of land use spatial patterns, respectively. Moreover, a new lacunarity dimension for describing the degree of self-organization of urban land use at the macro level is presented. A cloud-free Landsat ETM+ image acquired on 17 September 2010 was used to extract land use information in Wuhan, China. The results show that there are significant linear relationships represented by good statistical fitness related to these four indices. The results indicate that rapid urbanization has substantially affected the urban landscape pattern, and different land use types show different spatial patterns in response. This analysis reveals that multiple fractal/nonfractal indices provides a more comprehensive understanding of the spatial heterogeneity of urban land use spatial patterns than any single fractal dimension index. These findings can help us to gain deeper insight into the complex spatial patterns of urban land use.

A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing

This article presents a novel cellular automata (CA) approach to simulate the spatio-temporal process of urban land-use change based on the simulated annealing (SA) algorithm. The SA algorithm enables dynamic optimisation of the CA's transition rules that would otherwise be difficult to configure using conventional mathematical methods. In this heuristic approach, an objective function is constructed based on a theoretical accumulative disagreement between the simulated land-use pattern and the actual land-use pattern derived from remotely sensed imagery. The function value that measures the mismatch between the actual and the simulated land-use patterns would be minimised randomly through the SA process. Hence, a set of attribution parameters that can be used in the CA model is achieved. An SA optimisation tool was developed using Matlab and incorporated into the cellular simulation in GIS to form an integrated SACA model. An application of the SACA model to simulate the spatio-temporal process of land-use change in Jinshan District of Shanghai Municipality, PR China, from 1992 to 2008 shows that this modelling approach is efficient and robust and can be used to reconstruct historical urban land-use patterns to assist with urban planning policy-making and actions. Comparison of the SACA model with a typical CA model based on a logistic regression method without the SA optimisation (also known as LogCA) shows that the SACA model generates better simulation results than the LogCA model, and the improvement of the SACA over the LogCA model is largely attributed to higher locational accuracy, a feature desirable in most spatially explicit simulations of geographical processes.

Active-transport walking behavior: destinations, durations, distances

This paper fills a gap in our knowledge of active-transport (AT) walking, by presenting detailed aspects of walking behavior for a medium-sized North American city. It analyzes the frequency and length of walking episodes, categorized by origins, purposes, and destinations, and also investigates distance-decay functions for major destinations. The study employs day-after recall time diary and questionnaire data from the 2007–8 Space–Time Activity Research (STAR) survey conducted in Halifax, Canada. GPS co-ordinate data enhanced the accuracy of location information, start times, and end times of the 1790 AT walking episodes, while GIS software was used to compute a shortest-path distance between the origin and destination of each episode.Home is both the most common origin and destination for AT walks, and the most common purpose is travel-to-shop rather than travel-to-work. Most walks are to non-home locations, such as retail establishments and offices. Particularly important are restaurants and bars, grocery stores, shopping centers, banks, and other services. All major destinations show strong distance-decay effects: most walks are shorter than 600m, and very few exceed 1200m. The assumption employed in the walkability literature, that one should restrict the ‘neighborhood of opportunity’ to walking destinations within 1000m of the home, is seen to be well justified. However, a planning policy focus on the walker’s home neighborhood is revealed as questionable, since the majority of walking trips do not originate from the home. The relationship between urban land-use patterns and walkability may therefore require some rethinking.