Spatio-temporal land use analysis and urban growth dynamic prediction in the kingdom of Bahrain

2021 ◽  
Author(s):  
E.A. Alburshaid ◽  
M.A. Mangoud
Keyword(s):  
Land Use ◽  
Urban Growth ◽  
Growth Dynamic ◽  
Dynamic Prediction ◽  
Spatio Temporal

scholarly journals Modeling of Urban Growth Using Cellular Automata and GIS Case of Benslimane in Morocco

2020 ◽  
Author(s):  
BENCHELHA MOHAMED ◽  
Benzha Fatiha ◽  
Rhinane Hassan ◽  
BENCHELHA SAID ◽  
BENCHELHA TAOUFIK ◽  
...  
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Urban Growth ◽  
Urban Expansion ◽  
Confusion Matrix ◽  
Spatial Transformation ◽  
Urban Growth Modeling ◽  
Main Component Analysis ◽  
Spatio Temporal ◽  
Main Component

In this study, our goal was to research land-use change by combining spatio–temporal land use/land cover monitoring (LULC (1989–2019) and urban growth modeling (1999–2039) in Benslimane, Morocco, to determine the effect of urban growth on different groups based on cellular automata (CA) and geospatial methods. A further goal was to test the reliability of the AC algorithm for urban expansion modeling. To do this, four years of satellite data were used at the same time as population density, downtown distance, slope, and ground road distance. The LULC satellite reported a rise of 3.8 km2 (318% variation) during 1989–2019. Spatial transformation analysis reveals a good classification similarity ranging from 89% to 91% with the main component analysis (PCA) technique. The statistical accuracy between the satellite scale and the replicated built region of 2019 gave 97.23 %t of the confusion matrix overall accuracy, and the region under the receiver operational characteristics (ROC) curve to 0.94, suggesting the model's high accuracy. Although the constructed area remains low relative to the total area of the municipality's territory, the LULC project shows that the urban area will extend to 5,044 km2 in 2019, principally in the western and southwestern sections. In 2019–2039, urban development is expected to lead to a transformation of the other class (loss of 1,364 km2), followed by vegetation cover (loss of 0.345 km2). In spatial modeling and statistical calculations, the GDAL and NumPy Python 3.8 libraries were successful.

scholarly journals SPATIO-TEMPORAL ANALYSIS AND MODELING OF URBAN GROWTH OF BIRATNAGAR CITY, NEPAL

2019 ◽  
Vol IV-5/W2 ◽  
pp. 97-102
Author(s):  
S. Shrestha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Urban Expansion ◽  
Urban Change ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Spatial Metrics ◽  
Land Cover Map ◽  
Spatio Temporal

Abstract. Increasing land use land cover changes, especially urban growth has put a negative impact on biodiversity and ecological process. As a consequences, they are creating a major impact on the global climate change. There is a recent concern on the necessity of exploring the cause of urban growth with its prediction in future and consequences caused by this for sustainable development. This can be achieved by using multitemporal remote sensing imagery analysis, spatial metrics, and modeling. In this study, spatio-temporal urban change analysis and modeling were performed for Biratnagar City and its surrounding area in Nepal. Land use land cover map of 2004, 2010, and 2016 were prepared using Landsat TM imagery using supervised classification based on support vector machine classifier. Urban change dynamics, in term of quantity, and pattern was measured and analyzed using selected spatial metrics and using Shannon’s entropy index. The result showed that there is increasing trend of urban sprawl and showed infill characteristics of urban expansion. Projected land use land cover map of 2020 was modeled using cellular automata-based approach. The predictive power of the model was validated using kappa statistics. Spatial distribution of urban expansion in projected land use land cover map showed that there is increasing threat of urban expansion on agricultural land.

scholarly journals Assessment of Urban Growth Patterns using Spatio-temporal Data and Analysis

2019 ◽  
Vol 7 (5) ◽  
Author(s):  
Said Masoud Bakhit ◽  
Sbai Abdelkader
Keyword(s):  
Land Use ◽  
Urban Growth ◽  
Spatial Modeling ◽  
Urban Areas ◽  
Growth Patterns ◽  
Temporal Data ◽  
Spatial Trend ◽  
Growth Trends ◽  
Critical Issues ◽  
Spatio Temporal

Modeling urban growth trends has become one of the critical issues in the last decades. This study aims to evaluate spatio-temporal urban growth trends using spatial modeling. For this purpose, four land-use maps were used to visualize historical urban growth trends in Seremban, Malaysia. Land Change Modeller (LCM) was used to evaluate the spatial trend of Land-use and land-cover (LULC) in Seremban. The results of the study confirm that urban areas in Seremban hugely increased from 1984 to 2010. The main reasons to increase urban areas are that economic and population growth in Malaysia in general and Seremban in particular. This study confirms that the LCM model is one of the effective spatial techniques that should be taken into account in urban planning studies.

scholarly journals A regional spatial-retrofitting approach to geovisualise regional urban growth: An application to the Golden Horseshoe in Canada

2021 ◽  
Author(s):  
Eric Vaz ◽  
Amy Buckland ◽  
Kevin Worthington
Keyword(s):  
Decision Making ◽  
Land Use ◽  
Urban Growth ◽  
Urban Land ◽  
Urban Land Use ◽  
Urban Region ◽  
Web Environment ◽  
Urban Land Use Change ◽  
Spatio Temporal ◽  
Share Information

Understanding urban change in particular for larger regions has been a great demur in both regional planning and geography. One of the main challenges has been linked to the potential of modelling urban change. The absence of spatial data and size of areas of study limit the traditional urban monitoring approaches, which also do not take into account visualization techniques that share information with the community. This is the case of the Golden Horseshoe in southern Ontario in Canada, one of the fastest growing regions in North America. An unprecedented change on the urban environment has been witnessed, leading to an increased importance of awareness for future planning in the region. With a population greater than 8 million, the Golden Horseshoe is steadily showing symptoms of becoming a mega-urban region, joining surrounding cities into a single and diversified urban landscape. However, little effort has been done to understand these changes, nor to share information with policy makers, stakeholders and investors. These players are in need of the most diverse information on urban land use, which is seldom available from a single source. The spatio-temporal effect of the growth of this urban region could very well be the birth of yet another North American megacity. Therefore, from a spatial perspective there is demand for joint collaboration and adoption of a regional science perspective including land use and spatio-temporal configurations. This calls forth a novel technique that allows for assessment of urban and regional change, and supports decision-making without having the usual concerns of locational data availability. It is this sense, that we present a spatial-retrofitting model, with the objective of (i) retrofitting spatial land use based on current land use and land cover, and assessing proportional change in the past, leading to four spatial timestamps of the Golden Horseshoe’s land use, while (ii) integrating this in a multi-user open source web environment to facilitate synergies for decision-making. This combined approach is referred to as a regional-spatial-retrofitting approach (RSRA), where the conclusions permit accurate assessment of land use in past time frames based on Landsat imagery. The RSRA also allows for a collective vision of regional urban growth supporting local governance through a decision-making process adhering to Volunteered Geographic Information Systems. Urban land use change can be refined by means of contribution from end-users through a web environment, leading to a constant understanding and monitoring of urban land use and urban land use change.

A local and regional spatial index for measuring three-dimensional urban compactness growth

2017 ◽  
Vol 46 (1) ◽  
pp. 143-164 ◽  
Author(s):  
Olympia Koziatek ◽  
Suzana Dragićević
Keyword(s):  
Land Use ◽  
Urban Growth ◽  
Local Governments ◽  
Three Dimensional ◽  
Spatial Index ◽  
Time Interval ◽  
Suitability Analysis ◽  
Urban And Regional Planning ◽  
Spatio Temporal ◽  
Developing Area

The negative impacts resulting from urban sprawl are recognized as serious issues entailing environmental problems. Urban developments are moving towards a more compact form to mitigate many issues including pollution concerns, land depletion, and population growth demands. Urban compactness has been reported to be a more sustainable form of development that occurs through densification and mixed land use practices through spatial indicators that intensify the landscape. Urban modeling has been used extensively to aid in urban and regional planning as it can forecast possible scenarios of urban growth. The objective of this research is to develop and implement a spatial index for three-dimensional (3D) urban compactness to evaluate potential vertical development growth. The spatial index has two components, local and regional, and it is derived based on parameters accounting for a vertical urban growth suitability analysis, land designation, and average building height. Datasets used for this study were for the Metro Vancouver Region, Canada, a rapidly developing area with plans in place for sustainability and compact growth. The spatial index was derived for the study area for the year 2011 and projected to the year 2041 with a 10-year time interval, accounting for the spatio-temporal land use change. Results indicate concentrations of urban compactness growth near densely populated and transportation-oriented locations and also capture urban leap-frogging processes. The presented research aims to aid local governments in future planning processes related to regional sustainable development growth.

scholarly journals A regional spatial-retrofitting approach to geovisualise regional urban growth: An application to the Golden Horseshoe in Canada

2021 ◽  
Author(s):  
Eric Vaz ◽  
Amy Buckland ◽  
Kevin Worthington
Keyword(s):  
Decision Making ◽  
Land Use ◽  
Urban Growth ◽  
Urban Land ◽  
Urban Land Use ◽  
Urban Region ◽  
Web Environment ◽  
Urban Land Use Change ◽  
Spatio Temporal ◽  
Share Information

Understanding urban change in particular for larger regions has been a great demur in both regional planning and geography. One of the main challenges has been linked to the potential of modelling urban change. The absence of spatial data and size of areas of study limit the traditional urban monitoring approaches, which also do not take into account visualization techniques that share information with the community. This is the case of the Golden Horseshoe in southern Ontario in Canada, one of the fastest growing regions in North America. An unprecedented change on the urban environment has been witnessed, leading to an increased importance of awareness for future planning in the region. With a population greater than 8 million, the Golden Horseshoe is steadily showing symptoms of becoming a mega-urban region, joining surrounding cities into a single and diversified urban landscape. However, little effort has been done to understand these changes, nor to share information with policy makers, stakeholders and investors. These players are in need of the most diverse information on urban land use, which is seldom available from a single source. The spatio-temporal effect of the growth of this urban region could very well be the birth of yet another North American megacity. Therefore, from a spatial perspective there is demand for joint collaboration and adoption of a regional science perspective including land use and spatio-temporal configurations. This calls forth a novel technique that allows for assessment of urban and regional change, and supports decision-making without having the usual concerns of locational data availability. It is this sense, that we present a spatial-retrofitting model, with the objective of (i) retrofitting spatial land use based on current land use and land cover, and assessing proportional change in the past, leading to four spatial timestamps of the Golden Horseshoe’s land use, while (ii) integrating this in a multi-user open source web environment to facilitate synergies for decision-making. This combined approach is referred to as a regional-spatial-retrofitting approach (RSRA), where the conclusions permit accurate assessment of land use in past time frames based on Landsat imagery. The RSRA also allows for a collective vision of regional urban growth supporting local governance through a decision-making process adhering to Volunteered Geographic Information Systems. Urban land use change can be refined by means of contribution from end-users through a web environment, leading to a constant understanding and monitoring of urban land use and urban land use change.

Sign in / Sign up

Export Citation Format

Share Document