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The extension of urban perimeter markedly cuts available productive land. Hence, studies in urban sprawl analysis and modeling play an important role to ensure sustainable urban development. The urbanization pattern of the Greater Asmara Area (GAA), the capital of Eritrea, was studied. Satellite images and geospatial tools were employed to analyze...
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... Urban sprawl is associated with the growth, form and composition of urban areas (Pennock, 2006). It affects social, economic and environmental sustainability of cities. Shannon's entropy ( E n ) is the most widely used method for urban growth and sprawl analysis, measuring the degree of spatial concentration and distribution of the area (Tewolde & Cabral, 2011). E n is calculated with Eq. 1 and takes values between 0 and log(n). ...
... If the relative entropy value is 0, this indicates that the distribution of the urban areas in a region have achieved the highest density, resulting in a more condensed and clustered layout. However, a relative entropy value of 1 suggests a less compact distribution, which can stem from an irregular layout in the urban area (Tewolde & Cabral, 2011). ...
This study aims to investigate the impact of spatio-temporal dynamics of urban sprawl on habitats in Istanbul, using the CORINE 1990 and 2018 land cover datasets. In this context , by means of geographic information system (GIS) tools and techniques, land cover maps are created for land cover change analysis and visualization, the Shannon's entropy method is performed for measuring urban growth and sprawl, and the landscape metrics method is applied for assessing habitat loss and fragmentation. Results indicate that the rate of urban sprawl in the area increased by 0.1230 as per the Shannon's entropy index over the 28-year period. Similarly, the Shannon Diversity Index and the Shannon Evenness Index rose from 1.74 to 1.85 and from 0.68 to 0.70, respectively, indicating an increase in urbanization within the area. Moreover, an analysis of patch numbers reveals that habitat fragmentation increased in shrub and/or herbaceous vegetation associations (72.55%), heterogeneous agricultural areas (45.11%), arable lands (42.5%), forests (36.13%) and pastures (15.05%), due to urbanization. Habitat fragmentation has had a detrimental effect on the local biodiversity. While 15 flora species were identified as vulnerable, 13 as endangered and 9 as critically endangered, 19 fauna species were identified as vulnerable and 5 as endangered. This study highlights that the natural habitats and biodiversity of Istanbul will suffer further decline due to urbanization unless sustainable urban planning and management policies are put into practice. It is essential to have controlled urban development to preserve the ecosystem's carrying capacity, and urbanization decisions must consider this requirement.
... Hence, urban sprawl is a dynamic phenomenon (Batty et al. 1999;Torrens and Alberti 2000;Sudhira et al. 2004). It is defined as a dispersion of spatial expansion of the city or town towards its peripheral and sub-urban areas (Galster et al. 2001;Tewolde and Cabral 2011). Experts have different opinions about the causes of sprawl, as its patterns differ by characteristics (Habibi and Asadi 2011). ...
... However, it has been analysed effectively using Shannon's entropy and landscape metrics by integrating with RS and GIS techniques, and statistics in many studies. Based on these two approaches, monitoring and modelling of urban growth dynamics were used by several researchers over different cities worldwide (Sudhira et al. 2004;Aguilera et al. 2011;Ramachandra et al. 2014;Tewolde and Cabral 2011). Shannon's entropy is used to quantify the degree of compactness or dispersion of a certain geophysical variable like built-up area in a spatial unit (Lata et al. 2001;Jat et al. 2008;Bhatta 2012). ...
... Shannon's entropy or information entropy concept was proposed by Shannon as a concept based on which a measure of uncertainty about the occurrence of a certain event can be calculated (Shannon 1948). Shannon's entropy model is a common and widely used technique of measuring the magnitude of growth of an urban body in a region using RS and GIS (Lata et al. 2001;Tewolde and Cabral 2011;Mosammam et al. 2017). It measures the degree of concentration or dispersion of any geographical variable in any spatial unit (Sudhira et al. 2004;Bhatta 2012). ...
The rapid urban growth and anthropogenic activities have posed a threat to the local environment and ecosystem around the world. This situation has become a hindrance to planners and policy makers for sustainable urban development. Therefore, this study mainly focuses on the assessment of urban growth patterns in relation to urban sprawl in Jalpaiguri urban agglomeration. Multi-temporal Landsat data have been used for land use change detection and urban sprawl quantification. The maximum likelihood classifier technique has been performed to create land use land cover maps for each study year (2001, 2011 and 2021). Urban expansion intensity index has been applied to determine the magnitude of urban expansion. Landscape metrics and Shannon’s entropy have been employed to assess the urban sprawl to a spatial extent. Spatiotemporal land use changes reveal that the non-urban class (vegetation, agriculture, water bodies, and fallow) have been decreasing consistently with an increase in built-up areas over time. Built-up area has increased by almost seven times in the span of the last 20 years (2001–2021). In the first decade, the growth rate of urban areas was 145.42% with a medium speed of expansion and in the next decade, it was 180.83% with a very high speed. Landscape metrics show that the fragmentation of the entire urban landscape into small patches happened from 2001 to 2011 in a higher magnitude indicating the occurrence of sprawling characteristics. But in recent times, the entire landscape is aggregating into large single urban patches which indicate a clumpy situation and would affect the local ecological environment. Shannon’s entropy model also verifies the compact urban sprawl in different directions and distances from the city centre. The understanding of urban growth dynamics and land use changes is essential for addressing the rapid urbanization within this urban region. There is an immediate need for an appropriate strategy for effective utilization of land use and monitoring of uncontrolled and haphazard urban growth. This research study would help the urban planner to take a specific scope of action for future urban growth and development.
... Shannon's entropy quantifies the level of spatial concentration and dispersal on the surface [30]. The relative entropy can be applied to normalize the entropy value, scaling it to fall within the range of 0 to 1 according to (1) [20], [31]. ...
... The built-up areas exhibit their highest compactness (concentration or aggregation) within a particular region when the entropy value is 0. Conversely, an entropy value of 1 indicates that the built-up area displays a spatial distribution that is irregular and dispersed [30]. The 0.5 of entropy value is considered as the threshold value [31], [32]. ...
... where En is the relative entropy, P i is the probability or percentage of development in the area or P i = X i / n i=1 X i which x i is the density of land development, which is equal to the amount of built-up area divided by the total amount of built-up area in the i th of n total zones [30]- [33], and n is the zone number. ...
The objective of this study is to utilize geospatial technology and remote sensing data to assess the changes in land use and land cover, as well as urban sprawl, in Kabul city, which serves as the capital of Afghanistan, during the period spanning from 1993 to 2021. Urban sprawl has given rise to unsustainable patterns of urban growth when viewed from social, environmental, and economic perspectives. Hence, it is crucial to observe and regulate the city’s expansion to uphold sustainable planning and development. In this research, Landsat 5 and Landsat 8 for the years 1993, 2008, and 2021, as well as the integration of Shannon’s entropy model with GIS, were used to estimate changes in land cover and land use, as well as spatial dispersion and urban compactness. The results show that the built-up area increased dramatically from 137.7 km2 to 212.0 km2, which demonstrates 74.4 km2 of expansion, while the vegetation cover decreased significantly from 208.5 km2 to 173.7 km2, which shows a 34.8 km decline from 1993 to 2008. While between 2008 and 2021, the built-up area increased drastically from 212.0 km2 to 364.8 km2, which demonstrates 152.8 km2 of expansion, the vegetation cover decreased significantly from 173.7 km2 to 126.6 km2, which shows a 47.1 km2 decline. Furthermore, between 1993 and 2021, the built-up area expanded from 137.7 km2 to 364.8 km2, indicating a 227.2 km2 expansion, whereas the vegetation cover reduced significantly from 208.5 km2 to 126.6 km2, showing a −81.9 km2 reduction. Furthermore, the total values of relative Shannon’s entropy for the years 1993, 2008, and 2021 are 0.68, 0.71, and 0.70, respectively, which are closer to the upper limit of 1 and hence indicate the spatial dispersion within the study area during the study period. The findings of this research will serve as valuable tools for urban policymakers. They will aid in comprehending the spatial patterns of urban sprawl in Kabul city and in formulating appropriate strategies and policies. These measures aim to curtail the wasteful utilization of nonrenewable resources, maintain environmental equilibrium, address social inequalities, and promote comprehensive and sustainable development.
... Extended words in searching processes, such as urbanization and These models aim to derive an empirical relationship between several independent and dependent variables. It assumes that regressor variable/s have a nonzero influence on the change in the urban environment Agent-based model (ABM) (Kniveton et al., 2011;Shoko & Smit, 2013;Vermeiren et al., 2016;Wang et al., 2017) It assumes the decision and interaction of various independent urban agents, i.e. individual agents (residents, investors, and others) or collective units (government and other formal and informal organizations), can influence the urbanization pattern Land change modeler (LCM): (Jagarnath et al., 2019;Mahmoud et al., 2016;Tarawally et al., 2019;Tewolde & Cabral, 2011) It is a machine learning technique to model land use change processes in a particular area (Shade & Kremer, 2019). Based on the previous input land use maps, a computer program drives the change probabilities and replicates this into future prediction modelling, are represented by the symbol, '*'. ...
Africa’s urbanization rate has quadrupled from 14% in 1950 to about 44% in 2022. A variety of urban growth models have been used to measure and monitor urbanization, its drivers and implications for urban planning and sustainability. This paper reviews the performance of various Cellular Automata (CA)-based urban growth models and their implications for the urbanization processes in Africa. To this end, we employ a systematic review approach to identify the final 18 articles published in Web of Science-indexed journals until 2022. Our review found that the CA-based urban growth model has been successfully used in Africa to track the impacts of urban growth, assess different city growth scenarios and compare the performance of various CA-integrated models. Yet, most of the reviewed CA-based studies have focused on more urbanized regions of the continent. The result also reveals that integrating the CA model with other statistical methods improves its broader application and practicality than a conventional CA model. Our findings give planners, policy-makers, and other urban stakeholders a more in-depth understanding of the challenges of unplanned urbanization and the need for meaningful participation from urban stakeholders in city growth and sustainable land use management that balances urban growth and the environment.
... Infrastructure governance has multiple facets, integrating household necessities, community demands, and resource potentials. In most of the developing countries, population-induced urban growth has caused several issues (Pawe & Saikia, 2020;Tewolde & Cabral, 2011) in the provision of infrastructure. Whilst the household necessities deal with access to drinking water, electricity, sewerage, etc.; all-weather roads, access to public places, streetlights, and waste management are some notable community demands in this regard. ...
Of late, infrastructural issues have become the prime concern of urban governance in India. Urban Local Bodies (ULBs), which are constitutionally responsible for grass-root-level governance, do not have the sufficient financial independence to govern infrastructure comprehensively. Thus, a supply-demand anomaly is always evident. In the prevailing federal structure, the strategic framework of institutional devolution is incapable of satisfying the citizen’s demands. By applying inductive logic and identifying the attributes, roles, and responsibilities of urban infrastructure governance actors, this paper investigates the theoretical and normative trajectory. Accordingly, a wide range of literature was consulted and empirically evaluated in the existing framework of governance. Shedding some light on the global scenario of urban infrastructure governance, this study emphasises bridging the state, private, and individual gap. Though the central government is expected to set up the strategic framework, state and local governments will act on the field towards engineering an environment to promote pro-citizen governance towards sustainability.
... In urban growth and sprawl, Shannon entropy can be applied to measure population distribution or land use within a city or urban region. It provides a way to assess how concentrated or dispersed the growth and development patterns are (Tewolde and Cabral 2011;Punia and Singh 2012;Deribew 2020). In this study, we have used Shannon entropy to identify and measure the sprawling urban scenario in the KCC. ...
The spatio-temporal dynamics and regional land use driving factors are fundamental considerations in achieving suitable and sustainable urban development. These aspects play a significant role in shaping cities’ physical, social, and environmental dimensions. This article aims to document and analyze the detection of LULC changes and their concentration, along with urban sprawl and prediction for the future. The study utilized multi-temporal satellite imageries of 2001, 2011, and 2021 to analyze the historical land cover, urban expansion, land transformation, growth direction, and urban sprawl in the study area. Subsequently, to predict and simulate future land use/land cover scenarios, the study employed an integrated cellular automata (CA)–Markov model using the theTerrSet software. The change detection results revealed that the built-up area had drastically increased from 17.90 to 40.64% from 2001 to 2021, and the barren land and agricultural land had significantly decreased. The transition matrix shows that the maximum barren land was converted into a built-up area and fallow land; at the same time, agriculture lost its maximum area, and built-up gained maximum area. The predicted LULC map of 2031 indicates specific patterns of change, including converting barren land into built-up areas and expanding vegetation cover due to reforestation and agricultural activities. The built-up area is projected to experience a significant increase and is estimated to expand by 62.29 km2, representing 50.46% of the total land-use area. Further, the study predicts a decrease in barren land over the ten years; the estimated change in barren land is 14.33%. The findings demonstrate that the model performed well in projecting the LULC of 2021, achieving an AUC (Area Under the Curve) of 78%. Additionally, the kappa coefficient of 0.8 further supports the model’s capability as a feasible representation of the study area. The study’s findings contribute to understanding LULC dynamics, urban sprawl, and future projections, and it provides crucial data for planning and decision-making processes, supporting sustainable land use management and informing strategies for suitable urban development in the study area.
... It is an indicator that calculates the distribution of built up according to their area within a certain spatial unit (Jat et al. 2008). Shannon entropy proven technique is used to assess the degree of spatial concentration and dispersion of the surface (Tewolde, Cabral 2011). Currently, it has been inserted to remote sensing and GIS, in order to achieve valuable access that allows measuring the spatial distribution of built areas and indicating the spatial concentration (compactness) and dispersion (urban sprawl) of built areas of urban growth (Nelson 1999, Vanum, Hadgu 2012. ...
... If values are close to 0, the agglomerations are of compact structures (concentrated, aggregated) if values are close to 1, this means spread areas (an unequal dispersed spatial distribution) (Tewolde, Cabral 2011). If the entropy values cross the threshold (0.5), the city is considered as sprawling (Bhatta et al. 2010). ...
Fekkous N., alkama d., Fekkous K., 2023. Cartography and analysis of the urban growth, case study: Inter-communal grouping of Batna, Algeria. Quaestiones Geographicae 42(1), Bogucki Wydawnictwo Naukowe, Poznań, pp. 123-139. 10 figs, 3 tables. abstract: This paper focuses on the analysis of the urban macroform in terms of urban compactness and dispersion (urban sprawl) in the inter-communal grouping of Batna, which is composed of four adjacent interconnected communal districts: Batna, Tazoult, Oued Chaaba and Fesdis. First, the urban macroform is examined by mapping the urban areas that are characterised by morphological changes over a period of 36 years utilising remote sensing and geographic information system (GIS) through satellite images taken from Landsat TM and ETM +, Sentinel 2 (1984, 1996, 2008 and 2020). Next, the Shannon entropy method is utilised to determine compactness or dispersion of urban growth over time. In addition, a fractal analysis based on the box-counting method is used to assess the complexity and to explain the morphological reality of the macroform through urban changes. In order to predict the future change scenarios and spatial distributions of land use and land cover in the coming years the hybrid cellular automata (CA)-Markov method is used. The results of the remote sensing, Shannon entropy values and fractal indices demonstrate that Batna inter-municipal grouping has experienced moderate urban development according to the observed urban sprawl between 1984 and 2020. These data are helpful in the urban planning and to provide decision-making tools.
... Land use and cover change (LUCC) is beneficial to reflect the dynamic patterns of change in urban villages. LUCC has been an enduring research topic in environmental sciences, ecology, and geography since the 1990s [21]. In terms of the evolutionary mechanisms, the two main themes are the historical evolutionary characterization and the future-oriented spatiotemporal modeling. ...
How to recognize the land use change in urban villages during dynamic transformation in Haidian District, Beijing, has become a hot topic with the promotion of urban renewal. The GF-1 high-resolution remote sensing images of 2013, 2015, and 2020 were used in this study to reflect the land use change in urban villages before and after urban renewal by using a hierarchical machine learning recognition method based on scene-based and random forest classification. The overall scale of urban village blocks in Haidian was 10.46 km2, showing the distribution pattern along the traffic arteries in 2013. In 2015, it dropped to 10.11 km2. The scale of urban village blocks in 2020 decreased to 1.02 km2, 9.75% of that in 2013. Three kinds of urban village renewal logic are revealed by further taking Chuanying Village as an example: “urban village–blue–green space”, “urban village–real estate”, and “urban village–municipal facilities”.
... For projected LULC maps, Land Change Modeller (LCM) integrated within IDRIS Selva is used (Eastman et al., 2005b;Eastman, 2012;Adhikari & Southworth, 2012). In the past, LCM has been used to create projected land use changes for deforestation (Michalski et al., 2008), agriculture and pasture lands (Rodríguez Eraso et al., 2013), urban growth (Tewolde & Cabral, 2011), and of course, the hydrological response of watershed (Wilson & Weng, 2011). ...
The present study aims at documenting the impact of different climate and land use change scenarios on runoff in the Kangsabati River basin. While the study relies on India Meteorological Department (IMD), National Oceanic and Atmospheric Administration’s Physical Sciences Laboratory (NOAA-PSL), and a multi-model ensemble of six driving models from Coordinated Regional Downscaling Experiment-Regional Climate Models (CORDEX RCM) for climate data input, it depends on IDRISI Selva’s Land Change Modeller (LCM) and Soil and Water Assessment Tool (SWAT) model to generate projected land use land change maps and simulate its streamflow response, respectively. A total of four land use and land cover (LULC) scenarios, representing four projected land use change, were modelled across three climatic scenarios, called Representative Concentration Pathways (RCPs). With runoff being predominantly impacted more by climate change than LULC, volumetric runoff is expected to be 12–46% higher than the baseline period of 1982–2017. Conversely, while surface runoff is expected to decrease by 4–28% in lower parts of the basin, it will increase by 2–39% in the rest of it, depending on the subtle alterations in land use and climatic variability.
... Now it is estimated that, by 2028, India is expected to have the world's highest population, but in recent times, a declining trend of population growth has been observed (Seto et al. 2012;Bakr and Bahnassy 2019). According to Tewolde and Cabral (2011), the urban territory is expanding at a faster pace than the urban population. Rafiee et al. (2009) mentioned that cities are putting a lot of strain on existing lands and commodities as a result of their fast expansion. ...
Since producing a reliable land use land cover map is complex and time-consuming, the introduction of Google Earth Engine (GEE) and the availability of enormous volumes of Geosciences and Remote Sensing information provide a possibility for spatiotemporal monitoring of changing earth surface. The aim of this study is to utilise machine learning (random forest) on the Google Earth Engine framework with earth observation data to analyse land use land cover change in the Raiganj municipality. The research also uses a logistic regression-cellular automata model to evaluate the potential land use land cover changes by 2025. The findings of the study demonstrate that between 1990 and 2000, the study area experienced 1.87 km2 of urban expansion at an annual rate of 8.68%. The five-year land use land cover change study revealed that urban expansion was recorded at 59.88% from 1990 to 1995, followed by 2010–2015 (28.26%). With an average annual growth rate of 1.8% (0.41 sq. km), the lowest urban expansion was seen between 2005 and 2010. In Raiganj municipality, the majority of urban expansion and growth occurs in the southwest direction. According to the predicted land use land cover map for 2025, about 5.06% of the study area will be urbanised in the upcoming five years and urbanisation will spread in the northeastern part of the study region. The results highlight the requirement of monitoring land use land cover change and assisting policymakers in implementing policies to limit haphazard urbanisation and avoid human–environment conflict in the study region.
