A modified spatial entropy for urban sprawl assessment

Parvinnezhad, Davoud; Delavar, Mahmoud Reza; Claramunt, Christophe; Pijanowski, Bryan C.

doi:10.1080/10106049.2019.1678676

Cited by 14 publications

(7 citation statements)

References 28 publications

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“…Researchers have made several efforts to quantify sprawl. Shannon's entropy was employed to determine the extent of sprawl in Tripoli, Libya (Alsharif et al, 2015), Urmia City, Iran (Mohammadi et al, 2012), Tabriz city, Iran (Parvinnezhad et al, 2021), Mumbai, India , along the national highway from Mangalore to Udupi city, India (Sudhira et al, 2004), Dongguan City, China (Yeh & Li, 2001). Several researchers used landscape metrics to understand sprawl in various cities such as Ajmer city, India (Jat et al, 2008) and Idaho, United States of America (Dahal et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Urban Expansion and Identification of Sprawl Through Delineation of Urban Core Boundary

Sridhar

Rangarajan

2022

Journal of Landscape Ecology

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Cities are spatially expanding rapidly, leading to urban sprawl. This study aims to understand the nature of the urban expansion of Chennai city, located on India’s southeastern coast, by determining the urban growth pattern and identifying the urban sprawl areas. The urban growth pattern and sprawl areas between 1998 and 2019 are identified using remote sensing data through the delineation of the Urban Core Boundary (UCB). The urban areas were extracted from the Land Use Land Cover (LULC) classification using combined classification technique to delineate the UCB. All the findings were validated using ground truth information. LULC classification performed with an accuracy of more than 90 % for urban land cover revealed an increase in urban cover by 71.77% from 1998 to 2009 and 36.91 % from 2009 to 2019. The delineated UCB’s peripheral distance was measured from the city centre in an anticlockwise direction from 0˚ to 360˚ at every 10˚ interval. It is observed that the urban core boundary expanded to a maximum of 16.02 km along 240˚ and 11.93 km along 220˚ from the city centre, and the lands in the vicinity of the National Highway (NH 32), which is situated between these sectors, experienced maximum urban development. The study also pinpointed the sprawl areas during the study period, revealing that the urban sprawl occurs along the highways, around designated special economic zones, and industrial corridors.

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Section: Introductionmentioning

confidence: 99%

Assessment of Urban Expansion and Identification of Sprawl Through Delineation of Urban Core Boundary

Sridhar

Rangarajan

2022

Journal of Landscape Ecology

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“…Since the research object of information entropy is one-dimensional, while the reality is three dimensional or even multidimensional, so it is difficult to reflect the characteristics of actual spatial pattern [14] . To solve this problem, combined with the First Law of Geography [15] , Wang and Zhao (2018) proposed the spatial entropy (HS) considering spatial proximity, which is a quite useful attempt to apply entropy and information theory to landscape ecology research [16] .…”

Section: Introductionmentioning

confidence: 99%

Exploring spatial entropy in the analysis of landscape pattern and ecological sustainability in watershed ecosystem

Liu

Zhao

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Ecological sustainability research from the perspective of entropy has received extensive attention in recent years. Spatial entropy ( Hs ) is a new form of spatial heterogeneity analysis tool that applies entropy to distinguish and characterize different landscape patterns. In order to further explore the heterogeneity expression and ecological implication of Hs in the analysis of watershed ecosystem, this research takes Yanhe watershed as the study area. Based on the land use data of years 2000, 2010, and 2020, Hs in combination with 10 landscape metrics was applied for the study of landscape pattern and ecological sustainability. The results showed that: 1) Hs is correlated with landscape richness, aggregation and shape complexity, and it reflects the comprehensive heterogeneity information with high sensitivity to landscape variations, and also describes ecosystem stability with negative correlation; 2) The value of Hs showed a gradual increase from upstream to downstream in Yanhe watershed, and it dropped as a whole during the 20 years; 3) The stability of this basin from 2010 to 2020 had a greater variation range compared with the previous ten years, and the regions with large variation were mainly distributed in the midstream and downstream areas.

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“…While applying the concept of the thermodynamic entropy to describe urban systems is still problematic [ 1 ], spatial entropy [ 2 , 3 ], as a measure of disorder, can be successfully utilized to describe their geometric aspects. Several studies [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] employed entropy as an useful metric to describe the level of organization in various urban spatial structures. Spatial Shannon entropy was used [ 4 ] to analyze remote sensing data, and Geographic Information System (GIS) maps to characterize the urban growth patterns.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [ 8 ] demonstrated that the entropy of the spatial pattern of the land combined with its fractal properties [ 14 , 15 , 16 , 17 , 18 , 19 , 20 ] can be successfully applied to characterize the morphology of cities and regions. Spatial conditional entropies were employed [ 9 ] as a measure of the urban sprawl values. Huynh et al utilized [ 10 ] the idea of the entropy maximization to characterize the spatial pattern of urban locations.…”

Section: Introductionmentioning

confidence: 99%

Entropy of the Land Parcel Mosaic as a Measure of the Degree of Urbanization

Bitner

Fiałkowski

2021

Entropy

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Quantifying the urbanization level is an essential yet challenging task in urban studies because of the high complexity of this phenomenon. The urbanization degree has been estimated using a variety of social, economic, and spatial measures. Among the spatial characteristics, the Shannon entropy of the landscape pattern has recently been intensively explored as one of the most effective urbanization indexes. Here, we introduce a new measure of the spatial entropy of land that characterizes its parcel mosaic, the structure resulting from the division of land into cadastral parcels. We calculate the entropies of the parcel areas’ distribution function in different portions of the urban systems. We have established that the Shannon and Renyi entropies R0 and R1/2 are most effective at differentiating the degree of a spatial organization of the land. Our studies are based on 30 urban systems located in the USA, Australia, and Poland, and three desert areas from Australia. In all the cities, the entropies behave the same as functions of the distance from the center. They attain the lowest values in the city core and reach substantially higher values in suburban areas. Thus, the parcel mosaic entropies provide a spatial characterization of land to measure its urbanization level effectively.

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A modified spatial entropy for urban sprawl assessment

Cited by 14 publications

References 28 publications

Assessment of Urban Expansion and Identification of Sprawl Through Delineation of Urban Core Boundary

Assessment of Urban Expansion and Identification of Sprawl Through Delineation of Urban Core Boundary

Exploring spatial entropy in the analysis of landscape pattern and ecological sustainability in watershed ecosystem

Entropy of the Land Parcel Mosaic as a Measure of the Degree of Urbanization

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