Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area

Grekousis, George; Manetos, Panos; Photis, Yorgos N.

doi:10.1016/j.cities.2012.03.006

Cited by 149 publications

(86 citation statements)

References 35 publications

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“…This is integral to assessing risk and vulnerability to environmental phenomena such as climate change. It will also be worthwhile to compare the SVM based approached used in this study with other similar methods that have an integrated GIS approach with artificial neural network and fuzzy logic by incorporating additional driving force constraints based on additional data [32,74,75], and other tested integrated urban remote sensing and GIS approaches [76].…”

Section: Framework For Future Researchmentioning

confidence: 99%

Analysis of Settlement Expansion and Urban Growth Modelling Using Geoinformation for Assessing Potential Impacts of Urbanization on Climate in Abuja City, Nigeria

et al. 2016

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This study analyzed the spatiotemporal pattern of settlement expansion in Abuja, Nigeria, one of West Africa's fastest developing cities, using geoinformation and ancillary datasets. Three epochs of Land-use Land-cover (LULC) maps for 1986, 2001 and 2014 were derived from Landsat images using support vector machines (SVM). Accuracy assessment (AA) of the LULC maps based on the pixel count resulted in overall accuracy of 82%, 92% and 92%, while the AA derived from the error adjusted area (EAA) method stood at 69%, 91% and 91% for 1986, 2001 and 2014, respectively. Two major techniques for detecting changes in the LULC epochs involved the use of binary maps as well as a post-classification comparison approach. Quantitative spatiotemporal analysis was conducted to detect LULC changes with specific focus on the settlement development pattern of Abuja, the federal capital city (FCC) of Nigeria. Logical transitions to the urban category were modelled for predicting future scenarios for the year 2050 using the embedded land change modeler (LCM) in the IDRISI package. Based on the EAA, the result showed that urban areas increased by more than 11% between 1986 and 2001. In contrast, this value rose to 17% between 2001 and 2014. The LCM model projected LULC changes that showed a growing trend in settlement expansion, which might take over allotted spaces for green areas and agricultural land if stringent development policies and enforcement measures are not implemented. In conclusion, integrating geospatial technologies with ancillary datasets offered improved understanding of how urbanization processes such as increased imperviousness of such a magnitude could influence the urban microclimate through the alteration of natural land surface temperature. Urban expansion could also lead to increased surface runoff as well as changes in drainage geography leading to urban floods.

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Section: Framework For Future Researchmentioning

confidence: 99%

Analysis of Settlement Expansion and Urban Growth Modelling Using Geoinformation for Assessing Potential Impacts of Urbanization on Climate in Abuja City, Nigeria

et al. 2016

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“…A sprawling urban expansion can have on-site and off-site negative impacts [10], e.g., fragmenting the agro-forest landscape and reducing the environmental quality of entire regions [11]. These issues are clearly linked and inherently complex [12,13], since sprawl determinants are many and well differentiated (tourism growth, second-home expansion, decentralization of business activities, internal and foreign migration, change in lifestyles and consumption patterns, among others [10]). …”

Section: Introductionmentioning

confidence: 99%

Long-Term Urban Growth and Land Use Efficiency in Southern Europe: Implications for Sustainable Land Management

Zitti¹,

Ferrara

Perini³

et al. 2015

Sustainability

232

140

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Abstract:The present study illustrates a multidimensional analysis of an indicator of urban land use efficiency (per-capita built-up area, LUE) in mainland Attica, a Mediterranean urban region, along different expansion waves : compaction and densification in the 1960s, dispersed growth along the coasts and on Athens' fringe in the 1970s, fringe consolidation in the 1980s, moderate re-polarization and discontinuous expansion in the 1990s and sprawl in remote areas in the 2000s. The non-linear trend in LUE (a continuous increase up to the 1980s and a moderate decrease in 1990 and 2000 preceding the rise observed over the last decade) reflects Athens' expansion waves. A total of 23 indicators were collected by decade for each municipality of the study area with the aim of identifying the drivers of land use efficiency. In 1960, municipalities with low efficiency in the use of land were concentrated on both coastal areas and Athens' fringe, while in 2010, the lowest efficiency rate was observed in the most remote, rural areas. Typical urban functions (e.g., mixed land uses, multiple-use buildings, vertical profile) are the variables most associated with high efficiency in the use of land. Policies for sustainable land management should consider local and regional factors shaping land use efficiency promoting self-contained expansion and more tightly protecting rural and remote land from dispersed urbanization. LUE is a OPEN ACCESSSustainability 2015, 7 3360 promising indicator reflecting the increased complexity of growth patterns and may anticipate future urban trends.

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“…Many researchers and professionals have used diverse land use change models to explore the drivers and patterns of land use change, to make possible future scenarios and to provide appropriate policies affecting the changes by analyzing the causes and consequences of land use changes [31][32][33][34][35]. Most existing land use change models, however, have been criticized for not being able to provide reliable accuracy assessment processes of the output effectively [36].…”

Section: Literature Gaps and Research Objectivesmentioning

confidence: 99%

Forecasting Urban Vacancy Dynamics in a Shrinking City: A Land Transformation Model

Lee

Newman

2017

IJGI

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Abstract:In the past two centuries, many American urban areas have experienced significant expansion in both populating and depopulating cities. The pursuit of bigger, faster, and more growth-oriented planning parallels a situation where municipal decline has also been recognized as a global epidemic. In recent decades many older industrial cities have experienced significant depopulation, job loss, economic decline, and massive increases in vacant and abandoned properties due primarily to losses in industry and relocating populations. Despite continuous economic decline and depopulation, many of these so-called 'shrinking cities' still chase growth-oriented planning policies, due partially to inabilities to accurately predict future urban growth/decline patterns. This capability is critical to understanding land use alternation patterns and predicting future possible scenarios for the development of more proactive land use policies dealing with urban decline and regeneration. In this research, the city of Chicago, Illinois, USA is used as a case site to test an urban land use change model that predicts urban decline in a shrinking city, using vacant land as a proxy. Our approach employs the Land Transformation Model (LTM), which combines Geographic Information Systems and artificial neural networks to forecast land use change. Results indicate that the LTM is a good resource to simulate urban vacant land changes. Mobility and housing market conditions seem to be the primary variables contributing to decline.

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Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area

Cited by 149 publications

References 35 publications

Analysis of Settlement Expansion and Urban Growth Modelling Using Geoinformation for Assessing Potential Impacts of Urbanization on Climate in Abuja City, Nigeria

Analysis of Settlement Expansion and Urban Growth Modelling Using Geoinformation for Assessing Potential Impacts of Urbanization on Climate in Abuja City, Nigeria

Long-Term Urban Growth and Land Use Efficiency in Southern Europe: Implications for Sustainable Land Management

Forecasting Urban Vacancy Dynamics in a Shrinking City: A Land Transformation Model

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