Soft Computing Modelling of Urban Evolution: Tehran Metropolis

Exploring computational intelligence, geographic information systems and statistical information, a creative and innovative model for urban evolution is presented in this paper. The proposed model employs fuzzy logic and artificial neural network as forecasting tools for describing the urban growth. This dynamic urban evolution model considers the spatial data of population, as well as its time changes and the building usage patterns. For clustering the spatial features, fuzzy algorithms were implemented to represent different levels of urban growth and development. Then, these fuzzy clusters were modeled by the multi-layer neural networks to estimate the urban growth. Based on this novel intelligent model, the current state of development of Tehran’s population and the future of this urban evolution were evaluated by empirical data and the achieved outcomes were detailed in qualitative charts. The input data-set includes four censuses with five-year intervals. Tehran's demographic evolution model forecasts the next five years with an overall accuracy of 81% and Cohen's kappa coefficient up to 74% beside the qualitative charts. These performance indicators are higher than the previous advanced models. The primary objective of this proposed model is to aid planners and decision makers to predict the development trend of urban population

Geospatial approach using socio-economic and projected climate change information formodelling urban growth

Urban growth and climate change are two interwoven phenomena that are becoming global environmental issues. Using Niger Delta of Nigeria as a case study, this research investigated the historical and future patterns of urban growth using geospatialbased modelling approach. Specific objectives were to: (i) examine the climate change pattern and predict its impact on urban growth modelling; (ii) investigate the historical pattern of urban growth; (iii) embrace some selected parameters from United Nations Sustainable Development Goals (UN SDGs) and examine their impacts on future urban growth prediction; (iv) verify whether planning has controlled urban land use sprawl in the study area; and (v) propose standard operating procedure for urban sprawl in the area. A MAGICC model, developed by the Inter-Governmental Panel on Climate Change (IPCC), was used to predict future precipitation under RCP 4.5 and RCP 8.5 emission scenarios, which was utilized to evaluate the impact of climate change on the study area from 2016 to 2100. Observed precipitation records from 1972 to 2015 were analysed, and 2012 was selected as a water year, based on depth and frequency of rainfall. A relationship model derived using logistic regression from the observed precipitation and river width from Landsat imageries of 2012 was used to project the monthly river width variations over the projected climate change, considering the two emission scenarios. The areas that are prone to flooding were determined based on the projected precipitation anomalies and a suitability map was developed to accommodate the impact of climate change in the projection of future urban growth. Urban landscape changes between 1985 and 2015 were also analysed, which revealed a rapid urban growth in the region. A Cellular Automata/Markov Chain (CA-Markov) model was used to project the year 2030 land cover of the region considering two scenarios; normal projection without any constraint, and using some designed constraints (forest reserves, population and economy) based on some selected UN SDGs criteria and climate change. On validation, overall simulation accuracies of 89.25% and 91.22% were achieved based on scenarios one and two, respectively. The projection using the first scenario resulted to net loss and gains of - 7.37%, 11.84% and 50.88%, while that of second scenario produced net loss and gains of -4.72%, 7.43% and 48.37% in forest, farmland and built-up area between 2015 and 2030, respectively. The difference between the two scenarios showed that the UN SDGs have great influence on the urban growth prediction and strict adherence to the selected UN SDGs criteria can reduce tropical deforestation, and at the same time serve as resilience to climate change in the region

Impacts of Land Cover Change on Urban Heat Island (UHI) in Denver from 1985 to 2020

Rapid urbanization due to land use and land cover change has become one of the major problems in the fastest-growing cities during the past few decades. Land surface temperature has changed dramatically due to urban expansion, and it is a major driver of urban eco-environmental change. Increasing temperature leads to the Urban Heat Island (UHI) problem in rapidly growing cities like Denver, contributing to global warming at multiple scales. UHI study is significant to monitor and mitigate the urban heat islandrelated problem in the study area Denver. Satellite remote sensing analysis ready data (ARD) with 30 m resolution based on Landsat 4,5,7 and 8 were acquired for nine dates that correspond to summer, fall, and winter seasons in 1985, 2000, and 2020. Land cover change dynamics were derived using Land Change Monitoring Assessment and Projection (LCMAP) developed land cover classes, and land surface temperature (LST) has been extracted from seasonal and annual surface temperature data. Land cover data analysis observed changes within seven primary land cover classes; for instance, study area has gained 13% of developed land cover but lost a significant percentage of cropland from 1985 to 2020. The relationship between land cover and surface temperature has been explored by linear regression analysis using normalized difference vegetation index (NDVI) and LST. NDVI was taken as the explanatory variable, and LST was taken as a dependent variable to show the correlation between land cover and LST. Investigation of the correlation between NDVI and LST found that seasonal variability, spatiotemporal variations, and other underlying factors affect their relationship. Seasonal and annual Urban Heat Island intensity (UHII) distribution and variation have been investigated. The results found that the mean annual UHII in 2020 was 1°C which was greater than the mean UHII in 1985 and 2000. The UHII distribution was consistent in the central part of the city, and the scattered distribution of UHII was examined in non-urban extent over the past three decades. The methodology of this study can be a framework for future research on cities with a similar climate to Denver, and this can also help for sustainable urban planning and a better ecological environment

Simulación del crecimiento urbano de la zona metropolitana Tepic-Xalisco, México

The metropolitan area of Tepic-Xalisco (Nayarit, Mexico) has been experienced a fast growth in the last 30 years, generating situations that put the population and the environment at risk, being urgent and necessary to establish new approaches on strategies of urban planning. Understanding the processes of urban growth and simulating possible scenarios have proven to be an essential tool for decision making in the context of spatial planning. The objective of this project was simulating the urban growth the metropolitan area Tepic-Xalisco at the year 2045 horizon. Three different models were used: Multi-Criteria Evaluation Techniques (MCE), Logistic Regression (LR) and Cellular Automata with Markov chains (CA-Markov) to verify the one that better fits the spatial reality and establish a trend situation future. The results were validated with the actual data of urban occupation of 2015. The CA-Markov model showed the best results produced an overall accuracy of 75% and close coincidences in landscape metrics, so this model was used to generate a trend-based scenario of urban growth to the year 2045. The resulting information will be used to generate alternative scenarios that will help to design and evaluate sustainable urban development oriented urban planning strategies.La zona metropolitana Tepic-Xalisco (Nayarit, México) ha tenido un rápido crecimiento en los últimos 30 años, generando situaciones que han puesto en riesgo a la población y medio ambiente, siendo urgente y necesario establecer nuevos enfoques sobre estrategias de planificación urbana. Entender los procesos de crecimiento urbano y simular posibles escenarios futuros han demostrado ser una herramienta esencial para la toma de decisiones en el contexto de la ordenación del territorio. El objetivo del presente trabajo fue simular el crecimiento urbano de la zona metropolitana Tepic-Xalisco al año horizonte 2045. Se utilizaron tres modelos diferentes: técnicas de Evaluación Multi-Criterio (EMC), Regresión Logística (RL) y Autómatas Celulares con cadenas de Markov (AC-Markov), para comprobar el de mejor ajuste a la realidad espacial y establecer una situación tendencial futura. Los resultados fueron validados con datos reales de ocupación urbana del 2015. El modelo AC-Markov mostró mejores resultados al producir una exactitud general del 75 % y coincidencias cercanas en la comparación de las métricas del paisaje, por lo que este modelo fue utilizado para generar un escenario futuro tendencial de crecimiento urbano para el año 2045. La información resultante servirá para generar escenarios alternativos que ayuden a diseñar y evaluar estrategias de planificación urbana orientadas al desarrollo urbano sostenible

Uso y cobertura del suelo en las islas macaronésicas de Portugal y España: nuevos métodos para cuantificar y visualizar información de patrones espaciales

Tesis de la Universidad Complutense de Madrid, Facultad de Geografía e Historia, Departamento de Geografía Humana, leída el 23/11/2016The aim of this research is to propose novel methods for quantifying and visualizing geographical information, in order to aid the spatial planning decision-making process when addressing land use and land cover patterns. In doing so, several modeling and geographic visualization methods are developed and demonstrated by using the Macaronesian islands of Portugal and Spain as study areas. Macaronesia is a biogeographical region consisting of several archipelagos in the Atlantic Ocean belonging to three countries: Portugal, Spain, and Cape Verde. This research encompasses three archipelagos: the Azores, Madeira, and the Canary Islands. From these three archipelagos, the four most densely populated islands were further selected for the land use and land cover assessments: São Miguel, Madeira, Tenerife, and Gran Canaria. A common feature of the Macaronesian islands is that, ever since European colonization in the fifteenth century, up until the mid-twentieth century, anthropogenic land change was predominately attributable to agricultural activities consuming forests and natural areas. In the mid-twentieth century, owing to profound social and economic changes, the tertiary sector started its rise in becoming the main economic sector. Because the secondary sector in this region has always been minor, this substantial shift to the tertiary sector would dictate a progressive abandonment of the primary sector. Hence, agricultural areas started to recede. As a result, the last decades of the twentieth century were marked by a significant shift in land use dynamics. Agricultural activities ceased to be the main driving force of land change and were replaced by a rampant increase of the artificial surfaces, mainly on the southern coastal areas, where tourism-related and real estate pressure constitute a major impact on the landscape. A direct consequence of this pressure was the drastic transformation across the islands’ leeward coastal landscapes...El objetivo principal de esta investigación es proponer nuevos métodos para cuantificar y visualizar información geográfica, con el fin de facilitar el proceso de toma de decisiones en relación a los patrones de uso y ocupación del suelo. De este modo, se desarrollan y aplican varios métodos de modelación y visualización geográfica, utilizando las islas macaronésicas de Portugal y España como áreas de estudio. La Macaronesia es una región biogeográfica que integra varios archipiélagos en el Océano Atlántico pertenecientes a tres países: Portugal, España y Cabo Verde. Esta investigación abarca tres archipiélagos: Azores, Madeira y Canarias. Para una evaluación detallada de uso y cobertura del suelo se seleccionaron las cuatro islas más densamente pobladas: San Miguel, Madeira, Tenerife y Gran Canaria. Una característica común a las islas macaronésicas es que, desde de la colonización en el siglo XV hasta mediados del siglo XX, el cambio antropogénico del suelo se debió principalmente a las actividades agrícolas, que ocuparon bosques y áreas naturales. A mediados del siglo XX, debido a profundos cambios sociales y económicos, el sector terciario empezó su ascenso para convertirse en el principal sector económico. Debido a que el sector secundario en esta región siempre ha tenido una importancia menor, este proceso de terciarización de la economía supuso un progresivo abandono del sector primario. Por lo tanto, las áreas agrícolas comenzaron a experimentar un claro retroceso. Como resultado de este proceso, las últimas décadas del siglo XX se caracterizaron por un cambio significativo en las dinámicas de uso y cobertura del suelo. Las actividades agrícolas dejaron de ser la principal fuerza impulsora en el cambio de lo suelo y fueron reemplazadas por el aumento desenfrenado de las superficies artificiales, principalmente en las zonas costeras del sur, donde el turismo y la especulación inmobiliaria ejercen una gran presión sobre el paisaje. Consecuencia directa de esta presión fueron las drásticas transformaciones de los paisajes costeros de las islas...Esta investigação tem como principal objectivo propor novos métodos para quantificar e visualizar informação geográfica, de modo a auxiliar o processo de tomada de decisão quando seja necessário analisar padrões de uso e ocupação do solo. Ao longo da investigação são apresentados vários métodos de modelação e visualização geográfica, usando como área de estudo as ilhas da Macaronésia pertencentes a Portugal e Espanha. A Macaronésia é uma região biogeográfica no Oceano Atlântico constituída por vários arquipélagos pertencentes a três países: Portugal, Espanha e Cabo Verde. Este trabalho de investigação abrange três arquipélagos: os Açores, a Madeira e as Ilhas Canárias. Para uma avaliação mais detalhada quanto ao uso e ocupação do solo, foram seleccionadas as quatro ilhas mais densamente povoadas: São Miguel, Madeira, Gran Canaria e Tenerife. Uma característica comum às ilhas da Macaronésia reside na particularidade de, desde a sua colonização no século XV, até meados do século XX, as alterações antropogénicas do solo terem estado predominantemente associadas às actividades agrícolas que consumiram extensas áreas de floresta e espaços naturais. Em meados do século XX, devido a profundas alterações sociais e económicas, o sector terciário iniciou a sua ascensão para se tornar o principal sector económico. Uma vez que, nesta região, o sector secundário foi sempre pouco significativo, a terciarização da actividade económica ditou um progressivo abandono do sector primário. Deste modo, as áreas agrícolas começaram a recuar. Como resultado deste processo, as últimas décadas do século XX foram marcadas por uma mudança significativa na dinâmica de uso e ocupação do solo nas ilhas desta região. As actividades agrícolas deixaram de ser a principal força motriz para as alterações no uso do solo, sendo substituídas pelo aumento galopante das superfícies artificiais, principalmente nas áreas costeiras do sul, onde as actividades relacionadas com o turismo e a especulação imobiliária causaram um grande impacto na paisagem, e contribuiram para a transformação drástica do litoral sotavento das ilhas...Depto. de GeografíaFac. de Geografía e HistoriaTRUEunpu

A Spatially Heterogeneous Expert Based (SHEB) Urban Growth Model Using Model Regionalization

Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban growth models. Instead of applying a single method to the entire study area, we segment the study area into different regions and apply targeted algorithms in each subregion. The working hypothesis is that the integration of appropriately selected region-specific models will outperform a globally applied model as it will incorporate further spatial heterogeneity. We examine urban land use changes in Denver, Colorado. Two land use maps from different time snapshots (1977 and 1997) are used to detect the urban land use changes, and 23 explanatory factors are produced to model urbanization. The proposed Spatially Heterogeneous Expert Based (SHEB) model tested decision trees as the underlying modeling algorithm, applying them in different subregions. In this paper the segmentation tested is the division of the entire area into interior and exterior urban areas. Interior urban areas are those situated within dense urbanized structures, while exterior urban areas are outside of these structures. Obtained results on this model regionalization technique indicate that targeted local models produce improved results in terms of Kappa, accuracy percentage and multi-scale performance. The model superiority is also confirmed by model pairwise comparisons using t-tests. Th