Urban Land Expansion from Scratch to Urban Agglomeration in the Federal District of Brazil in the Past 60 Years

Li, Zhichao; Gurgel, Helen; Li, Minmin; Dessay, Nadine; Gong, Peng

doi:10.3390/ijerph19031032

Cited by 12 publications

(8 citation statements)

References 47 publications

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“…Urban growth between 2005 and 2014 was found in all three zones (urban, rural and peri-urban), which is also observed in other worldwide studies (Dou & Han 2022;Li et al 2022). This net increase was higher in the urban zone, followed by the one present in the peri-urban and rural zones; this agrees with the urban expansion reported by Li et al (2022) for cities in Brazil, who state that this increment takes place in a polycentric way. Consequently, more than 90% of the urban growth in the peri-urban and rural zones in this period was infill of the gaps left by this fragmented and scattered urban form.…”

Section: Discussionsupporting

confidence: 88%

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Urban growth in peri-urban, rural and urban areas: Mexico City

Cruz-Bello

Galeana-Pizaña²,

Arellano

2023

Buildings and Cities

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This remote sensing study compares growth occurring in three urban types between 2005 and 2014: peri-urban, rural and urban, in a fast-growing metropolitan region west of Mexico City. Future urban growth for the period 2014-24 is modelled using the land-use/cover change (LUCC) model Geomod. Urban expansion is correlated with some socio-territorial factors and the impacts are assessed for the loss of biomass. In both periods, the urban zone differed the most from the other two in terms of urban expansion. The Geomod predictions overestimate the urban expansion in the urban zone and underestimate it in the peri-urban and rural zones. Significant differences exist in the average urban expansion between zones. The main urban growth drivers were elevation, population density, distance to previous urban land and distance to roads. A substantial loss of biomass is due to urban growth, including expansion and infill. The research reveals significant differences in growth between peri-urban, rural and urban areas, and contributes spatial information for designing focused land-use policies in dynamic urban contexts. POLICY RELEVANCEThis article contributes to understanding the differentiated urban growth of urban, periurban and rural areas, which can translate into more precise and effective public policies. Urban expansion and infill patterns differ. For peri-urban and rural areas, the main growth is infill, so actions should be implemented to sustainably manage the vacant or undeveloped land within an existing human settlement to prevent further expansion, but also to avoid the loss of priority areas for the provision of ecosystem services. In urban areas where the main urban increment is expansion, sensible consolidation decisions need to be taken to avoid further urban expansion and the incorporation of urban green space.*Author affiliations can be found in the back matter of this article 2 Cruz-Bello et al.

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Section: Discussionsupporting

confidence: 88%

“…It is partially explained due to the relative similarity between the peri-urban and the rural zones in population density which is one of the primary drivers of urban expansion (Li et al 2022). This was one of the main urban growth drivers along with elevation, distance to previous urban land and distance to roads.…”

Section: Discussionmentioning

confidence: 99%

Urban growth in peri-urban, rural and urban areas: Mexico City

Cruz-Bello

Galeana-Pizaña²,

Arellano

2023

Buildings and Cities

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“…To evaluate the performance of the proposed framework, we selected the Federal District of Brazil and Fortaleza as study sites. The Federal District of Brazil is the smallest federative unit without municipalities, and intense urban land expansion and population growth make dengue a major public health issue in recent years [ 40 , 41 , 42 ]. Moreover, Fortaleza is the fifth most densely populated city in Brazil and exhibits great socioeconomic inequality where intense dengue epidemics occurred in recent decades due to population concentration and urban inequality [ 25 , 41 , 43 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil

2022

IJERPH

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Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013–2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (Rsum), mean temperature (Tmean), mean relative humidity (RHmean), and mean normalized difference vegetation index (NDVImean). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.

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“…Dominguez et al [ 46 ] modelled the spatial structure of Bogotá and 17 municipalities to analyze their land occupation and distribution of land use. (2) From the perspective of externality, some scholars discuss the relationship between urban expansion [ 47 ], economic growth [ 32 ], industrial upgrading [ 48 ] and ecological environment change [ 49 ] in the formation of a polycentric spatial structure.…”

Section: Literature Reviewmentioning

confidence: 99%

Influence Mechanism of Polycentric Spatial Structure on Urban Land Use Efficiency: A Moderated Mediation Model

Zhu

Wang

Peng

et al. 2022

IJERPH

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Under the background of green development, the spatial structure of urban agglomerations (UA) has an important impact on urban land use efficiency (ULUE), but few studies have explored the impact mechanism between the two. This research explores the impacts of polycentric development on ULUE of UA, using data for 140 cities in China’s top ten key UA covering the period from 2004–2019. The linkage between polycentric development and ULUE is explored by estimating models of determinants of ULUE. This research also examines the mechanism of the polycentric spatial structure of UA on ULUE by using a moderated mediation model. The main findings of the research can be concluded as below. The eastern UAs show a mostly polycentric spatial structure, whereas the central and western UAs show a weak polycentric spatial structure. The polycentric spatial structure of UA has a positive impact on ULUE. An inverted U-shape curve depicts the relationship between the polycentric spatial structure of UA and ULUE. However, the mediating variables, integration of industrial structure and factor mobility have a positive and partially mediating effect between the polycentric spatial structure of UA and ULUE. The infrastructure level has a positive U-shaped regulation effect, in which the impact coefficient of transport infrastructure is more significant. These findings provide empirical evidence for the coordinated development of China’s regional space planning and ULUE.

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Urban Land Expansion from Scratch to Urban Agglomeration in the Federal District of Brazil in the Past 60 Years

Cited by 12 publications

References 47 publications

Urban growth in peri-urban, rural and urban areas: Mexico City

Urban growth in peri-urban, rural and urban areas: Mexico City

Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil

Influence Mechanism of Polycentric Spatial Structure on Urban Land Use Efficiency: A Moderated Mediation Model

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