The Evolution of Urban Spatial Structure in Brasília: Focusing on the Role of Urban Development Policies

Costa, Cayo; Lee, Sugie

doi:10.3390/su11020553

Cited by 14 publications

(9 citation statements)

References 42 publications

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“…The city was expected to transform into a sustainable metropolis. However, in the past decades, significant urban structural changes, population growth, and various urban development policies occurred in the FDB [37]. According to the Brazil Institute of Geography and Statistics (IBGE), the population continuously increased from 139,796 people in 1960 to 3,015,268 people in 2019.…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

“…The population had a very significant (p-value < 0.001) correlation with the area of urban land, with a Pearson coefficient of 0.995. In fact, compared with other regions in Brazil, the high income per capita, quality of life, and access to public services in the FDB attracted the migrants in the past [50], and the construction of a series of peripheral urban nuclei in this region solved the housing problem [37]. Together with the results of SDG indicator 11.3.1 (Section 3.5), it was found that population growth is an important driver of urban expansion in the FDB, and the urban expansion rate is greater than the population growth rate during 1960-2015.…”

Section: Some Implications For Sustainable Urban Expansion In the Fdbmentioning

confidence: 99%

“…The urban structure patterns could be explained by the time and place of urban development policies. An important study summarized the main urban development policies implemented in the FDB, and described their association with the evolution of the spatial structure of urban land over time [37]. Some important points were highlighted (Figure 9): (1) Brasilia was created as the new national capital in 1960.…”

Section: Some Implications For Sustainable Urban Expansion In the Fdbmentioning

confidence: 99%

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

Gurgel

et al. 2022

IJERPH

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Empirical studies of urban expansion have increased rapidly in recent decades worldwide. Previous studies mainly focused on cities in China, the United States or African countries, with Brazilian cities receiving less attention. Moreover, such studies are rare in purpose-built cities. Taking the urban expansion from scratch (1960) to urban agglomeration (2015) in the Federal District of Brazil (FDB) as an example, this study aims to quantify the magnitude, patterns, modes, types and efficiency of urban land expansion and attempts to reveal some implications within sustainable urban expansion thinking. Annual expansion, landscape metrics, local Moran’s I index, area weighted mean expansion index, and land-use efficiency were computed. The suitability of diffusion–coalescence theory and the impact of population growth and urban development policies on urban expansion were discussed. Urban land continuously expanded and became more fragmented during 1960–2015, which mainly occurred in SSW and WSW directions. Urban land evolved in a polycentric way. Edge expansion was identified as the stable contributor, and the importance of infilling and spontaneous growth alternated. Urban expansion in this region supported the diffusion–coalescence theory. Population growth promoted urban expansion, and the creation of peripheral urban nuclei and their development were associated with the urban expansion and the changes in urban land structure. This study adds new empirical evidence of urban expansion to Brazil urbanization, and compact urbanization, population control, and efficient urban land use should be considered in the future.

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Section: Study Area and Data Sourcesmentioning

confidence: 99%

Section: Some Implications For Sustainable Urban Expansion In the Fdbmentioning

confidence: 99%

Section: Some Implications For Sustainable Urban Expansion In the Fdbmentioning

confidence: 99%

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

Gurgel

et al. 2022

IJERPH

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“…Expansion of the city into peripheral areas of the region due to Rapid population growth however, enhanced the unplanned and informal character of the region. More generally, top-down land governance led to socio-spatial fragmentation (Costa and Lee, 2019). From an 8R perspective, one could say that the original design was not in accordance with needs of citizens, or at least not in line with consultative processes, hence not responsive.…”

Section: Resultsmentioning

confidence: 99%

Urban Greening for New Capital Cities. A Meta Review

Vries

Timo²

2021

Front. Sustain. Cities

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In light of the discussions on relocating the capital city of Indonesia to a new location in Kalimantan, and create a new green capital city (referred to as IKN), the purpose of this meta-review paper is learn from experiences from other relocations of capital cities and creations of green cities in the world. Specific emphasis is hereby given to urban greening and gentrification. This article applies a meta-analytical approach by connecting the basic tenets of the 8R framework of responsible land management to assess the pros and cons of a selected set of capital city relocations and green cities. From the comparison, it is possible to generate general recommendations for Indonesia's new green capital city. The comparison reveals that each of the selected cases falls short in one or more aspects of the 8R framework. In all cases, constructing green capitals requires a mixed and integrated land use planning, a transparent regulatory framework toward land use control, extensive consultation with both local, national and international stakeholders, and participation with local residents. Only under these conditions, one can ensure ownership, respect and trust in the decision. The quandaries highlight the complexity of capital city relocation and green city creation. The originality lies in the specific land management framework perspective and discursive analysis of documented discourses on constructing new capital and green cities. This provides new options for devising and extending regulatory guidelines and for assigning responsibilities for such new mega-endeavors. Given the conceptual and discursive character of the paper, a limitation of the approach may be that there are no specific empirical data collected, yet several recommendations for further research include expanding the boundary work between the land management, the spatial planning and governance domains.

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“…The Federal District of Brazil (FDB) was selected as the study area, which was created in 1960 to house the new national capital, Brasilia, with rapid urbanization and population growth in the past decades [ 25 ]. The urban agglomeration in the FDB has become the third-largest metropolis in Brazil, and has been greatly affected by dengue epidemics in past years [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling

Gurgel

et al. 2022

Biology

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Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007–2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007–2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts.

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The Evolution of Urban Spatial Structure in Brasília: Focusing on the Role of Urban Development Policies

Cited by 14 publications

References 42 publications

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

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

Urban Greening for New Capital Cities. A Meta Review

Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling

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