Zhichao Li

Helen Gurgel
email: helengurgel@unb.br

Minmin Li
email: limm@szu.edu.cn

Nadine Dessay
email: nadine.dessay@ird.fr

Peng Gong
email: penggong@mail.tsinghua.edu.cn

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

Keywords: purpose-built city, urban expansion, spatial pattern, Brazil

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.

Introduction

With global urbanization, more than half of the world's population were living in cities in 2018, and this proportion is expected to reach 68% by 2050 [1]. Urban expansion and changes in urban land structure have social, economic, ecological, and environmental impacts on urban populations and on urban sustainable development [2][3][4][5]. The Sustainable Development Goal (SDG) target 11.3 underlines the goal of enhancing sustainable urbanization in all countries by 2030. Implementing empirical studies of urban expansion permits to provide the baseline information of urban land-use change, enabling to reveal some implications for urban sustainable development [6,7].

A better understanding of urban expansion of planned cities could make a timely contribution to the literature, especially in the context of numerous new planned cities worldwide. Previous studies mainly focused on cities in China, the United States or African for urban expansion in the FDB? (3) How did population growth and urban development policies affect urban expansion in the FDB?

This study quantified the magnitude, patterns, modes, types, and efficiency of urban land expansion based on a comprehensive set of indicators to answer the first question. Based on quantitative results, this study discussed the temporal dynamics of the pattern and types of urban expansion, and the impact of urban expansion efficiency and the timing and place of urban development policies on urban expansion to answer the second and third research questions, respectively.

Materials and Methods

Study Area and Data Sources

This study was carried out in the FDB, which is located on the central-western highlands in Brazil, with an area of approximately 5760 km 2 (Figure 1). It is reserved for Brasilia, the new capital of Brazil founded in 1960. Brasilia is a planned city and its initial layout was proposed by Lucio Costa. Its central area was named as a World Heritage Site in 1987 by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) due to its artistic urban planning and modernist architecture [START_REF]World Heritage List[END_REF]. 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 [START_REF] Costa | The Evolution of Urban Spatial Structure in Brasília: Focusing on the Role of Urban Development Policies[END_REF]. 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. Today, the FDB is the third largest metropolis in Brazil, which is divided into 33 administrative regions and is characterized by spatio-economic inequalities, such as lack of infrastructure in new urban land, low-income people concentrated in satellite towns, and fragmented landscapes and transportation. Thus, the FDB could be an ideal study area to provide a comprehensive understanding of rapid urban land expansion from a city's origin to urban agglomeration, and examine urban expansion toward sustainable development. for urban expansion in the FDB? (3) How did population growth and urban development policies affect urban expansion in the FDB? This study quantified the magnitude, patterns, modes, types, and efficiency of urban land expansion based on a comprehensive set of indicators to answer the first question. Based on quantitative results, this study discussed the temporal dynamics of the pattern and types of urban expansion, and the impact of urban expansion efficiency and the timing and place of urban development policies on urban expansion to answer the second and third research questions, respectively.

Materials and Methods

Study Area and Data Sources

This study was carried out in the FDB, which is located on the central-western highlands in Brazil, with an area of approximately 5760 km 2 (Figure 1). It is reserved for Brasilia, the new capital of Brazil founded in 1960. Brasilia is a planned city and its initial layout was proposed by Lucio Costa. Its central area was named as a World Heritage Site in 1987 by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) due to its artistic urban planning and modernist architecture [START_REF]World Heritage List[END_REF]. 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 [START_REF] Costa | The Evolution of Urban Spatial Structure in Brasília: Focusing on the Role of Urban Development Policies[END_REF]. 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. Today, the FDB is the third largest metropolis in Brazil, which is divided into 33 administrative regions and is characterized by spatio-economic inequalities, such as lack of infrastructure in new urban land, low-income people concentrated in satellite towns, and fragmented landscapes and transportation. Thus, the FDB could be an ideal study area to provide a comprehensive understanding of rapid urban land expansion from a city's origin to urban agglomeration, and examine urban expansion toward sustainable development.

Two Types of Spatial Scales

To quantify urban expansion in the FDB, we used two types of analysis: an analysis within the FDB border and an analysis based on the concentric buffers-geographic directions system (Figure 1). The former approach permits to provide the general information of urban expansion within the entire study area, and the latter approach integrates the concentric rings and geographic directions, and permits to provide a better comprehen- The multi-date urban expansion data downloaded from the FDB Geoportal website (https://www.geoportal.seduh.df.gov.br, accessed on 1 December 2019) were used as The multi-date urban expansion data downloaded from the FDB Geoportal website (https://www.geoportal.seduh.df.gov.br, accessed on 1 December 2019) were used as input data for characterizing the urban expansion of the FDB. The data were produced by the State Secretariat for Housing and Urban Development of the Federal District, Brazil (SEDUH-DF) by manually vectorizing the mosaics of the high-resolution aerial photographs taken by Brasilia Real Estate Company, Federal District, Brazil (Terracap) and urban plans drawn up by SEDUH. Urban land in this dataset refers to the developed areas, including residential, industrial, commercial, transportation, administration/public services, and green space. In this study, due to the data availability, we focused on the urban land in 1960 and the expanded land for 1975,1986,1997,2009, and 2015 (Figure 1). According to World Population Review, the population of Brasilia for 1960Brasilia for , 1975Brasilia for , 1986Brasilia for , 1997Brasilia for , 2009Brasilia for , and 2015 was 136,643, 827,361, 1,615,853, 2,549,093, 3,624,703, and 4,168,288 was 136,643, 827,361, 1,615,853, 2,549,093, 3,624,703, and 4,168,288, respectively.

Two Types of Spatial Scales

To quantify urban expansion in the FDB, we used two types of analysis: an analysis within the FDB border and an analysis based on the concentric buffers-geographic directions system (Figure 1). The former approach permits to provide the general information of urban expansion within the entire study area, and the latter approach integrates the concentric rings and geographic directions, and permits to provide a better comprehension of the characteristics of the urban expansion in different locations of cities that are often different [START_REF] Li | Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil[END_REF][START_REF] Lal | Spatio-temporal landscape modeling of urban growth patterns in Dhanbad Urban Agglomeration, India using geoinformatics techniques. Egypt[END_REF][START_REF] Qian | Study on Urban Expansion Using the Spatial and Temporal Dynamic Changes in the Impervious Surface in Nanjing[END_REF][START_REF] Zhang | A Comparative Study of Urban Expansion in Beijing, Tianjin and Tangshan from the 1970s to 2013[END_REF]. Here, we first created twenty concentric rings at intervals of 3 km around the center of Brasilia (i.e., the center point defined at the beginning of the establishment of Brasilia) until the largest ring covered all urban land patches in 2015. Each ring was then divided into sectors located in eight geographic directions (i.e., NNE, ENE, ESE, SSE, SSW, WSW, WNW, and NNW). Finally, in each direction, twenty sectors of different sizes were generated. As shown in Table 1, urban expansion magnitude and urban land patterns were both evaluated at two types of spatial scales. The types, modes, and efficiency of urban expansion were analyzed within the entire study area. The detailed steps are presented hereafter.

Urban Expansion Magnitude

To evaluate the urban expansion magnitude, we computed the annual expansion (AE) of urbanized land within the entire study area and sectors (Figure 1) for five time periods (1960-1975, 1975-1986, 1986-1997, 1997-2009, and 2009-2015) as follows [8,[START_REF] Terfa | Urban Expansion in Ethiopia from 1987 to 2017: Characteristics, Spatial Patterns, and Driving Forces[END_REF]:

AE = U t2 -U t1 T , (1)
where T, U t1 and U t2 represent the time period, the urban land area at the start of the time period, and the urban land area at the end of the time period, respectively.

Urban Land Patterns

The description of a landscape-by-landscape metric computation includes two aspects: (1) compositional patterns, referring to the abundance and variety of patch types in a landscape; and (2) configurational patterns, a broader and more vague notion related to the spatial character, arrangement, and context of the patches in the landscape [START_REF] Mcgarigal | FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps[END_REF]. In this study, considering our objective and the interpretability of the metric, we selected six commonly used and easily interpreted landscape metrics (Table 2) from the literature based on the following two reasons: (1) they characterize the main urban land patterns, including the area, edge, shape, isolation, and subdivision of urban patches; and (2) they were commonly used in the test of diffusion-coalescence urban expansion theory, and their temporal variations (e.g., increasing and decreasing) in the model of diffusion-coalescence urban expansion theory are known [START_REF] Dietzel | Diffusion and Coalescence of the Houston Metropolitan Area: Evidence Supporting a New Urban Theory[END_REF]. Moreover, class-level computation of landscape metrics permits to measure the spatial patterns of a focal landscape type [START_REF] Wang | Measuring habitat fragmentation: An evaluation of landscape pattern metrics[END_REF]. Therefore, in this study, the landscape metrics were calculated at the class level with an 8-connectivity implementation of the algorithm within the whole study area and each sector (Figure 1), using Fragstats software 4.2 (Amherst, MA, USA). In the analysis based on the concentric buffers-geographic directions system (Figure 1), we first used the β-score and γ-score to examine the ability of the metrics to capture the changes in urban expansion over time (i.e., 1960, 1975, 1986, 1997, 2009, and 2015) and space (i.e., the sectors of different sizes), respectively. For each metric, we computed a β-score per year and a γ-score per sector. This method was adopted from the multiscale analysis of the behavior of the landscape metrics [START_REF] Wu | Effects of changing scale on landscape pattern analysis: Scaling relations[END_REF], the scalogram method used for studying the effects of the different spatial extents of the area of urban land using a series of concentric circles with increasing radii [START_REF] Ma | Spatial scaling of urban impervious surfaces across evolving landscapes: From cities to urban regions[END_REF], and the rank-based landscape metric selection process used for spatial land cover pattern monitoring [START_REF] De Clercq | A method for the selection of relevant pattern indices for monitoring of spatial forest cover pattern at a regional scale[END_REF][START_REF] Sinha | Rank-Based Methods for Selection of Landscape Metrics for Land Cover Pattern Change Detection[END_REF]. Then, all the metrics were ranked in descending order according to the mean value of β-scores and the mean value of γ-scores, respectively. Effective metrics should have higher values of the two scores, reflecting the better suitability to recognize the urban expansion patterns using our multi-date urban expansion data and spatial scales.

β = (Max(S 1 , . . . , S m ) -Min(S 1 , . . . , S m )) Max(S 1 , . . . , S m ) , (2)
where Max(S 1 , . . . , S m ) and Min(S 1 , . . . , S m ) are the maximum and minimum values of a metric in each sector, and m represents the number of sectors. A total of 160 sectors (i.e., m = 160) were included in the β-score computation.

γ = (Max(Y 1 , . . . , Y n ) -Min(Y 1 , . . . , Y n )) Max(Y 1 , . . . , Y n ) , (3)
where Max(Y 1 , . . . , Y n ) and Min(Y 1 , . . . , Y n ) are the maximum and minimum values of a metric in each year, and n represents the number of years. A total of 6 years (i.e., n = 6) were included in the γ-score computation.

Urban Expansion Modes

The local Moran's I index [START_REF] Anselin | An Introduction to Spatial Data Analysis[END_REF] has been used in a case of a polycentric urban expansion to reveal the temporal changes of urban land agglomeration [6]. In this case, a spatial analysis grid was defined, and the area of urban land per grid cell was computed. Local Moran's I index was then computed based on the grid, and only the grid cells with a p-value ≤ 0.05 were presented for displaying the agglomeration of urban land. The spatial agglomeration of urban land was indicated by high-high clusters that would be the grid cells with a high area of urban land surrounding by other grid cells with a high area of urban land. Low-high indicates the grid cells surrounded by other grid cells with a higher area of urban land, and high-low indicates isolated urban land. In this study, based on a 1 km × 1 km grid covering the entire FDB, we computed the area of urban land per grid cell for each study year. Then, the local Moran's I index was estimated as follows, and we obtained the spatio-temporal distribution of the local Moran's I per study year.

I i = v i -v σ 2 ∑ n j=1 w ij v j -v (i = j), (4)
where v i is the area of urban land in the grid cell i, v is the mean value of the areas of urban land with the sample number of n, v j is the area of urban land in other grid cells, σ 2 is the variance of the variable v, and w ij is the spatial weight that could be defined as the inverse of the distance among grid cells i and j or decided by the use of a defined distance band.

Urban Expansion Types

To investigate the spatio-temporal dynamics of the urban expansion types, we computed the LEI within the entire study area (Figure 1) for five time periods (i.e., 1960-1975, 1975-1986, 1986-1997, 1997-2009, and 2009-2015), which is defined as Formula (5). Such an index quantifies the spatial relationship between newly expanded patches and pregrowth urban patches [START_REF] Liu | A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data[END_REF]. Urban expansion type is defined as infilling for LEI > 50, edge-expansion for 0 < LEI ≤ 50, and spontaneous growth for LEI = 0. Then, the relative dominance of each expansion type over a given time period was estimated by the use of the Area Weighted Mean Expansion Index (AWMEI) as Formula (6) [START_REF] Liu | A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data[END_REF]. Higher values of AWMEI correspond to a more compact expansion of urban land, whereas smaller values correspond to more dispersal expansion of urban land.

LEI = 100 × A o A o -A v (5)
where LEI represent the value of the land expansion index for a newly expanded urban patch, A o represents the intersection between the buffer zone with a specified distance around a newly expanded patch and old urban patch, and A v represents the intersection between the buffer zone and vacant land.

AW MEI = N ∑ i=1 LEI i × a i A (6)
where LEI i represents the value of landscape expansion index for the new urban patch i, a i is the area of the new urban patch i, and A represents the total area of the new urban patches.

Urban Expansion Efficiency

To measure how efficiently the urban land expanded, we computed the land consumption rate (LCR), population growth rate (PGR), and the ratio of LCR and PGR for five time periods (1960-1975, 1975-1986, 1986-1997, 1997-2009, and 2009-2015) as follows [START_REF] Nicolau | Ratio of Land Consumption Rate to Population Growth Rate-Analysis of Different Formulations Applied to Mainland Portugal[END_REF]:

LCR = ln U t2 U t1 T , (7)
PGR = ln Popt2 Popt1 T (8)
LCRPGR = LCR PCR , (9)
where T, U t1 , U t2 , Popt1, and Popt2 represent the time period, the urban land area at the beginning time of the time period, the urban land area at the end time of the time period, the population at the beginning time of the time period, and the population at the end time of the time period, respectively.

Results

Urban Expansion Magnitude

Table 3 lists the AE of the urban land in the entire study area for the five time periods. The highest and lowest value of AE were found in 1986-1997 and 1975-1986, respectively. Moreover, the AE per geographic direction across concentric rings for the five time periods are presented in Figure 2. Evidently, AE varied significantly among different directions and depended on the distance from the city center. The faster urban expansion could be found in the SSW and the WSW directions, in particular for the periods 1960-1975, 1986-1997, and 1997-2009. Moreover, it is very important to verify whether landscape metrics adapt well to the multi-date urban expansion data and spatial sectors in the analysis of urban land patterns. Among the thirty-one metrics, less than half of the metrics had relatively high values (greater than 0.80) of both mean β-score and mean γ-score (Figure A1), including CA, AREA_MN, PARA_MN, ED, NP, and ENN_MN (Table 4). It indicates that they performed relatively consistently over space and time; that is, they adapted well to our multi-date urban expansion data and sectors used in this study. Such result ensures the interpretation of urban land patterns at different distances and in different directions using the six metrics. Nonetheless, only CA and NP were displayed hereafter as they are more intuitive to reflect the temporal changes in urban land at different distances and in different directions. In terms of the area of urban land (Figure 4), a consistently increasing trend in CA values from 1960 to 2015 showed that urban expansion occurred in all eight directions, and the significant urban expansion could be observed in the SSW and WSW directions. Moreover, in each direction, urban expansion ended at a different distance from the city center during 1960-2015 (see the black vertical lines in Figure 4). For example, in the WSW direction, urban patches in 1960 were located within 21 km from the center, and they reached the distance of 52 km from the center in 2015. In terms of the subdivision of urban land (Figure 5), NP presented a consistently increasing trend from 1960 to 2015 for the NNE, ENE, and SSE directions. For the ESE, SSW, WSW, and WNW directions, NP increased from 1960 to 1997, decreased from 1997 to 2009, and then increased again from 2009 to 2015. For the NNW direction, there was a slight variation in NP values from 1960 to 1997, and NP presented an evident increase from 1997 to 2015.

Temporal Changes in Urban Land Patterns

Temporal Changes in Urban Land Patterns

Temporal Changes in Urban Land Patterns

Polycentric Urban Expansion

Polycentric Urban Expansion

Spatio-Temporal Dynamics of Urban Expansion Types

Figure 7 shows the quantitation of the three urban expansion types (i.e., infilling expansion, edge expansion, and spontaneous growth) in terms of the number and area of newly expended patches and the AWMEI value in the FDB during 1960-2015. The number and the area of new urban patches correspond to the frequency and extensiveness of urbanization, respectively, and they provide the complementary information on temporal dynamics of urban expansion types [START_REF] Li | Quantifying the speed, growth modes, and landscape pattern changes of urbanization: A hierarchical patch dynamics approach[END_REF]. As presented in our study, the temporal dynamics of the three expansion types derived from the two analyses were consistent for each

Spatio-Temporal Dynamics of Urban Expansion Types

Figure 7 shows the quantitation of the three urban expansion types (i.e., infilling expansion, edge expansion, and spontaneous growth) in terms of the number and area of newly expended patches and the AWMEI value in the FDB during 1960-2015. The number and the area of new urban patches correspond to the frequency and extensiveness of urbanization, respectively, and they provide the complementary information on temporal dynamics of urban expansion types [START_REF] Li | Quantifying the speed, growth modes, and landscape pattern changes of urbanization: A hierarchical patch dynamics approach[END_REF]. As presented in our study, the temporal dynamics of the three expansion types derived from the two analyses were consistent for each time period. Overall, edge expansion was the important contributor to urban expansion for all time periods, and the importance of infilling and spontaneous growth alternated. The dispersal and compact form of urban land appeared alternately according to the temporal changes in AWMEI values. Specifically, in 1960-1975, spontaneous growth and edge expansion dominated and the value of AWMEI is lowest during that time span. Compared with edge expansion, the number of newly expanded patches in the form of spontaneous growth was more than the ones in the form of edge expansion; it presented a similar status of the composition of urban expansion types in 1986-1997. The value of AWMEI was relatively small in this period, corresponding to a more dispersed urban land. In 1975In -1986In and 1997In -2009, edge expansion and infilling expansion were dominant, and the values of AWMEI were relatively high, representing a more compact urban land. In 2009-2015, the status of the urban expansion types was different from other time periods. Infilling expansion contributed the least, which was much less than the other types. A lower value of AWMEI indicated that urban land became more dispersed after 2009. time period. Overall, edge expansion was the important contributor to urban expansion for all time periods, and the importance of infilling and spontaneous growth alternated. The dispersal and compact form of urban land appeared alternately according to the temporal changes in AWMEI values. Specifically, in 1960-1975, spontaneous growth and edge expansion dominated and the value of AWMEI is lowest during that time span. Compared with edge expansion, the number of newly expanded patches in the form of spontaneous growth was more than the ones in the form of edge expansion; it presented a similar status of the composition of urban expansion types in 1986-1997. The value of AWMEI was relatively small in this period, corresponding to a more dispersed urban land. In 1975In -1986In and 1997In -2009, edge expansion and infilling expansion were dominant, and the values of AWMEI were relatively high, representing a more compact urban land. In 2009-2015, the status of the urban expansion types was different from other time periods. Infilling expansion contributed the least, which was much less than the other types. A lower value of AWMEI indicated that urban land became more dispersed after 2009.

Ratio of Land Consumption Rate to Population Growth Rate

Table 5 presents the LCR, PGR and the ratio of LCR to PGR for 1960-1975, 1975-1986, 1986-1997, 1997-2009, and the entire study period. Based on the results of LCRPGR, urban land expanded relatively faster than population growth in 1997-2009 and 2009-2015, and it enables catch up with the population growth over the entire study period (i.e., 1960-2015).

Discussion

This study explored the characteristics of urban expansion from scratch to urban agglomeration in the FDB that has taken place during 1960-2015. The results support the discussion of the suitability of diffusion-coalescence urban expansion theory and the impact of population growth and urban development policies on urban expansion in the FDB.

Ratio of Land Consumption Rate to Population Growth Rate

Table 5 presents the LCR, PGR and the ratio of LCR to PGR for 1960-1975, 1975-1986, 1986-1997, 1997-2009, and the entire study period. Based on the results of LCRPGR, urban land expanded relatively faster than population growth in 1997-2009 and 2009-2015, and it enables catch up with the population growth over the entire study period (i.e., 1960-2015).

Discussion

This study explored the characteristics of urban expansion from scratch to urban agglomeration in the FDB that has taken place during 1960-2015. The results support the discussion of the suitability of diffusion-coalescence urban expansion theory and the impact of population growth and urban development policies on urban expansion in the FDB.

Suitability of the Diffusion-Coalescence Urban Expansion Theory in the FDB

Taking together, the temporal dynamics of CA, NP, AREA_MN, ENN_MN (Figure 3), and spatio-temporal shifts of each expansion type during 1960-2015 (Figures 7 and8), we identified the cyclical process of diffusion and coalescence in the FDB, with the diffusion phases in 1960-1975, 1986-1997 and 2009-2015, and the coalescence phases in 1975-1986 and 1997-2009 (Table 6). In 1960-1975, the increase in NP and AREA_MN and the decrease in ENN_MN were not consistent with their temporal dynamics proposed in urban expansion theory. However, urban expansion in the FDB started from multiple urban cores of 1960, and there were also the relatively large distances between these cores (Figure 8). Newly expanded patches scattered around these cores, and the area of newly expanded land, were much larger than the area of urban land in 1960 (Figure 8). These facts explain why AREA_MN increased and ENN_MN decreased with the increase in NP. Thus, a diffusion phase could be identified for this time period. In 1975-1986, NP was relatively stable and AREA_MN increased greatly. The new urban patches linked the neighborhood urban patches or extended outward from previous patches (Figure 8). These facts clearly indicated a coalescence phase of urban expansion. In 1986-1997, the increase in NP, the decrease in AREA_MN, the spatial distribution of new urban patches (Figure 8), and their contribution in the form of edge expansion and spontaneous growth clearly pointed out a diffusion phase of urban expansion in this period. In 1997-2009, NP increased and AREA_MN increased slightly. Moreover, edge-expansion and infilling expansion were more pronounced in this period. The two results are opposed. According to the value of AWMEI, a high value in this period indicated that new urban patches are closer to existing urban patches. Therefore, we preferred a coalescence phase in this period. In 2009-2015, the changing trend in NP and AREA_MN is similar to the period 1986-1997. Moreover, ENN_MN increased, and new urban patches of a small size that dispersed in the FDB (Figure 8) mainly contributed in the form of edge expansion and spontaneous growth. These facts indicated a typical diffusion phase of urban expansion in this time period.

Some Implications for Sustainable Urban Expansion in the FDB

Regarding to driving factors of urban expansion, this study solely considered the population growth and urban development policies due to data availability in such a long time period (i.e., 1960-2015). 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 [START_REF] Madaleno | The frontier capital[END_REF], and the construction of a series of peripheral urban nuclei in this region solved the housing problem [START_REF] Costa | The Evolution of Urban Spatial Structure in Brasília: Focusing on the Role of Urban Development Policies[END_REF]. 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. However, the land-use efficiency in the FDB is smaller than the average value of cities worldwide, with the LCRPGR equal to 1.5.

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 [START_REF] Costa | The Evolution of Urban Spatial Structure in Brasília: Focusing on the Role of Urban Development Policies[END_REF]. Some important points were highlighted (Figure 9): (1) Brasilia was created as the new national capital in 1960. Three urban nuclei (i.e., Taguatinga, Sobradinho, and Gama) were already established before 1960, and two other urban nuclei (i.e., Guara and Ceilandia) were developed in the 1960s. These urban nuclei were created to accommodate workers and inhabitants from illegal settlements. (2) The sectors in the WSW and SSW directions were regarded as the main zone of urban expansion as the nuclei, such as Guara, Ceilandia, Samambaia, Riacho Fundo I, Riacho Fundo II, Santa Maria, Agua Claras, and Recanto das Emas were created around Brasilia in the 1980s and 1990s to address the housing shortage. 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. However, the land-use efficiency in the FDB is smaller than the average value of cities worldwide, with the LCRPGR equal to 1.5.

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 [START_REF] Costa | The Evolution of Urban Spatial Structure in Brasília: Focusing on the Role of Urban Development Policies[END_REF]. Some important points were highlighted (Figure 9): (1) Brasilia was created as the new national capital in 1960. Three urban nuclei (i.e., Taguatinga, Sobradinho, and Gama) were already established before 1960, and two other urban nuclei (i.e., Guara and Ceilandia) were developed in the 1960s. These urban nuclei were created to accommodate workers and inhabitants from illegal settlements. (2) The sectors in the WSW and SSW directions were regarded as the main zone of urban expansion as the nuclei, such as Guara, Ceilandia, Samambaia, Riacho Fundo I, Riacho Fundo II, Santa Maria, Agua Claras, and Recanto das Emas were created around Brasilia in the 1980s and 1990s to address the housing shortage. Our findings permit to provide some implications for urban expansion and local sustainable development in the FDB. For example, recent urban expansion is in the stage of diffusion, and it should promote the policies to encourage the compact urban growth within sustainable development thinking. Furthermore, historical population growth and urban development policies form the basis of urban sustainability, and urban expansion faster than population growth often leads to a low urban population density. This implies the importance of strategies of population control and improving the efficiency of urban land use. Future studies could focus on the urban expansion in recent years and consider more aspects of urban sustainable development in terms of urban form and urban expansion. Our findings permit to provide some implications for urban expansion and local sustainable development in the FDB. For example, recent urban expansion is in the stage of diffusion, and it should promote the policies to encourage the compact urban growth within sustainable development thinking. Furthermore, historical population growth and urban development policies form the basis of urban sustainability, and urban expansion faster than population growth often leads to a low urban population density. This implies the importance of strategies of population control and improving the efficiency of urban land use. Future studies could focus on the urban expansion in recent years and consider more aspects of urban sustainable development in terms of urban form and urban expansion.

Conclusions

This study first provides a retrospect of urban expansion in the FDB from scratch to urban agglomeration in the past 60 years, in terms of urban expansion magnitude, urban land patterns, urban development modes, urban expansion types, and land-use efficiency. It could be found that urban land continuously expanded in a polycentric way, and it became more and more fragmented and complex. Edge expansion was the important contributor, and the importance of infilling and spontaneous growth alternated. Urban land expanded faster than population growth.

Then, urban expansion theory adapted well the urban expansion from scratch to urban agglomeration in the FDB, with an alternation in the diffusion and coalescence phases. Population growth was positively related to urban expansion, and urban development policies related to the creation and development of a series of peripheral urban nuclei affected urban expansion in this region. This study provides not only new evidence of urban expansion but also some implications within sustainable urban development thinking, including encouraging compact urban expansion, controlling population growth, and improving the efficiency of urban land use.

Conclusions

This study first provides a retrospect of urban expansion in the FDB from scratch to urban agglomeration in the past 60 years, in terms of urban expansion magnitude, urban land patterns, urban development modes, urban expansion types, and land-use efficiency. It could be found that urban land continuously expanded in a polycentric way, and it became more and more fragmented and complex. Edge expansion was the important contributor, and the importance of infilling and spontaneous growth alternated. Urban land expanded faster than population growth.

Then, urban expansion theory adapted well the urban expansion from scratch to urban agglomeration in the FDB, with an alternation in the diffusion and coalescence phases. Population growth was positively related to urban expansion, and urban development policies related to the creation and development of a series of peripheral urban nuclei affected urban expansion in this region. This study provides not only new evidence of urban expansion but also some implications within sustainable urban development thinking, including encouraging compact urban expansion, controlling population growth, and improving the efficiency of urban land use.

  Int. J. Environ. Res. Public Health 2022, 19, 0 3 of 19

Figure 1 .

 1 Figure 1. The administrative border (grey polygon) of the FDB, multi-date urban expansion data, and the concentric buffers-geographic directions system.

Figure 1 .

 1 Figure 1. The administrative border (grey polygon) of the FDB, multi-date urban expansion data, and the concentric buffers-geographic directions system.

Figure 1 .

 1 Figure 1. The administrative border (grey polygon) of the FDB, multi-date urban expansion data, and the concentric buffers-geographic directions system.

Figure 2 .

 2 Figure 2. Annual expansion per direction across concentric rings during 1960-2015.

Figure 3

 3 Figure 3 presents the trends in the changes of urban land patterns in the entire study area during 1960-2015. CA, ED, and NP showed a rising trend that indicates urban land became more fragment and complex. However, ENN_MN sharply declined from 1960 to 1997, and it had a slight increase after 1997. This indicates that before 1997, urban land mainly expanded in the areas between the urban patches of 1960. After 1997, urban land expanded surrounding the pre-existing urban land patches of 1997, particularly in 2009-2015. AREA_MN reflects the changing trend in both CA and NP that sharply increased from 1960 to 1986, and then it had a slight decline.

Figure 3 .

 3 Figure 3. Temporal changes in landscape metrics in the FDB during 1960-2015.

Figure 2 .

 2 Figure 2. Annual expansion per direction across concentric rings during 1960-2015.

Figure 3 19 Figure 2 .

 3192 Figure 3 presents the trends in the changes of urban land patterns in the entire study area during 1960-2015. CA, ED, and NP showed a rising trend that indicates urban land became more fragment and complex. However, ENN_MN sharply declined from 1960 to 1997, and it had a slight increase after 1997. This indicates that before 1997, urban land mainly expanded in the areas between the urban patches of 1960. After 1997, urban land expanded surrounding the pre-existing urban land patches of 1997, particularly in 2009-2015. AREA_MN reflects the changing trend in both CA and NP that sharply increased from 1960 to 1986, and then it had a slight decline.

Figure 3

 3 Figure 3 presents the trends in the changes of urban land patterns in the entire study area during 1960-2015. CA, ED, and NP showed a rising trend that indicates urban land became more fragment and complex. However, ENN_MN sharply declined from 1960 to 1997, and it had a slight increase after 1997. This indicates that before 1997, urban land mainly expanded in the areas between the urban patches of 1960. After 1997, urban land expanded surrounding the pre-existing urban land patches of 1997, particularly in 2009-2015. AREA_MN reflects the changing trend in both CA and NP that sharply increased from 1960 to 1986, and then it had a slight decline.

Figure 3 .

 3 Figure 3. Temporal changes in landscape metrics in the FDB during 1960-2015.Figure 3. Temporal changes in landscape metrics in the FDB during 1960-2015.

Figure 3 .

 3 Figure 3. Temporal changes in landscape metrics in the FDB during 1960-2015.Figure 3. Temporal changes in landscape metrics in the FDB during 1960-2015.

  Int. J. Environ. Res. Public Health 2022, 18, x 10 of 19

Figure 4 .

 4 Figure 4. The total area of urban land in different directions and within concentric rings from 1960 to 2015. The x-axis represents the distances to the city center. The y-axis represents the normalized value of the metric. The black vertical line represents the distance where the metric value became stable for each year.

Figure 4 .

 4 Figure 4. The total area of urban land in different directions and within concentric rings from 1960 to 2015. The x-axis represents the distances to the city center. The y-axis represents the normalized value of the metric. The black vertical line represents the distance where the metric value became stable for each year.

Figure 4 .

 4 Figure 4. The total area of urban land in different directions and within concentric rings from 1960 to 2015. The x-axis represents the distances to the city center. The y-axis represents the normalized value of the metric. The black vertical line represents the distance where the metric value became stable for each year.

Figure 5 .

 5 Figure 5. The number of urban patches in different directions and within concentric rings from 1960 to 2015. The x-axis represents the distances to the city center. The y-axis represents the normalized value of the metric. The black vertical line represents the distance where the metric value became stable for each year.

Figure 5 .

 5 Figure 5. The number of urban patches in different directions and within concentric rings from 1960 to 2015. The x-axis represents the distances to the city center. The y-axis represents the normalized value of the metric. The black vertical line represents the distance where the metric value became stable for each year.

Figure 6

 6 Figure 6 presents the spatio-temporal distribution of the local Moran's I computed based on a 1 km × 1 km grid covering the entire FDB. The results indicate that urban land in the FDB evolved in a polycentric way during 1960-2015. Moreover, urban expansion was characterized by the expansion of pre-existing urban land patches, and the emergence of new urban patches. The number of urban land clusters varied from three to five, with the main agglomeration of urban land in the middle of the FDB.

Figure 6

 6 Figure 6 presents the spatio-temporal distribution of the local Moran's I computed based on a 1 km × 1 km grid covering the entire FDB. The results indicate that urban land in the FDB evolved in a polycentric way during 1960-2015. Moreover, urban expansion was characterized by the expansion of pre-existing urban land patches, and the emergence of new urban patches. The number of urban land clusters varied from three to five, with the main agglomeration of urban land in the middle of the FDB.

Figure 6 .

 6 Figure 6. Spatio-temporal distribution of the local Moran's I computed based on a 1 km × 1 km grid in the FDB.

Figure 6 .

 6 Figure 6. Spatio-temporal distribution of the local Moran's I computed based on a 1 km × 1 km grid in the FDB.

Figure 7 .

 7 Figure 7. Temporal dynamics of urban expansion types and AWMEI values in the FDB from 1960 to 2015.

Figure 8

 8 Figure 8 shows the spatial distribution of newly expended patches in each time period. Particularly, in 1960-1975, newly expanded patches scattered around the urban patches of 1960. In 1975-1986 and 1997-2009, newly expanded patches mostly linked the neighborhood patches or extended outward from old patches. In 2009-2015, newly expanded patches dispersed in this region.

Figure 7 .

 7 Figure 7. Temporal dynamics of urban expansion types and AWMEI values in the FDB from 1960 to 2015.

Figure 8

 8 Figure 8 shows the spatial distribution of newly expended patches in each time period. Particularly, in 1960-1975, newly expanded patches scattered around the urban patches of 1960. In 1975-1986 and 1997-2009, newly expanded patches mostly linked the neighborhood patches or extended outward from old patches. In 2009-2015, newly expanded patches dispersed in this region.

Figure 8 .

 8 Figure 8. Spatial distribution of urban expansion types in the FDB during 1960-2015.

Figure 8 .

 8 Figure 8. Spatial distribution of urban expansion types in the FDB during 1960-2015.

( 3 )

 3 Land-use plans, such as PDOT-1992, PDOT-1997, and PDOT-2009, reinforced the development of urban nuclei, by regarding Taguatinga as a complementary center of Brasilia, and creating the industrial poles in Santa Maria, Guara, and Recanto das Emas.

( 3 )

 3 Land-use plans, such as PDOT-1992, PDOT-1997, and PDOT-2009, reinforced the development of urban nuclei, by regarding Taguatinga as a complementary center of Brasilia, and creating the industrial poles in Santa Maria, Guara, and Recanto das Emas.

Figure 9 .

 9 Figure 9. Location of the peripheral urban nucleus around Brasilia created from 1960 to 2015. Source: State Secretariat for Housing and Urban Development of the Federal District (SEDUH-DF).

Figure 9 .

 9 Figure 9. Location of the peripheral urban nucleus around Brasilia created from 1960 to 2015. Source: State Secretariat for Housing and Urban Development of the Federal District (SEDUH-DF).

Figure A1 .

 A1 Figure A1. Landscape metrics computed based on the sectors of the concentric buffers-geographic directions system. (a) Mean β-score and (b) Mean γ-score reflect the capability of the landscape metrics to recognize the urban expansion patterns in the Federal District of Brazil over space and time, respectively.

Figure A1 .

 A1 Figure A1. Landscape metrics computed based on the sectors of the concentric buffers-geographic directions system. (a) Mean β-score and (b) Mean γ-score reflect the capability of the landscape metrics to recognize the urban expansion patterns in the Federal District of Brazil over space and time, respectively.

Table 1 .

 1 Spatial scales and urban expansion analysis used in this study. represents the analysis was implemented, and X represents the analysis was not implemented.

	Urban Expansion Analysis	Spatial Scales Sectors FDB	Description
	Urban expansion magnitude Urban land patterns Urban expansion modes Urban expansion types Urban expansion efficiency	X X X	Quantifying the area of annual expansion of urbanized land Quantifying the compositional and configurational patterns of urban land Identifying the expansion modes: monocentric or polycentric Identifying the expansion types of newly urban land: Infilling expansion, edge expansion and spontaneous growth Indicating whether the urban expansion in the FDB is inclusive and sustainable

Table 2 .

 2 Landscape metrics used in this study.

	Metric (Abbreviation)	Description Adapted from [41]	Units	Range
	Class area (CA) Mean patch area (AREA_MN) Mean perimeter-area ratio (PARA_MN) Edge density (ED) Number of patches (NP) Mean Euclidean neighbor distance (ENN_MN)	The sum of the area of urban land patches in the analysis unit The average area of urban land patches in the analysis unit Simple ratio of perimeter to area for each urban land patch in the analysis unit Total length of urban land edges in the computation unit, per hectare Total number of urban land patches in the analysis unit Average distance between two urban land patches in the nearest cell center-to-center computation unit, based on the	Hectares Hectares None Meters/ hectare None Meters	≥0 ≥0 ≥0 ≥0, no limit >0 ≥0, no limit

Table 3 .

 3 Annual expansion in the FDB during 1960-2015.

	Time Periods	Annual Expansion (km 2 )
	1960-1975 1975-1986 1986-1997 1997-2009 2009-2015	10.6 6.5 12.5 12.1 10.3

Table 4 .

 4 Mean β-score and mean γ-score of the landscape metrics used in this study.

	Landscape Metrics	Mean β-Score	Mean γ-Score
	CA AREA_MN PARA_MN ED NP ENN_MN	0.987 0.956 0.965 0.976 0.965 0.968	0.961 0.850 0.873 0.913 0.870 0.838

Table 5 .

 5 Land consumption rate, population growth rate, and land-use efficiency in the FDB during 1960-2015.

	Time Periods	LCR	PGR	LCRPGR
	1960-1975	0.120	0.138	0.867
	1975-1986	0.061	0.025	2.510
	1986-1997	0.041	0.045	0.925
	1997-2009	0.029	0.026	1.126
	2009-2015	0.023	0.017	1.337
	1960-2015	0.062	0.059	1.047

Table 5 .

 5 Land consumption rate, population growth rate, and land-use efficiency in the FDB during 1960-2015.

	Time Periods	LCR	PGR	LCRPGR
	1960-1975 1975-1986 1986-1997 1997-2009 2009-2015 1960-2015	0.120 0.061 0.041 0.029 0.023 0.062	0.138 0.025 0.045 0.026 0.017 0.059	0.867 2.510 0.925 1.126 1.337 1.047

Table 6 .

 6 Summary of the temporal dynamics of landscape metrics, urban growth types, and their abundance and urban expansion phases. ↑, ↓, and -indicate increase, decrease, and relatively stable, respectively. E, S, and I represent edge-expansion, spontaneous growth, and infilling, respectively.

	Time Periods	CA	NP	AREA_MN	ENN_MN	Urban Expansion Types	AWMEI	Urban Expansion Phases
	1960-1975	↑	↑	↑	↓	E + S	Low	Diffusion
	1975-1986	↑	-	↑	-	E + I	High	Coalescence
	1986-1997	↑	↑	↓	↓	E + S	Low	Diffusion
	1997-2009	↑	↑	-	↑	E + I	High	Coalescence
	2009-2015	↑	↑	↓	↑	E + S	Low	Diffusion

Funding: This research was supported by the National Natural Science Foundation of China (Project No.: 41801336). This research was funded by the Humanity and Social Science Youth Foundation of the Ministry of Education of China, grant number 19YJCZH081, and the Open Fund of the Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, grant numbers KF-2020-05-013. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Funding: This research was supported by the National Natural Science Foundation of China (Project No.: 41801336). This research was funded by the Humanity and Social Science Youth Foundation of the Ministry of Education of China, grant number 19YJCZH081, and the Open Fund of the Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, grant numbers KF-2020-05-013. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.

Conflicts of Interest:

The authors declare no conflict of interest.

Author Contributions: Conceptualization, Z.L.; methodology, Z.L.; data analysis, Z.L.; funding acquisition, Z.L.; writing the manuscript, Z.L.; reviewing the bibliography, Z.L.; data curation and data description, H.G.; providing important inputs and reviewing the manuscript, M.L., N.D. and P.G. All authors have read and agreed to the published version of the manuscript.

Appendix A

Among the thirty-one landscape metrics evaluated using β-score and γ-score, and only fourteen metrics had relatively high values (i.e., greater than 0.80) of both mean β-score and mean γ-score (Figure A1). Thus, we encourage the evaluation of landscape metrics in further studies of urban expansion, especially using different spatial scales and multi-date urban expansion data.

Author Contributions: Conceptualization, Z.L.; methodology, Z.L.; data analysis, Z.L.; funding acquisition, Z.L.; writing the manuscript, Z.L.; reviewing the bibliography, Z.L.; data curation and data description, H.G.; providing important inputs and reviewing the manuscript, M.L., N.D. and P.G. All authors have read and agreed to the published version of the manuscript.

Appendix A

Among the thirty-one landscape metrics evaluated using β-score and γ-score, and only fourteen metrics had relatively high values (i.e., greater than 0.80) of both mean βscore and mean γ-score (Figure A1). Thus, we encourage the evaluation of landscape metrics in further studies of urban expansion, especially using different spatial scales and multi-date urban expansion data.


Céline Badouard
email: celine.badouard@univ-reims.fr

Christophe Bliard

Guillaume Polidori

Fabien Bogard

Development and characterization of a new composite based on grape stalks and potato starch for building insulation

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Nowadays, energy consumption is increasing in all areas around the world. Meanwhile, the cost of energy has increased due to the exhaustion of energy sources and the high environmental impact (greenhouse gases, global warming, etc) [1]. The French building industry represents 43% of energy consumption and 25% of total greenhouse gas emissions. It is an industry that is evolving to reduce its environmental footprint.

France has committed to drastic reductions in its carbon emissions. In order to meet this commitment, a new environmental regulation of the energy transition of buildings has been implemented, RE 2020 [2]. It requires the improvement of building insulation performance (thermal insulation, absorption, and acoustic insulation), by promoting lowemission construction methods, biosourced construction materials, and finally by integrating a summer comfort objective to adapt buildings to global warming. The development of materials that respect health and the environment is a priority issue and these new materials will have to meet new criteria such as a renewable resource, the recyclability of a material, and a moderate cost.

Insulation is a key factor in the energy performance of buildings. Hence the necessity to develop materials for civil engineering using minimal fossil resources. Agricultural waste represents a vast potential to be used as biosourced materials for insulation. The valorization of these raw materials as agromaterials would diversify the sources of supply, provide a complementary source of revenues for farmers and adapt the composition of particleboard to locally available resources.

Grapes are one of the most cultivated fruits in the world. The production of wine generates a considerable amount of grape pomace, in France, 850,000 tons are produced per year. The grape pomace is called "marc" or "aignes", in the Champagne region which corresponds to the totality of the solid parts (skins, stalks, and seeds) that remain at the end of the pressing.

Starch is a polysaccharide widely present in agricultural products such as cereals, tubers, and legumes. Starch has already been used for the development of bio-based composites for civil engineering applications such as hemp/starch composites, date palm fiber/cassava starch, and beet pulp/potato starch [START_REF] Bourdot | [END_REF]4,5].

In this work, we describe new agrocomposites based on stalks or crushed stalks and a biosourced binder based on potato starch. The fabrication and characterization of these agro-composites by different methods and techniques are presented. An investigation of thermal, acoustic, and hygric behavior was carried out. Two parameters are studied: the influence of stalk grinding and the stalk to starch ratio. The ratio of stems to starch is fixed at 10, 20, and 30 %, and two types of stalks are used: whole stalks and crushed stalks.

Materials and Methods

Composite Preparation Methods

The wine co-products, stalks, and crushed stalks were recovered from a local plant during harvest. The stalks (S) and crushed stalks (CS) were mixed with a binder at different ratios (10, 20, and 30%). The chosen binder was potato starch (A), which was supplied by Roquette (Lestrem, France). By heating the starch powder to 95°C in water, one obtains an irreversible gelatinisation caused by the swelling of the grains. The resulting gel serves as a binding agent for solid aggregates. The starch/water ratio was set at 0.1. The water and starch were heated under strong mechanical agitation until they had a translucent appearance and high viscosity. Stalks, and crushed stalks were mixed and kneaded with the starch gel for a few minutes in order to obtain a good homogeneity. The resulting mixtures were formed into moulds and then compacted at a pressure of 40 kPa for five minutes. Starch gel alone was also prepared as a reference. After being frozen, the samples were freeze-dried at 3 mbars during several days.

Composite analysis

The density ρ bulk of the composites is calculated by weighting the samples and determining their total volume. The volume is calculated based on the dimensions (width, length, and height or diameter and height) of the composites measured five times for each sample. The bulk density of the composite was averaged over twelve samples for each composition.

The absolute density ρ abs of the composites is calculated using the absolute densities of the raw materials. The densities and porosity of stalks and starch were analyzed by the authors in a previous paper [6] and are presented in Table 1.

Thermal properties

The thermal conductivity (λ) of the composites was determined using an Applied Precision ISOMET 2114 probe. The heat flux was generated by heating an electrical resistor inserted into the sample to ensure direct thermal contact with the sample. This measurement analyzes the temperature response of the measured composite to heat flux pulses. The evaluation of thermal conductivity (λ) is based on temperature measurements taken periodically as a function of time.

A series of three cubic samples (10 x 10 x 10 cm) for each composition was made. After drying, the samples were equilibrated at 23°C and 50% RH before measurements. A total of ten measurements was made for each sample and the results were averaged.

Acoustic properties

The Kundt tube 4206 (tube with two fixed microphones) from Brüel & Kjaer was used to measure the sound absorption coefficient α. This device consists of a cylindrical tube with two quarter-inch microphones type 4187 BK, a power amplifier BK 2735, and a 3160-A-042 analyzer type. The Kundt tube measures sound absorption as well as surface impedance according to NF EN ISO 10534-2.

The sound absorption coefficient was measured in a frequency range of 50 Hz to 6 400 Hz, which corresponds to the frequency range of noise usually emitted in buildings.

Cylindrical test pieces of each formulation were prepared with various thicknesses. The edges of the samples were sealed with adhesive tape and an adapter was used to fill in the small gap caused by shrinkage during the drying procedure and avoid acoustic leakage. Each batch of measurements was averaged over six tests with a 2 Hz measurement resolution.

According to ISO 11654, the sound absorption coefficient (α w ) is calculated by using the practical sound absorption coefficient (α p ) values based on standard frequencies and compared to a reference curve. The α w parameter corresponds to the value of the reference curve at 500Hz.

Moisture Buffer Value (MBV)

For this test, the edges and the backsides of samples were sealed to obtain a onedimensional moisture flow. Samples were stabilized at 23°C and 50% RH in a climatic chamber and weighted until they reach equilibrium. Three 10cm diameter and 4 cm thick cylindrical samples were tested for each formula. All results have been averaged. After stabilization, the Nordtest protocol [7] defines cyclic step-changes in relative humidity between high (75%) and low (33%) values for 8 and 16h, respectively. During the periodic exposure, the samples were weighed five times during the absorption phase and twice during the desorption phase. The moisture buffering value (MBV) was calculated using to Eq.1:

(1)

Where A (m 2 ) is the area of the sample in contact with air. RH high and RH low represent, respectively, the high relative humidity (75% RH) and the low relative humidity (33% RH), and Δm represents the change in mass during the absorption/desorption phase (g).

Density and porosity

Results of densities and porosity are shown in Table 2. These composites have a bulk density of less than 350 kg/m 3 . Absolute density increases with the starch ratio, but bulk density evolves differently. Composites containing 10 and 20% starch have a higher bulk density. From 30% starch on, the bulk density of the composites decreases. The presence of starch aerogel fills the inter-particle space between the stalk aggregates. However, during the drying step, water escapes from the samples and forms capillary voids between the stalk aggregates and the starch binder. In consequence, the composites contain both closed pores and more or less accessible open pores. The porosity of the composites ranges from 67% to 82%.

Among the composites with the least starch, S/A-0.1 and CS/A-0.1 have the lowest bulk density and the highest porosity, 82%. The grinding of the stalks increases the bulk density of the composites. Moreover, the complex structure of the stalks allows to create composites with high porosities. With regard to vegetable insulation materials, hemp/starch mixtures have densities of about 130 kg/m 3 [START_REF] Bourdot | [END_REF] and cork-based panels have very variable densities depending on particle size and range from 108-390 kg/m 3 [8]. However, commercial petroleum and mineral insulation materials generally have densities less than 100 kg/m 3 .

Table 2. Densities and porosity of composites

Thermal characteristics

Table 3 shows the thermal measurements λ for different aggregate-starch mixtures and freeze-dry starch gel. The thermal conductivity is close for all composites around (ca 0.0663 W/(m.K)).

Table 3. Thermal conductivity of the different compositions

Composites with 20% starch had a higher thermal conductivity than composites with 10% or 30% starch as well as starch aerogel. S/A-0.2 and CS/A-0.2 have lower porosities than the other composites, which may explain the observed difference.

The incorporation of stalks in the starch aerogel lowers the thermal conductivity at 30% starch, on the other hand; the grinding of the stalks does not lower thermal conductivity of the obtained composites. As a matter of fact, the value of λ for composites based on whole stalks is lower than the value for composites based on crushed stalks, no Starch Ratio 0.1 0,0651 ± 0,0005 0,0656 ± 0,0004 -0.2 0,0693 ± 0,0039 0,0771 ± 0,0015 -0.3 0,0590 ± 0,0015 0,0617 ± 0,0006 -1 --0,0658 ± 0,0042 matter how much starch is added. Considering that starch aerogel has had a conductivity of 0.0658 W/(m.K), the arrangement of aggregates in the composites does affect the thermal conductivity.

Hemp-starch composites have a thermal conductivity of ~ 0.066 W/(m.K) [START_REF] Bourdot | [END_REF], as do cork-based panels, which have a thermal conductivity of between 0.047 and 0.081 W/(m.K) (due to differences in particle size in the panels) [8]. The thermal conductivity of commercial petroleum or mineral insulation materials ranges from 0.02 to 0.08 W/(m.K).

Acoustic characteristics

Fig. 1 shows the evolution of the weighted absorption coefficient α w as a function of sample thickness and different classes of sound absorption. From A to E, the parameter α w ranges from 0 to 1. Class A is the highest absorption class.

For the starch aerogel, increasing the thickness does not improve the α w parameter. As a matter of fact, from 6cm on, there is even a decrease of the αw from 0.40 to 0.25. For the stalks-based composites, there is a progressive increase of the α w with the thickness but also with the starch content in the composites. The S/A-0.2 and S/A-0.3 composites have had a weighted absorption coefficient that seems to stabilize from 8 cm thickness at 0.80. For the composites based on crushed stalks, the evolution of the acoustic properties has not been so well defined. It is interesting to notice that for the composite CS/A-0.2, there is a progressive evolution followed by stabilization, whereas for the composites CS/A-0.1 and CS/A-0.3, there is a slight increase at first followed by a fluctuation of α w with thickness. The porosity with the presence of open or/and closed pores has a noticeable influence on the acoustic properties of the materials.

Moisture Buffer Value (MBV)

It is possible to simulate realistically a house's daily climatic conditions using this method of dynamic characterization of water buffering capacities and to translate the relative humidity control ability of the material. During these cycles, composite materials will absorb and desorb water at a rate of 75% and 33% respectively. As the mass variation of According to the classification proposed by Rode [7], the results presented in Fig. 2. clearly show that all composite materials are excellent relative humidity regulators (MBV > 2 g/(%RH.m²)). The grinding of the stalks and the starch content do not seem to have a direct influence.

By absorbing and desorbing water vapor, these composites can regulate the humidity of the interior air, reducing the ventilation rate and the need for heat in the winter and air conditioning in the summer.

Conclusion

The composite based on stalks with 30% starch, S/A-0.3, displays the best thermal and acoustic characteristics, it does not have the best MBV among all the studied composites, but it is still classified as an excellent moisture regulator.

Furthermore, materials of plant origin are sensitive to excess moisture since it favors the development of microorganisms, discomfort, but also deterioration of performance and thermal stability. Studies are conducted on the durability of composites (fungal evolution, water immersion, resistance and fire tests). In order to address these problems, other surface treatments using linseed oil or grape seeds are being studied to create a resistance to microbial degradation and make these materials usable in the building industry.

In addition, it will be interesting to examine how much carbon can be sequestered in these materials, as well as comparing life cycle analysis between vegetable materials from agro-waste and mineral and petroleum-based products.

Fig. 1 .

 1 Fig. 1. Evolution of the weighted absorption coefficient αw as a function of sample thickness for the different composites and sound absorption classes

Table 1 .

 1 Densities and porosity of raw materials[6]

	Raw materials	ρ bulk (kg/m 3 )	ρ abs (kg/m 3 )	Porosity (%)
	Grape Stalks (S)	61 ± 3	1022 ± 71	94 ± 1
	Crushed Stalks (CS)	116 ± 5	1022 ± 71	89 ± 1
	Freeze-dry starch gel (A)	178 ± 7	1200 ± 82	85 ± 2


domain: info shs

text:
The tutorial is available at https://escriptorium-tutorial.readthedocs.io All collaborations are welcomed: https://github.com/alix-tz/escriptorium-tutorial

❏❏❏❏❏❏❏❏❏

  Chiffoleau From Source to Full Text: Workshop on Using Automatic Text Recognition (ATR) ATR or Automatic Text Recognition is the process of transforming a digitized document into a machine-readable text, through the use of segmentation and text recognition tools ❏ Various software for ATR with some specificities: Proprietary or open source ❏ Printed documents, handwritten documents or both ❏ Charged, freemium, free ❏ Example of open-source, semi-free OCR engine: ❏ Tesseract (no interface) ❏ Transkribus (interface) + PyLaia (transcription engine) ❏ eScriptorium (interface) + Kraken (transcription engine) ❏ eScriptorium is an open-source software (web application), for transcribing textual documents, developed by the research team Scripta (PSL). ❏ It provides a graphical user interface (GUI) for: ❏ document management, ❏ layout annotation (aka segmentation), ❏ transcribing (manually or automatically), ❏ and training models. ❏ and more! ❏ It uses Kraken (Benjamin Kiessling -PSL) as a transcription engine. Kraken is language-agnostic and fully open source. Get overall metrics on the project'What is training data ? → Sets of images and their exact transcription (for text recognition) or layout structure (for layout recognition), used to train models ❏ There are two categories of training data: Three formats for the training data: ❏ Sets of ALTO XML files and their images Sets of PAGE XML files and their images Sets of text line images and their transcription In each case, same filename, different extension (xml/txt, jpg/png/tiff) ❏ Additionally to your own training data, you can find other training data online, such as thanks to the HTR-United catalog ❏ It contains metadata on training datasets available for the creation of transcription or segmentation models Fantastic models (and where to find them) Fantastic models (and where to find them) ❏ What is a model ? → It is a file that has been trained to recognize certain types of patterns. ❏ A model can be developed for 2 types of process ❏ Layout recognition → Process that determines the constituents of an image and locates the regions and lines of the document where data have been written ❏ Text recognition → Process that recognizes the characters in document lines and produces a machine-readable text ❏ Specific to the software that produced them (no standardization) In the tab "My models" on eScriptorium → it contains public models, available for every user and the models the user uploaded/trained themself ❏ In the repository "OCR/HTR model" created on Zenodo ❏ For other software ❏ Inside software, but not downloadable (Transkribus, FineReader, etc.) (most common case) ❏ Along with software source code (Tesseract) Assess the transcription ❏ How to assess? ❏ Eyeballing the result (Can I read it? Does it look gibberish?) The tutorial aims to provide a set of (written) walkthroughs detailing eScriptorium's features, for example: ❏ Managing projects and collections of documents, ❏ Learning how to segment, annotate and transcribe a text image, ❏ Instructions for training models.

SegmentationTranscription

Predict the transcription

❏ A prediction is the act of using a model to generate (or predict) a layout recognition or a text recognition using an image and a segmentation/transcription model.

Image

Layout Text

Thank you for your attention

Any questions ?

Contact: alix.chague[at]inria.fr floriane.chiffoleau [at]inria.fr