- The Bartlett
Transcription
- The Bartlett
University College London Bartlett School of Graduate Studies M.Sc. Built Environment: Advanced Architectural Studies Final Report Rediscovering the peripheral area of the historic centre of São Paulo Fernanda Lima Sakr September 15th, 2010 I, Fernanda Lima Sakr, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. 2 Abstract This thesis explores the socio-economic and spatial characteristics of the areas located in the periphery of the historic core of São Paulo, Brazil. The key objective of the study is to investigate the different contributions that socio-economic variables and spatial properties make to the generation of diversity in urban areas. It examines whether the areas on the periphery of the historic core can, in their current spatial form, play a role in creating a sustainable urbanism in São Paulo, or whether they are essentially unsustainable without some radical intervention. Case studies are presented of six different sub-areas – Brás, Gasômetro, Luz, Oriente, Pari and Santa Cecília. The theoretical framework and techniques of space syntax are used to identify the relationship between the spatial properties and the socio-economic variables of these areas. In addition, the paper discusses previous studies and hypotheses about São Paulo. It will be suggested here that the current pattern of land use and activities of the six subareas have deficiencies: there is low residential use, low resident density and monofunctionality. The statistical correlations between the socio-economic and spatial characteristics of each area are weakly related. The specialised retail of the areas plays an ambiguous role: it is considered to hold economical potential, but at the same time it impedes the diversification of activities, people and movement. Finally, it concludes that the theory of centrality could be slightly more flexible in its predictions. One hypothesis is that the pattern of occupation and activities has to do with the patchwork layout of the city. The form of the city is neither regular grid structure nor ‘deformed wheel’, but a patchwork of offset grids in which areas might differ morphologically from each other depending upon their origins. Key words: morphology, socio-economic characteristic, periphery of the historic core, centrality, space syntax. 3 Contents Chapter 1 Introduction........................................................................ 10 Chapter 2 Study Area.......................................................................... 16 Chapter 3 Research methodology........................................................ 44 Chapter 4 Literature Review................................................................ 55 Chapter 5 Analysis and Results............................................................ 62 Chapter 6 Discussion........................................................................... 111 Chapter 7 Conclusion.......................................................................... 117 Bibliography....................................................................... 119 Appendix............................................................................ 124 4 List of Illustrations Figure 1 Map of São Paulo in 1890 showing the firsts settlements of the city. Figure 2 Map of São Paulo Central Area Figure 3 Aerial photo showing the areas of study according to EMPLASA classification of UITs. Table 1 UIT classification according to EMPLASA. Figure 4 Brazil in the context of South America. Figure 5 Satellite photo of São Paulo, 2002. Figure 6 Map of São Paulo and its Central Area. Figure 7 Map of São Paulo in 1897 showing the railway and the rivers Tamanduateí and Tietê Figure 8 Victor da Silva Freire diagram (1911). Figure 9 ‘Plano de Avenidas’ by Prestes Maia (1930). Figure 10 Map of São Paulo showing the shift of the financial centres over years. Table 3. Evolution of the resident population: 1991-2000 Table 4. General Information Table 5. Employment according to activities Figure 11 Aerial photo highlighting Brás area Figure 12 Alcantara Machado Ave. Figure 14 Alcantara Machado Ave. Figure 15 Alcantara Machado Ave. Figure 17 Piratininga street Figure 18 Piratininga street Figure 19 Piratininga street Figure 20 Brás subway station Figure 22 Mooca street Figure 23 Mooca street Figure 24 Mooca street Figure 25 Visconde de Parnaíba street Figure 26 Visconde de Parnaíba street Table 6.General Information Table 7. Employment according to activities Figure 27 Aerial photo highlighting the Gasômetro area Figure 29 Largo do Pari (old Pari railway yard) Figure 31 Estado Avenue Figure 33 Mercurio – Palacio das Industrias Figure 34 Santa Rosa street Figure 39 Gasômetro street Figure 43 Aerial photo highlighting Luz area Table 8. General information Table 9. Employment according to activities Figure 45 São Caetano street Figure 46 São Caetano street 5 Figure 44 Land use map of Luz Figure 50 25 de Janeiro street Figure 54 Cel Fernanda Prestes Figure 61Silva Pinto street Table 11. Employment according to activities Figure 64 Land use map of Oriente Figure 71 Rangel Pestana Table 12.General information Table 13. Employment according to activities Figure 80 Tiers street Figure 78 Land use map of Pari Figure 81 Das Olarias street Figure 82 Das Olarias street Table 14. General information Table 15. Employment according to activities Figure 90 Land use map of Santa Cecília Figure 91 Nothman street Figure 93 Helvética street Figure 94 Helvética street Figure 95 Duque de Caxias street Figure 96 Duque de Caxias street Figure 98 São João Ave. Figure 99 São João Ave. Figure 100 Angélica Ave Figure 101 Angélica Ave Figure 105 Predominant land use map (Luz area) – GIS Table Figure 112 Segment Map of São Paulo showing historic centre and surrounding area and the Y System at logCH+1 at global radius (n) Figure 113 Diagram of the Y System: 9 de Julho Ave., 23 de Maio Ave. And Prestes Maia Ave. Extracted from Meyer (2010) Figure 114 Segment Map of São Paulo showing logCH+1 at global radius (n) Figure 115 Segment Map of São Paulo showing historic centre and surrounding area at logCH+1 at local radius (750m) Figure 116 Segment Map of São Paulo showing logCH+1 at local radius (750m) Figure 117 Estado Avenue Figure 118 Segment Map of São Paulo showing Patchwork: Metric Mean Depth local radius (2000m) Figure 119 Segment Map of São Paulo showing local Integration (NC/MD) at radius 1000m. Figure 120 Land use value Table 16. General information Table 17. Family average income (salary) Table 18 Number of land use according to activities Figure 122 Correlation between industrial (%) land use and density 6 Figure 123 Correlation between retail (%) land use and density Figure 124 Correlation between service (%) land use and density Figure 125 Correlation between house (number) and housing Figure 126 Correlation between apartment (number) and housing Figure 127 Correlation between room (number) and housing Table 19. Typology of housing Figure 128 Areas on the periphery of the historic centre of São Paulo. Segment map global Choice logCH+1 radius n Figure 129 Correlation between global integration (Rn) and local integration (R750) Figure 130 Correlation between global choice (Rn) and local choice (R750) Table 20. Integration (NC/MD) of each study area Figure 131 Correlation between global integration (Rn) and local integration (R3000) Figure 132 Correlation between global choice (Rn) and local choice (R5000) Table 21. Summary of comparison: integration (NC/MD) and choice (LogCH+1) at different measures Figure 134 Correlation between local choice (R750) and local integration (R750) Table 22. Relationship between integration (NC/MD) and choice (LogCH+1) Figure 136 Correlation between local integration (R750) and retail (%) land use Figure 138 Correlation between local choice (R3000) and retail (%) land use Figure 139 Correlation between global integration (Rn) and service (%) land use Figure 140 Correlation between local integration (R3000) and service (%) land use Figure 141 Correlation between global integration (Rn) and industrial (%) land use Figure 142 Correlation between local integration (R3000) and industrial (%) land use Figure 144 Correlation between local choice (R3000) and industrial (%) land use Figure 145 Correlation between global integration (Rn) and house (%) Figure 147 Correlation between global integration (Rn) and apartment (%) Figure 148 Correlation between local integration (R3000) and apartment (%) Figure 149 Block size graph of the area of Brás Table 24. Comparison between centre and non-centre Figure 150 Land Use Map of Brás Figure 151 Segment Map of Brás showing logCH+1 at global radius (n) Figure 152 Segment Map_logCH+1 at local radius (750m) Figure 153 Segment Map_Integration (ND/MD) at global radius (n) Figure 154 Segment Map_Integration (ND/MD) at local radius (750m) Figure 159 Block size graph of the area of Gasômetro Table 25. Comparison between centre and non-centre Figure 160 Land Use Map of Gasômetro Figure 161 Segment Map of Gasômetro showing logCH+1 at global radius (n) Figure 162 Segment Map_Integration (ND/MD) at global radius (n) Figure 163 Segment Map_Integration (ND/MD) at local radius (750m) Figure 164 Segment Map_logCH+1 at local radius (750m) Figure 166 Global and local choice Figure 167 Block size graph of the area of Luz 7 Table 26. Comparison between centre and non-centre Figure 168 Land Use Map of Luz Figure 169 Segment Map of Luz showing logCH+1 at global radius (n) Figure 170 Segment Map_logCH+1 at local radius (750m) Figure 171Segment Map_Integration (ND/MD) at global radius (n) Figure 172 Segment Map_Integration (ND/MD) at local radius (750m) Figure 177 Block size graph of the area of Oriente Figure 178 Land Use Map of Oriente Figure 179 Segment Map of Oriente showing logCH+1 at global radius (n) Figure 180Segment Map_logCH+1 at local radius (750m) Figure 181Segment Map_Integration (ND/MD) at global radius (n) Figure 182 Segment Map_Integration (ND/MD) at local radius (750m) Table 28. Comparison between centre and non-centre Figure 188 Land Use Map of Pari Figure 189 Segment Map of Pari showing logCH+1 at global radius (n) Figure 190 Segment Map_logCH+1 at local radius (750m) Figure 191Segment Map_Integration (ND/MD) at global radius (n) Figure 192 Segment Map_Integration (ND/MD) at local radius (750m) Figure 193 Synergy Figure 194 Global and local choice Figure 196 local integration and choice Figure 198 Land Use Map of Santa Cecília Figure 199Segment Map of Santa Cecília showing logCH+1 at global radius (n) Figure 200 Segment Map_logCH+1 at local radius (750m) Figure 201Segment Map_Integration (ND/MD) at global radius (n) Figure 202 Segment Map_Integration (ND/MD) at local radius (750m) Figure 203 Synergy Figure 204 Global and local choice Figure 205 Global integration and choice Table 30. Comparison of the shopping streets in each area 8 Acknowledgments I wish to express my gratitude to my supervisors Professor Sam Griffths and Professor Bill Hillier for their guidance throughout the research. I would like to thank Dr. Beatriz Campos for her advice and enthusiasm, Professor Regina Meyer for her valuable insights, João Pinelo for his technical support with the software used, and Bruno Netto for providing pictures of central São Paulo. I would also like to thank Dr. Laura Vaughan, Dr. Ruth Conroy-Dalton, Dr. Kerstin Sailer, and Dr. Alan Penn for their commitment throughout the academic year. A special thanks to my family and to all my friends at the AAS course, thank you for making this journey a memorable one. 9 1. INTRODUCTION 10 Chapter 1 1. Introduction The city of São Paulo is a metropolis in flux, growing constantly according to changes in its economy and society. It has limited physical resources to support these transformations and rapid expansion. This research looks at settlements on the periphery of the historic core, exploring the spatiality of their economic activities and its determinants. The six areas selected for study were the first settlements to emerge along the north and east border of the historic centre, and along the railway track. Since the rapid development of the city, and the shift of the financial centre towards to southwest, these areas have been left behind by the wealthier population, as well as by the public and private sectors. An aim of this paper is to “rediscover” the peripheral area of the historic centre of São Paulo; what can be discovered there from a spatial perspective? It also asks whether the peripheral areas of the historic core, in their current form, have a role to play in creating a sustainable urban city, or whether their run-down situation means that they are essentially unsustainable without some radical intervention. A sustainable city in this context means a city that is able to support itself socially and economically. Furthermore, the paper considers whether there are there any socio-economic benefits to the modern city in preserving the current spatial aspect of these areas; which spatial factors are of value to the city? The study analyses the six sub-areas of the city using the theoretical framework of space syntax since they hold tradition research and techniques on correlations between spatial configuration and urban centrality. The comparison presents statistical correlations of spatial-syntactical analyses, based on data from a demographic census, empirical data and maps, in order to identify the degree of spatial accessibility and influence on the pattern of land use, activity, and pedestrian and vehicular movement. The analysis will inform a discussion of the role of spatial configuration in generating diversity and sustainable urbanism in São Paulo. 11 Chapter 1 Boundary of the study area Figure 1 Map of São Paulo in 1890 showing the firsts settlements of the city. Source: SEMPLA The areas of study were selected according to previous research titled São Paulo Centro uma nova abordagem (2000), in which Regina Meyer proposes the concept of the Central Area, which is formed by both the historic core (Sé and República) and the settlements located on its periphery (Bela Vista, Bom Retiro, Pari, Brás, Santa Cecília, Campos Elíseos, Barra Funda, Santa Ifigênia, Cambuci, Glicério, Ponte Pequena, Liberdade, Luz and Consolação) (Meyer 2000:12) (Figures 2, 3). These areas have individual identities and play a distinctive role in the local and global economy of the city. 12 Chapter 1 The physical boundaries of the areas are defined precisely according to the administrative division of district sub-areas developed by EMPLASA1 (Empresa Paulista de Planejamento Metropolitano SA – Paulista Metropolitan Planning Organization); classified as UITs (Sistema de Unidades Informarções Territorializadas –Units System of Territorialized Information) according to the following factors: land use and pattern of occupation; constructive aspects of the buildings; location of the relevant land use; urban or rural functionality; morphology and retail streets; poles of traffic; socio-economic factors and demographic census2. Each UIT is part of one administrative district (EMPLASA report 2007). It seems relevant to emphasise that this administrative division (zoning system) is not based on the natural morphological boundaries. Bearing in mind the common and different features of each area, the research explores the areas located to the north and east of the historic centre: Santa Cecilia district – Santa Cecilia (UIT28); Bom Retiro district – Luz (UIT8); Bras district – Bras (UIT13), Gasômetro (UIT16), Oriente (UIT15) and Pari district – Pari (UIT 10). The socioeconomic information about these areas was extracted from Regina Meyer literature, EMPLASA, SEMPLA3 and Viva Centro4 websites. 1 http://www.emplasa.sp.gov.br/portalemplasa/ It is based on information developed by IBGE (Instituto Brasileiro de Geografia e Estatistica – Brazilian Institute of Geography and Statistics) - http://www.ibge.gov.br/ 3 Secretaria Municipal do Planejamento http://www.prefeitura.sp.gov.br/cidade/secretarias/planejamento/ 4 Viva Centro Association - http://www.vivaocentro.org.br/vivaocentro/index.htm 2 13 Chapter 1 Figure 2 Map of São Paulo Central Area. Source: Meyer et. al. São Paulo Centro: uma Nova Abordagem (São Paulo: Associação Viva o Centro, 2000) D. PEDRO II PARK District boundary UIT boundary Figure 3 Aerial photo showing the areas of study according to EMPLASA classification of UITs. Source: EMPLASA report (2009). Map by author 14 Chapter 1 DISTRICT UIT DISTRICT UIT SÉ Sé Parque Dom Pedro Praca Joao Mendes República Santa Ifigenia Ladeira da Memoria Santa Cecilia/ Campos Eliseos Marechal Deodoro Várzea da Barra Funda BOM RETIRO Bom Retiro Armênia Luz Brás Gasômetro REPÚBLICA SANTA CECILIA BRAS Oriente Bresser PARI Pari Canindé CDC Vigor Table 1 UIT classification according to EMPLASA. Source: EMPLASA report 2009 The thesis is structured in six sections. Chapter 2 introduces the area of study, providing a brief description of its historical formation, and outlining important events and criteria which were used to define the sub-areas. It also discusses the current socio-economic characteristics of the peripheral area of the historic core of the city of São Paulo. Chapter 3 is the literature review which presents a background to the current debate surrounding areas on the periphery of the historic core, including the current situation of the city of São Paulo and a discussion of sustainable cities as generators of social and economical diversity. It also establishes connections between space syntax literature and other theories. Chapter 4 describes the research methodology used. It explains the available dataset provided by different institutions; the detailed field survey that, when used alongside existing data, helped to provided an understanding of the current dynamic of the area of study; the syntactic measures developed by space syntax; the role of the different software that was utilised; the model of centrality which was applied; and the implications found in the process. In Chapter 5 interpretations of the findings are developed in the light of empirical, analytical and syntactic analyses of the data collected. The performance of each area is discussed and its pattern of centrality; for instance, how the centre of each area is embedded in the surrounding area, as well as its spatial shape. In this case, the centres are considered as the busiest shopping streets, and non-centres the other streets located within the administrative boundary. In addition, statistical correlations are developed by comparing the socio-economic data and the syntactic measures extracted from spatial analyses. Chapter 6 discuss the implications of the analyses relating to the literature previously discussed. Chapter 7 provides a brief conclusion and propose lines for further study. 15 2. STUDY AREA 16 Chapter 2 2. Study Area This section will give a brief summary of the history of the city of São Paulo, outlining the relevant periods with regards to this research. It aims to show the important facts that have influenced the spatial and economic development of the city. São Paulo is located in the southeast of Brazil at a distance of approximately 70km from the Atlantic Ocean. It is located on a plateau that is part of the Serra do Mar (Sea Range), which forms part of the vast region know as the Brazilian Highlands, with an average elevation of around 800m (2,000ft) above sea level. São Paulo is the most populous city in the Americas, with approximately 11,037,593 residents within an area of 1,523 km2 (IBGE). Figure 4 Brazil in the context of South America. Source: Guia Geográfico 17 Chapter 2 Figure 5 Satellite photo of São Paulo, 2002. Source: Empresa Brasileira de Pesquisa Agropecuária EMBRAPA. Figure 6 Map of São Paulo and its Central Area. Source: Guia Geográfico and Prefeitura de São Paulo In the sixteenth century the city was strategically formed between two valleys plentiful with water, vegetation, animals and fertile lands, in order to support the Jesuit mission in the region. The location was chosen for settlement as it guaranteed good visibility while at the same time being difficult for potential enemies to access. The opening of the railway in 1867 can be considered the first big change in the history of the city. In the late nineteenth and early twentieth centuries it enabled the exportation of coffee beans which attracted international investment and intense immigration from Europe. The first railroad ran from the seaport of Santos to the western lands beyond the city of São Paulo. According to Barbosa (2001), the railroad brought about a considerable transformation of the region; the city became a central point for economic development. 18 Chapter 2 Meyer (1999) suggests that the railway drastically affected the development of the city. The infrastructure lead an intense process of population growth (from 32,000 to 65,000 inhabitants between 1870 and 1890) and urbanization, especially on land located within the border of the railway. In terms of urban organisation, the railroad and the rivers Tamanduateí and Tietê formed an extensive territory to the north of the historic centre where there was a concentration of industry. Meyer (2010) adds that other factors influenced the location of industry: the flatlands of the valley, the low cost and facility of converting farm constructions into factories; and the presence of an immigrant workforce from the coffee production. Around this region, and particularly in the east, the boroughs of Brás, Pari, Bom Retiro, Santa Ifigenia and Mooca emerged within the new urban fabric, generating the firsts ‘vila operárias’ (workers villages). Tietê river Tamanduateí river Railroad Boundary of the study area Figure 7 Map of São Paulo in 1897 showing the railway and the rivers Tamanduateí and Tietê Source: SEMPLA The rapid process of urbanization resulted in accelerated population growth. In 1886 the city was a village with 47,697 inhabitants and in 1950 it had 2 198,000 inhabitants within 420 km2 (Meyer 2010:22) (Table 2). 19 Chapter 2 Table 2. Evolution of the population: 1886-1950 1886 1900 1920 47,697 239,820 579,033 São Paulo Source: IBGE, Demographic Census 1991 and 2000 – from EMPLASA 2008 1950 2 198,000 In 1911 Victor da Silva Freire developed a street diagram with radial lines emanating from a central triangle towards new boroughs, emphasising the importance of the links between neighbourhoods (Figure 8). Since that time vehicular traffic increased continuously, so in 1930 a new urban model was proposed: the radio-centric ‘Plano de Avenidas’ (Roads Plan) by Francisco Prestes Maya. Many avenues leading to the peripheries converge on the centre of this model, with a few ring roads connecting them at different distances from the centre (Figure 9). Later in 1945 a new system was added: the Sistema Y (Y System), formed by the avenues 9 de Julho, 23 de Maio and Prestes Maia, which cross in the centre, intensifying the transversal north-south axis of vehicular flow. The system particularly affected the central boroughs located to the north of the historic centre. As a solution it only improved the streets which privileged vehicular movement. Subsequently, in order to keep traffic controlled, other complexes were built: 23 de Maio, modifications to the Dom Pedro II park such as the Radial Leste expressway (1960), and the elevated express west-east axis Elevado Costa e Silva (1970) (Meyer 1999, 2010). Figure 8 Victor da Silva Freire diagram (1911). Source: Meyer (2010) 20 Chapter 2 Figure 9 ‘Plano de Avenidas’ by Prestes Maia (1930). Source: Meyer (2010) The intense expansion of the city from the mid-1940s onwards is mostly explained by the heavy industrialization, and by the opening of new roadways connecting major Brazilian cities – a parallel process that simultaneously stimulated remote demands for new production. This policy was intensified from 1956 under the government of President Juscelino Kubitschek, who made possible the implementation of heavy industries throughout the country. But São Paulo was by then both the most attractive market and the most propitious place for new industries. Under Kubitschek’s guidance, nearly the entire automobile production industry was implemented in São Paulo’s region known as ABCD (Santo Andre, São Bernardo, São Caetano, and Diadema). This industrialization occurred at the fringes of the city, specifically along the new expressways. The need for easy transportation access at a national scale, for plentiful electric energy, and for large sites, made the industrial infrastructure from the early 21 Chapter 2 1900s obsolete. Thereafter, the city observed the emptying process of the central industrial neighborhoods adjacent to the historical centre. From the mid-1950s, the upper classes turned their attention to Paulista Avenue, with the transference of many offices and much luxury commerce from downtown to this region. This process, in which upper class offices and retail activities move away from stabilized areas to new areas of development, replicates itself continuously in waves even nowadays. Furthermore, the Paulista Avenue region, so successful in the 1960s, also suffered from a partial evacuation of offices after the opening of Brigadeiro Faria Lima Avenue further south, on which São Paulo’s first shopping mall was constructed in the 1970s. From the late 1980s to the 1990s, some office space was then transferred to Eng. Luis Carlos Berrini Avenue and, more recently, to Marginal Pinheiros. This fast shift of the most prestigious office space and, consequentially, the upper class residential sector, is referred to as the “southwest vector” of development (Rolnik 2001, Meyer 2004, 2010) (Figure 10). Paradoxically, while the old centre was being left solely for the economically lower classes, one of the most expensive known urban elements was introduced there in 1970: the subway. The existence of both the new subway stations and large bus terminals within the inner city motivated the movement of pedestrians in the area, making vehicular flow difficult; however, the majority of pedestrians were from the lower class population who were not car owners. Figure 10 Map of São Paulo showing the shift of the financial centres over years. Source: Meyer (2004) 22 Chapter 2 Nevertheless, the fast process of urbanization – the rapid growth of the city facilitated by the railway, industrial development, the large road system interventions over the years and the shift of the financial centre to the southwest – may have favoured the macro accessibility of the system affecting the urban network, with highly accessible vehicular routes and neighbourhoods juxtaposed with large blocks and mono-functional uses. Indeed this process directly affected the central area of the city – the historic centre and boroughs located at its periphery – since this is where the first urban settlements were located. Bearing in mind the consequences of this rapid transformation on the urban fabric of the historic centre’s peripheral areas, it seems relevant to focus on how this development affects these sub-areas today, in order to better understand the relationship of their current spatial configuration and populations. For instance, what role do these areas play for the city and its society? Meyer (2000) argues that the Central Area of São Paulo reflects s the identity of the city through its urban morphology, socio-economic aspects and functions. Although the public sector has invested in these areas in transport and roads, since the 1950s the private sector has concentrated its investments in the more privileged areas to the southwest, which are occupied by an elite population. As a consequence, some boroughs with good accessibility, located close to clusters of services, have suffered from emptiness caused by the shift in the population towards to the new financial centres in 1950s. By the 1960s the city has a centralized low density and lower income population (Meyer 2000). The Census shows that density growth has decreased significantly between 1991 and 2000 (Table 3). The impact of the current conditions of the Central Area falls not only on this area itself, but also in the areas located to the north and east side of the city. The rapid process has left these areas with an undesirable physical and social fabric. As follows, the next section reports descriptions of each selected sub-area in order to provide a more comprehensive social and spatial framework for the case studies. Table 3. Evolution of the resident population: 1991-2000 Population 1991 Population 2000 Growth rate 12,359 9,626 -2,733 Brás 5,089 3,240 -1,849 Gasômetro 18,440 13,726 -4,714 Luz 6,343 4,150 -2,193 Oriente 17,626 11,415 -6,211 Pari 40,227 33,305 -6,922 Santa Cecília Source: IBGE, Demographic Census 1991 and 2000 – from EMPLASA 2008 % -2.76% -4.94% -3.26% -4.65% -4.76% -2.10% 23 Chapter 2 2.1 Case study areas 2.1.1 Brás Figure 11 Aerial photo highlighting Brás area Source: image: Google earth, map: by author Brás is a region that in the past was characterized by small farms and country houses and has grown traditionally as a working class population. It is known as a place that at the beginning received many Italian immigrants and, afterwards, migrants from the northeast of the country. The majority of the current population is made up of Korean and Bolivian immigrants who work in the production of clothes and accessories. It has a population of 9,626 inhabitants within an area of 1,095km2 (8,791hab/km2) (Table 4). Its residents are mostly a middleclass and low income population. In terms of the built environment, many houses (old, horizontal, up to one floor) are considered deteriorated located in specific streets. There are also mixed-use buildings and squats. There are some vertical residential buildings which were constructed in 1980s around the railway track and close to the subway station (Brás station). The busiest street is Alcantara Machado Avenue, which has a specialized retail offering of machinery and equipment, as well as electronic devices, clothing and accessories (EMPLASA report and Meyer 2010). Table 4. General Information Population 2000 (hab) Area (km2) Housing 2000 Demographic density (hab/km2) 9,626 1,095 3,476 8,791 Brás Source: IBGE, Demographic Census 2000 and IGC (Instituto Geografico e Cartografico) – from EMPLASA 2008 Table 5. Employment according to activities Industrial % Retail % Services % Total 2,024 24.84% 2,926 35.92% 3,197 39.24% 8,147 Brás Source: Ministerio do Trabalho, Rais (Relacao Annual de Informacaoes Sociais) 2005; SEMPLA/Dipro – from EMPLASA 2008 24 Chapter 2 Figure 12 Alcantara Machado Ave. Figure 13 Alcantara Machado Ave. Figure 14 Alcantara Machado Ave. Figure 15 Alcantara Machado Ave. Figure 17 Piratininga street Brás subway station Figure 16 Land use map of Brás Figure 18 Piratininga street 25 Chapter 2 Figure 19 Piratininga street Figure 20 Brás subway station Figure 21 Mooca street Figure 22 Mooca street Figure 23 Mooca street Figure 24 Mooca street Figure 25 Visconde de Parnaíba street Figure 26 Visconde de Parnaíba street Source (all pictures): Author 26 Chapter 2 2.1.2 Gasômetro Figure 27 Aerial photo highlighting the Gasômetro area Source: image: Google earth, map: by author The Gasômetro area, within the same district as Brás, became of note in 1872 when the presence of a gas power station provided light to the streets of São Paulo. It is one of the oldest boroughs in the city and its settlements are mainly characterized by storehouses and warehouses, most of which have converted from industrial into retail and service uses. It is known for having the first traditional ‘zona cerealista’ (cereal distribution zone), which supplies the whole city and parts of the country with fruit and vegetables. Besides this wholesale market, the Largo do Pari (old Pari railway yard) is also used for this traditional function and it is where the most famous coconut supplier is located. It has a specialized retail offering of wood, glue, resin and household accessories. The majority of the shops are located on Gasômetro street. Its demographic density is 4,675hab/km2 (3,240 inhabitants in 0,693km2) (Table 6). Similar to Brás, its residents are a mainly middleclass and low income population. Its housing is predominantly horizontal (maximum one or two floors), semi-detached and fronting directly onto the street. The weaknesses of the area are compounded by its spatial configuration: large block sizes, a lack of residents and its function as a destination for wholesale and retail activities (EMPLASA report and Meyer 2010). Table 6.General Information Population 2000 (hab) Area (km2) Housing 2000 Demographic density (hab/km2) 3,240 0.693 1,062 4,675 Gasômetro Source: IBGE, Demographic Census 2000 and IGC – from EMPLASA 2008 Table 7. Employment according to activities Industrial % Retail % Services % 332 6.98% 3,560 74.82% 866 18.20% Gasômetro Source: Ministerio do Trabalho, Rais 2005; SEMPLA/Dipro – from EMPLASA 2008 Total 4,758 27 Chapter 2 Figure 28 Largo do Pari (old Pari railway yard) Figure 29 Largo do Pari (old Pari railway yard) Figure 30 Largo do Pari (old Pari railway yard) Figure 31 Estado Avenue Largo do Pari Figure 33 Mercurio – Palacio das Industrias Figure 32 Land use map of Gasômetro Figure 34 Santa Rosa street 28 Chapter 2 Figure 35 Coconut supplier (Largo do Pari) Figure 36 Gasômetro street Figure 37 Gasômetro street Figure 38 Gasômetro street Figure 39 Gasômetro street Figure 40 Gasômetro street Figure 41 Alfândega sreet Figure 42 Alfândega street Source (all pictures): Author 29 Chapter 2 2.1.3 Luz Figure 43 Aerial photo highlighting Luz area Source: image: Google earth, map: by author Different from the previous two areas, Luz is characterized by being a centre of culture and popular retail in the city. Recently it has received investments in cultural facilities, with renovated buildings including the Pinacoteca do Estado, Estação da Lingua Portuguesa, São Paulo Symphony Hall and others. It also has a strong local economy with concentrated retail and wholesale activities within the clothing/fashion sector located mainly on José Paulino and Graça streets (popular fashion) and São Caetano street (wedding clothes and accessories to buy or hire). These shops attract shoppers from different parts of the city and country during the whole year. During the nineteenth century the area was formed by small farms and country houses used by the wealthier population of the city as leisure retreats. It is where the oldest park in the city is located – the Jardim da Luz (Luz Gardens) – however, this scenery changed when the earthenware industry settled in the area. Later, with the opening of the São Paulo Railway in 1867, new warehouses and factories were built and there was an intensification of the working class population, mainly formed of Italian immigrants. The area not only underwent spatial transformations (the railway and industrial occupation), but also social transformations, with the process of immigration. For instance, José Paulino street was firstly managed by the Portuguese, Turkish, Lebanese and Arabic, and then by a Jewish population after World War II. Later, a South-Korean population bought most of the main shops and helped to install a weaving industry within the fashion sector. Nowadays, within the area, it is possible to find a mix of ethnicities and cultures, a variety of restaurants and cafés, Jewish theatres, a synagogue, Christian churches, Presbyterian churches, schools, and so on. Its settlement has a different typology of both vertical and horizontal housing. 30 Chapter 2 Table 8. General information Population 2000 (hab) Area (km2) Housing 2000 Demographic density (hab/km2) 13,726 1,541 4,500 8,907 Luz Source: IBGE, Demographic Census 2000 and IGC – from EMPLASA 2008 Table 9. Employment according to activities Industrial % Retail % Services % Total 11,584 57.42% 4,751 23.55% 3,839 19.03% 20,174 Luz Source: Ministerio do Trabalho, Rais 2005; SEMPLA/Dipro – from EMPLASA 2008 Oficina Oswald de Andrade Cel Fernando Tiradentes subway station Militar Police museum Luz park Figure 44 Land use map of Luz Figure 45 São Caetano street São Paulo Symphony Hall Luz station Pinacoteca do Estado Figure 46 São Caetano street 31 Chapter 2 Figure 47 São Caetano street Figure 48 Estado Ave. Figure 49 São Caetano street Figure 50 25 de Janeiro street Figure 51 Cantareira street Figure 52 Cantareira street Figure 53 Oficina Cultural Oswald de Andrade Figure 54 Cel Fernanda Prestes 32 Chapter 2 Figure 55 Luz station Figure 56 Pinacoteca do Estado Figure 57 José Paulino sreet Figure 58 José Paulino street Figure 59 José Paulino sreet Figure 60 Graça street Figure 61Silva Pinto street Figure 62 Aimorés street Source (all pictures): Author 33 Chapter 2 2.1.4 Oriente Figure 63 Aerial photo highlighting Oriente area Source: image: Google earth, map: by author Oriente is located within the same district as Brás and Gasômetro. It has a density of 3,825hab/km2 (4,150 inhabitants in 1,085km2) (Table 10). The majority of its residents are considered middleclass and they mostly live in houses (old, horizontal, up to one floor) and mixed-use buildings. Its local economy is concentrated in the fashion sector – manufacturing and retail – which brings people from different parts of the city and country. It is mainly located at Miller street and Orient street, and on the north side of the Largo do Pari (old Pari railway yard) there is the ‘Feira da Madrugada’ (wee market) or ‘Shopping Popular da Madrugada’ (wee popular shopping) that covers about 70,000m2, has around 4,000 stalls and is open for work during the hours of 10am to 6pm and from 3am to 10am. This initiative is supported by public and private institutions. Table 10. General information Population 2000 (hab) Area (km2) Housing 2000 Demographic density (hab/km2) 4,150 1,085 1,278 3,825 Oriente Source: IBGE, Demographic Census 2000 and IGC – from EMPLASA 2008 Table 11. Employment according to activities Industrial % Retail % Services % Total 11,936 56.79% 7,030 33.45% 2,023 9.77% 21,019 Oriente Source: Ministerio do Trabalho, Rais 2005 ;SEMPLA/Dipro – from EMPLASA 2008 34 Chapter 2 Mesquita do Brás Largo da Concórdia Figure 64 Land use map of Oriente Figure 65 Shopping Popular da Madrugada Figure 66 Mesquita do Brás Figure 67 Shopping Popular da Madrugada Figure 68 Oriente street (Sunday) 35 Chapter 2 Figure 69 Shopping Popular da Madrugada-entrance Figure 70 Rangel Pestana Figure 71 Rangel Pestana Figure 72 Rangel Pestana Figure 73 Rangel Pestana Figure 74 Rangel Pestana Figure 75 Railway track Figure 76 Largo da Concórdia Source (all pictures): Author 36 Chapter 2 2.1.5 Pari Figure 77 Aerial photo highlighting Pari area Source: image: Google earth, map: by author Pari is known as a traditional working class borough, which grew because of the railway track and industry. Nowadays it has a middleclass population of 11,415 residents within 1,398km2 (8,165hab/km2) (Table 12). Its buildings – horizontal (one or two floors) and semi-detached – are considered deteriorated. During the last decade vertical buildings (more than 5 stories) have been built for middleclass and high income populations in some points of the borough. Its busiest streets are Silva Teles and Maria Marcolina. They specialise in manufacturing and selling of household goods and popular shops which attract a variety of buyers from the whole city and country (there are group excursions to the area of shoppers from different places) (EMPLASA report and Prefeitura de São Paulo). Table 12.General information Population 2000 (hab) Area (km2) Housing 2000 Demographic density (hab/km2) 11,415 1,398 3,654 8,165 Pari Source: IBGE, Demographic Census 2000 and IGC – from EMPLASA 2008 Table 13. Employment according to activities Industrial % Retail % Services % Total 6,738 43.25% 5,385 34.56% 3,457 22.19% 15,580 Pari Source: Ministerio do Trabalho, Rais 2005; SEMPLA/Dipro – from EMPLASA 2008 37 Chapter 2 Figure 78 Land use map of Pari Figure 79 Vautier street Figure 80 Tiers street 38 Chapter 2 Figure 81 Das Olarias street Figure 82 Das Olarias street Figure 83 Das Olarias street Figure 84 Rio Bonito Figure 85 Carlos de Campos Figure 86 Carlos de Campos Figure 87 Maria Marcolina street Figure 88 Maria Marcolina Source (all pictures): Author F 39 Chapter 2 2.1.6 Santa Cecília Figure 89 Aerial photo highlighting Santa Cecília area Source: image: Google earth, map: by author The Santa Cecília area differs from the others in social and economic characteristics. It has 33,305 inhabitants within 1,441km2 (23,112hab/km2) (Table 14), living mainly in high rise, high density buildings with different typologies, the majority being mixeduse. In comparison to the other areas, it has both the largest higher income and no income populations. The economy of the area is based more in the services sector than retail. Its busiest street is Duque de Caxias, which has diverse retail and activities. Another point is the presence of an elevated concrete expressway, named Elevado Costa e Silva and nicknamed ‘Minhocão’. Inaugurated in 197I, it is an iconic symbol that covers an area of 3.4km at 5.5 metres above the ground, its width varying between 15.5 and 23 metres (Prefeitura de São Paulo). It connects the central areas to the west side of the city, running from Franklin Roosevelt Square up to the Largo Pericles (Francisco Matarazzo Avenue). This “overwhelming concrete structure sitting cheek by jowl with adjacent buildings” (Urban Age Report 2008:154) has contributed to the deterioration and deprivation of the urban environment of the area (sometimes the distance between buildings and the expressway is less than 4 metres). During the peak hours it is usually highly congested, though every night and on Sundays it is closed and ‘converted’ into an open public space to serve the local population. This built structure has been part of the different discussions and competitions held by urban planners and governmental institutions over the years in order to propose new solutions. In May 2010 it was announced that parts of the expressway will be demolished according to new proposals that form part of policy interventions developed by the current municipal administration. 40 Chapter 2 Table 14. General information Population 2000 (hab) Area (km2) Housing 2000 Demographic density (hab/km2) 33,305 1,441 14,204 23,112 Santa Cecília Source: IBGE, Demographic Census 2000 and IGC – from EMPLASA 2008 Table 15. Employment according to activities Industrial % Retail % Services % 2,622 10.07% 4,900 18.83% 18,504 71.10% Santa Cacília Source: Ministerio do Trabalho, Rais 2005; SEMPLA/Dipro – from EMPLASA 2008 Total 26,026 Elevado Costa e Silva – ‘Minhocão’ Santa Cecília subway station Figure 90 Land use map of Santa 41 Chapter 2 Figure 91 Nothman street Figure 92 Guaianazes Figure 93 Helvética street Figure 94 Helvética street Figure 95 Duque de Caxias street Figure 96 Duque de Caxias street Figure 97 São João Ave. Figure 98 São João Ave. 42 Chapter 2 Figure 99 São João Ave. Figure 100 Angélica Ave Figure 101 Angélica Ave Figure 102 Santa Cecília Subway station Figure 103 Santa Cecília church Figure 104 Santa Cecília church Source (all pictures): Author 43 3. LITERATURE REVIEW 44 Chapter 3 3. Literature Review 3.1 The city of São Paulo and the areas located on the periphery of its historic centre What can be said about São Paulo today? This chapter investigates how the current physical layout of the city affects the social and economic aspects of its society, and what importance the areas on the periphery of its historic centre hold. The preliminary and most obvious question that shall be addressed in this section is: what is São Paulo today? “Today’s São Paulo is a paradigm of a local metropolis in the global world. It is at once a world city linked to global networks, and a local city, where public spaces manifest social inequality and urban dereliction.” (Urban Age Report 2008:173) Sassen (2008) adds São Paulo today is one of the major global financial centres in the world. It is integrated into the network of global cities. Alongside this, the fragmented areas left behind by industry, which new services and technological industries should/could occupy, also play an important role in the city today. With the end of industrial occupation, the railroad lost its primary function. The decay of industrial areas brought about the spatial disintegration that generated a fragmented territory: the abandoned buildings of the inner city and the dormant rail yards. Meyer (2010) examines the performance of the current physical situation in São Paulo. In the boroughs adjacent to the historic core, which reflect the changes of the past fifty years, Meyer argues that the spaces are fragmented and disconnected from the whole network because of the industrial urban fabric and its physical barriers: railway tracks, elevated roads and expressways. According to Meyer (2010), in the past years the change from industry to the production of knowledge and services has directly impacted São Paulo’s functionality and spatiality. New technologies based on micro-electronics and communications generate a different logic for the location of industry. Nowadays, the contemporary scenario comprehends the generic idea of ‘New Industrial Space’ (Castells, 1989), which is flexible in its site of production, resulting in an emptying of traditional industrial sectors and the abandonment of its built complexes to allow for the expansion of service-based industry and activities in these areas. “Telecommunications allows work at home in ‘electronic cottages’, while firms become entirely footloose in their location, freed in their operations by the flexibility of information systems and by the density and speed of the transportation network.” (Castells 1989:1) 45 Chapter 3 Castells (1989) discussion of ‘new information technologies’ affirms that the high production of knowledge and technology is the key for new economic growth, and that this production does have an impressive impact on cities and societies at different levels: economic, social, political and cultural. Castells’s concepts lead to the conclusion that the spatial and socio-economic needs of cities and their societies have changed and are changing constantly. At time of the industrial revolution, the main goal was to provide more space for people, for machines and for mass production lines. Nowadays the evolution of technology and information has lead to a division of labour in society, with people specialising in diverse, independent areas. Also the economies of cities/ regions/ countries are more competitive in the global network. Instead of ‘industrial revolution’, today there is the ‘technological revolution’, in which the soft industries, with innovative technologies, extend the productive capacity of working hours while overcoming spatial distance in all realms of social activities. Technology has been able to change the basic condition of human life: space and time. Considering the city of São Paulo and its society, this discussion can be further developed in order to understand how this contemporary process is occurring there today and how it is reflected in the urban space and activities of its citizens. 3.2 The importance of the areas located on the periphery of the historic core To understand what role the areas on the periphery of the historic centre play within the context of the whole city, the idea of centre itself, and its surroundings, needs to be brought into the discussion. According to Sieverts (2000), the centre is a place in which all major developments start and it used to be considered a hierarchical tree structure. As a consequence of contemporary urbanization, the structure of cities has changed surreptitiously towards to a configuration formed of distributed specialisation, both in terms of the division of labour and the function of space. Today a single centre no longer exists within the city (Sieverts 2000). In São Paulo, with the shift of the financial centre towards to southwest, not only has the historic core has been left behind, but also the areas immediately to its north and east. Centres and sub-centres have emerged in different regions, once more making the historic core and its periphery a forgotten place. A wide range of theories emphasize the importance of regenerating and preserving the inner areas of the city in order to bring back social and economic life, and to limit the urban sprawl beyond the city. 46 Chapter 3 Bromley et al (2005) suggest that the implementation of more residential development within the city centre could help to enhance the ‘vitality and viability’ of the area, by bringing in more residents, who demand greater facilities, thus increasing the diversity of uses during day and night. The daytime economy of city centres is dominated by retail and office activities, with a high density of people and movement. However, the night-time economy does not attract the same amount of the population; as a result there are empty areas and fear of crime. The residents of the inner city, who support the local facilities and activities, contribute to the sustainability of the city centre, its local economy and transport infrastructure. “Regeneration through residential development and its contribution to sustainability are key issues in the city centre. The city centre is an urban area where threats to long-term viability and vitality have prompted regeneration policies which incorporate housing, and where the rapidity of change provides an ideal arena for assessing aspects of sustainability in the regeneration strategy”. (Bromley et al 2005:2411) So, the idea of ‘residentialisation’ – denser resident areas – is an important element for the functionality of inner city areas. It helps to intensify the mixed occupations and activities at all times. As a result, higher urban densities can help to improve the local infrastructure, public transportation and facilities, diminishing social segregation (Bromley et al 2005). In addition, Griffiths et al (2008) in their research on suburbs, argue that sustainable suburban centres help to control urban sprawl. The ‘sustainability’ within suburban town centres is interpreted as ‘adaptability’, in the sense of “persistence of socioeconomic activity overtime” (Griffiths et al 2008:2); in other words, the potential of centres to preserve their vitality and viability over the years rather than running down towards their complete deterioration. This ability can be facilitated by diverse socioeconomic activities, by mixed-use high streets rather than mono-functional consumer destinations. The centres are able to offer a variety of employment and social activities. As it was demonstrated in the research about sustainable suburban high streets, the layout of the centre and its determinants for land use occupation and people’s movement is fundamental for its ‘vitality and viability’. A study about the Central Unplanned Areas in the city of Jeddah, Saudi Arabia, developed by Karimi et al (2007) presents analyses which demonstrate that the reason for the decline and deprivation of the central areas lay in the configuration of their urban fabric: fragmented systems as a result of the rapid development of the city. The layout of Jeddah’s streets is unable to relate to either the wider context of the city as a whole, 47 Chapter 3 or the historic core. This incompatibility impacts on the socio-economic aspects of the city. In order to find a solution for the problem, a multi-layered analysis with two methods was proposed: one brought together a detailed analysis of urban and socioeconomic data (land use, building height, density and so on) with a view to preserving the integrity of the local structure of the unplanned settlements; the other looked at the best way to improve the urban configuration and reconnect its core (route structure) to the wider grid, by identifying weak points in the potential street network and proposing solutions to fix them. For the improvement of the urban fabric, it is possible to both propose new route networks and modify the existing ones. This targets the segregation of the unplanned areas, enhancing the morphology of space and its interplay with socioeconomic variables. From this literature it is possible to retrieve indicators that seem essential for the discussion of central areas of the city: socio-economic aspects including land use and activities, resident density, adaptability to change and urban morphology. Moreover, when dealing with urban research is important to bear in mind what authors such Willian H. Whyte say about cities. One of the requirements is to learn how the real city works by observing in depth what is going on within the streets. It is emphasized that looking at the material world is essential to understanding the interface between the physical space and people’s behaviour. Indeed, to appreciate the real streets and their population, for instance, it is recommended to analyse how they act in parks, plazas and streets by standing there as long as is necessary. After carrying out a variety of observations, White concludes that streets suffer because of emptiness, for having too few people, not for being overcrowded (Whyte 2000). 3.3 Urban spatial form The topic of urban spatiality is triggered by Hillier’s studies of urban morphology, in which the configuration of the urban fabric plays an important role in people’s movement and has the potential to determine the location of specific land use and activities shaping the whole city (Hillier et al 1993). The theory of how space itself influences the socio-economic aspects of a society was considered relevant for this research since space syntax covers a wide range of empirical studies of urban environments. It provides a relevant support to the analytical investigations of space in such a complex city as São Paulo, whose configuration has changed rapidly over the years. Moreover, it helps us to understand the degree to which the peripheral areas of the historic centre became fragmented and disconnected from the system as a whole, in 48 Chapter 3 the sense that the pedestrian and vehicular accessibility of these areas was made difficult because its street network. Hillier et al (1993) say that the relationship between space and people is through their behaviour in the space, or their movement in the space; such movement is referred to as ‘natural movement’. The theory of ‘natural movement’ relies on the configuration of the urban grid itself having the potential to generate more movement in some places than in others. It means that the proportion of pedestrian movement is determined, other things being equal, by the structure of the layout itself, rather than by the presence of specific attractors or magnets (land use). And the grid is affected by both global properties, which relate each spatial element to every other element in a system, and local properties: the relation of each space to its neighbours. This process shows that “movement is fundamentally a morphological issue in urbanism, a functional product of the intrinsic nature of the grid” (Hillier et al 1993:32). In other words, the spatial form generates pedestrian flow in the physical city, reflecting the functional formation of the city and creating probabilistic interfaces between social groups and activities in the space. Hillier (1996) then suggests that the previous idea of ‘natural movement’ initiates a process where areas with high movement rates will attract uses that are high movement dependent, and this in return will attract more movement. Hillier defines this phenomenon as “movement economy” (1996:126). It creates a ‘multiplier effect’ – a social, economical and physical cycle where attractive land uses attract more activities and pedestrian movement, generating diversity and new enterprises, which in turn attract further uses and more people. It creates a feedback loop of life potential. So instead of making initial speculations about the possible problems of the peripheral areas of São Paulo’s centre being based only on land use and activities (for instance, because of the shift of the financial centre), the theory suggests that, initially, it is necessary to investigate the areas’ spatial performance. Indeed, how the physical properties of the areas facilitate or make difficult the pattern of occupation and movement in the system. Subsequently, Hillier (1999) proposed ‘centrality as a process’ in which the movement economy, acting within the spatial network, generates ‘live centres’; areas with a concentration of movement and dependent land use. Centrality is based on the interplay between configuration and attraction, a process in which both spatial properties (town plan) and functionality (land use and activities) drive people to the centre (pattern of 49 Chapter 3 centre and sub-centres - attraction inequalities). Hillier argues that it is the relationship of these centres and sub-centres that characterises the evolution of cities in general, and that they can be understood in terms of the movement economy. He argues that within a vibrant town centre it should be possible to get from any facility to any other by a quick and easy route, which stays within the town centre and which itself is lined with town centre facilities to maximize natural access to all facilities. In short, the network system in a centre makes itself accessible and obvious. Wherever people go they can easily find their way back without taking the same route, from all spaces to all others the orientation facilitates movement. Hillier (2009) also says that the pattern of centre and sub-centres spread throughout the urban fabric in a more intricate way than the polycentric form whose hierarchical location can be called ‘pervasive centrality’. This pattern of linked centres emerges from a consistent self-organising process at the interface between the grid configuration of space and movement at different scales. After considering studies about English city centres and Siksna’s resarch The effects of block size and form in North American and Australian City Centres (1997), Hillier argues that “successful live centres require both a global position in the settlement, and a compact and interaccessible local layout condition” (Hillier 1999:21). According to Hillier, the shape of a ‘live centre’ is formed by a linear and convex process. Firstly it is linear. As a settlement grows, a convex and compact process of intensification and metric integration is formed. With linear growth away from the ‘live centre’, local subcentres develop along radial lines, and with further growth smaller scale sub-centres develop away from the main radials. Furthermore, cities are formed by a ‘dual process of generic form’, both on a global and local scale, in which space and movement are explored in different ways (Hillier 2001 and Hillier and Vaughan 2007). The public space process, that brings people together, is driven by micro-economic factors which are invariant and tend to give cities similar universal and global structures. The residential spatial process, which structures the relation between inhabitants and visitors, is driven by socio-cultural factors and thus tends to make cities locally different to each other. On the one hand, public spaces work as part of a generative process, maximising pedestrian movement. On the other hand, residential spaces are conservative in the way that they control the space according to the needs of the culture. The effects of generative and conservative spaces impact on: the presence and movement of people, land use distribution, centre and sub-centre formation, as well as cultural and social features. In later papers, Hillier et al (2007) and 50 Chapter 3 Hillier (2009) reinforce the idea of the duality, suggesting two terms: ‘foreground network’ – linked centres of all scales, global; and ‘background network’ – primarily residential space, local. In terms of the urban block itself, Siksna (1997), after studying the spatial form of different cities, suggests a classification for block size: small (under 10,000m2), medium (10,000-20,000m2) and large (over 20,000m2). Siksna suggests larger blocks size are more suitable for residential use and smaller for public use And in relation to people’s movement she classifies 60-70m as very good for pedestrians, c.100m as very convenient and c.200m as inconvenient for pedestrians. This model helps us to understand the relation between block sizes, land use and circulation patterns in each area of study. Bearing in mind that parts of the study areas are old industrial settlements that emerged rapidly on the periphery of the historic core, they are constituted by blocks of irregular sizes and form. This results in different patterns of movement. Since the large blocks of the industrial occupation, one hypothesis to be investigated is if the configuration of the block makes more difficult the pedestrian flow in the area. Additionally, from studies of block morphology, Hillier et al (2007) and Hillier (2009) developed the ‘patchwork theory’. Based on the characteristics of the size and shape of the block (considering its physical distortion), the measures identify different local areas with similar values as representing natural partitions of the background. The patchwork of areas is determined by their spatial differentiation, rather than a well-delimitated boundary. Yang and Hillier (2007) add that the urban patchwork can be affected by the external structure of an area, its spatial context acting as a reference against which the area is outlined, and the boundary is defined as a fuzzy boundary since it depends not only on how space is formed within the patchwork, but also on how it is embedded within the whole area. The identified patchwork of areas has no clear boundary, but a fuzzy boundary defined by its external structures. A fuzzy boundary emerges from the internal structure of the space and how it relates to the external form. In identifying it, it helps to look at the different functions of areas, or their spatial characteristics, although it does not depend on their form, size or specific boundaries. This is about continuity and discontinuity in the urban grid, and it helps to identify the natural boundary of the areas according to their spatial form, as well as the nature of the base formation of the structure of the city. It is to think of the city of São Paulo as a morphological object, divided by its natural spatial characteristics rather than by administrative and economical aspects. 51 Chapter 3 3.4 Diversity and Density The issue of diversity and density is discussed by a variety of authors in urban literature, within different contexts (Jacobs 1961; Penn et al 2009; Marcus 2007). When looking at these two variables in the city of São Paulo, we should consider the concept of density in terms of demographic population, and diversity as mixed land use and activities. To help comprehend these urban indicators and how they relate to the configuration of the space itself, it seems relevant to bear in mind their different definitions. Jane Jacobs in The Death and Life of Great American Cities (1961) shows her social concern for neighbourhood life, demonstrating components that are able to achieve successful urbanisation and a sustainable society. These are based on the idea that cities are able to generate diversity and stimulate their economy, motivating new enterprises, when four spatial and social principles are effectively combined: ‘mixed primary uses, small blocks, aged buildings and a concentration of people’. For Jacobs, cities contain the seeds of their own destruction, but lively and diverse cities contain the seeds of their own regeneration, with enough energy to compensate for problems and needs outside of themselves. According to Jacobs (1961), her first principle is that streets need the presence of people at different times of the day, going in and out, from one place to another, because of distinct purposes, generating movement and natural encounters within a district. To achieve this it is essential to combine effective elements that work as anchors, helping to stimulate the natural economy, such as parks, squares, public buildings and dwellings. These attractors help to intensify the variety of uses and sub-areas, creating pools of economy that reach different levels of complexity. Her second principle is concerned with urban morphology. She emphasizes that the use of small blocks enables people to turn corners successively; they have more route choices. There are frequent streets on their way that increase the patterns of movement within the grid. This creates a fabric of streets as a continuous system throughout the sub-area district, attracting a mixture of uses along them. Her third principle is to support old and new enterprises, as well as mixed uses and activities. Cities must have mixed buildings and infrastructure with different aspects and conditions. In addition, they should have a variety of buildings with a high enough concentration of residents in order to guarantee the constant intensification of presence of people. The more these elements are mixed, the more diverse the economic and social possibilities. Her fourth principle is a high concentration of people in a district – individuals have to be there at different times of 52 Chapter 3 the day, as residents, shoppers or workers. This dense concentration can be achieved by the combination of the three principles already described and by adding densities of dwellings. The concept of diversity interpreted by Jacobs – mix of land use and activities, configuration of the urban fabric and movement of people – is directly related to the idea examined earlier about centrality: form and block size affects pedestrian movement, which subsequently determines land use and activities. Penn et al (in Cooper et al 2009) propose a definition for ‘the generation of diversity’ from the combination of Jacobs’ concepts and the theory of ‘movement economy’ from Hillier, emphasising the human cognitive dimension of intelligibility. The definition of diversity is the outcome from the relation of physical material, spatial configuration, social, economic and cultural properties, as well as the communities’ behaviour affected by their sensory perceptions in urban system. “Land use and the effects that these have upon the physical detail of the building; human occupation densities, movement patterns and behaviours; the economic life of land parcels, and the relation of these to development density and human occupancy – all these seem to be key factors in our experience of urban diversity.” (Penn et al in Cooper et al 2009:220) In other words, different aspects of the spatial system are said to play an essential role in affecting people’s behaviour, but they need to be strategically combined; activities and uses cannot be mixed randomly. An intelligible spatial structure is required, sensitive to how humans create their environment. Intelligibility in space syntax theory discusses the degree to which the immediate environment (local scale structure) conveys information about the outside immediate perception (global scale structure), thus encouraging movement flow in the system, the easy navigation by people. (Penn et al in Cooper et al 2009). So diversity in a neighbourhood lies in having a rich environment in terms of significant information, which generates socio-economic and cultural relations. Another way to investigate the determinants of density and diversity was proposed by Marcus (2007). He argues that density is a geographic variable, and the degree of ‘accessibility to density’ results from the layout of the urban network of streets and buildings, while diversity relates to the definition stated by Jacobs. The intention of his research is not only to correlate density and diversity, but to analyse these two variables within the context of space syntax’s theory of accessibility within the urban system. In his research on Stockholm, Marcus found correlations: ‘between integration and people’s movement; accessible building density and population; accessible plots and 53 Chapter 3 diversity indices (amount of age groups and amount of lines of business)’. From these correlations he proposes that when density, diversity and movement are combined they are able to capture the ‘use-value and exchange-value’ of the land defined by ‘spatial capital’. “The exchange-value of spatial capital suggests how the value of the urban form literally can be translated into economical capital” (Marcus 2007:10). It means that spatial capital quantifies the effects of the urban fabric on land-value. The urban form is able to generate variables in accessibility and diversity that it is possible to measure. In short, once more, it is proposed that the physical city, at an urban design level, affects urban life in social and economic ways – a relation of cause and effect, which, firstly, is affected by high accessibility and high density. 54 4. RESEARCH METHODOLOGY 55 Chapter 4 4. Research Methodology This section describes the methodology used to conduct this research, as well as presenting the limitations encountered during the investigations. The methods applied consisted of direct site observations, spatial analysis, syntactic analyses and statistical correlations based on space syntax techniques. 4.1 Available data A land use map (predominant use of the block) and a block size map were provided by Space Syntax Limited in the form of MapInfo GIS tables. A more detailed map of the area – plot size – was procured from Viva Centro Association and EMPLASA in the form of images (jpg) and digital drawings (dwg). All image maps used were converted into digital drawings, which form the base maps for land use analyses. Figures 105 and 106 show an example of the land use map available and a digital drawing of the Luz area. In the map, information on predominant block use is divided into residential, retail and service, industrial, and institutional uses. The socio-economic data was obtained from SEMPLA (Secretaria Municipal do Planejamento – Administrative Municipal Planning) and the EMPLASA report (2007) which were based on the IBGE (Censo Demográfico do Instituto Brasileiro de Geografia e Estatística – Brazilian Institute of Geography and Statistics), and Rais (Relação Anual de Informações Sociais - Ministério do Trabalho - Anual Relation of Social Information). Figure 105 Predominant land use map (Luz area) – GIS Table 56 Chapter 4 Figure 106 Digital drawing (Luz area) 4.2 Survey data A detailed field survey was conducted in order to qualify the data available with further information on the occupation of the sites, and also to have a better comprehension of each area. Observations were carried out from 12th to 21st July 2010 (Monday-Sunday): - 12th and 19th (1pm-5pm): Santa Cecília; - 13th (1pm-5pm) and 14th (10am-3pm): Luz; - 16th (10am-4pm) and 17th (2pm-4pm): Brás; - 17th and 20th (10am-1pm): Gasômetro; - 18th (11am-2pm) and 20th (2pm-5pm): Oriente. Observations examined the existing land use of the built environment at the site scale, plotting the different kinds of occupation. In addition to residential, retail and/or service, industrial (industry, storehouse, and warehouse) and institutional (public, religious, cultural buildings), the categories of mixed-use (retail and/or service on the ground floor and residential on upper floor(s)) and parking were also recorded (Figure 107). Photos were taken to register the visual characteristics of the streets (Figure 108-111). 57 Chapter 4 Figure 107 Digital drawing with collected land use information (Luz area) Figure 108, 109, 110, 111 Examples of residential, retail, mixed-use and institutional land use. Source: author 4.3 Syntactic data The spatial structure data is based on a segment map, which examines the shortest path in a system. It uses the least number of streets to get to a destination. (Turner, 2008). The syntactic analysis is based on space syntax theory and techniques. It was found relevant to consider this methodology given its traditional use in analyses of the urban environment. According to Turner (2008), to calculate a syntactic measurement of space there are three definitions of distance to consider: metric, topological and angular (geometrical). The metric is the system of shortest mapped paths for integration and choice, the topological is the one of fewest turns, and the angular is the system of least angle 58 Chapter 4 changes. Within each definition it is possible to extract the measures of integration and choice using different radii (global and local). For the global scale, radius n (infinity) is used, which measures each line in the system in relation to all other lines. For the local scale, the measurement of routes from any line is restricted to only those lines that are up to the metric distance specified; for instance, the typical radii of 750m, 1000m, 1250m, 1500m, 2000m and so on. It is known that integration is related to ‘to movement’ in a system. Integration reveals the streets that have greater potential as destinations. Using segment analysis, integration is calculated by the degree to which a line is closer to every other segment in the network, considering the simplest route (using minimum angular depth). “It is a measure of how accessible each segment is from all the others, and so how much potential it has as a destination for movement” (Hillier 2009a:2). Turner (2004) suggests that in segment analysis the measure of integration is obtained by the formula Node Count/Mean Depth (NC/MD) in which NC is the number of segments encountered on the route from the current segment to all others, and MD is the mean depth of the nodes with respect to the root node. In contrast, the measure of choice is linked to ‘through movement’, with the potential of each segment providing indications of movement flow, It measures the degree to which an individual is likely to pass through the segment, from all segments to all others (Hillier and Iida 2005). According to Hillier (2009a) it is possible to create another measure by combining the measures of integration and choice. This relationship reveals the combined potential of a segment as both a destination and a route. To identify spatial patchworks and area boundaries (Fuzzy boundaries) Hillier and Yang (2007) developed a method that calculates the metric measures with different metric radii – to calculate the metric mean depth from segments within the metric radius. When increasing the radius of any measure, the scale of the patchwork increases proportionally. Finally, the analysis of the segment map explores the measures: Choice (logCH+1) and Integration (NC/MD) at different metric radii: 750m, 1000m, 1250m, 1500m, 2000m, 3000m, 4000m, 5000m, 7500m, 10000m. 59 Chapter 4 4.4 Integrated software Spatial and syntactic analyses were carried out using Depthmap. The software is used to perform spatial analyses that are designed to understand social processes within the built environment (VR Centre for the Built Environment). Scattergrams were plotted in Depthmap after processing, the analyses.. Later, the map was imported into MapInfo to create graphs of the segment map using the tool ‘Create Thematic Map’- method: Natural Break, Ranges: 16, Round by: 0.001. It generated maps with a colour range from red (highest line value) to dark blue (lowest line value). MapInfo also assisted in the comparison of socio-economic data and spatial data: land use map, block size and segment map. AutoCad (software developed by Autodesk) was used to convert the maps from image format (jpeg) into digital drawings (dwg), as well as to plot the information from the detailed onsite survey (land use). The statistical correlations between different variables were developed using Excel and JMP programs. Firstly, the syntactic measures of the segment map were extracted using tools in MapInfo and put into a single table. Later the syntactic values were compared with the socio-economic data available. 4.5 Analytical Model The analytical method applied in the syntactic analyses of the pattern of centre and noncentre areas was based on recent research on centrality, developed in London by Hillier (2008a). Hillier’s suggested method involves taking the linear extension of the main high street (centre line), measuring its global and local spatial properties, and comparing them to the surrounding area (non-centre lines). In order to identify the spatial signature of centrality, the centres should be significantly and statistically distinguishable from non-centres. In applying the theory of centrality in São Paulo, either the linear or the convex extension was taken, depending upon the shape of the centre of each area. Also, the centre here is defined as the busiest shopping street. The analysis aims to find if: 1) the centre has shorter segments; 2) if it has higher local choice; 3) if the centre has more comparable choice as the radius is increased from local to global. 4.6 What the method can help to find The aim of the spatial, syntactic and statistical analyses is to investigate how the configuration of the grid itself influences pedestrian and vehicular movement flow 60 Chapter 4 within the urban fabric, whether as a route or as a destination; and, furthermore, how this impacts on land use occupation and activities. Finally, the analyses can help us to understand how the relationship between spatial properties and socio-economic characteristics work within the peripheral areas of São Paulo’s historic core. Research developed by Space Syntax using this methodology shows that the urban form is not only directly associated to people’s movement, but also it shapes the pattern of land use within the system. 4.7 Implications One challenge for the research was the on-site observation and the development of the detailed land use map. Due to the lack of time available and the size of the selected area, and also for reasons of personal security, a few blocks were not completely observed. Also, weather conditions (cold and rain) during the first week (12th -16th) may have impacted on the understanding of pedestrian movement in the area. 61 5. ANALYSIS AND RESULTS 62 Chapter 5 5. Analysis and Results As follows, the findings will be divided into two categories: descriptive, which includes the syntactic, geometric, geographic and demographic analyses of the whole São Paulo and its sub areas; and statistical, which tests for correlations between the socioeconomic and spatial measures. 5.1 Descriptive: spatial configuration of São Paulo This section reveals the outcomes of the analysis. The segment map presented in Figure 114 at measure LogCH+1 global radius (Rn) clearly show the macro situation described, with a high value in terms of road systems: the radio-centric model, Radial Leste, and the Y System – 9 de Julho, 23 de Maio and Prestes Maia (Figure 112). Moreover, at this radius the map picks up other main roads that privilege vehicular movement – the Costa e Silva elevated expressway – those constructed on the border of important rivers – Estado Avenue (Tamanduateí river), Marginal Tietê (Tietê river) and Marginal Pinheiros (Pinheiros river) – as well as arterial roads that connect the historic core and the non-central areas. Interestingly, it also outlines the streets where the shift of the financial centre towards to southwest occurred: Paulista, Brigadeiro Faria Lima Avenue and Marginal Pinheiros. So this change of land use and activities might be related to the spatial form of the city. One hypothesis is that the financial centres, where the wealthier populations are located and which have received more private investment, are also the areas that have the potential for routes at the global scale. Within the map, the configuration as a whole presents a relatively fragmented spatial system. There are both several clusters of regular grids settled within different directions where original main routes may be linked to new settlements, and a patchwork pattern. A recent article about Brazilian cities concludes that the configurational features of Brazilian cities indicate a “labyrinthine structure derived from a ubiquitous patchwork layout. The exception is the oasis in the labyrinth – the old urban core preserving the positive configurational features from the city of the past.” (Medeiros and Holanda 2010:88) In the map (Figure 114) it can be seen that the areas located immediately to the northeast and east of the historic core do not have concise structures. There are a few clusters of grid formations but they become weak in the context. A strong grid structure appears further east, within a more peripheral, noncentral area, although it has less potential than the grids in the west and southwest where the financial centres are found. 63 Chapter 5 The presence of the Marginal Tietê considerably affects the structure of the north part of the city. It looks like a labyrinth network, but it seems that there is logic in its formation. There are strong roads (similar to a tree’s roots) which connect the expressway to the peripheral settlements. It can be justified by the topological aspect of the area – it is close to the natural border ‘Serra da Cantareira’. 64 Prestes Maia Ave. 9 de Julho Ave. 23 de Maio Ave. Figure 112 Segment Map of São Paulo showing historic centre and surrounding area and the Y System at logCH+1 at global radius (n) Border study area Figure 113 Diagram of the Y System: 9 de Julho Ave., 23 de Maio Ave. And Prestes Maia Ave. Extracted from Meyer (2010) Figure 114 Segment Map of São Paulo showing logCH+1 at global radius (n) high low Prestes Maia Ave. 9 de Julho Ave. 23 de Maio Ave. Figure 115 Segment Map of São Paulo showing historic centre and surrounding area at logCH+1 at local radius (750m) Border study area Figure 116 Segment Map of São Paulo showing logCH+1 at local radius (750m) high low Figure 117 Estado Avenue Figure 118 Segment Map of São Paulo showing Patchwork: Metric Mean Depth local radius (2000m) Border study area high low Chapter 5 Looking at the maps at a local scale, radius 750m (Figure 115 and 116), they display a different picture: the main roads that privilege vehicular flow lose their potential for movement, for instance Estado Avenue, which at radius n has an average of 7.585, decreases to 2.596 at radius 750m, and 9 de Julho Avenue changes from 7.682 to 2.793. The segments that maintain higher values are the ones closer to the historic core, a few on Paulista Avenue and Brigadeiro Faria Lima Avenue. In this case, the settlements located to the north and east of the historic centre considerably lose their values. The map also picks up some streets that will be discussed in more depth when describing each area. Another point to draw attention to is the different line values of the settlements located along the railway track, from the southeast to the northwest. Locally, these are considered very segregated compared with the global scale. This reveals the effect of the large blocks from the industrial occupation on the area: fewer streets and less potential for through-movement within the streets. Similarly, this happens with segments of Estado Avenue located within Dom Pedro II Park. They have a high value at the global scale, but low at the local. The low potential for routes matches what was observed in the area: high vehicular movement, traffic in peak hours and very low pedestrian flow. Furthermore, at this radius the whole area to the north of Marginal Tietê sees an increase in values. Its potential for movement flow is found not only in the roads that link the Marginal to the periphery, but also on others more localised streets. Looking at the map of Land use value (Figure 120), it reveals the economic effect of the shift of the financial centre and higher income population to southwest. Spatially speaking, there is a better correlation between land value and integration (NC/MD) rather than choice measures. At the local scale, radius 1000m (Figure 119), Paulista Avenue and its surrounding area, Brigadeiro Faria Lima Avenue and Eng. Luis Carlos Berinni Avenue are shown to have very high values as destinations, while the areas to the north and east of the historic centre have low values, especially the streets located along the railway track. So far there has been a descriptive review of the whole São Paulo, the central and noncentral areas. The initial findings show that, at a global scale, the roads highlighted with high values match the main roads of the city. On the other hand, at the local scale those roads lose their potential as routes and destinations. This allows us to hypothesise that most of these roads prioritise vehicular movement rather than pedestrian flow – there is an intense use of cars and buses by the population due to the lack of subway and train 68 Chapter 5 infrastructure in the city. Moreover, not only the railway track itself and the settlements along it, but also Dom Pedro II Park might intensify the physical separation between the historic core and its peripheral areas, acting as physical barriers in the inner city. 69 Financial centre Study area Figure 119 Segment Map of São Paulo showing local Integration (NC/MD) at radius 1000m Figure 120 Land use value. It shows the effect in land use value resulted by the shift of the financial centre and residential population to southwest high low Chapter 5 5.2 Descriptive socio‐economic characteristics of the areas located on the periphery of the historic centre of São Paulo In addition to the social information about the city and its sub areas previously discussed, as follows is a socio-economic and spatial correlation in order to find the relevant relationships between the areas. According to the Demographic Census developed in 2000 by IBGE, it seems that there is a direct relationship between the size and the population of the areas, r² = 0.4239 – the smaller the area the less populated, and vice versa, but with one exception, the area of Santa Cecília which is 1,441.00km2. The geographical order from the biggest to the smallest area is: Luz, Santa Cecília, Pari, Brás, Oriente and Gasômetro. Santa Cecília is the most densely populated among the areas since the majority of its housing is based on high-density vertical buildings. It is where both the higher income and no income population live together, as well as the place that has the highest number of jobs within the service sector, 18,504 – more than five times greater than the other regions (Table 16). This can be explained by its location close to more costly, noncentral boroughs – Higienópolis and Pacaembu – and its proximity to the historic centre, the first financial centre of the city. The relationship between density (x-axis) and the total employed population (y-axis) in the areas is positive and direct (r² = 0.4501). Figure 121 shows that Santa Cecília has the highest value in both variables; Oriente, Luz and Pari have a high value of employed population but low density, while Brás and Gasômetro are both low density and have low employment figures. The least dense area is Oriente, where the lack of residents gives over space to retail, service and industrial uses. Also it is the region that offers the greatest amount of employment and has the greatest amount of established industry and retail. Similar to Oriente, Gasômetro has some common features: lack of residents, low density, a mainly middleclass population and employment within the retail sector, but it offers more service than industrial activities Brás and Pari also have established retail sectors which offer employment. 71 Chapter 5 Table 16. General information Population 2000 (hab) Area (km2) Housing 2000 Demographic density (hab/km2) 9 626 1 095 3 476 8 791 Brás 3 240 0.693 1 062 4 675 Gasômetro 13 726 1 541 4 500 8 907 Luz 4 150 1 085 1 278 3 825 Oriente 11 415 1 398 3 654 8 165 Pari 33 305 1 441 14 204 23 112 Santa Cecília Source: IBGE, Demographic Census 2000 and IGC (Instituto Geografico e Cartografico) – from EMPLASA 2008 Table 17. Family average income (salary) no More than % 5 to 10 % % income 20 130 3.82 1022 30.07 330 9.71 Brás 63 6.1 288 27.88 34 3.29 Gasômetro 204 4.78 1,154 27.01 393 9.2 Luz 67 5.36 348 27.82 40 3.2 Oriente 210 5.91 968 27.23 281 7.9 Pari 702 5.54 4,232 33.4 1,201 9.48 Santa Cecília Source: IBGE, Demographic Census 2000, Setores Censitarios – from EMPLASA 2008 Table 18. Number of land use according to activities Bras Gasometro Luz Oriente Pari Santa Cecilia Industrial 105 43 1,153 1,431 547 147 % 17.95% 6.90% 42.19% 51.25% 39.72% 8.91% Retail 313 449 945 1,089 552 562 % 53.50% 72.07% 34.58% 39.00% 40.09% 34.08% Services 167 131 635 272 278 940 % 28.55% 21.03% 23.23% 9.74% 20.19% 57.00% Total 585 623 2,733 2,792 1,377 1,649 Source: Ministerio do Trabalho, Rais -Relacao Annual de Informacaoes Sociais 2005; Taking the demographic density (Table 16) on the x-axis and the proportional percentage of industry, retail and service activities respectively on the y-axis (Table 18), the results are as follows: - Industry (Figure 122): Oriente, Pari and Luz have a high percentage of industrial land use but low density. Santa Cecília is high density but has low industrial land use. Brás and Gasômentro have neither a high industrial percentage nor high demographic density. - Retail (Figure 123): Gasômetro and Brás have a high percentage within retail activities but low density, although with Santa Cecília the opposite occurs. Luz, Pari and Oriente have both low percentages of retail activities and low density. - Services (Figure 124): Santa Cecília has a higher proportion of service activities and is a high density area. On the contrary, Oriente has the lowest industrial 72 Chapter 5 percentage and demographic density. Brás, Gasômetro, Luz and Pari have higher percentages and densities than Oriente, but they are still weakly correlated. Finally it shows that the areas with a higher percentage of industrial activity are the ones with lower densities. The same finding is taken in places with a high percentage of retail. On the other hand, the areas where the majority of uses fall within the service sector are the ones with higher densities. Figure 121 Correlation between total employed population and density Figure 123 Correlation between retail (%) land use and density Figure 122 Correlation between industrial (%) land use and density Figure 124 Correlation between service (%) land use and density Another point to consider when dealing with social and economic characteristics is the typology of housing (its historic and economic formation). When the total number of residential properties and the different typologies – house, apartment and rooms (Table 73 Chapter 5 19) is compared - it is found r² = 0.0002 (Figure 125), r² = 0.9728 (Figure 126) and r² = 0.3644 (Figure 127) respectively. This impressively illustrates the culture of high-rise, high density buildings in São Paulo. Despite the general characteristics of these areas – old industrial sites with working class villages, and the existence of one-two floor housing – the number of residents in apartments is still much higher than the predominant typology of the areas. Figure 125 Correlation between house (number) and housing Figure 126 Correlation between apartment (number) and housing Figure 127 Correlation between room (number) and housing 74 Chapter 5 Table 19. Typology of housing Brás Gasômetro Luz Oriente Pari Santa Cecília House 640 219 814 313 2,289 515 % 18.83% 21.20% 19.05% 25.02% 64.39% 4.06% Apartment 2,634 772 3,122 865 1,131 11,903 % 77.49% 74.73% 73.08% 69.14% 31.81% 93.94% Room 125 42 336 73 135 253 % 3.68% 4.07% 7.87% 5.84% 3.80% 2.00% Total 3,399 1,033 4,272 1,251 3,555 12,671 Source: IBGE, Demographic Census 2000 – Setores Censitarios – from EMPLASA 2008 These comparisons were established in order to give us a better understanding of the relationships between the areas selected, and to help in the further spatial, syntactic and statistical analysis developed below. 5.3 Descriptive spatial configuration of the areas on the periphery of the historic centre Pari Luz Santa Cecília Oriente Gasômetro historic centre Brás Figure 128 Areas on the periphery of the historic centre of São Paulo. Segment map showing global Choice - logCH+1 radius n 75 Chapter 5 Figure 129 Correlation between global integration (Rn) and local integration (R750) Figure 130 Correlation between global choice (Rn) and local choice (R750) Taking the integration measure (NC/MD), with radius 750m (the smallest radius considered due to the size of the city) as the local variable and radius n as the global, and with global integration on the x-axis and local on the y-axis, a weak result of r² = 0.0111 is generated (Figure 129). It suggests that: Santa Cecília is both globally and locally integrated, Pari is locally but not globally integrated, Brás, Luz and Gasômetro are globally but not locally integrated, and Oriente is neither globally nor locally integrated. When it briefly consider the different local measures analysed, for instance, when the radius is increased from 750m to 10,000m, Santa Cecília remains the most locally integrated area until the radius 4000m (1,052.17), while from the radius 5000m it changes and Brás becomes the most locally integrated area ( Table 20). Taking the measure logCH+1 radius n on the x-axis and logCH+1 radius 750m on the yaxis, it shows a stronger, but still weak r² = 0.2135 (Figure 130), suggesting that Santa Cecília and Gasômetro are both global and local, Pari is globally but not locally, Brás is locally but not globally and Oriente and Luz are neither globally nor locally. Table 20. Integration (NC/MD) of each study area Int Rn Brás Gasômetro Luz Oriente Pari Santa Cecilia 6,570.72 6,598.82 6,636.26 6,366.28 6,415.20 6,748.55 Int R750 65.169 74.434 71.191 78.926 93.541 90.545 Int R1000 101.784 119.195 110.817 123.336 141.408 141.802 Int R2000 316.268 387.255 349.273 365.862 360.576 412.391 Int R3000 641.43 702.205 654 664.058 614.056 727.155 Int R4000 1,041.78 1,042.51 957.402 988.326 917.309 1,052.17 Int R5000 1,449.98 1,413.28 1,286.42 1,366.97 1,278.78 1,404.97 Int R10000 4,377.44 4,362.66 4,052.15 4,044.59 3,945.30 4,098.91 76 Chapter 5 Figure 131 Correlation between global integration (Rn) and local integration (R3000) Figure 132 Correlation between global choice (Rn) and local choice (R5000) Table 21. Summary of comparison: integration (NC/MD) and choice (LogCH+1) at different measures Int Rn vs R750 Choice Rn vs R750 Int Rn vs R3000 Choice Rn vs R5000 Global Global Local Local Global Local Global Local Brás Gasômetro Luz Oriente Pari Santa Cecília significant non-significant However, it is very important to make clear that in this case, considering the lowest radius used – 750m – both correlations, integration (NC/MD) and choice (logCH+1), have very low r² values. Looking to other radii it is found that on the integration measures the highest correlation is between radius n and radius 3000m, r² = 0.4499 (Figure 131), which indicates that Santa Cecília and Gasômetro are both globally and locally integrated, Oriente is locally but not globally integrated, Brás and Luz are globally but not locally integrated, and Pari is neither globally nor locally integrated. Also, the choice correlation on the radius 5000m is r² = 0.5589 (Figure 132) which suggests that Brás and Santa Cecília are both globally and locally, Gasômetro, Oriente and Pari are locally but not globally, and Luz is neither globally nor locally. The intelligibility, the correlation between connectivity and integration, of all areas selected 77 Chapter 5 is very weak, r² = 0.0324; and so is synergy, the relation of local and global integration, within the radius 750m, r² = 0.0111 (Figure 129), but when considering the radius 3000m it increases to r² = 0.4499 (Figure 131). Finally, by considering the correlation between integration (R3000 X Rn) (Figure 131) and choice (R 5000 X Rn) (Figure 132) it can be argued that the areas of Santa Cecília and Gasômetro have good potential as destinations, while Brás and Santa Cecília are the places with higher potential for movement flow within local and global scales. Figure 133 Correlation between global choice (Rn) and global integration (Rn) Figure 134 Correlation between local choice (R750) and local integration (R750) Table 22. Relationship between integration (NC/MD) and choice (LogCH+1) NC/MD int Global (n) Brás Gasômetro Luz Oriente Pari Santa Cecília 6,570.72 6,598.82 6,636.26 6,366.28 6,415.20 6,748.55 LogCH+1 Local (750m) 65.169 74.434 71.191 78.926 93.541 90.545 Global (n) 6.066 5.688 5.444 5.535 5.507 5.873 Local (750m) 2.473 2.594 2.514 2.613 2.737 2.614 High Mediun Low In addition, it is possible to create another measure by combining the integration and the choice measures in order to see which areas have potential as both destinations and routes. Taking the global (n) choice on the x-axis and global integration on the y-axis, the result is (Figure 133): Santa Cecília has the potential for both through and to movement. Brás has high through movement but lower to movement. Luz and Gasômetro are strong destinations but weak routes. Pari and Oriente are weak on both 78 Chapter 5 through and to movement in the global scale. At the local scale radius 750m the correlation between both is high, r² = 0.8140 (Figure 134). This result indicates: Pari has potential as both a route and destination; Santa Cecília has higher to movement than through movement; Oriente and Gasômetro have equal potential for both but with lower values; and Luz and Brás have neither to nor through movement. In summary, when comparing all areas, one hypothesis to be suggested is that the area of Santa Cecília is better embedded in the context of the whole city, locally and globally. It means that Santa Cecília’s system allows people to change easily from global to local scales, facilitating the movement within the area. As a result, this spatial potential for movement attracts more people and activities, which in turn attract further uses and more people. It can be argued that this area is more successful in terms of urban fabric than the others. On the other side, Oriente and Luz are the areas with very low correlation between scales, and so less spatially successful. 5.4 Descriptive statistical correlations of the areas on the periphery of the historic centre An exploration of the syntactic and socio-economic data shows that the correlation between the size (km2) of the areas and their global integration (n) is very low, r² = 0.0007, but that local integration (750m), considering the highest relation, is strong, r² = 0.8518. This strongly indicates that there is no relationship at all between size and global integration, only local integration. Figure 135 Correlation between global integration (Rn) and retail (%) land use Figure 136 Correlation between local integration (R750) and retail (%) land use 79 Chapter 5 Figure 137 Correlation between global choice (Rn) and retail (%) land use Figure 138 Correlation between local choice (R3000) and retail (%) land use Bearing in mind that the spatial variables which predict retail distribution are likely to be associated to the global and local integration measure, in this case it seems to be weakly relevant, since the correlation between the percentage of retail within each area and the global (Rn r² = 0.0002) (Figure 135) and local integration (R750m r² = 0.1887 and 3000m r² = 0.0260) is very low (Figure 136). What seems interesting is that the relationship value between retail (%) and the choice (logCH+1) measure is higher than integration, though still weak (Rn r² = 0.0964) (Figure 137) and (R750m r² = 0.0322 3000m r² = 0.233) (Figure 138). It seems that the pattern of retail occupation is more associated with streets that have potential as routes rather than destinations. For instance, in Brás the location of retail seems to follow the logic of space at the global scale for route movement, but not at the local scale. Gasômetro in all correlations has a high percentage of retail in the area, but it is not located in streets that have potential as either routes or destinations. 80 Chapter 5 Figure 139 Correlation between global integration (Rn) and service (%) land use Figure 140 Correlation between local integration (R3000) and service (%) land use Looking to other variables, it was found that there is a good correlation between services (%) and global integration (r² = 0.6833) (Figure 139) in which Santa Cecília is on top with maximum correlations, Brás, Gasômetro and Luz are in the middle with considerable correlations, and Pari and Oriente are on the bottom with very low correlations. It means that in Santa Cecília the location of services follows the logic of the most integrated spaces at the global scale. Although in Pari and Oriente the location of services seems random. At the local scale the radius 3000m was considered (r² = 0.3691) (Figure 140) since it has better correlations than the radius 750m (r² = 0.100), but it is still weak. The results are as follows: Santa Cecília and Pari maintain the same position compared to the others; Brás, Gasômetro and Luz are still in the middle; and Oriente’s integration value increases. 81 Chapter 5 Figure 141 Correlation between global integration (Rn) and industrial (%) land use Figure 142 Correlation between local integration (R3000) and industrial (%) land use Figure 143 Correlation between global choice (Rn) and industrial (%) land use Figure 144 Correlation between local choice (R3000) and industrial (%) land use Surprisingly, industrial occupation seems to locate according to the urban system. There are considerable correlations between industrial location, integration and choice measures. The integration correlations at the global scale (Rn r² = 0.5131) (Figure 141) and at the local scale (R3000m r² = 0.4136) (Figure 142) can be considered relevant. In both cases Santa Cecília and Gasômetro are shown to have low percentages of industrial occupation but they are relatively highly integrated, while Pari has a high percentage of industrial occupation but is less integrated. Compared to integration, choice reveals a better correlation at the global radius (Rn r² = 0.5179) (Figure 143) and at the local (R3000m r² = 0.5768) (Figure 144). Globally, Luz, Oriente and Pari have a high 82 Chapter 5 percentage of industries, but they are not located on streets with high potential as routes, but in Santa Cecília and Brás the industries are located in more potential streets. Locally, Oriente and Pari have a higher percentage of industrial area and choice value while Santa Cecília and Brás have a lower percentage of industries but higher choice value. In short, the findings reveal that industrial occupation in the area over the years has tended to follow the systematic logic of streets that have potential as routes rather than destinations. Figure 145 Correlation between global integration (Rn) and house (%) Figure 146 Correlation between local integration (R3000) and house (%) Figure 147 Correlation between global integration (Rn) and apartment (%) Figure 148 Correlation between local integration (R3000) and apartment (%) Observing the typology of housing of the area – houses and apartments – and correlating it with the integration measure – the variable that predicts if the area is more 83 Chapter 5 segregated or integrated within the whole system – the results are as follows: in Pari both houses and apartments are found to be located on more segregated streets in both measures integration and choice. This indicates that they might be located in more private and conservative spaces where it is easier to control the public – either residents or visitors. While in Santa Cecília residential occupation is located in more integrated areas with easier accessibility for the public. This reveals that these areas might be culturally distinguishable from each other since they have different spatial formations. Table 23. Summary of correlations and r² values Correlations r² Int (NC/MD) Rn vs Retail (%) Int (NC/MD) R750 vs Retail (%) Int (NC/MD) R3000 vs Retail (%) logCH+1 Rn vs Retail (%) logCH+1 R750 vs Retail (%) logCH+1 R3000 vs Retail (%) 0.0002 0.1880 0.0260 0.0964 0.0322 0.2330 Int (NC/MD) Rn vs Service (%) Int (NC/MD) R750 vs Service (%) Int (NC/MD) R3000 vs Service (%) logCH+1 Rn vs Service (%) logCH+1 R750 vs Service (%) logCH+1 R3000 vs Service (%) 0.6833 0.1000 0.3691 0.3145 0.0065 0.2015 Int (NC/MD) Rn vs Industry (%) Int (NC/MD) R750 vs Industry (%) Int (NC/MD) R3000 vs Industry (%) logCH+1 Rn vs Industry (%) logCH+1 R750 vs Industry (%) logCH+1 R3000 vs Industry (%) 0.5131 0.0048 0.4136 0.5179 0.0435 0.5768 significant Finally, it can be argued that, in general, the statistical correlations between the socioeconomic and spatial characteristics of each area are weakly related. Results have shown that the location of retail is hardly predicted by the configuration of the space – shops might not be located in streets that are well embedded in the whole system with high potential for movement. The location of services is organized slightly differently. They might be located in streets more integrated within the whole city at the global scale. On the other hand, results have shown that the location of industry has a higher relationship with the spatial form of the city. Industrial uses are likely to be located in streets with better potential for movement. This pattern might have a connection with the historical occupation of the areas. They were originally developed by the heavy industries located along the railway track and adjacent to the historic core, which in turn 84 Chapter 5 attracted more people and infrastructure. So it can be said that this attraction occurred not only because of the land use, but also because of the spatial properties. 5.5 Descriptive statistical correlations of each study area As follows, a comparison between the centre and non-centre areas is developed. The micro-structure of each area is considered in order to find out how the internal-toexternal spatial pattern relates to the different kind of activities – pedestrian and vehicular movement as well as land use distribution. While doing this, Hillier (2008) suggests to keep in mind some variables that will index the spatial value of centrality: “shorter segments, higher local choice and more comparable choice as we increase radius from local to global” (Hillier 2008:01). 5.5.1 Brás The spatial structure of the area appears disorganised, without a concise grid form. The land use pattern is mixed throughout the area, with clusters of residences located in the inner area and industrial settlements located closer to the railway track (Figure 150). Its block size varies from 20m to 350m, so some are optimal for pedestrians while others can be very inconvenient (Figure 149). According to observations, it was found that the main vehicular and pedestrian movement occurs along Alcantara Machado Avenue and it is where the majority of shops are located. It is considered the busiest street of the area. Also high pedestrian movement was observed in parallel streets such as Moóca and Visconde de Parnaíba. Rangel Pestana Alcantara Machado Da Figueira Mooca Figure 149 Block size graph of the area of Brás 85 Chapter 5 Looking the segment map of the area and at global choice (logCH+1 radius n) (Figure 151) it can be seem that both Alcantara Machado Avenue and Mooca street have high potential for through movement at the global scale. At the local scale (logCH+1 radius 750m) (Figure 152) these roads lose potential, but the segments located closer to Da Figueira street can still be considered to have relatively high potential for through movement. When looking at global integration (NC/MD radius n) (Figure 153) both Alcantara Machado Avenue and Mooca street appear to have high values. At the local radius (750m) (Figure 154) both Alcantara Machado Avenue and Mooca street maintain high values in the segments closer to Da Figueira street and they lose potential when closer to the railway track. This reveals that the presence of the railway track considerably affects the system. It induces the ‘edge effect’, that is the tendency for the streets/segments on the edges of the spatial network to be distinct from interior areas since they are close to the edge (Hillier 1996). The correlation between logCH+1Rn and integration (Rn r² = 0.5744) (Figure 157) shows that Alcantara Machado Avenue and Mooca street have higher potential for both ‘to and through’ movement at the global radius. At the local radius (750m, r² = 0.5866) (Figure 158) some segments of Da Figueira street are differentiated. Synergy (r² = 0.5478) (Figure 155) and the relation between global and local (1000m) choice (logCH+1) (Figure 156) reveals Mooca and Da Figueira streets. This means that these streets are the ones better embedded within the system, and so have better potential for movement. It is not a coincidence that the centrality of the area located along Alcantara Machado Avenue is linear. The relationship between centre and non-centre reveals that the centre has shorter segments, as the theory predicts, by about 56%, although it has higher local choice only in its average, not in its maxima. The difference diminishes with increase radius in the maxima only up to radius 4000m, after that it increases, but in its average the difference increases rather than decreases; for instance, the radius 2000m is 14% and 4000m is 20%. At the global radius its maxima is more or less indistinguishable with only 2% of difference (Table 24). Table 24. Comparison between centre and non-centre Area Segment L_Max Bras Centre 184.622 Non-centre 416.358 %difference -56% LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R n_Max n_Av 1000_Max 1000_Av 2000_Max 2000_Av 3000_Max 3000_Av 4000_Max 4000_Av 5000_Max 5000_Av 8.484 8.352 2% 8.468 5.898 44% 3.514 4.052 -13% 3.139 2.823 11% 4.399 4.948 -11% 4.172 3.661 14% 5.013 5.422 -8% 4.853 4.110 18% 5.452 5.796 -6% 5.318 4.416 20% 1.205 6.186 -81% 1.127 4.640 -76% 86 8.467 8.484 Figure 151 Segment Map of Brás showing logCH+1 at global radius (n) Linear 3.106 Figure 150 Land Use Map of Brás 2.462 Figure 152 Segment Map_logCH+1 at local radius (750m) 7231.35 97.056 7120.81 49.062 high Figure 153 Segment Map_Integration (ND/MD) at global radius (n) Figure 154 Segment Map_Integration (ND/MD) at local radius (750m) low Chapter 5 Mooca Da Figueira Figure 155 Synergy Moóca Da Figueira Figure 156 Global and local choice Moóca R² = 0.574481 Alcantara Machado Figure 157 Global integration and choice Da Figueira Figure 158 Local integration and choice 88 Chapter 5 5.5.2 Gasômetro Gasômetro has no concise grid structure in its system. It has the biggest block size of all the areas – 460m – while its smallest block size is 20m (Figure 159). According to Siksna (1997:24) 200m or more is ‘very coarse meshed: inconvenient for pedestrians’. This biggest block is where the old Pari railway yard is located (today it serves as a fruit and vegetable market). Its industrial settlements are mainly located along the railway track and close to the railway yard. Its residential buildings are located sprawling throughout the area, but there are clusters located closer to Rangel Pestana Avenue and Gasômetro street (Figure 160). The main shopping street is Gasômetro and, according to observations, is where there is both high pedestrian and vehicular movement. At the same time, Mercúrio Avenue and Santa Rosa street (especially at the intersection with Mendes Caldeira street) have intense pedestrian and vehicular movement due to their proximity to Estado Avenue. Many people from the west side of the avenue take this way (by car) to go into the area. Old Pari railway yard Figure 159 Block size graph of the area of Gasômetro Taking the segment map and both measures global choice (LogCH+1 radius n) (Figure 161) and global integration (NC/MD radius n) (Figure 162) Mercúrio Avenue and Estado Avenue are highlighted as having higher values. Gasômetro street and Rangel Pestana Avenue have lower values, but still considerable. At the local choice (logCH+1 89 Chapter 5 radius 750m) (Figure 164) Mercúrio Avenue, Rangel Pestana Avenue and Gasômetro street have high values, but Estado Avenue loses considerable local potential for movement. As said before, this avenue prioritises fast vehicular flow. At the local integration radius 750m (Figure 163) Mercurio Avenue and Gasômetro street have higher values than Rangel Pestana Avenue. Also, the analyses reveal that Mercúrio Avenue, which marks the limit between the historic core and the Gasômetro area, is well embedded in the system, locally and globally. Comparing residential locations and the spatial system shows that the cluster of housing is mainly located in a more segregated area with less movement and less potential as a route or destination at the local scale (750m), repelling the accessibility to the area. The spatial structure of Gasômeto has high synergy (correlation of global and local integration) using radius 1000m (r² = 0.7820) (Figure 165), and so does the correlation global and local choice (logCH+1) (r² = 0.8062) (Figure 166). Taking the choice correlation Gasômetro street, which is the busiest shopping street of the area, has a high correlation but lower than Mercúrio Avenue. It can be argued that the centrality of the area is distributed linearly along Gasômetro street, since this is where the majority of shops are located. As the theory predicts, the centre has shorter segments by about 60%. However, as increase the local radius, the difference at its maxima is negative and increases (radius 2000m is -8% and 4000m is 10%). The non-centre has higher values than the centre. At its average, the centre has higher local choice and this diminishes as you increase the radius (2000m is 19% and 4000m is 16%). Also at global radius (n) the difference is distinguishable by about 22% (Table 25). Table 25. Comparison between centre and non-centre Area Segment L_Max Gasometro Centre 170.782 Non-centre 424.557 %difference -60% LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R n_Max n_Av 1000_Max 1000_Av 2000_Max 2000_Av 3000_Max 3000_Av 4000_Max 4000_Av 5000_Max 5000_Av 6.462 8.263 -22% 6.241 5.620 11% 3.845 4.001 -4% 3.471 2.927 19% 4.644 5.033 -8% 4.472 3.756 19% 5.018 5.534 -9% 4.863 4.168 17% 5.292 5.867 -10% 5.144 4.430 16% 5.486 6.087 -10% 5.357 4.613 16% 90 high 5.994 low 6.452 Linear Figure 161 Segment Map of Gasômetro showing logCH+1 at global radius (n) Figure 160 Land Use Map of Gasômetro 6636.07 6790.13 Figure 162 Segment Map_Integration (ND/MD) at global radius (n) 2.635 62.927 103.51 Figure 163 Segment Map_Integration (ND/MD) at local radius (750m) 3.452 Figure 164 Segment Map_logCH+1 at local radius (750m) Chapter 5 Mercúrio Figure 165 Synergy R² = 0.8062 Mercúrio Gasômetro Figure 166 Global and local choice 92 Chapter 5 5.5.3 Luz Physically speaking, Luz is divided in two by Tiradentes Avenue – the east side between Tiradentes Avenue and Estado Avenue, and the west between Tiradentes Avenue and Tenente Pena street. Both Tiradentes Avenue and Estado Avenue have high vehicular movement and low pedestrian movement, but they are spatially and functionally differentiated. Figure 168 shows that the predominant land use in Tiradentes Avenue is institutional (important museums and cultural buildings), retail and services, while in Estado Avenue it is abandoned buildings, squatters and storehouses. The local fashion economy is also separated. On the east side retail is linearly concentrated along São Caetano street and the pedestrian movement falls considerably in João Teodoro and 25 de Janeiro street. According to observations, João Teodoro has very low pedestrian movement with high vehicular flow. The existing land uses do not seem to work as attractors – there are local restaurants, a gas station, parking spaces and the Police Museum. What seems to help the high values is the size and the number of streets: on the south side of the João Teodoro block size varies from 20m to 115m and on the north side from 118m to 287m (Figure 167). The number of frequent streets increases the potential of the grid for movement. In João Teodoro street it helps the vehicular flow, while in São Caetano street it helps the pedestrian flow, which may explain its shopping function. Tiradentes Ave. João Teodoro Figure 167 Block size graph of the area of Luz On the west side centrality occurs convexly along José Paulino, Silva Pinto, Aimorés and Graça streets (Figure 168). Pedestrian movement decreases towards the north of the area where mixed land use and spots of housing are found. In the case of José Paulino, it 93 Chapter 5 seems that block size does not influence the intensification of movement, but rather the shops themselves – the blocks vary from 55m to 370m (Luz Park) (Figure 167). Looking at the map and at global choice (logCH+1 radius n) (Figure 169) shows that José Paulino, São Caetano and João Teodoro streets have high potential as routes. At the local scale (logCH+1 radius 750m) (Figure 170) they considerably lose their values, as they do at the local integration measure (NC/MD 750m) (Figure 172). Looking at global integration (NC/MD radius n) (Figure 171) the map reveals significant changes: José Paulino does not appear to have a high level of global integration. Only São Caetano and João Teodoro streets show potential as destinations. The synergy in Luz (local radius 1000m) (r² = 0.6983) (Figure 173) reveals that the street with the highest correlation is Cantareira, that is, one of the streets that crosses the railway track, connecting both sides. This street is also highlighted on the correlation between global and local (750m) choice (r² = 0.7778) (Figure 174). The relationship between choice and integration at the global scale shows that Cantareira Avenue has potential both as routes and destinations. In short, it is suggested that Cantareira is the street with the highest potential for movement attraction, whether ‘to’ or ‘through’ movement. Once more, the street that seems to have the most potential for movement and, consequently, for location of the land use, is not the one considered to be the main shopping street of the area. Considering the parameters suggested, the measures of centre (busiest streets) and noncentre say that the shopping streets have shorter segments by about 28%. Looking at the different measures, in the maxima the non-centre has higher values than the centre, but in the means it is the other way round, the centre has higher values. Also, as the radius is increased the difference between centre and non-centre does not diminish. (Table 26) Table 26. Comparison between centre and non-centre Area Segment L_Max Luz Centre 282.314 Non-centre 394.824 %difference -28% LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R n_Max n_Av 1000_Max 1000_Av 2000_Max 2000_Av 3000_Max 3000_Av 4000_Max 4000_Av 5000_Max 5000_Av 6.486 8.550 -24% 5.812 5.368 8% 3.613 3.812 -5% 3.077 2.787 10% 4.657 4.772 -2% 3.984 3.547 12% 5.150 5.414 -5% 4.442 3.929 13% 5.392 5.867 -8% 4.710 4.194 12% 5.537 6.215 -11% 4.888 4.387 11% 94 5.924 6.051 6.352 6.208 Cantareira Convex Figure 169 Segment Map of Luz showing logCH+1 at global radius (n) 2.649 2.720 Figure 168 Land Use Map of Luz Linear 2.761 3.071 Cantareira Figure 170 Segment Map_logCH+1 at local radius (750m) 76.510 6609.87 68.881 7101.44 high 6643.47 67.740 7114.56 Cantareira Figure 171 Segment Map_Integration (ND/MD) at global radius (n) 100.017 Cantareira Figure 172 Segment Map_Integration (ND/MD) at local radius (750m) low Chapter 5 Cantareira José Paulino São Caetano Figure 173 Synergy Cantareira Figure 174 Global and local choice Cantareira Figure 175 Global integration and choice Cantareira Figure 176 Local integration and choice 96 Chapter 5 5.5.4 Oriente Oriente is the area with the highest amount of land use in retail (Brás-313, Gasômetro449, Luz-945, Oriente-1,089, Pati-552 and Santa Cecília-562), and according to observations during the day there is intense pedestrian movement not only in the main shopping streets – Oriente street and Miller street – but also around Largo do Pari (old Pari railway yard) where there are thousands of shopping stalls (Figure 178). However, during the weekend (after 3:00pm on Saturday when the shops close) the area becomes empty, with no pedestrians, especially the main streets where the uses are basically retail. The area lacks residents – they are sprawling located in mixed-use buildings, so very low density. Also there are few institutional buildings. Looking at the segment map, it reveals that for global choice (logCH+1) radius (n) (Figure 179) movement is more intense on the periphery and the main streets – Oriente and Miller – and has low potential for through movement. Actually, it matches with the high vehicular flow of the area. People arrive by car and leave the area mainly taking São Caetano and João Teodoro streets, since they cross Estado Avenue (towards the west) and Rangel Pestana further south. At the local radius 750m (Figure 180), it highlights with high value the Oriente street that correspond with the real situation on week days. Although the Miller street maintains with low value. Interestingly, there is a significant differentiation between the global and local scales in the area. The integration (NC/MD) map global radius (n) (Figure 181) shows that Miller street is highly integrated while Oriente is very poorly integrated. At the radius 750m (Figure 182) Miller street has low values while Oriente is very integrated. In this case, it seems that block size does not have much influence on the intensification of pedestrian movement. The size of the blocks in the whole area varies from 25m to 385m. But the majority of the blocks along Oriente are more than 100m, which is very convenient for pedestrians according to Siksna (1997) (Figure 177). 97 Chapter 5 Oriente Figure 177 Block size graph of the area of Oriente Synergy (r² = 0.6040) (Figure 183) and the relationship between integration and choice at global and local (1000m) scales shows that segments of João Teodoro have higher values. Global and local choice correlation (r² = 0.8479) (Figure 184) outlines João Teodoro and Rangel Pestana streets. It suggests that João Teodoro has high potential for movement attraction at both global and local scales. It allows people to change scales easily. Also, the uses that are high movement dependent, for example retail, would benefit from this location. It can be argued that the centrality of the area expands convexly not only along Oriente street and Miller street, but also around Mns. Andrade street, Elisa Witacker street and others (Figure 178). Within the area, the analysis between centre and non-centre shows that the centre has significantly shorter segments by about 55%, as the theory predicts. At the global radius (n) the difference is distinguishable by about 27%. As the radius is increased the difference at the maxima rises, but from radius 750m to 1000m it decreases. Considering the average, the difference increases up to radius 3000m, afterwards it diminishes (Table 27). Table 27. Comparison between centre and non-centre Area Segment L_Max O riente Centre 190.307 Non-centre 424.557 %difference -55% LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R n_Max n_Av 1000_Max 1000_Av 2000_Max 2000_Av 3000_Max 3000_Av 4000_Max 4000_Av 5000_Max 5000_Av 6.007 8.241 -27% 5.934 5.480 8% 3.669 3.787 -3% 3.371 2.907 16% 4.500 4.847 -7% 4.312 3.675 17% 4.809 5.476 -12% 4.707 4.085 15% 4.981 5.896 -16% 4.919 4.374 12% 5.127 6.171 -17% 5.081 4.588 11% 98 6.007 5.920 Figure 179 Segment Map of Oriente showing logCH+1 at global radius (n) Convex 2.636 2.761 Figure 178 Land Use Map of Oriente Figure 180 Segment Map_logCH+1 at local radius (750m) 85.065 6635.63 6350.19 79.413 high Figure 181 Segment Map_Integration (ND/MD) at global radius (n) Figure 182 Segment Map_Integration (ND/MD) at local radius (750m) low Chapter 5 João Teodoro Figure 183 Synergy João Teodoro Rangel Pestana Figure 184 Global and local choice João Teodoro Figure 185 Global integration and choice João Teodoro Figure 186 Local integration and choice 100 Chapter 5 5.5.5 Pari Pari is also an area with a high amount of retail compared to other uses and, differing from Oriente, the area has a higher number of residents and blocks in which the predominant use is housing. The locations of the residences are better defined and are located mainly in the centre and further north. There is fewer numbers of institutional and industrial buildings (Figure 188). As observed, there is high pedestrian movement in Silva Teles and Maria Marcolina streets (both main streets for retail). Also, the streets perpendicular to João Teodoro, such Carnot, Vautier, Tiers, Rodrigo dos Santos and Barão de Ladário have considerably high pedestrian movement within the blocks close to João Teodoro, but they change when moving towards the north, where residences are found (Figure 188). Looking at the segment map at global choice (logCH+1 radius n) (Figure 189) it highlights João Teodoro and Silva Teles streets, as well as Carlos de Campos street, which is characterized by high vehicular flow. Maria Marcolina, at this radius, has lower values than other streets but it is still strong. The areas where there is more residential use are also more segregated, they have lower global values. At the radius 750m (Figure 190) Silva Teles street has segments with both high and low values, so does Maria Marcolina. Carlos de Campos maintains higher choice measure. Similar to global choice, global integration (NC/MD radius n) (Figure 191) reveals that João Teodoro, Silva Teles and Carlos de Campos streets are highly integrated. At the local radius (750m) (Figure 192) they maintain high values. In this case it is suggested that the centre has expanded linearly along Silva Teles and Maria Marcolina streets (Figure 188). Moreover, the concise grid structure of the blocks – grids with different orientation – intensifies the potential for movement in the area. The size of the blocks varies from 25m to 285m (Figure 187). 101 Chapter 5 Figure 187 Block size graph of the area of Pari Pari has a low synergy correlation (r² = 0.3971) (Figure 193), which highlights Carlos de Campos and João Teodoro streets. The global and local choice relation is high (r² = 0.7985) (Figure 194) and shows Carlos de Campos as having higher values. So does the relationship between integration and choice at the local radius (1000m) (Figure 196). In this case it suggests that Carlos de Campos street is the one best embedded in the network, with high potential as both a route and destination. The comparison between centre and non-centre streets reveals that the centre has shorter segments than the non-centre (by about 39%). The centre has high local choice only in its means not in its maxima. As the radius is increased, its maxima decreases and its mean increases the difference; for example, maxima radius 2000m is 6% difference while 4000m is 5%, and means radius 2000m is 18% difference while 4000m is 22%. At the global radius (n) the maxima difference is 39% and means is 3% (Table 28). Table 28. Comparison between centre and non-centre Area Segment L_Max Pari Centre 187.810 Non-centre 308.046 %difference -39% LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R n_Max n_Av 1000_Max 1000_Av 2000_Max 2000_Av 3000_Max 3000_Av 4000_Max 4000_Av 5000_Max 5000_Av 7.051 8.779 -20% 6.701 5.438 23% 3.554 3.897 -9% 3.404 3.035 12% 4.572 4.874 -6% 4.408 3.726 18% 5.175 5.476 -5% 4.936 4.100 20% 5.602 5.896 -5% 5.310 4.365 22% 5.909 6.235 -5% 5.601 4.580 22% 102 6.6190 5.869 Figure 189 Segment Map of Pari showing logCH+1 at global radius (n) 2.905 2.734 Linear Figure 188 Land Use Map of Pari Figure 190 Segment Map_logCH+1 at local radius (750m) 109.591 6738.84 6633.45 Figure 191 Segment Map_Integration (ND/MD) at global radius (n) 89.312 Figure 192 Segment Map_Integration (ND/MD) at local radius (750m) high low Carlos de Campos Chapter 5 João Teodoro Figure 193 Synergy Carlos de Campos Figure 194 Global and local choice Estado Figure 195 Global integration and choice Carlos de Campos João Teodoro Figure 196 local integration and choice 104 Chapter 5 5.5.6 Santa Cecília According to observations, the area of Santa Cecília is predominantly mixed use. It has higher pedestrian and vehicular movement at its north and south boundaries – Angelica Avenue, Eduardo Prado street and Duque de Caxias Avenue – and on the avenues that cross the area – Rio Branco and São João (Costa and Silva). From these avenues the pedestrian and vehicular movement tends to decrease when going towards the inner area. Within the area a clear differentiation between the right and left side of Rio Branco Avenue was observed. In this context, the avenue seems to be a barrier to the area in terms of movement and functionality. On its right side, between the avenue and the railway track, the area has more empty, abandoned and degenerated buildings than the left side. Also, there are more parking places because of the proximity to the São Paulo Concert Hall and other cultural buildings, resulting in emptier streets without natural surveillance by residents and retailers (Figure 198). Another physical and visual barrier in the area is the elevated expressway – ‘Minhocão’– which at ground floor level has considerably high pedestrian movement, (despite being dark and degraded) due to the presence of bus stops and its proximity to the tube stations Marechal Deodoro and Santa Cecília. Indeed, the street where Santa Cecília station is located has high pedestrian flow. This might be explained not only by the presence of the station, which works as attractor, but also by the fact that the blocks around it are less organised (since this area has a concise grid structure) and are smaller in size compared to the area as a whole. The size of the blocks varies from about 15m to 250m. The majority of blocks in which the predominant use is residential are located at the north border – Angelica Avenue, Eduardo Prado street – where there is higher movement(Figure 197). The segment maps show that in all measures – global and local choice (logCH+1 radius n and radius 750m) (Figure 199, 200) and global and local integration (NC/MD radius n and 750m) (Figure 201, 202) – Duque de Caxias street has high potential as both a destination and route. Also, this street is considered the busiest street of the area; the shops are located linearly along it. 105 Chapter 5 Figure 197 Block size graph of the area of Santa Cecília The synergy (Figure 203) is low and highlights Duque de Caxias street and São João Avenue. Also the relationship between choice and integration in the global radius (n) picks these streets, but with higher correlations (r² = 0.6038) (Figure 205). Both correlations – choice (logCH+1) (global n vs local 1000m) and choice vs integration (1000m) (Figure 204, 206) – highlight Jaguaribe street, the street located at the border of the area close to Santa Cecília subway station. It can be argued that in this area the main shopping street matches with the spatial system. Duque de Caxias has potential as both a route and destination on the global scale. An examination of the difference between centre and non-centre shows that centre has significantly shorter segments than non centre by about 38%, as the theory predicts. The maxima difference at radius n is indistinguishable by about 0.44%. By increasing the local radius, the maxima difference decreases up to radius 4000m (9%), and then increases (5000m – 10%). Similarly, the average difference diminishes and at radius 4000m is 11% but at 5000m is 12% (Table 29). Table 29. Comparison between centre and non-centre Area Segment L_Max Santa Ce cilia Centre 207.891 Non-centre 335.602 %difference -38% LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R n_Max n_Av 1000_Max 1000_Av 2000_Max 2000_Av 3000_Max 3000_Av 4000_Max 4000_Av 5000_Max 5000_Av 7.727 7.761 -0.438% 7.722 5.767 34% 3.490 3.923 -11% 3.347 2.965 13% 4.287 4.696 -9% 4.220 3.784 12% 4.779 5.237 -9% 4.663 4.222 10% 5.143 5.650 -9% 5.027 4.516 11% 5.424 6.030 -10% 5.316 4.738 12% 106 Jaguaribe 7.723 Figure 199 Segment Map of Santa Cecília showing logCH+1 at global radius (n) ão sti ba Se Linear ira re Pe Figure 198 Land Use Map of Santa Cecília Jaguaribe 3.112 Figure 200 Segment Map_logCH+1 at local radius (750m) high Jaguaribe 7189.11 Figure 201 Segment Map_Integration (ND/MD) at global radius (n) Jaguaribe 121.409 Figure 202 Segment Map_Integration (ND/MD) at local radius (750m) low Chapter 5 São João Duque de Caxias Figure 203 Synergy Jaguaribe Sebastião Pereira Figure 204 Global and local choice São João R² = 0.603844 Duque de Caxias Figure 205 Global integration and choice Jaguaribe Figure 206 Local integration and choice 108 Chapter 5 Table 30. Comparison of the shopping streets in each area Area Segment L_Av LogCH+1Rn LogCH+1 LogCH+1 _Av R750_Av R1000_Av LogCH+1 R2000_Av LogCH+1 R3000_Av LogCH+1 LogCH+1 R4000_Av R5000_Av NC/MD Integration Rn_Av NC/MD Integration R750_Av NC/MD Integration R1000_Av NC/MD Integration R2000_Av NC/MD Integration R3000_Av NC/MD Integration R4000_Av NC/MD Integration R5000_Av Bras Alcantara Machado 119.291 8.468 2.716 3.139 4.172 4.853 5.318 1.127 7189.700 71.493 107.908 316.633 687.785 1162.690 1610.790 Gasometro Gasômetro 82.343 6.241 3.020 3.471 4.472 4.863 5.144 5.357 6693.820 82.721 128.724 403.854 721.032 1075.230 1454.600 Luz José Paulino São Caetano 142.936 69.213 6.242 6.119 2.705 2.992 3.201 3.321 4.327 4.142 4.888 4.575 5.147 4.783 5.287 4.937 6638.550 7117.390 71.020 87.418 119.298 135.793 381.181 455.529 693.316 820.042 1009.240 1115.520 1316.330 1485.000 Oriente Oriente Miller 120.065 104.462 5.945 5.919 3.044 2.834 3.481 3.217 4.416 4.166 4.731 4.674 4.910 4.932 5.063 5.106 6344.040 6627.140 96.575 78.047 144.588 125.954 384.979 383.576 686.567 714.164 994.718 1044.840 1352.440 1415.960 Pari Silva Teles Maria Marcolina 96.244 95.827 6.911 6.238 2.991 2.891 3.422 3.365 4.449 4.318 5.042 4.703 5.450 5.003 5.760 5.251 6756.800 6725.300 108.930 93.021 172.577 158.330 438.358 398.873 721.065 724.887 1086.350 1042.480 1482.310 1417.080 Santa Cecilia Duque de Caxias 84.817 7.722 2.970 3.347 4.220 4.663 5.027 5.316 7157.430 116.824 180.809 486.804 836.458 1198.920 1587.110 Highest values Chapter 5 In short, after looking at the micro structure of each area and taking their busiest shopping streets into consideration, (Table 30) it is revealed that the street that seems to be best integrated in the whole system at different local scales is Duque de Caxias (Santa Cecilia). It is located within the area which is also the one best embedded within the whole system. Although on a global scale Alcantara Machado is slightly more integrated than Duque de Caxias. This means that Duque de Caxias is easy accessible and has higher potential as a destination compared to the other streets. As a result, land uses such as retail can benefit from being located along it. Considering the potential for routes between spaces, Oriente and Silva Teles streets are the ones with highest values at different local scales, while Alcantara Machado has the highest value at the global. As explained before, the analyses of the areas were developed using the determined administrative boundaries, rather than the natural limits defined by their spatial morphology. One hypothesis is that this might affect the relation between centre and non-centre of each area. As it was shown, the outcome didn’t always follow the conjecture predicted by the theory. At the same time, Hillier (2008) argues that in some cases centres cannot be distinguished from their context, and there is a way to test this: by considering the spatial values of the centre segments as a ratio against the average for the 800 metric pedestrian context (Hillier 2008). By doing this, the typological difference of the centre would be powerfully distinct from its context. To investigate this contrast, it would be necessary to repeat the process of analyses adopting the natural boundary of each area (within 800m) as a further research study that could be compared to the one developed here. 110 Chapter 6 6. DISCUSSION 111 Chapter 6 6. Discussion The previous chapters have suggested how the issues of sustainable urbanisation – in the sense of cities that are self-sufficient – and urban diversity can be approached. Thinking about São Paulo and the areas located on the periphery of its historic core, what factors motivate or impede the generation of diversity in these areas? According to Hillier (1996, 2009), the streets of the urban system can be subtle and complex. Both have universal characteristics and cultural properties, and reflect the economic and social forces of the city and its society. These variables are expressed spatially in different ways, depending upon how they are embedded in the system globally and locally. The configuration of space is an essential driving factor in the location of land use and people’s movement in the system. So the structure of the urban fabric plays a vital role in the existence of a diverse and dense population. That is why spatial form is the most powerful indicator of the presence of people, and of successful urbanization. Moreover, to generate diversity all elements need to be combined – mixed land use and activities, configuration of space and population density. Even for Jacobs (1961), spatial form is, in itself, an essential factor. She suggests careful observation of the potentiality of block configuration (size and form) to create movement and an intensification of people presence. So far, it has been possible to analyse these variables in all areas. To summarise the results there follows here a table with the findings from the social, spatial and syntactic analyses. 112 Chapter 6 Table 31. Summary of the areas With regards to the mixed land use column of the table, it shows that Santa Cecilia has the highest amount of services. The socio economic factors reveal information about residential populations. With the exception of the Santa Cecília area, the whole central area is lacking in residential use. As discussed before, the presence of people is the most important factor in keeping a place alive. During the day all areas are busy and liveable, but during the night they become empty. This lack of natural surveillance generates dangerous areas. People presence is so essential for the vitality and viability of the city that even within Santa Cecilia the streets closer to the railway track, where there are fewer residents, are emptier. In this case, public investment could work in partnership with the real estate market, by developing incentive policies to encourage residential development in the areas. As suggested by Bromley et al (2005), a process of 113 Chapter 6 ‘residentialisation’ needs to be introduced, in which houses replace other land uses. In the case of São Paulo, parking space and other infrastructure could be replaced, while abandoned and deteriorated buildings could be revitalised. The implementation of new residential areas will change not only the density of residents, but also land use in general. New uses and activities will be created in order to support the local population. The areas should avoid ‘mono-functionality’, where one kind of activity is dominant. For instance, in Santa Cecília the proportion of land use of services is much higher than other areas, 57%; Gasômetro has 72.07% in retail and Oriente 51.25% industrial use. In urban studies there is no fixed formula to quantify the ideal land use pattern to achieve successful urbanisation, but what is recommended is a mixed balance of activities. This generates diversification of people and activities within the area: workers, shoppers and residents. As found, the study areas are not diversified in their land use or their activities. In terms of retail activities, the areas slightly differ from each other, but within each area there is mono-specialisation. Brás, Luz and Oriente specialise in fashion and accessories, Gasômetro in wood and Pari in household goods. Santa Cecília is the only area that has no specialized retail. In theory it could be argued that this specialisation actively prevents the ‘generation of diversity’; however, since Brazilian retail culture is based on shopping malls, not shopping streets, specialisation is considered to be the potential of these areas located on the periphery of the city’s historic centre. People from all over the country come to these areas in search of products at better prices, and they can find them as they have different options of places to shop. In other words, what makes these areas important for the city is their local economy, the specialised retail and activities along the streets. But taking into consideration their lack of residential areas and mono-functionality it must be said that they are neither self-contained nor self-supporting. Another factor to be considered is how the pattern of this land use is distributed in the system. We should not only think of the areas’ functions and activities, but also how they are affected by their spatial configuration. The findings suggest that within these areas there is no logical pattern of occupation to be followed: - The area with the highest density, Santa Cecília, is also the one that is best spatially embedded in the whole city. It has lower industrial and retail activities but they are located on streets that have good potential as routes and destinations. Santa Cecília’s service activities also track the logic of space: they are located on streets with good choice and integration values. 114 Chapter 6 - The two areas with the lowest densities do not have the same pattern of occupation or spatiality. Gasômetro has a high number of retail activities located on more globally integrated streets, but they are not located on streets with either local integration or high through movement. Oriente has more industrial land use that is globally located in more segregated areas with low choice; although locally it is integrated with higher choice. Its retail and services activities are lower and located in less integrated places. - Brás, Luz and Pari have similar demographic densities. In Brás retail activities are located in streets with good potential for ‘to and through’ movement at the global scale, but not the local. So it goes for Brás’ industrial and services land use. Land use in Luz (industrial, retail and services) is located globally in well-integrated, high choice streets, but this is not the case locally. Pari has a similar amount of industrial and retail activities. Its industries and services are located in more segregated, lower choice streets. Its retail is globally segregated with lower choice, but locally it has potential as both route and destination. One reason which could be suggested for the above findings, that does not seem to be so evident, is the patchiness of the grid system. As argued by Ortiz-Chao (2007), many Latin American cities have ended up as patchworks of areas with different spatial characteristics because of their rapid growth over the last few decades. They are not similar to western cities, which grew organically with a spatial hierarchy that shapes the pattern of land use, such as London with its ‘deformed wheel’ structure: “an integration core and a number of integrated spikes that radiate from it to the limit of the settlement” (Ortiz-Chao 2007:12). Indeed, the analysis of São Paulo has shown that the structure of the city could be considered as a patchwork system and a ‘labyrinthine structure’. The results suggest that the theory of centrality could be adapted to the context of São Paulo, since the performance aspects of the space match some of the variables required. The analysis of the relationship between centre and non-centre has as a result: - Centre has shorter segments; - Higher local choice, but on the average not on the maxima; - The difference does not necessarily diminish with an increase in radius; - At radius n the difference is not necessarily indistinguishable. 115 Chapter 6 Indeed, it was found that the area with the best spatial performance, as predicted by the analytical model applied, is the one that is embedded in a regular grid structure. Also, it is the place where there is higher demographic density, more services than industries and no specialised retail. Incorporating the relationships between integration and movement, accessibility and diversity that can capture the variables of the social performance of urban form discussed by Marcus (2007), how does spatial capital occur in the study areas? Considering the analyses developed by measuring the spatial properties of each area and comparing them to the socio-economical variables, it was shown that density, diversity and movement are not always combined. Each area was found to have its own pattern of configuration and occupation, i.e. they are weakly related. The areas lacking in demographic density and a mix of functionality might have a role to play within the configuration of the urban fabric. As argued, the higher the global and local integration, the more potential an area has for attracting people and activities, and the higher the accessibility and diversity, the higher the spatial capital. But this does not mean that higher spatial capital is always better. Spatial capital helps to translate the value of the urban form in distinct ways for everyday life; socially, economically and culturally. Indeed, not all needs require high spatial capital, since in each society people have different requirements (Marcus 2007). Different spatial capital outcomes can emerge from diverse societies according to their cultural and social aspects. It could be suggested that the peripheral areas of São Paulo’s historic core have ‘lower spatial capital’ because of their statistically weak socio-economic-spatial relationship. Finally, in the case of the study areas in São Paulo, special attention should be given to the spatial structure of each one; and not only to the micro-structure of each individual area and its socio-economic interfaces, but to each area within the city as a whole, in order to improve the socio-economic relationship of the city. Strategic intervention in the street network could be proposed in order to improve the accessibility of all areas. The areas located on the periphery of the historic centre have the potential to become a more successful urban network embedded in the city. They could benefit the city and, in turn, they could benefit from the city and its contemporary society. 116 7. CONCLUSION 117 Chapter 7 7. Conclusion This thesis has examined the socio-economic and spatial characteristics of six areas located on the periphery of the historic centre of São Paulo. The key objective of the investigation was to explore the socio-economic variables and the spatial properties in the theoretical context of sustainable urbanisation (cities that are socially and economically self-sufficient) and the generation of diversity. The framework and techniques of space syntax were used in order to identify these relationships. The study has shown that the socio-economic and spatial components of each area have deficiencies: 1) they lack residential areas and, therefore, have a low density of residents and pedestrian movement; 2) the areas tend to be mono-functional, with high numbers of one specific activity and land use. As a consequence, their retail offering is specialised; 3) the statistical correlations between the socio-economic and spatial characteristics of each area are weakly related; 4) they have ‘lower spatial capital’ because of this weak statistical socio-economicspatial relationship. These findings lead to an ambiguous correlation: the areas are considered important for the city because of their specialised retail and local economy, but at the same time this impedes the diversification of activities, people and movement. Finally, when analysing the spatial performance of the areas considering the administrative boundaries, it has been found that the centres (busiest shopping streets) do not follow a fixed rule in terms of distribution. The centre of each area has its own logic of occupation. Analyses have shown that the outcomes did not always follow the conjecture predicted by the theory. This suggests that the theory of centrality could be slightly more flexible in its predictions. One hypothesis is that this pattern has to do with the patchwork layout of the city. It was shown that the form of the city is neither regular grid structure nor ‘deformed wheel’, but a patchwork of offset grids in which areas might differ morphologically from each other depending upon their origins. This patchwork form could be attributed to the rapid process of development which the city has undergone in its formation. 118 BIBLIOGRAPHY 119 BIBLIOGRAPHY Barbosa, E. 2001. Evolução do uso do solo residencial na área central do município de São Paulo. Master thesis: USP, São Paulo. Batty, M; Besussi, E; Maat, K; Harts, J. 2003. Representing Multifunctional Cities: Density and Diversity in Space. Centre for Advanced Spatial Analysis, University College London. Bromley, R; Tallon, A; Tomas, C. 2005. City centre regeneration through residential development: contributing to sustainability. Urban Studies 42 (13): 2407-2429. Castells. Manuel.1989. The Informational City. United Kingdom: Blackwell Publishing Ltda. Castells.Manuel.1999. Information Technology, Globalization and Social Development. UNITED NATIONS RESEARCH INSTITUTE FOR SOCIAL DEVELOPMENT. UNRISD Discussion Paper No. 114, September 1999. Chiaradia, A; Hillier B; Schwander C; Wedderburn, M. 2009. Spatial Centrality, Economic Vitality/Viability: Compositional and Spatial Effects in Greater London. Paper presented at The 7th International Space Syntax Symposium. Stockolm: KTH Cooper, R; Evans, G; Boyko, C.2009.Designing Sustainable Cities. United Kingdom: Blackwell Publishing Ltda. EMPLASA report. 2009. Empresa Paulista de Planejamento Metropolitano AS. Downloaded (June 2010) from http://www.emplasa.sp.gov.br/portalemplasa/ Griffiths, S; Vaughan, V; Haklay, M; Jones, C. 2008. The sustainable suburban high street: a review of themes and approaches. In Geography Compass 2, Blackwell Publishing Ltd. Hillier, B. 1999. “Centrality as a process: accounting for attraction inequalities in deformed grid”. In Urban Design International 3 & 4: 107-127. Hillier, B. 1996. Cities as movement economies. In Space is the Machine, 111-137. Cambridge: University Press, Cambridge. Hillier, B. 2008. “Space and Spatiality: what the built environment needs from social theory. In Building & Research 36:3, 216-230. 120 Hiller, B. 2008a. Working Paper on Centrality in London - Unpublished with kind permission of the author. Hillier, B. 2009. Spatial Sustainability in Cities: Organic patterns and sustainable forms. Paper presented at The 7th International Space Syntax Symposium. Stockolm: KTH Hillier, B.2009a. Using DepthMap for urban analysis: a simple guide on what to do once you have an analysable map in the system. Bartlett School of Graduate Studies, UCL, London. Downloaded (June 2010) from: http://moodle.ucl.ac.uk/mod/resource/view.php?id=67969 Hillier, B. 1996. The fundamental city. In Space is the Machine, 262-286. Cambridge: University Press, Cambridge. Hillier B, Penn A, Hanson J, Grajewski T, Xu J.1993. “Natural movement: or configuration and attraction in urban pedestrian movement”. In Environment & Planning B: Planning & Design 20: 29-66 Hillier B, Vaughan L. 2007. The city as one thing. In Progress and Planning, 67 (3): 205-230. Jacobs Jane.1961. The Death and Life of Great American Cities. New York: Cooper Square Press. Jenks, M; Burton, E; Willians, K. 1996. The Compact City: A sustainable urban form? London: E & FN Spon Karimi, K et al. 2007. Evidence-based spatial intervention for regeneration of informal settlements. In Kubat, A. S. (Ed.) 6th International Space Syntax Symposium. Istanbul, Technical University of Istanbul. Marcus, L. 2009. Urban Form and Urban Capital: How spatial capital creates, attracts and enhances social and human capital in regional growth. Proceedings 7th International Space Syntax Symposium. Meyer, R; Izzo Junio, A. 2000. Polo Luz. Associação. Associação Viva o Centro: São Paulo Meyer, R; Grostein, D; Biderman, C. 2004. São Paulo Metrópole. Edusp e Imprensa Oficial: São Paulo. 121 Meyer, R; Grostein, D. 2010. A leste do centro. Edusp e Imprensa Oficial: São Paulo. Meyer, R et. al.2000. São Paulo Centro: uma Nova Abordagem. Associação Viva o Centro: São Paulo Ortiz-Chao C, Hillier B. 2007. In search of patterns of land-use in Mexico city using logistic regression ate the plot level. Proceedings of the 6th International Space Syntax Symposium, Istanbul Penn A and Perdikogianni I. 2006. Is Urban Diversity Synonymous with Urban Sustainability? What do people “suggest” for Clerkenwell in London?. Paper presented at the 2006 Annual General Conference of the Canadian Society for Civil Engineering, Alberta, Canada, May 23-26. Sassen, Saskia. The Specialised Differences of Global Cities. Essay presented at South America Conference in December 2008. Downloaded (July 2010): http://www.urbanage.net/10_cities/08_saoPaulo/_essays/SA_Sassen.html Sieverts, T. 2000. Cities without cities. Birkhäuser: Switzeland Turner, A.2004. Depthmap 4: A Researcher's Handbook", Bartlett School of Graduate Studies, UCL, London. Downloaded (June 2010) from: http://www.vr.ucl.ac.uk/depthmap/depthmap4.pdf Turner. A. 2008. Getting Serious with Depthmap-Segment Analysis and Scriptin. Bartlett School of Graduate Studies, UCL, London. Downloaded (June 2010) from: http://moodle.ucl.ac.uk/mod/resource/view.php?id=67969 Urban Age South America detailed Report. 2008. London School of Economics and Political Science. Downloaded (June 2010) from: http://www.urban-age.net/03_conferences/conf_saoPaulo.html Yang,T. 2007. The fuzzy boundary: the spatial definition of urban areas. Proceedings of the 6th International Space Syntax Symposium, Istanbul Whyte, William. 2000. From City: Rediscovering the Center (1988). In The essential William Whyte. EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária): http://www.embrapa.gov.br/ (accessed August, 2010) 122 Folha Online: 07/05/2010 - http://www1.folha.uol.com.br/folha/cotidiano/ult95u731572.shtml 06/05/2010 - http://www1.folha.uol.com.br/folha/cotidiano/ult95u731094.shtml (accessed June, 2010). GIS (Geographic Information System): http://www.gis.com/content/what-gis Guia Geográfico: http://www.guiageo-americas.com (accessed July, 2010). IBGE: Instituto Brasileiro de Geografia e Estatistica: http://www.ibge.gov.br/ (accessed June, 2010). Prefeitura de São Paulo: http://www.prefeitura.sp.gov.br/portal/ (accessed June, 2010). Relação Anual de Informações Sociais: http://www.rais.gov.br/ (accessed July, 2010). SEMPLA: Secretaria Municipal do Planejamento: http://www.prefeitura.sp.gov.br/cidade/secretarias/planejamento/ (accessed June, 2010). Urban Age Conference: http://www.urban-age.net (accessed May, 2010). Viva Centro Association: http://www.vivaocentro.org.br/vivaocentro/index.htm (accessed June, 2010). VR Centre of the Built Environment: http://www.vr.ucl.ac.uk/depthmap/ (accessed July, 2010). 123 APPENDIX 124 Appendix Table 32. Employment according to activities Industrial % Retail % Services % Total 2,024 24.84% 2,926 35.92% 3,197 39.24% 8,147 Brás 332 6.98% 3,560 74.82% 866 18.20% 4,758 Gasômetro 11,584 57.42% 4,751 23.55% 3,839 19.03% 20,174 Luz 11,936 56.79% 7,030 33.45% 2,053 9.77% 21,019 Oriente 6,738 43.25% 5,385 34.56% 3,457 22.19% 15,580 Pari 2,622 10.07% 4,900 18.83% 18,504 71.10% 26,026 Santa Cecília Source: Ministerio do Trabalho, Rais (Relacao Annual de Informacaoes Sociais) 2005; SEMPLA/Dipro – from EMPLASA 2009 125 Table 33. Different measures Mean Angular Integration Connectivity Connectivity Rn 4.509 3.064 6,570.72 Brás 4.633 3.154 6,598.82 Gasômetro 4.557 3.074 6,636.26 Luz 4.707 3.164 6,366.28 Oriente 4.781 3.244 6,415.20 Pari 4.966 3.277 6,748.55 Santa Cecilia Integration R750 65.169 74.434 71.191 78.926 93.541 90.545 Integration R1000 101.784 119.195 110.817 123.336 141.408 141.802 Integration R1250 145.938 174.011 158.26 175.21 192.334 201.674 Integration R1500 196.019 238.996 214.645 232.535 245.942 267.047 Integration R2000 316.268 387.255 349.273 365.862 360.576 412.391 Integration Integration R2500 R3000 463.817 641.43 542.046 702.205 502.631 654 511.311 664.058 481.799 614.056 569.425 727.155 Integration R4000 1,041.78 1,042.51 957.402 988.326 917.309 1,052.17 Integration R5000 1,449.98 1,413.28 1,286.42 1,366.97 1,278.78 1,404.97 Integration R7500 2,716.84 2,703.86 2,490.32 2,567.65 2,492.54 2,591.23 Integration R10000 4,377.44 4,362.66 4,052.15 4,044.59 3,945.30 4,098.91 Segment Length 115.968 108.47 100.65 100.119 93.185 109.325 Metric Step Depth 1,381.59 1,124.59 1,401.35 2,151.02 2.526.72 1,220.94 Table 34. Choice measures logCH+1 Rn 6.066 Brás 5.688 Gasômetro 5.444 Luz 5.535 Oriente 5.507 Pari 5.873 Santa Cecilia logCH+1 R750 2.473 2.594 2.514 2.613 2.737 2.614 logCH+1 R1000 2.844 2.987 2.837 2.946 3.056 2.986 logCH+1 R1250 3.134 3.265 3.087 3.206 3.287 3.261 logCH+1 R1500 3.353 3.491 3.289 3.411 3.478 3.477 logCH+1 R2000 3.694 3.835 3.621 3.730 3.763 3.808 logCH+1 R2500 3.944 4.070 3.849 3.956 3.974 4.050 logCH+1 R3000 4.159 4.244 4.017 4.141 4.145 4.246 logCH+1 R4000 4.475 4.509 4.282 4.427 4.417 4.544 logCH+1 R5000 4.709 4.695 4.472 4.640 4.636 4.769 logCH+1 R7500 5.161 5.110 4.876 5.023 5.022 5.191 logCH+1 R10000 5.529 5.425 5.158 5.300 5.266 5.492 Table 35. Relation Centre and Non-centre Segment L_Max Segment L_Av LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R LogCH+1R n_Max n_Av 750_Max 750_Av 1000_Max 1000_Av 2000_Max 2000_Av 3000_Max 3000_Av 4000_Max 4000_Av 5000_Max 5000_Av Brás Centre Non-centre %difference 184.622 416.358 -56% 119.291 115.735 3% 8.484 8.352 2% 8.468 5.898 44% 3.106 3.564 -13% 2.716 2.456 11% 3.514 4.052 -13% 3.139 2.823 11% 4.399 4.948 -11% 4.172 3.661 14% 5.013 5.422 -8% 4.853 4.110 18% 5.452 5.796 -6% 5.318 4.416 20% 1.205 6.186 -81% 1.127 4.640 -76% Gasômetro Centre Non-centre %difference 170.782 424.557 -60% 82.343 111.702 -26% 6.462 8.263 -22% 6.241 5.620 11% 3.452 3.599 -4% 3.020 2.541 19% 3.845 4.001 -4% 3.471 2.927 19% 4.644 5.033 -8% 4.472 3.756 19% 5.018 5.534 -9% 4.863 4.168 17% 5.292 5.867 -10% 5.144 4.430 16% 5.486 6.087 -10% 5.357 4.613 16% Luz Centre Non-centre %difference 282.314 394.824 -28% 103.763 100.010 4% 6.486 8.550 -24% 5.812 5.368 8% 3.322 3.454 -4% 2.687 2.478 8% 3.613 3.812 -5% 3.077 2.787 10% 4.657 4.772 -2% 3.984 3.547 12% 5.150 5.414 -5% 4.442 3.929 13% 5.392 5.867 -8% 4.710 4.194 12% 5.537 6.215 -11% 4.888 4.387 11% Oriente Centre Non-centre %difference 190.307 424.557 -55% 113.564 100.306 13% 6.007 8.241 -27% 5.934 5.480 8% 3.201 3.407 -6% 2.957 2.582 15% 3.669 3.787 -3% 3.371 2.907 16% 4.500 4.847 -7% 4.312 3.675 17% 4.809 5.476 -12% 4.707 4.085 15% 4.981 5.896 -16% 4.919 4.374 12% 5.127 6.171 -17% 5.081 4.588 11% Pari Centre Non-centre %difference 187.810 308.046 -39% 96.114 93.016 3% 7.051 8.779 -20% 6.701 5.438 23% 3.117 3.582 -13% 2.960 2.725 9% 3.554 3.897 -9% 3.404 3.035 12% 4.572 4.874 -6% 4.408 3.726 18% 5.175 5.476 -5% 4.936 4.100 20% 5.602 5.896 -5% 5.310 4.365 22% 5.909 6.235 -5% 5.601 4.580 22% Santa Cecilia Centre Non-centre %difference 207.891 335.602 -38% 84.817 110.729 -23% 7.727 7.761 -0.438% 7.722 5.767 34% 3.146 3.614 -13% 2.970 2.593 15% 3.490 3.923 -11% 3.347 2.965 13% 4.287 4.696 -9% 4.220 3.784 12% 4.779 5.237 -9% 4.663 4.222 10% 5.143 5.650 -9% 5.027 4.516 11% 5.424 6.030 -10% 5.316 4.738 12% 126