TRANSPORT RELATED
SOCIAL EXCLUSION IN
AMSTERDAM
CHIEL VAN DE CAMP (11200049) HUMAN GEOGRAPHY AND URBAN PLANNING
RESEARCH INTO THE RELATION
BETWEEN SOCIO-ECONOMIC STATUS OF
NEIGHBOURHOODS AND ACCESSIBILITY
OF KEY ACTIVITIES
Course: Thesis project Urban Planning
Department: Human Geography and Urban Planning
University: University of
Amsterdam
Mentor: DR. R.(Rowan) Arundel MSC
Second reader: Dr Y.(Yannis) Tzaninis
Date: 17-6-19
Author: CMG van de Camp
Studentcard Nr: 11200049
Address: Kinkerstraat 336K, 1053GE Amsterdam
Email: Chielvandecamp@icloud.com
TABLE OF CONTENTS
Table of contents 3
1. Introduction 5
1.1 Goal of this research 5
1.2 Research Question 6 1.3 Sub-questions 6 1.4 Social relevance 6 1.5 Academic relevance 6 2. Theoretical framework 7 2.1 Social exclusion 7
2.2 Social exclusion and transport 8
2.3 Just city theory 10
3. Methodology 12
3.1 Research design 12
3.2 Definition of the research unit 12
3.3 Study area 13
3.4 Operationalisation of key concepts 18
3.5 Data gathering 20
3.6 Methods of analysis 21
3.7 Conceptual model 24
4. Results 25
4.1 Sub-question 1: What is the spatial distribution of neighbourhoods with different socio-economic status in Amsterdam? 25 4.2 Sub question 2: What is the spatial distribution of key activities in Amsterdam? 34 4.3 Sub-question 3: What is the spatial distribution of transit accessibility to key activities and how does this relate to neighbourhood
socio-economic status in Amsterdam? 40
4.4 Sub-question 4: What is the spatial distribution of biking accessibility to key activities and how does this relate to
neighbourhood socio-economic status in Amsterdam? 46 4.5 Sub-question 5: What is the spatial distribution of walking accessibility to key activities and how does this relate to
neighbourhood socio-economic status in Amsterdam? 56
5. Conclusion and discussion 69
Conclusion 69
Discussion 70
6. References 72
8. Appendix 75
8.1 Calculation of travel speeds Metro, Tram, bus, and Ferry 75
8.2 Example of newly created transit stop 76
8.3 Examples of service areas for transit, biking and walking 77
1. INTRODUCTION
The city of Amsterdam is growing at a fast rate as it attracts thousands of new inhabitants each year (Het Parool, 2017). In 2018, Amsterdam had 854.316 inhabitants and the Amsterdam’s Research, Information and Statistics bureau expect that in 2030, 1 million inhabitants can call themselves Amsterdammer (OIS, 2019). One of the reasons for this rapid grow is the great number of opportunities and activities in the city. According to the Atlas voor gemeenten, Amsterdam is the most attractive city to live in The Netherlands, as access to jobs is high, the range of cultural facilities is large, and the city has lots of good restaurants an cafes (Atlas voor gemeenten, 2019; Verkaik, 2019). Inhabitants are looking for opportunities in life and therefore move to the city.
However, not everyone living in cities can take advantage of the opportunities and activities they offer. Within cities, some groups have to deal with what is called ‘social exclusion’. Social exclusion can be described as ‘circumstances where individuals or groups of inhabitants are unable to participate in activities or to access goods, services and opportunities that are available to others as a fundamental part of belonging to society’ (Mackett, 2015). Social exclusion is also present in Amsterdam. In 2016, 14,6% of all households in Amsterdam had a risk on poverty (CBS, 2018). Even though poverty is only a part of social exclusion, the risk on poverty goes along with social backlogs. Some groups with specific socio-economic characteristics are more at risk of being socially excluded than others. These groups include inhabitants over the age of 55, ethnic minority groups, low income groups, inhabitants who do not participate in paid work, or inhabitants living of a benefit (SCP, 2003: 53).
Social exclusion, and therefore the inability to participate in activities or to access goods, services and opportunities can have multiple causes, such as personal health issues, low educational attainment, having a mental breakdown, ethnical/sexual discrimination, and characteristics of the area a person is living in. One of this characteristics is the availability of transport links. Many researchers showed interest into the relation between available transport links and social exclusion. According to Preston and Rajé (2007) transport barriers can cause social exclusion, as often the problem of social exclusion is not caused by a lack of activities or opportunities, but moreover by a lack of access to those activities or opportunities. This would imply that the spatial distribution of access to key activities is uneven between different areas in a city.
According to Fainstein, this difference in access can be explained by the fact that city policies are focussed on growth promotion (Fainstein, 2010). These growth promoting policies exacerbate the disadvantages of minority and lower income groups. Investments usually involve a quantification of benefits, that is biased in favour of the already well off. Therefore, uneven access between groups with different socio-economic status could evolve.
1.1 GOAL OF THIS RESEARCH
Research into the relation between social exclusion and access through transport has been conducted in multiple cities across the globe, but not in Amsterdam. Therefore, this research will aim to find if uneven spatial distribution of access to activities in Amsterdam through transport could be one of the causes of social exclusion. This research does not only focus on the question if there is possible social exclusion through transit in Amsterdam, but especially pays attention to the question whether there is a relation between the socio-economic status of neighbourhoods in Amsterdam and access to activities within the city. As it is not possible toinvestigate access to all activities in Amsterdam, the research will focus on specific activities that have been set as ‘key’ by Burchardt et al. (1999).
1.2 RESEARCH QUESTION
To answer to these questions, the research question of this research is formulated as:What is the relation between socio-economic status of neighbourhoods and access to
key activities through the transit/biking/walking network of Amsterdam?
1.3 SUB-QUESTIONS
To answer this research question, a set of 5 sub-questions has been used:1. What is the spatial distribution of neighbourhoods with different socio-economic status in Amsterdam? 2. What is the spatial distribution of key activities in Amsterdam?
3. What is the spatial distribution of transit accessibility to key activities and how does this relate to neighbourhood socio-economic status in Amsterdam?
4. What is the spatial distribution of biking accessibility to key activities and how does this relate to neighbourhood socio-economic status in Amsterdam?
5. What is the spatial distribution of walking accessibility to key activities and how does this relate to neighbourhood socio-economic status in Amsterdam?
1.4 SOCIAL RELEVANCE
As mentioned in the introduction, the phenomenon of social exclusion is seen as a problem in society, as the opportunities that are present are not accessible to everyone. Social exclusion is also present in Amsterdam. Governments and research institutes, try to understand the causes of social exclusion, and the Dutch and has programs on the prevention of social exclusion, as it has a negative effect on society. This research tries to find out if access to activities could be one of the many causes of social exclusion in Amsterdam, and which neighbourhoods and socio-economic groups are affected by it. Therefore, it could contribute to visualisation of social exclusion in Amsterdam.1.5 ACADEMIC RELEVANCE
Many researchers showed interest into social exclusion and what causes social exclusion. As the article by Burchardt (1999) shows, social exclusion has many causes, such as personal health issues, low educational attainment, having a mental breakdown, ethnical/sexual discrimination, and characteristics of the area a person is living in. One of this characteristics is the availability of transport links. Therefore, much research paid attention to this relation and what the effects are of poor transport links. However, not that many researchers conducted research into the relation between socio-economic status and access to activities through transport. Therefore, this research tries to broaden the knowledge on this relation, as there seems to be a gap in the literature on this.2. THEORETICAL FRAMEWORK
2.1 SOCIAL EXCLUSION
Since the 90’s, the issue of social exclusion has attracted growing attention in the EU, especially in Great Britain. It has become the focus of action on different policy levels, from the global to the local level. The origins of the term are associated with the work of French social scientists Lenoir and Lefebvre (1974). However, even though the term has been used by many different institutions over a long period of time, there is still no solid agreement on what social exclusion means, and its interpretation varies across the world.The absence of an agreed meaning for the term social exclusion could imply that the concept is imprecise, but the opposite is more plausible. The term is used for varying phenomena and processes. Terms such as poverty, marginalisation or deprivation do not take into account the full spectrum of problems that can be defined as social exclusion. According to the Social Exclusion Knowledge Network (SEKN), social exclusion can either be described as a state or a process. Hine and Mitchell describe social exclusion as a process ‘which causes individuals or groups, who are geographically resident in a society, not to participate in the normal activities of citizens in that society’ (Hine and Mitchell 2000). This definition is derived from the definition of Burchardt, saying that ‘an individual is socially excluded if (a) he or she is geographically resident in a society and (b) he or she does not participate in the normal activities of citizens in that society’ Burchardt et al. 1999). In a policy context, social exclusion is mostly described as a state in ‘which inhabitants or groups are assumed to be excluded from social systems and relationships’ (SEKN 2015).
For this research, social exclusion will be defined as: ‘circumstances where individuals or groups of inhabitants are unable to participate in activities or to access goods, services and opportunities that are available to others as a fundamental part of belonging to society’ (Mackett, 2015). This definition was chosen, because it explicitly mentions the dimension of accessibility, and this research will focus on the relationship between accessibility of key activities and social exclusion.
Not all activities, goods, services or opportunities are a fundamental part of belonging to society. Therefore, a clear definition of the activities, goods and services that are is necessary. Burchardt (1999: 231) uses four dimensions of activities that could be seen as fundamental and explains them as follow:
I. Consumption activities: dimension that examines if inhabitants are being able to consume at least up to
some minimum level of goods and services which are considered normal to take part in society, such as hospitals, shopping centres, or parks. It is closely linked to measures that are being used for measuring poverty or deprivation, therefore it is marked as one of the key components of social exclusion by Burchardt.
II. Production activities: this dimension is focussed on whether a person engages in socially or economically
valued activities, such as paid work, education, training, retirement (when inhabitants reached the age of 65), and looking over family. One can think of the next activities: employees/self-employed, parents, students, pensioners, or carers of others.
III. Political activities: dimension that reviews if inhabitants engage in a collective effort to support or improve the social or physical environment. This includes any political activity, such as voting (national, local), a membership of a political party, or a membership of campaigning organizations.
IV. Social interaction: this dimension investigates whether inhabitants are able to engage in social interaction
with family, friends, neighbours. Can they be part of the community that they identify themselves with? One should feel that emotional support is available. Isolation is seen as one of the key factors of social exclusion.
Not being able to participate in one of the four dimensions, can lead to exclusion on other types of activities as well (SEKN, 2008 and Burchardt, 1999: 232). The different dimensions are interlinked and can influence each other. For example, when individuals are not able to participate in paid work (Production activity), this can lead to a low income, which in its turn can lead to not being able to take part in consumption activities, that can lead to malnutrition or health problems. This again can have influence on the individual’s ability to take part in the production activity.
The problems that social exclusion can cause are taken serious by governments across Europe. In 1997, the British government established the social exclusion unit (SEU). The remit of the SEU is ‘to help improve government action to reduce social exclusion by producing joined-up solutions to joined-up problems’ (SEU, 2004). This approach of a joined-up solution to a joined-up problem again acknowledges the interlink between the problems of social exclusion, but also the interlink of causes of social exclusion. The Dutch Sociaal en Cultureel Planbureau (SCP) also conducted research upon social exclusion. In a 2003 report called ‘Sociale uitsluiting, een conceptuele en empirische verkenning’ (SCP, 2003), the SCP tried to find a working definition for social exclusion, and to find the causes and effects of social exclusion in the Netherlands. Also, they found that groups with specific socio-economic characteristics are more at risk of being socially excluded than others. These groups include inhabitants over the age of 55, single person households, ethnic minority groups, low income groups, groups with low educational attainment, groups that do not participate in paid work, inhabitants living of a benefit and groups that live in so called ‘achterstandswijken’ (SCP, 2003: 53).
The inability to participate in activities or to access goods, services and opportunities can have multiple causes, which include (Burchardt, 1999: 232):
I. Individuals own characteristics, such as personal health or educational attainment;
II. Events in the life of a person, such as having a relationship or having a mental breakdown;
III. Characteristics of the area a person is living in, such as the number of jobs in the neighbourhood or the
available transport links;
IV. Social, civil and political institutions in society, for example, ethnical/sexual discrimination.
This research will focus on cause number III: characteristics of the area a person is living in, such as the number of jobs in the neighbourhood or the available transport links
2.2 SOCIAL EXCLUSION AND TRANSPORT
There has been growing attention on the link between social exclusion and transport barriers. In 2003, the SEU published ‘Making the Connections: Final Report on Transport and Social Exclusion’ that examines the links between social exclusion, transport and the location of services (SEU, 2003). They found that transport barriers can be a significant barrier to social inclusion, as inhabitants are not able to access activities and opportunities. The SEU highlights work, educational facilities, healthcare, food shops, and social, cultural and sporting facilitiesas activities that are key to belonging to society. Preston and Rajé also recognise transport barriers as a cause of social exclusion, as often the problem of social exclusion is not caused by a lack of activities or opportunities, but moreover by a lack of access to those activities or opportunities (Preston and Rajé, 2007). Inhabitants do not have the means to access the transport system because it is too expensive, they do not have a car or bicycle or the network service of the transport system does not provide access to activities.
Accessibility can be described and measured in many ways. The Oxford English Dictionary defines accessibility as ‘the quality of being able to be reached or entered’ (OED, 2019). Hansen (1959) describes accessibility as ‘the potential for interaction’. According to Handy (1997) accessibility consists of the spatial distribution of activities, the ease of getting to those activities, and the magnitude, quality and character of the activities. Central to the term accessibility is travel cost. Travel cost can be measured in different ways; such as travel time or the money one has to spent on travelling to an activity. With most definitions of accessibility, choice is an important element as more choices in destinations and modes of travel mean greater accessibility (Handy, 2002). More choices in destinations also mean more potential for interaction which Hansen describes as accessibility.
In 2000, Church et al. identified and categorised the factors that may reduce or remove the ability of inhabitants to access activities or opportunities in the paper ‘Transport and social exclusion in London’ (2000). A three-fold categorisation of interrelated processes that influence a person’s ability to access activities and opportunities that are fundamental to belonging to society was presented. Firstly, there are processes that relate to the time-space organisation of a household and to how time-space budgets influence the ability of a person to travel. Secondly, the costs that come with travelling, network coverage of the system, personal security and security of the public space can influence a person’s mobility. Thirdly, the time-space budget of the activities a person wants to reach can influence mobility.
In what way these processes influence once mobility depends on multiple factors. These factors consist of material circumstances, such as income, but also household and personal and cultural characteristics, such as gender, age ethnic background, physical and intellectual abilities, sexuality and beliefs (Church, 2000).
2.3 JUST CITY THEORY
An important theory to understand why social exclusion exists is a theory by Fainstein called the just city theory. The deindustrialisation and globalisation changed the fortunes of cities in the western world. Local governments tried to respond to this by entering into the competition for private investments. Most local governments focussed their objectives on economic growth, because economic growth of the city would lead to the greatest good for the greatest number. Projects in cities are mostly justified by their ability to enhance competitiveness instead of their social effect on the inhabitants of a city. According to Fainstein, This narrow focus on growth leads to capital investments into development projects that promote growth, rather than improving the quality of peripheral neighbourhoods (Fainstein, 2010: 1). Policymakers overlook the consequences of these growth promoting policies on minority groups. Only focussing on growth will lead to an uneven distribution of activities and opportunities. This could be explained by the fact that investments usually involve a quantification of benefits, that is biased in favour of the already well off in a city.An example of this is can be found in investments in commuter rail. When investments in a commuter rail will lead to time savings by high income groups, it will have a greater pay-off than expenditures on busses that will lead to time savings for low-income groups whose time has less monetary value (Fainstein, 2014: 6). Therefore, it is more plausible that investments will be made to increase the accessibility of neighbourhoods with high income groups. City policies that are focused on growth promotion therefore do not lead to greater good for all groups in a city, but mainly favour the in this example group that already has the highest income. The gap between the accessibility between high and low income groups only grows by these kind of policies. They exacerbate the disadvantages of low income and minority groups, and therefore increase social exclusion of (socio-economic) disadvantaged groups within the city.
Fainstein sees these policies that lead to inequalities as a problem. Scholars of urban politics such as Fainstein and Jacobs have criticised urban decision makers for imposing policies that exacerbated the disadvantages suffered by socio-economic disadvantaged (Fainstein, 2010: 3). As a reaction to this, they came with the model of the ‘just city’. In their opinion, a just city is a city ‘in which public investment and regulation would produce equitable outcomes rather than support those already well off’ (Fainstein, 2010:3). A just city should be more focussed on creating social equity by ensuring that everyone has access to resources, rather than focussing on economic growth.
Building upon this model, Fainstein developed an urban theory of justice as a tool for urban policy evaluation. According to this tool, the conceptualisation of the just city requires the incorporation of three principles into the development and evaluation of public policy: Democracy, Diversity, and Equity (Fainstein, 2010: 5). She recognises that there can be tension between these three principles. For example, between growth and equity, and growth and sustainability. In addition, she argues that diversity can undermine democracy. However, these values can, according to Fainstein, also be mutually reinforcing (Fainstein, 2010: 49). Incorporation of these three principals could help to minimise the amount of social exclusion in a city, as it would provide more equity to all its residents.
To examine how different cities measure up these criteria, she uses the three case studies of New York, London, and Amsterdam. Through these case studies, Fainstein shows the effects of projects and policies in these cities on the urban justice of the cities.
In 1999, Fainstein used Amsterdam to demonstrate the potential for a just city within a capitalist political economy (Fainstein, 1999). Even though commitment to redistributive policies had been receding, and open friction among ethnic groups had been developing in the recent past, compared to London and New York, Fainstein still calls Amsterdam the most just city as it is ‘a place of considerable greater equality; its culture continues to be defined in terms of tolerance; and it offers substantial public amenities, excellent, cheap transit, and extensive social services’ (Fainstein, 2010: 139). According to Fainstein, Amsterdam remains the strongest of the three examined cities in terms of diversity, democracy, and equity and it remains exemplary for the incorporation of these three principals (Fainstein, 2010: 164). Gilderbloom also praises Amsterdam as just city, because of ‘its ability to ensure basic necessities, freedom, and creativity (Gilderbloom et al., 2009: 473). In comparison with many other cities, it is a far better place for citizens of all races, religions, and incomes as ‘it has created progressive policies and initiatives that are designed to reach every section of society, thereby benefiting everyone’ (Gilderbloom et al. 2009: 489). Inhabitants living in Amsterdam appear to be more tolerant, secure, happy and healthy compared with citizens in the USA.
Not everyone agrees with the view of Fainstein and Gilderbloom. In the paper An in memoriam for the just
city of Amsterdam, Uitermark takes a stand against the view on Amsterdam as (still) a just city. Gilderbloom
emphasizes that Amsterdam is a city where inhabitants seem tolerant, secure, happy and healthy. Even though these features make a city nice or good, they do not necessarily make it just according to Uitermark. In his view, a just city is ‘a city where exploitation and alienation are absent’ (Uitermark, 2009: 350). This view is closer to that of Fainstein for whom equity is central to a just city. However, where Fainstein also empathises on growth, in Uitermark’s opinion, growth can help to promote justice but I might just as well exacerbate injustice. Also, a city can be sustainable and diverse, yet replete with inequalities between different groups (Uitermark, 2009:350). He concludes by saying that Amsterdam indeed could be called a just city when compared with other cities in the world. However, when Amsterdam is analysed historically instead of comparatively, one could ask themselves whether Amsterdam can still be called a just city. (Uitermark, 2009: 359). Actors such as social housing corporations that first helped to build and maintain Amsterdam (through, for instance, redistributive housing policies) as a just city also helped it to die around 1990.
Different stands are taken on the performance of Amsterdam as a just city. The question remains in what extent Amsterdam is still able to provide equitable opportunities for all groups within the city. Some argue that compared to other cities, Amsterdam performs quite well, others mainly focus on the downfall of its ability to provide equitable outcomes to all, compared to the past. Because this research is not longitudinal, it is not possible to comment on the question if Amsterdam has become less effective in providing equitable access to opportunities, however it tries to evaluate in what way the transport network of Amsterdam provides access to opportunities at this moment and in what way this access is equitable.
3. METHODOLOGY
3.1 RESEARCH DESIGN
For this research, quantitative methods of research have been used. The aim of the research is to find if a relation exists between socio-economic status of neighbourhoods and accessibility of key activities through the transit/ walking/ biking system. To find out if there is variation in the accessibility of key activities between neighbourhoods with different socio-economic status, the research is built upon a cross sectional design (Bryman 2008 p.59). This research design has been chosen, because with a cross sectional design it is possible to tell if there is variation between the research units (neighbourhoods), at one point in time using quantitative data. Because quantitative data has been used, it is possible to find if there is a relation between socio-economic status and access through transit by using a correction model.3.2 DEFINITION OF THE RESEARCH UNIT
Amsterdam can be classified on different levels. OIS Amsterdam uses 5 levels: Amsterdam, City districts, so called 22 areas, wijken, and buurten* (OIS, n.d.). For this research the lowest scale of areas that OIS offers, neighbourhoods have been used, as this will provide the most detailed accessibility per area possible. This definition leads to a division into 476 areas.Not all 476 neighbourhoods of Amsterdam have been used as research units. Only neighbourhoods with more than 200 inhabitants have been picked. Not only residential areas are called neighbourhoods, also industrial zones and public parks are registered as neighbourhoods. These neighbourhoods could provide distorting analysis as they are not representative for the average neighbourhood in Amsterdam. Almost all areas with residential parks and industrial zones within its borders have less than 200 inhabitants. Therefore, the lower limit for the number of inhabitants of a neighbourhood has been set at 200. When a neighbourhood with an industrial zone or public park within its borders has more than 200 inhabitants, the neighbourhood has been taken into the analysis. There are 389 neighbourhoods in Amsterdam with more than 200 inhabitants.
To conclude, the research units of this research are: neighbourhoods within the municipality of Amsterdam
with more than 200 inhabitants.
*The Dutch words ‘Wijk’ and ‘Buurt’ both translate into neighbourhood, but they define different scales. The lowest scale is ‘Buurt’. In the upcoming parts of this research, neighbourhood will be used to define ‘Buurt’.
3.3 STUDY AREA
The study area of this thesis is (the municipality of) Amsterdam, the capitol of The Netherlands. In 2018, Amsterdam had 854.316 inhabitants (OIS, 2019). Different layers of Amsterdam have been investigated. The first two maps presented in this paragraph will be used to locate neighbourhoods with different socio-economic status, and to locate activities. The last two maps have been used to built the network datasets for accessibility of activities through transit, biking, and walking.On the first map (map 1), the division of Amsterdam into 8 districts is presented: Noord (North), Oost (East), Zuidoost (Southeast), Centrum (Center), Zuid (South), West (West), Nieuw West (New West), and Westpoort. The division of Amsterdam into these districts will be used to locate neighbourhoods in the analysis. For example when concentrations of certain socio-economic characteristics are described, this is done through the districts (‘Most neighbourhoods with a low percentage of inhabitants receiving WWB are located in Centrum, Zuid,
Noord, and Nieuw West’, P.30)
On the second map (map 2), a division of neighbourhoods inside and outside the RING A10 is presented. The RING A10 is a highway that forms a ring around the pre-war neighbourhoods of Amsterdam. Almost all neighbourhoods outside this highway ring are post-war neighbourhoods. Many social studies on socio-economic status segregation in Amsterdam use this division of neighbourhoods in a post-war part and pre-war part, as often segregation patterns are divided along this line. For example, a study by Boterman and Van Gent in 2015 on segregation in Amsterdam used this division (Boterman and Van Gent, 2015). Therefore, differences of socio-economic variables will be presented as inside and outside the RING A10 (The mean percentage of
social housing is 37,4%. Inside the RING A10 of Amsterdam, the mean percentage of social housing is 35,0%, outside the RING A10 the mean percentage is 43,4%, P.27).
On the third map (map 3), an overview of all the transit lines in Amsterdam is presented. On this map, all metro lines (green), tram lines (red), and bus lines (orange) are indicated. All these lines have been used to make a network dataset to find the accessibility of activities through transit.
On the fourth map (map 4), an overview of all city streets of Amsterdam is presented. All highways have been removed from this map, therefore the RING A10 that is used as a division line in map 1 and other highways in the municipality of Amsterdam are not present at this map. This street layer is used to built network datasets to find the accessibility of activities through biking and walking.
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3.4 OPERATIONALISATION OF KEY CONCEPTS
In this paragraph, the key concepts will be operationalised. First, the key activities are operationalised. Then, the measure for accessibility, and last, socio-economic status of neighbourhoods is operationalised.3.4.1 KEY ACTIVITIES
Key activities will be defined through the concept of Burchardt (1999: 231). Burchardt divides key activities into four dimensions: production activities, consumption activities, political activities, and social interaction. Not all dimensions will be evaluated. Firstly, because evaluation of all the dimensions would cost more time than was set for this thesis project. Secondly, the available data on neighbourhood level was limited, and therefore, not all dimensions could be evaluated. The dimensions that have been evaluated are production and consumption activities. Within these dimensions, various activities are included. Access to greens spaces, outdoor sporting facilities, hospitals, and paid work have been investigated. These key activities have been operationalised as:
• Green spaces: all public parks and grasslands that are within the ‘Hoofdgroenstructuur’ of Amsterdam
(City of Amsterdam, n.d.).
• Outdoor sporting facilities: all sport fields and tracks in the open air, such as football/ hockey/ tennis
fields, athletics tracks and golf courts. All outdoor sporting facilities can also be found in the Hoofdgroenstructuur of Amsterdam (City of Amsterdam, n.d.).
• Hospitals: an institution providing medical and surgical treatment and nursing care for sick or injured
inhabitants (Lexico, n.d.). Next to this, an institution is only counted for as a hospital as it was listed on the Zorgkaart.
• Paid work: a job performed as an employee or self-employed person’ (Centraal Bureau voor de Statistiek,
n.d.).
3.4.2 ACCESSIBILITY
As mentioned in the literature by Handy, accessibility can be described as the spatial distribution of activities, the ease of getting to those activities, and the magnitude, quality and character of the activities (Handy, 1997). Central to this is travel cost. This description has been used as a basis to operationalise accessibility in this research.
Access to jobs, has been operationalised as: the number of jobs that can be reached from the center of a neighbourhood in 15 minutes. Accessibility to jobs is investigated through walking, biking, and the transit
system. The reason for the maximum travel time of 15 minutes is, that Amsterdam is a small city where almost every place can be reached through biking or the transit system within a bigger time frame. When the number of minutes would have been extended to, for example 30 minutes, almost everyone would have had access to all jobs, even though differences in accessibility to jobs within the city do exist. To show these differences between neighbourhoods, a maximum of 15 minutes walking, biking or using the transit system has been set as the measure of accessibility to jobs.
Access to hospitals has been operationalised as: the number of minutes to the nearest hospital from the center of a neighbourhood. Accessibility to hospitals is investigated through walking, biking, and the transit
system.
Access to outdoor sporting facilities has been operationalised as: the number of minutes to the nearest outdoor sporting facility from the center of a neighbourhood. Accessibility is only investigated through biking
and walking, because it is assumed that most inhabitants do not use the transit system to access an outdoor sporting facility.
Access to green spaces has been operationalised as: the number of minutes to the nearest green space from the center of a neighbourhood through walking. Accessibility to green spaces is only investigated through
walking, as access to green spaces is very important.
Because of climate change, the average temperature in the region of Amsterdam is increasing. Since 1950, the annual average temperature increased with 1,6 degrees Celsius (KNMI, 2018). Next to this increasing annual temperature, cities also have to deal with the effect of the urban heat island (UHI) effect: air and surface temperatures that are higher than in the surrounding rural areas (van Hove, 2011: 11). In Amsterdam, the UHI has a strong effect on the ability of the air to cool down at night. Therefore, the annual temperature in Amsterdam is higher than in the areas around the city (van der Hoeven, 2013: 6). According to the CBS, heat has a strong effect on mortality rates. When temperature increases, mortality risk (strong increase with age) shows an increase (Garssen, 2005). This implies that heat has a negative effect on health. Green spaces such as parks have a cooling effect on the temperature in cities (van der Hoeven, 2013). Small parks have a cooling effect within the park itself, larger parks such as het Vondelpark also have a cooling effect on the surrounding built environment (van Hove, 2011). Therefore, one could say that green spaces have a positive effect on the health of inhabitants. Because of this positive effect, everyone should be able to reach a green space, especially vulnerable groups, such as disabled and elderly and other groups who have difficulties with walking, biking or using the transit system. Only the most basic way of access, access through walking is investigated.
3.4.3 SOCIO-ECONOMIC STATUS
Lastly, the concept socio-economic status is operationalised. The socio-economic status of neighbourhoods is conceptualised in 4 dimensions: tenure situation, age, ethnic background, and income. Each dimension is operationalised by one or two variables that will be reviewed below. Each variable represents data aggregated on the level of neighbourhoods. These dimensions have been chosen as socio-economic characteristics, because the SCP recognises variables within these dimensions as risk factors for being socially excluded (SCP, 2003).
Often, average household income is used as a variable for income. However, this was not possible, as no data on the average income per neighbourhood is available on neighbourhood level in Amsterdam. Therefore, two other variables that also have to do with economic situation have been used.
The two used variables to operationalise income are the average property value per square meter of a
neighbourhood, and the percentage of inhabitants in a neighbourhood that receives social benefits that are
regulated through the Wet werk en bijstand (Wwb) (English: Law work and assistance). The percentage of inhabitants receiving WWB benefits has been used, because the SCP marks inhabitants living on a benefit as a risk group for social exclusion and CBS (Centraal Bureau voor de Statistiek) also uses the percentage of inhabitants receiving WWB benefits of an area as a variable to measure socio-economic status (CBS, 2018).
Next to this variable, the average property value per square meter is used to get an indication of the average household income per neighbourhood. It is assumed that neighbourhoods with a high average property value are more likely to have inhabitants with a higher income, as property value per square meter. The average property value was also used as a variable for socio-economic status in a study on healthy food access in Amsterdam by Helrich et al. (2017).
The used variable for tenure situation is the percentage of social housing in a neighbourhood. The used variable for age is the percentage of inhabitants over the age of 65 per neighbourhood.
The used variable for ethnic background is the percentage of inhabitants with a non-western background per
neighbourhood.
3.5 DATA GATHERING
Data has been gathered in multiple ways. The different data gathering methods will be explained below. Also a short description of the data is added.3.5.1 DATA OF NETWORKS
The data on the transit network of Amsterdam is collected through a contact at the Vervoerregio. The data that has been used consists of all the metro, tram, and bus lines of the GVB (Gemeentelijk VervoersBedrijf) from 2019. This means that the newly introduced North-South metro line is already implemented and the trams and busses drive via the new routes.
Data on the travel speeds of trams, metros busses and ferries could not be gained through official statistics. To make an estimation of the speeds, Google maps has been used. Speeds are calculated with a Google Map route planner. For every mode of transport, 3/4/5 lines have been selected and the travel time (minutes) that Google Maps showed is adopted, then the length of the line (meters) is calculated with Arcmap. Then, the length of the line in meters is divided by the number of seconds the line lasts, which will give the speed in meter/second. All the travel times and the length of the used lines to estimate travel speeds can be found in a
tablein the appendix, chapter 8.1. The data on the average travel speed of cyclists in Amsterdam was obtained
through the Dutch cyclists union. The average travel speed of cyclists in Amsterdam is estimated at 14,4 km/h (Fietsersbond, 2019). The speed for walking is set on 5 km/h.
Data on the street network of Amsterdam has been gathered via the ministry of infrastructure and water management. Rijkswaterstaat, part of the ministry has an index of geo data of the street network of The Netherlands. The data consists of all the Highways, (A- and N-wegen), city streets, and biking lanes in 2019.
3.5.2 DATA OF NEIGHBOURHOODS
The data on neighbourhoods contains large sets of data with a lot of variables and research units. Therefore, gathering this by myself would have been impossible. The data on neighbourhoods is therefore collected through the municipal service OIS Amsterdam (Onderzoek, Informatie en Statistieken). Data has been gathered
via their website ois.amsterdam.nl where data can be downloaded. The following datasets from OIS have been used:
• Population neighbourhoods by age groups (data of 1st of January, 2018)
• Population neighbourhoods by migration background (data of 1st of January, 2018).
• Property value per square meter in euros (data of the 1st of January, 2018).
• Social assistance benefits to neighbourhoods (Data of 1st of January, 2018).
• Housing stock according to the tenure situation (Data of 1st of January, 2018).
A basemap of all neighbourhoods in Amsterdam has been obtained via the municipality of Amsterdam, as well as a map that divides Amsterdam in 8 districts that will be used to locate neighbourhoods is obtained through the municipality.
3.5.3 DATA OF ACTIVITIES
Data on the location of activities that are being counted for as key activities has been obtained via the municipality of Amsterdam (maps.amsterdam.nl), OIS Amsterdam (ois.amsterdam.nl) and Zorgkaart Nederland (zorgkaartnederland.nl). The following datasets have been obtained:
• Ecological structure (Municipality of Amsterdam, n.d.). This data has been used to locate outdoor sporting
facilities and green spaces.
• Business locations and working inhabitants neighbourhoods per sector (Municipality of Amsterdam, data
of 1st of January 2018). This data has been used to find the number of jobs in each neighbourhood.
• Address of hospitals in Amsterdam (Zorgkaart Nederland, n.d.).
3.6 METHODS OF ANALYSIS
To find the spatial distribution of key activities, the spatial distribution of neighbourhoods with different socio-economic status, and the spatial distribution of accessibility, multiple methods have been used. The methods of this research can be split into two parts.In the first part, the distribution of key activities across the city of Amsterdam has been mapped out. Then the different characteristics of socio-economic status of neighbourhoods have been mapped out. The location of key activities and the socio-economic status have been mapped out with the help of Geographical Information Systems (GIS).
In the second part, the accessibility of key activities through walking, biking has been calculated with the network analyst of ArcMap. Then with the help of SPSS (Statistical Package for the Social Sciences) has been used to find if access to key activities has a relation with socio-economic status of neighbourhoods.
A geographic information system is a framework for gathering, managing, and analysing data (ESRI, n.d.). GIS is rooted within the science of geography. It integrates many types of data. It can analyse spatial locations and can organise layers of information into visualisations using maps. Because of these features, GIS gives an extra dimension to data, as it can find (spatial) patterns and relationships. The GIS programmes that have been used are ArcMap and ArcCatalog from ESRI. ArcMap is the programme that shows the projected map layers. It is possible to build new layers and make analysis with the program. ArcCatalog stores all the featured map layers. It is also possible to create new (empty) point, line, and polygon layers with ArcCatalog.
To find the distribution of neighbourhoods with different socio-economic status, data on neighbourhood socio-economic status has been gathered. Most data consists of a dataset in an excel sheet. This data can be imported into ArcMap and then coupled to a map layer of all the neighbourhoods with more than 200 inhabitants. This was done for all the socio-economic variables. Only the number of jobs per neighbourhood was added to a separate map layer of all the neighbourhoods in Amsterdam, because otherwise jobs in neighbourhoods with less than 200 inhabitants would not have been included in the calculation of the number of jobs that can be accessed.
To find the spatial distribution of key activities, the data on the location of key activities has been gathered in multiple ways as described in the data gathering paragraph. Most data consists map out of layers. These map layers can be added to ArcMap as separate layers. This was done for the location of green spaces and outdoor sporting facilities. Also all entrances of green spaces and outdoor sporting facilities were added in a newly created point layer. The number of jobs per neighbourhood is added to the layer of all neighbourhoods in Amsterdam.
There was no map layer with the location of hospitals in Amsterdam. A point layer was created with ArcCatalog, then the location of the hospitals was obtained through Zorgkaart Nederland. The location of the hospitals was found using Google Maps. In the created point layer, points were added on the location of the hospitals.
To find the spatial distribution of accessibility of the key activities, different data sources have been used. First, all streets where obtained through the of infrastructure and water management. This data was already in a map layer. All highways were filtered out of this layer, as pedestrians and cyclists are not allowed to use these streets. Then the average walking and biking speed were added in the attribute table. All transit lines and stops of the lines in Amsterdam were obtained through the Vervoerregio. This data was delivered in a line layer for the transit lines and a point layer for the stops. The travel speeds of the different transit modes that were calculated with Google Maps were added to the attribute table of the line layer. The location of the transit stops has to be connected with the street layer. Otherwise the network analyst tool does not recognise that it is possible to get from the stop to the street. This however was not the case. to overcome this error, a buffer of 50 meters was built around every transit stop. Then the perimeter of each buffer was changed into a line. this line was integrated in the street layer. The speed of the new lines was set at the walking speed. At the intersection of the street and the transit line, a new stop was added. This leads to two stops at the location of each stop. This is not a problem, as there is no time calculated for the stop. Now the new stops are connected with the street layer through the line of the perimeter. An example of this can be found in the appendix, chapter 8.2.
The new street layer, the transit network layer, and the new transit stop layer were all used as source layers to make network datasets in ArcCatalog. Network datasets store the connectivity of source layers. Three network datasets have been created: a walking network dataset, a biking network dataset, and a transit network dataset. The transit network dataset is multimodal, as it consists of walking and transit lines. With the network datasets, transportation networks were modelled. The one-way streets, turn restrictions, and overpasses/tunnels options were not used. As cost attribute, the travel speeds of each mode of transport were added.
The transportation networks were used to calculate the travel time from an activity to the center of a neighbourhood. A service area around each activity was built. Travel times are displayed in a number of classes of service areas. Examples of this can be found in the appendix, chapter 8.3.
It is only possible to find the transport time between two points. Therefore, a point in the middle of each neighbourhood was added. This point was snapped to the closest street. This point is the reference point for the whole neighbourhood.
Through the ‘select by location’ tool, the points in the middle of neighbourhoods within a class can be selected. Each neighbourhood can be classified into a class. The outcome is a map with the average travel time in minutes from the center of a neighbourhood to the activity. An example is shown in map 5.
" v " v " v " v " v " v
Data from: OIS Amsterdam and Zorgkaart Nederland, Made by: Chiel van de Camp
Travel time to hospitals through the transit system in Amsterdam, 2019
Legend " v Hospital 0-5 minutes 5-10 minutes 10-15 minutes 15-20 minutes 20-25 minutes 25-30 minutes
Neighbourhood with less than 200 inhabitants
±
0 1.25 2.5 5 7.5 10
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3.7
CONCEPTUAL MODEL
4. RESULTS
4.1 SUB-QUESTION 1: WHAT IS THE SPATIAL DISTRIBUTION OF
NEIGHBOURHOODS WITH DIFFERENT SOCIO-ECONOMIC STATUS IN
AMSTERDAM?
SOCIAL HOUSING
On map 7, the percentage of social housing per neighbourhood showed in deciles can be found. The darker the colour, the higher the percentage of social housing.
The mean percentage of social housing is 37,4%. Inside the RING A10 of Amsterdam, the mean percentage
of social housing is 35,0%, outside the RING A10 the mean percentage is 43,4%. Some neighbourhoods in Amsterdam totally consist of social housing, some have no social housing. The percentage of social housing therefore varies from 0 to 100 percent of the houses in a neighbourhood. Two neighbourhoods consist of 100% social housing, thirty-two neighbourhoods have 0% social housing. Almost all of these are located inside the RING A10.
Most neighbourhoods with a low percentage of social housing can be found in the districts Centrum, and Zuid. The spatial distribution of neighbourhoods with a high percentage social housing is quite disperse as they can be found in all parts of the city. Concentrations of neighbourhoods with high percentages can be found in old parts of Noord, parts of Nieuw-West, Oost, and Zuidoost. Most neighbourhoods with high percentages are close to the borders of Amsterdam with most of them located in Nieuw West or Zuidoost.
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WWB BENEFITS
On map 8, the spatial distribution of neighbourhoods on percentage of inhabitants receiving WWB benefit per neighbourhood is presented. The darker the colour, the higher the percentage of inhabitants receiving WWB benefits.
The mean percentage of inhabitants receiving a WWB benefit is 5,5%. Inside the RING A10 the mean
percentage is 4,9%, outside the RING A10 the percentage is 6,9%. In 73 neighbourhoods, 0 percent of the
inhabitants receives a WWB benefit. With 70,8% of all inhabitants receiving a WWB benefit, Mercatorpark is the neighbourhood with the highest percentage of inhabitants receiving a WWB benefit.
Most neighbourhoods with a low percentage of inhabitants receiving WWB are located in Centrum, Zuid, Noord, and Nieuw West. Neighbourhoods with a high percentage are mostly located districts at the borders of Amsterdam: Noord, Oost, Zuidoost, Nieuw West and West. At the borders of Amsterdam (also in districts with many neighbourhoods with high percentages), many neighbourhoods have low percentages of inhabitants receiving WWB benefits are located.
PROPERTY VALUE PER SQUARE METER
On map 9 the spatial distribution of neighbourhoods on property value per square meter is presented. The darker the colour of the neighbourhood, the higher the average property value per square meter.
The mean property value per square meter in Amsterdam is 4437 euro. Teleport in the districts Westpoort is
with an average property value per square meter of 7587 euro the neighbourhood with the highest value per square meter. The Rechte H-buurt in the district Zuidoost has the lowest average property value per square meter with an average of 1874 euro per square meter.
A clear distinction is visible inside and outside the RING A10. Most neighbourhoods inside the RING A10 have a high property value per square meter, most neighbourhoods outside the RING A10 have a lower property value per square meter. Most neighbourhoods with a low average property value per square meter are located in the districts Zuidoost, followed by Nieuw West and Noord. Almost all neighbourhoods with an average below the mean are located in these city districts, some are located in Oost. Almost all neighbourhoods with a high property value per square meter are located in Zuid, Centrum or West.
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INHABITANTS OVER 65 YEARS OLD
On map 10 the spatial distribution of neighbourhoods on the percentage of inhabitants over the age of 65 is presented. The darker the colour of a neighbourhood, the higher the percentage of inhabitants over the age of 65 in a neighbourhood.
The mean percentage of inhabitants over the age of 65 in Amsterdam is 12,7%. Inside the RING A10, the
mean percentage is 12,7 percent, outside the RING A10 the mean percentage is 12,5%. In 4 neighbourhoods, 0% of all inhabitants is over the age of 65. With 45,7% of all inhabitants over the age of 65, Sportpark Middenmeer is the neighbourhood with the highest percentage.
The spatial distribution of neighbourhoods with low percentages of inhabitants over the age of 65 is disperse. Neighbourhoods with less than 10% of the inhabitants over the age of 65 are mostly located in West, Nieuw West, and Oost. However, all districts house a large amount of neighbourhoods with low percentages. The spatial distribution of neighbourhoods with a high percentage of inhabitants over the age of 65 is more concentrated with concentrations in Zuid, Oost, Noord, and Centrum. Overall, most neighbourhoods with high percentages of inhabitants over 65 are located at the borders of Amsterdam and inside the RING A10.
NON-WESTERN BACKGROUND
On map 11 the spatial distribution of neighbourhoods on the percentage of inhabitants with a non-western background is presented. The darker the colour of a neighbourhood, the higher the percentage of inhabitants with a non-western background.
The mean percentage of inhabitants with a non-western background is 30,8%. There is a clear difference
inside and outside the RING A10 on the mean percentages, as inside the RING A10, the mean percentage is 23,7%, and outside the RING A10, the mean percentage is 48,5%. With only 2.2% of the inhabitants with a non-western background, Nieuwendammerdijk West in Noord is the neighbourhood with the lowest percentage. With 87,1% of the inhabitants with a non-western background, G-buurt Noord in Zuidoost is the neighbourhood with the highest percentage.
The spatial distribution of neighbourhoods on percentage of inhabitants with a non-western background is shows clear concentrations within Amsterdam. In the districts Nieuw West, Zuidoost, Noord, and Oost, there are concentrations of neighbourhoods with high percentages of inhabitants with a non-western background. In Centrum and Zuid, most neighbourhoods have a low percentage of inhabitants with a non-western background. Almost all neighbourhoods with high percentages are located outside the RING A10, Noord, or just inside the RING A10.
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CONCLUSION
- For almost all socio-economic characteristics, the spatial distribution is unevenly spread over the city.
Almost all neighbourhood with characteristics that are mostly seen as negative, such as high percentages opfinhabitants receiving a benefit, a low average property value per square meter, and high percentages of non-western inhabitants are clustered outside the RING A10 or in Noord. Only for the characteristics social housing and inhabitants over 65 this clear division between neighbourhoods inside or outside the RING A10 is not visible.
- For social housing, the spatial distribution of neighbourhoods with high percentages is disperse, although
the overall percentage of social housing is higher outside than inside the RING A10. Neighbourhoods in Zuid have the lowest percentages of social housing.
- When looking at the spatial distribution of neighbourhoods on the percentage of inhabitants receiving
WWB benefits, neighbourhoods with high percentages are mostly located outside the RING A10. Again, neighbourhoods in Zuid but also in Centrum have the lowest percentages of inhabitants receiving WWB benefits.
- The spatial distribution of neighbourhoods when looking at the average property value per square meter
shows a strong difference inside and outside the RING A10. Inside the RING A10, almost all neighbourhoods have a high average property value per square meter, expect for neighbourhoods in Noord. Outside the RING A10, almost all neighbourhoods have a lower average property value per square meter except neighbourhoods at the borders of Amsterdam.
- The spatial distribution of neighbourhoods with high percentages of inhabitants older than 65 is also
uneven. Most neighbourhoods with high percentages can be found inside the RING A10 and at the borders of Amsterdam.
- The spatial distribution of neighbourhoods with high percentages of inhabitants with a non-western
background is very uneven. Most neighbourhoods with higher percentages are located outside the RING A10, or in the districts Noord or Oost. In the districts Centrum and Zuid, most neighbourhoods have a low percentage of inhabitants with a non-western background.
4.2 SUB QUESTION 2: WHAT IS THE SPATIAL DISTRIBUTION OF
KEY
ACTIVITIES
IN AMSTERDAM?
JOBS
On map 12, the spatial distribution of the job density per square meter per neighbourhood is presented. The darker the colour, the higher the number of jobs per square meter in the neighbourhood.
The average job density in Amsterdam is 6141 jobs per square kilometer. The neighbourhood with the
highest job density, Zuidas Zuid has a job density of 62.039 jobs per square kilometer and is situated in Zuid. The neighbourhood with the lowest job density, Holysloot has a job density of 83 jobs per square kilometer and is situated in Noord. Holysloot is situated in a rural part of the municipality of Amsterdam.
The spatial distribution of the job density in Amsterdam is uneven. The map shows clear concentrations of jobs in Amsterdam. The first concentration is in Zuid. In Zuid, the South-Axis, the central business district of Amsterdam can be found. The overal South-Axis region has a job density of 30981 jobs per square meter. The second job concentration can be found in Centrum. In the whole district the job density is high, with a mean average of 15811 jobs per square meter. The third job concentration can be found in Oost, around the Amstelstation. The average mean job density around this station is 18701 jobs per square meter. The fourth job concentration is located in Zuidoost, around the BijlmerArenA station. the average mean job density in this job concentration is 19738 jobs per square meter. The fifth job concentration can be found in the neighbourhood Telepoort, with a job density of 19333 jobs per square meter. This neighbourhood is located in Westpoort. All job concentrations, expect the one in Centrum, are concentrated around Metro stations.
The number of jobs per district: Centrum: 105.331 Nieuwpoort: 22.178 West: 49.775 Nieuw West: 79.914 Zuid: 111.616 Oost: 61.037 Noord: 33.744 Zuidoost: 80.606 Total number: 544.201
HOSPITALS
on map 13, *the spatial distribution of hospitals is presented. The location of a hospital is indicated with the symbol H.
There are 6 hospitals in Amsterdam. One hospital is located in Noord, one in Oost, one in Zuidoost, one in Zuid and two in Nieuw West. The only districts without a hospital are Nieuwpoort, West, and Centrum. However, the hospital that is situated in Oost is close to the Centrum district, and the hospitals in Nieuw West are close to West. 4 out of 6 hospitals are close to the RING A10.
