The Path-Dependency of Low-Income Neighbourhood Trajectories: An Approach for Analysing Neighbourhood Change
The Path-Dependency of Low-Income Neighbourhood Trajectories: An Approach for Analysing Neighbourhood Change
Merle Zwiers 0 1
Reinout Kleinhans 0 1
Maarten Van Ham 0 1
0 School of Geography and Geosciences, University of St Andrews , Irvine Building, North Street, St Andrews, Fife KY16 9AL, Scotland
1 Department of OTB - Research for the Built Environment, Faculty of Architecture and the Built Environment, Delft University of Technology , P.O. Box 5043, 2600 GA Delft , The Netherlands
The gap between wealthy and disadvantaged neighbourhoods seems to be increasing in many contemporary Western cities. Most studies of neighbourhood change focus on specific case-studies of neighbourhood downgrading or gentrification. Studies investigating socio-spatial polarisation in larger urban areas often compare neighbourhoods at two points in time, neglecting the underlying dynamic character of neighbourhoods. In the current literature, the question if neighbourhoods with similar characteristics experience similar changes over time remains unanswered. As a result, it is unclear why some neighbourhoods appear to be more prone to change than others. In this paper, we propose a dual approach for analysing neighbourhood change. We argue that researchers should both adopt a long-term perspective (20-40 years), because significant changes are only visible after longer periods of time, and focus on more detailed neighbourhood trajectories to understand how neighbourhood change is interrelated with context. Focussing on Dutch neighbourhoods over the period 19712013, we analyse the role of physical characteristics on low-income neighbourhood trajectories using an innovative visualisation technique. A tree-structured discrepancy analysis allows for the visualisation of complete neighbourhood pathways, enabling the analysis of complex, contextualised patterns of change. We find that the original quality of neighbourhoods and dwellings seems to be an important predictor for future neighbourhood trajectories, indicating a high level of path-dependency.
Socio-spatial polarisation is increasing in many large cities throughout Europe
(Tammaru et al. 2016)
. Socio-spatial polarisation refers to the process where the
gap between the rich and the poor is increasing, which is translated into spatial
segregation along ethnic or socioeconomic lines. In the European context, this has
resulted in distinctive spatial patterns in large cities where the rich are increasingly
located in historic city centres, while the poor reside in the more disadvantaged
(cf. Van Eijk 2010)
. Despite substantial government
investments to counteract such socio-spatial polarisation over the past few
decades, this process seems to be persistent, though it varies over time and between
In most of the studies on socio-spatial polarisation the continuous dynamic character
of neighbourhoods is neglected, reducing neighbourhood change to the difference
between two points in time. However, neighbourhoods are constantly changing in their
population composition as a result of residential mobility and demographic events,
thereby changing the aggregate status of neighbourhoods. Many studies investigating
neighbourhood change focus on exceptional cases of gentrifying or declining
(e.g. Bailey 2012; Jivraj 2013; Hochstenbach and Van Gent 2015)
Although these studies have provided important insight in the drivers behind
neighbourhood change, they are typically limited to time-specific case-studies in
particular cities. As a result, we do not know if neighbourhoods with similar
characteristics experience similar processes of change over time – or if processes of
gentrification or downgrading are the exception to the rule. In addition, we have limited
understanding of how processes of gentrification and downgrading affect other
neighbourhoods. As neighbourhoods do not operate in a societal and policy vacuum,
changes in one neighbourhood are likely to affect other neighbourhoods as well. It has,
for example, been argued that processes of urban restructuring or gentrification are
likely to lead to new concentrations of deprivation in other neighbourhoods through the
displacement of low-income groups
(Bolt et al. 2009)
. As such, the upgrading of one
neighbourhood might go hand-in-hand with the deterioration of another neighbourhood
(Musterd and Ostendorf 2005; Bråmå 2013)
In addition, many studies in this field rely on percentile shifts and point-in-time
measures to analyse change, neglecting the possibility that development over time
might be more non-linear than linear or need much more time to take effect
Van Ham and Manley 2012)
. Because the physical structure of neighbourhoods hardly
changes, neighbourhoods can maintain their overall status for longer periods of time
(Meen et al. 2013; Tunstall 2015)
. However, selective mobility and demographic events
lead to a constantly changing population composition
(Van Ham et al. 2013)
. In this
paper we argue that to fully understand processes of neighbourhood change, the next
step in neighbourhood research is to focus on detailed neighbourhood trajectories and
to identify typologies of neighbourhood change over longer periods of time. Analysing
interrelated neighbourhood trajectories and understanding why some neighbourhoods
are more prone to change than others is therefore highly relevant to the debate on
spatial manifestations of inequality and neighbourhood development.
In this paper, we present an approach for analysing neighbourhood change
by focussing on long-term neighbourhood change combined with a detailed analysis
of neighbourhood trajectories. Focussing on the trajectories of low-income
neighbourhoods in the Netherlands over the period 1971–2013, we analyse the role of
physical characteristics in neighbourhood change. In the Dutch context, neighbourhood
and housing quality is often related to the debate on neighbourhood change, however,
few empirical studies try to analyse to what extent physical characteristics are related to
today’s spatial patterns. Different starting positions in terms of housing quality can have
long-lasting effects on neighbourhood statuses through processes of path-dependency
(Meen et al. 2013)
. In addition, because the Dutch government has invested heavily in
urban restructuring by changing the share of owner-occupied and social rented
dwellings in particular neighbourhoods, we analyse the effect of demolition and construction
on the different neighbourhood trajectories. Changes in the housing stock generate
mobility processes and may thus affect neighbourhoods in both direct and indirect ways.
To analyse neighbourhood trajectories we use a combination of methods. Sequence
analysis allows for the analysis of complete pathways through time and is therefore a
promising method for longitudinal neighbourhood research. Sequence analysis is
gaining popularity in the social sciences and is increasingly used by researchers
interested in patterns of socio-spatial inequalities
(e.g. Coulter and Van Ham 2013;
Van Ham et al. 2014; Hedman et al. 2015)
. However, sequence analysis is ultimately a
descriptive method and its potential for explaining trajectories is limited. Researchers
have therefore developed a methodological framework that combines sequence analysis
and a tree-structured discrepancy analysis, allowing for the analysis of the relationship
between covariates and sequences (Studer et al. 2011). As such, this framework can
provide insight in how different covariates affect neighbourhood trajectories in different
ways. To our knowledge, this paper offers the first empirical application of this
combination in the field of urban research, constituting a new approach towards
researching neighbourhood dynamics and a move towards the visualisation and analysis
of complex trajectories. In this paper, we only highlight the most important aspects of the
combination between sequence analysis and the tree-structured discrepancy analysis.
Based on our presentation, researchers should be able to get a basic understanding of
(for a full understanding of sequence analysis researchers are referred to
Gabadinho et al. 2011; for the tree-structured discrepancy analysis to Studer et al. 2011)
The remainder of this paper is organised as follows. We start with expounding our
approach for analysing neighbourhood change. We then move to describe the
combination of sequence analysis and the tree-structured discrepancy analysis in more detail.
In the BData and Methods^ section, we elaborate on the structure of the dataset and the
methodological choices made. We then discuss the substantive results and reflect on the
applicability of the methods for neighbourhood research.
Longitudinal Neighbourhood Change
Time is an important dimension in neighbourhood research. There are generally two
viewpoints on this: one emphasises the general stability of neighbourhood status over
longer periods of time as a result of path-dependency
(Dorling et al. 2007; Meen et al.
. Another viewpoint argues that neighbourhoods are highly dynamic and are
constantly experiencing population change
(Van Ham et al. 2013)
. These two views on
neighbourhood change are however rather complimentary than competing. On the one
hand, neighbourhoods are indeed very dynamic and are constantly changing in their
population composition as a result of residential mobility and demographic events
(Simpson & Finney, 2009; Van Ham et al. 2013)
. On the other hand, because the
housing stock of neighbourhoods is rather static, the overall socio-economic status of
neighbourhoods does not change much over time
(Meen et al. 2013)
. In other words:
because the physical spatial structure of neighbourhoods remains broadly unchanged,
similar types of residents move in and out of these neighbourhoods, thereby
maintaining the status quo.
This is not to say that there are no changes in neighbourhood status at all:
neighbourhoods can experience processes of decline or gentrification over time because
the population in-situ experiences changes in employment status
because of selective out- and inflow of different income groups
(Van Ham et al. 2013)
However, extreme processes of decline or gentrification whereby neighbourhoods
experience a complete transformation of their population composition and overall status
(Cortright and Mahmoudi 2014; Tunstall 2015)
. Moreover, when
neighbourhoods experience processes of decline or gentrification, the effects of these
processes on the urban mosaic are often only visible after longer periods of time
When such extreme changes do occur, they can often be explained by the physical quality
of the neighbourhood. Processes of gentrification have been related to the desirable location
and high quality and architectural aesthetics of pre-war or other historic neighbourhoods
(e.g. Zukin 1982, 2010; Bridge 2001)
. As higher income groups gradually move into these
neighbourhoods, house values and prices go up, thereby pushing lower income households
out. In a similar vein, many unattractive post-war neighbourhoods have experienced
processes of extreme neighbourhood decline over the past few decades. Researchers have
argued that this decline can be explained by the low quality of and technical problems with
dwellings and neighbourhoods built after the Second World War
(Prak and Priemus 1986;
Van Beckhoven et al. 2009)
In the Netherlands, these extreme processes of neighbourhood decline in post-war
neighbourhoods (built between 1945 and 1970) led to the development of large-scale
urban restructuring programmes. These urban restructuring programmes were aimed at
creating a social mix in these neighbourhoods by demolishing social housing and
constructing more upmarket owner-occupied or rental dwellings
Researchers have argued that urban restructuring programmes have led to minor
improvements in the socio-economic position of these neighbourhoods
et al. 2013; Kleinhans et al. 2014)
. This can be explained by the fact that while a large
number of social rented dwellings has been demolished, the overall share of social
housing remained high in most restructuring neighbourhoods (Dol and Kleinhans
2012). Urban restructuring is only effective in reducing socio-spatial segregation when
a substantial part of the social housing stock in a neighbourhood is replaced by
(Bolt et al. 2009)
. Quite often (part of) the original residents in
restructuring neighbourhoods moved back in the newly constructed dwellings. This
meant that while these neighbourhoods have experienced a physical upgrade; the
socioeconomic status of the population remained largely unchanged (see e.g. Kleinhans et al.
There are thus two important, yet related, gaps in the literature on neighbourhood
change. First, many studies focus on exceptional cases of change involving
gentrification, downgrading or urban restructuring in particular cities or neighbourhoods, failing
to answer the question if neighbourhoods with similar characteristics experience similar
changes over time. Second, few studies have analysed the role of path-dependency
of the physical characteristics of neighbourhoods in processes of change for a large
sample of neighbourhoods in different cities. As a result, we have little insight into
which neighbourhoods are more prone to change than others. Analysing the effects of
physical characteristics and/or physical changes on neighbourhood trajectories is
important for our understanding of why some neighbourhoods experience change while
others remain stable for longer periods of time and help to answer the question which
neighbourhood characteristics are predictors of future processes of change.
However, research on neighbourhood change is complicated because
neighbourhoods have different starting positions, may experience different paces and
processes of change over time, and the effects of changes in context might be
nonlinear or might only be visible after longer periods of time
(Van Ham and Manley
. To fully capture patterns of neighbourhood change, it is therefore necessary to
adopt a twofold approach: 1) Change should be analysed over longer periods of time
(20–40 years) to capture the effects of longer term processes; and 2) The focus should
be on continuous change of neighbourhood trajectories instead of simply comparing
two points in time. As such, a dual approach would contribute to the identification of
neighbourhood change typologies providing insight in (the drivers of) different spatial
Analysing Neighbourhood Trajectories
The methods for analysing trajectories are limited: the most common statistical
methods treat time as another level (in multilevel models), as dummy variables (in
regression models), or as growth curves (time-series models). While all of these models
have their advantages and disadvantages for studying change over time, they generally
do not easily allow for the identification of patterns of change. Sequence analysis, a
method that originates from the biological sciences to map DNA patterns, however,
allows for the study of patterns of change and is gaining increasing popularity within
the social sciences because of its ability to show complete pathways. Social researchers
are using sequence analysis to explore class careers
(Halpin and Chan 1998)
(Abbott and Hrycak 1990; Pollock et al. 2002; McVicar and
AnyadikeDanes 2002; Brzinsky-Fay 2007)
, family histories (Elzinga and Liefbroer 2007), and
(Billari and Piccarreta 2005; Wiggins et al. 2007; Martin et al.
The main goal of sequence analysis is to explore trajectories of subjects (individuals,
neighbourhoods, et cetera) over time and to identify groups of subjects that experience
(see also Gabadinho et al. 2011)
. Sequences are comprised of
different states that show the order and duration that the individual subject occupied
in each state. Focussing on neighbourhood trajectories, a neighbourhood can, for
example, be in the 6th socio-economic neighbourhood category in 1971, then move up
to the 5th category in 1999, and the 4th category in 2000, to end up in the 3th category
in 2013. The neighbourhood categories in this example represent the different states
that a neighbourhood can move through. The sequence of this particular neighbourhood
would then look like this: 6th category -5th category -4th category -3rd category. This
is an example of the most straightforward state sequence format (STS), however, other
sequence representations are also possible
(for a detailed understanding of state
sequence representations, see Gabadinho et al. 2011)
. All sequences together can then
be visualised as a series of individual neighbourhood trajectories, which represent how
each neighbourhood moves through the different states over time. There are different
ways to visualise sequences depending on the objective of the researcher
Gabadinho et al. 2011)
Many researchers are however interested in going a step further and explain
variation in sequences. For that reason, sequence analysis is often combined with
cluster analysis where similar sequences are clustered together. However, cluster
analysis has several disadvantages. First of all, the clusters can be very arbitrary
because different algorithms generate different clusters. In addition, cluster membership
tends to be unstable and the optimal number of clusters is very difficult to assess
also Studer 2013)
. Cluster analysis reduces sets of sequences to a number of standard
trajectories which are a rather crude approximation that consider deviations from the
standard as noise
(Studer et al. 2011)
In a few recent papers Studer and colleagues (2010, 2011, 2012, 2013) indicate a
tree-structured discrepancy analysis as a valuable alternative to cluster analysis. The
advantage of this method over cluster analysis is that a tree-structured discrepancy
analysis does not create a number of groups that is supposedly representative for the
entire population, instead it shows the effect of different variables on the set of
sequences in a stepwise approach. Discrepancy analysis is similar to the analysis of
variance (ANOVA)-types of analyses and measures the variability between sequences
(Studer et al. 2011)
. The researcher can select a number of explanatory variables
which are hypothesised to be related to the different sequences. Based on these
predictor variables, the tree-structured discrepancy analysis will group similar
sequences together. This is done by using the pairwise dissimilarities between
sequences to compute the discrepancy within groups
(Studer et al. 2010, 2011)
practice, this means that two sequences are compared to determine to what extent
they are different from one another. This level of mismatch is then quantified by the
dissimilarity measure (Studer and Ritschard 2016). In this paper, we use Optimal
Matching distances to quantify dissimilarity. Optimal Matching computes the distance
between pairs of sequences using a chosen cost scheme. This cost scheme constitutes
of (1) insertion and deletion costs (indel) which capture whether the same state occurs
in two sequences, and (2) substitution costs that focus on the timing of states and
whether the same state occurs at the same time point in two sequences
and Fasang 2010)
. Here we have set the indel costs to one and we base the
substitution costs on the inverse transition frequencies between different states, which
is in line with previous studies
(e.g. Aassve et al. 2007; Widmer and Ritschard 2009;
Barban 2013; Kleinepier et al. 2015)
. This means that we are more focussed on
distinct trajectories (i.e. a change from the 1st category to the 6nd category is
considered to be more costly than a change from the 1st category to the 2nd category)
than on timing (i.e. we place less importance on differences in neighbourhood states
at different points in time). We have replicated our results using a different
dissimilarity matrix to ensure robustness. We have used Optimal Matching with indel costs
of 1 and substitution costs of 2, which is equivalent to the Longest Common
Subsequence distance (Studer and Ritschard 2016). All of our results remain the
There are different ways to measure dissimilarity and the choice of dissimilarity
algorithm has been subject to debate for many years
(see Abbott and Tsay 2000;
Aisenbrey and Fasang 2010; Gabadinho et al. 2011)
. Different dissimilarity
measures focus on different aspects of the trajectories; researchers interested in
change are advised to use one of the Optimal Matching algorithms; researchers
focussed on timing should employ one of the Hamming algorithms; while
researchers interested in duration are recommended to use algorithms such as the
Longest Common Subsequence, Chi2 or Euclidian distances
(for an excellent
overview, see Studer and Ritschard 2016)
. Optimal Matching remains the most
popular dissimilarity matrix used in the social sciences because of its flexibility
and can generally be used to understand the ‘common narrative’ between
(Elzinga and Studer 2015)
The tree-structured discrepancy analysis visualises the relationship between
predictor variables and the sequences trajectories. The tree starts with all
sequences in an initial group. The tree-structured discrepancy analysis then selects
the most important (significant) predictor and its most important values to split the
group into two distinctly different groups using the dissimilarity measure and a
pseudo R2 and a pseudo F test. Significance is assessed through permutation tests
(5000 permutations are sufficient to assess the results at the 1 % significance level,
see Studer et al. 2011)
. Looking at, for example, the share of social housing, the
model identifies the threshold value at which the sequences differ most, resulting
in two significantly different groups of sequences that show different trajectories
below and above the threshold value. In practice, this could mean that the model
illustrates the trajectories for a group of neighbourhoods with low shares of social
housing and a group of neighbourhoods with high shares of social housing. For
each of the newly created groups, the discrepancy analysis splits the groups into
two again, using the second most important predictor and its values (for that
group) for which the highest pseudo R2 is found. Using our example, for the
group of neighbourhoods with high shares of social housing, the model then
shows the effect of a different variable, again creating two groups that show
distinctly different trajectories. The process is repeated until a stopping criterion
is reached or when a non-significant F for the selected split is encountered
et al. 2010)
. The overall quality of the model can be assessed through the pseudo
F test and the pseudo R2 that provide information on the statistical significance of
the tree and the part of the total discrepancy explained, respectively
(Studer et al.
A tree-structured discrepancy analysis can be seen as the next step in sequence
analysis and contributes to the creation of meaningful groups of sequences
(Studer et al.
. In this paper, we adopt an exploratory approach and use the tree-structured
discrepancy analysis to understand how variation in neighbourhood sequences can be
explained by the physical characteristics of neighbourhoods.
Data and Methods
Data and Measures
Research on neighbourhood change ideally requires individual-level georeferenced
data at short-time intervals over a longer period of time. Unfortunately, in many
countries, such longitudinal data are unavailable or inconsistent through time.
Researchers are therefore confronted with a trade-off between data quality and data
availability. This paper used longitudinal register data from the System of social
Statistical Datasets (SSD) from Statistics Netherlands. For 1999 to 2013, we have
data for the full Dutch population. Historic neighbourhood-level data from before the
1990s is extremely scare in the Netherlands due to the move from a census based
system to a register based system. The last Dutch census was conducted in 1971, and
the alternative country-wide individual-level registration system was installed by
1995. Data on neighbourhood income levels is however only available from 1999
onwards, hence our focus on 1999 to 2013. Combining the recent register data with
the last census from 1971 allowed us to analyse long-term neighbourhood change,
however, this meant that there was a 28-year time-gap in our dataset. Nevertheless,
the inclusion of 1971 data provides a unique viewpoint on long-term neighbourhood
change in the Dutch context.
Our definition of a neighbourhood is based on 500 by 500 m grids. The use of 500
by 500 m grids enabled the comparability of geographical units over time (as other
administrative definitions of neighbourhoods have changed drastically over the last
40 years) and allowed for a detailed analysis on a relatively low level of aggregation.
We focussed on the 31 largest cities of the Netherlands, resulting in a total of 8917
500 by 500 m grids
(including newly constructed neighbourhoods in the period
. The choice for the 31 largest cities in the Netherlands is related to the
scale of urban restructuring programmes over the past few decades and can therefore be
understood as a political construct. To ensure the stability of spatial boundaries over
time, we used the city boundaries of 2013. Because of the high density in these cities,
the average grid consists of 900 residents. For privacy reasons, grids with less than 10
residents have been excluded from the analyses.
We analysed changes in the share of low-income households in neighbourhoods
over time. Low-income households are defined as the bottom 20 %, which in 1971
included households with an income below 8000 guilders and in 2013 households with
an income below 17167 euros. Neighbourhoods have been categorised according to
their share of low-income households into deciles. Because there were few
neighbourhoods with more than 50 % low-income households, the last four deciles
have been grouped together.
To examine the role of the physical characteristics of neighbourhoods on their
trajectories over time, we have included several control variables. We first included a
dummy variable for the four largest cities in the Netherlands (Amsterdam, Rotterdam,
Utrecht and the Hague) because we expected more dynamics in big cities. To analyse
the path-dependency of neighbourhood quality, we included the share of social housing
and the share of post-war housing in 1971. We included the change in the share of
owner-occupied dwellings between 1971 and 2013 as an indicator for high-quality
construction. To assess the effect of changes to the physical structure, we analysed the
effect of the demolition, defined as the cumulative number of demolished post-war
rental dwellings over the period 1999 to 2013. We have no information on demolition
in 1971, however, as many post-war dwellings were still relatively new in 1971 and as
large-scale urban restructuring of post-war areas started in the 1990s, it is highly
unlikely that the demolition of post-war rental dwellings in 1971 was more than
incidental. A summary of all the variables used in the analyses is presented in Table 1.
To provide a detailed illustration of long-term neighbourhood change, we first zoomed
in on Amsterdam and Rotterdam. We visualised how the spatial distribution of
lowincome neighbourhoods has changed in Amsterdam and Rotterdam between 1971 and
2013. Amsterdam and Rotterdam are the two largest cities in the Netherlands, but have
experienced different neighbourhood trajectories over time. The economy of
Amsterdam is characterised by a strong service sector, while Rotterdam’s economy
remains tied to the harbour
(Burgers and Musterd 2002; Hochstenbach and Van Gent
. The average income level of the population is therefore higher in Amsterdam
(Hochstenbach and Van Gent 2015). Amsterdam has experienced strong gentrification
in the past few decades, which is often ascribed to the historic architecture of inner-city
neighbourhoods. Although some neighbourhoods in Rotterdam have also experienced
processes of gentrification, the dominant process in Rotterdam has been neighbourhood
downgrading since the 1970s
(Hochstenbach and Van Gent 2015)
To come to a better understanding of patterns of neighbourhood change, we
next focussed on neighbourhood trajectories of the 31 largest cities using a
combination of sequence analysis and a tree-structured discrepancy analysis. We
have first conducted a multifactor discrepancy analysis to assess the raw effects of
the variables on the sequences trajectories (see Table 3). The multifactor approach
offers insight in which covariates are significantly associated with the
neighbourhood trajectories and provides information on the significance of the
variables (using permutation tests) and the strength of the model using a pseudo F
and a pseudo R2
(see also Studer et al. 2011)
We then combined sequence analysis and a tree-structured discrepancy analysis
to analyse variation in neighbourhood trajectories. Sequence analysis is used for
the visualisation of neighbourhood trajectories showing the neighbourhood status
at each point in time using a colour scheme. Each neighbourhood category is
assigned a different colour where the grey scheme represents the low to high
neighbourhood status scale. There are different ways to visualise sequences
overview, see Gabadinho et al. 2011)
. In this paper, we used a sequence
distribution plot showing the overall neighbourhood distribution instead of individual
sequences. Importantly, this means that we are focussed on the general pattern of
neighbourhood trajectories rather than individual neighbourhoods. The
treestructured discrepancy analysis then visualised how our control variables affect
the trajectories in a tree-structured sequence plot
(Studer et al. 2011)
. We have
used the default stopping criteria of a p-value of 1 % for the F test (R = 5000),
fixing the minimal group size at N = 446 (5 % of the total N = 8917), and allowing
for the creation of five levels
(see also Studer et al. 2011)
. The analyses were
conducted in R version 3.2.1 BWorld-Famous Astronaut^
(R Core Team 2015)
using the TraMineR package (Gabadinho et al. 2011).
We first zoom in on Amsterdam and Rotterdam in Figs. 1 and 2. Table 2 tabulates the
neighbourhood categories in 1971 and 2013 for each city. Both illustrate a process of
increasing poverty concentration these cities. Table 2 shows that the share of
lowincome neighbourhoods in the last two categories has remained stable over 40 years:
the share of neighbourhoods with more than 40 % low-income households has not
increased. However, the spatial distribution of these neighbourhoods is characterised by
increasing spatial concentration as shown in Figs. 1 and 2. While both cities were
characterised by a large share of high-income neighbourhoods (category 1) in 1971,
they show more variation in the neighbourhood income distribution by 2013.
The maps show the distribution of low-income households in 1971 and 2013.
Figure 1 illustrates how inner-city neighbourhoods in Amsterdam have maintained
their high status over time, while the post-war neighbourhoods at the outskirts of the
city have experienced downgrading. Low-income neighbourhoods in Amsterdam are
now increasingly concentrated outside the city centre
(cf. Van Gent 2013)
. Figure 2
shows significant downgrading of large parts of Rotterdam over the last 40 years.
Fig. 1 Percentage low-income households in Amsterdam 1971 and 2013. Source: System of social Statistical
Datasets, Statistics Netherlands, 2015
Contrary to Amsterdam, Rotterdam’s inner city neighbourhoods have experienced
downgrading, while the high-status neighbourhoods in the northern part of the city
have maintained their status
(cf. Hochstenbach and Van Gent 2015)
We are interested in the neighbourhood trajectories underlying the patterns described
above and how these trajectories are related to a set of predictors. We are particularly
interested in how the physical characteristics of neighbourhoods are associated with
neighbourhood trajectories over time. As mentioned earlier, we have first conducted a
multi-factor discrepancy analysis to assess the raw effect of our variables on the
neighbourhood sequences. The results are shown in Table 3. The global statistics show
that the model is significant (F = 43.584, R = 5000) with an R2 of 14.4 %, meaning that
our set of variables provides overall significant information about the diversity of
neighbourhood trajectories. All variables are significant at the 1 % level (assessed
through 5000 permutations), with the exception of our dummy variable for the four
largest cities. The share of social housing in 1971 and the number of demolished
dwellings appear to be the most important predictors of neighbourhood trajectories
between 1971 and 2013.
Source: System of social Statistical Datasets, Statistics Netherlands, 2015
Figure 3 shows the tree-structured discrepancy analysis for the neighbourhood
trajectories in the 31 largest Dutch cities. The initial node shows the distribution of
neighbourhood states by year (box 1). Overall, the 31 largest cities are characterised by
a more or less even distribution of neighbourhoods. Over time, the share of
highincome neighbourhoods is decreasing while the share of low-income neighbourhoods
In the tree, the most significant variables and their most significant values are used in
respective order. For each group, we see how the selected variable (and the threshold
Significance is assessed through permutations (R = 5000)
Source: System of social Statistical Datasets, Statistics Netherlands, 2015
values of the variable) affects the neighbourhood trajectories, showing the group size,
the within-discrepancy, and the R2 for that split. Our overall model has an R2 of 19.5 %,
which is higher than the R2 from the multifactor discrepancy analysis, meaning that the
tree has better explanatory power, which can be explained by the fact that the tree
automatically accounts for interaction effects
(Studer et al. 2011)
. Our neighbourhood
characteristics explain 19.5 % of the variability in neighbourhood trajectories. We have
forced the model to use the dummy variable for the four largest cities – Amsterdam,
Rotterdam, the Hague and Utrecht – for its first split because we were interested to see
how the trajectories of neighbourhoods in these large cities differ from the trajectories
in the other cities. We find that neighbourhoods in the four largest cities (box 3) are
characterised by more neighbourhood dynamics than the other cities (box 2). Since
1971, the four largest cities have experienced a substantial decrease in their share of
high-income neighbourhoods and an increase in low-income neighbourhoods. The
model shows that the share of social housing in 1971 is the most important indicator
in explaining variance in neighbourhood trajectories in the four largest cities (box 6 and
7). The neighbourhoods with hardly any social housing in 1971 are characterised by
high-income trajectories, while the neighbourhoods with higher shares of social
housing show more downward trajectories. For this latter group, the number of demolished
dwellings between 1999 and 2013 seems to matter (box 10 and 11). Demolition took
place in neighbourhoods that were experiencing downgrading (box 11). These
processes of decline were the reason for the Dutch government to target these neighbourhoods
for urban renewal through the demolition of low-quality social rented dwellings
The left side of the tree shows that changes in the share of owner-occupied dwellings
between 1971 and 2013 is the most important predictor for neighbourhood trajectories
in the other 27 cities (box 4 and 5). Box 4 consists almost solely of newly constructed
neighbourhoods with high shares of owner-occupied dwellings since 1999. These
neighbourhoods are characterised by more neighbourhood stability. Existing
neighbourhoods that have seen increases in their share of owner-occupied dwellings
are characterised by more downward trajectories (box 5). Here the share of
owneroccupied dwellings interacts with the share of social housing in 1971. For those
neighbourhoods that have seen an increase in the share of owner-occupied dwellings
(box 5), the share of social housing seems to matter. Higher shares of social housing in
1971 are associated with more downward trajectories (box 9). For this latter group,
higher increases in the share of owner-occupied dwellings are associated with more
high-income trajectories (box 15). This interaction between the share of social housing
in 1971 and changes in the share of owner-occupied dwellings captures the Dutch
policy of social mixing by changing the tenure composition in neighbourhoods.
Discussion and Conclusion
Especially in the four largest Dutch cities, our results show an increase in the share of
low-income neighbourhoods since 1971. Amsterdam and Rotterdam, in particular,
have experienced increasing poverty concentrations in specific neighbourhoods. Most
of these neighbourhoods were built after the Second World War and were characterised
by concentrations of social housing. The Netherlands, historically, had a large social
housing sector with relatively high-quality housing. Contrary to many other countries,
social rented dwellings were inhabited by a mix of socioeconomic groups, not just
(Van Kempen and Priemus 2002)
. In 1971, many post-war
neighbourhoods were still relatively new and were considered to be high
(Van Beckhoven et al. 2009)
. By 2013, these post-war
neighbourhoods have experienced significant downgrading and are characterised by
concentrations of poverty as is shown in Figs. 1 and 2. The downgrading of these
neighbourhoods can be explained by their phsyical characteristics, in particular,
the current low-quality housing and its multiple technical and physical problems.
This, combined with relative downgrading due to new housing construction elsewhere,
fuelled processes of neighbourhood decline
(Prak and Priemus 1986; Kleinhans 2004)
At the same time, this process led to the residualisation of the social housing stock in
the Netherlands, where the social housing sector increasingly became the domain of
(Van Kempen and Priemus 2002)
In the 1990s, the Dutch government launched large-scale urban renewal
programmes to target the most disadvantaged neighbourhoods. In practice, this meant
that many low-quality post-war social rented dwellings were demolished to make room
for more expensive privately rented or owner-occupied dwellings
Fig. 3 captures this process very well: we see that demolition took place in
downgrading neighbourhoods with relatively high shares of post-war rental dwellings
in the four largest cities. At the same time, we see that the changes in the share of
owner-occupied dwellings interacts with the share of social housing in 1971 in the
other 27 cities. If we interpret a rising share of owner-occupied dwellings in these
neighbourhoods as an indicator of the Dutch policy of mixing tenure, it then seems to
be most effective in neighbourhoods that have experienced substantial increases in the
share of owner-occupied dwellings, thereby contributing to more high-income
(see also Bolt et al. 2009)
. The question however remains if such changes to the
housing stock will lead to significant neighbourhood upgrading and to what extent
these effects will be temporary or long-lasting
(Van Ham and Manley 2012; Tunstall
2015; Zwiers et al. 2016)
Our analyses seem to indicate a high degree of path-dependency as the initial quality
of dwellings and neighbourhoods was found to be associated with neighbourhood
trajectories over time. While the four largest cities generally show a change towards
a more equal neighbourhood distribution, there is some indication of increasing poverty
concentration. Especially neighbourhoods with high shares of social housing in 1971
have experienced strong processes of neighbourhood decline. Zooming in on
Amsterdam and Rotterdam in Table 2 and Figs. 1 and 2, we see that both cities were
characterised by high shares of high-income neighbourhoods in 1971, but show more
variation in neighbourhood income groups by 2013, albeit with more poverty
concentration in many post-war neighbourhoods.
The main contribution of this paper is the introduction of a new method for
exploring neighbourhood trajectories. Our empirical exercise confirms the need for
an approach that incorporates both long-term neighbourhood changes and a more
detailed analysis of neighbourhood trajectories, because neighbourhoods are extremely
dynamic but the effects of downgrading and upgrading on neighbourhoods are only
visible after longer periods of time. A focus on neighbourhood trajectories lends itself
for the identification of different patterns of change over time. The combination of
sequence analysis and a tree-structured discrepancy analysis contributes to an
understanding of how changes in a particular group of neighbourhoods are related to the
trajectories of other neighbourhoods. As such, these methods provide an integrated
approach towards neighbourhood change, by focussing on trajectories and by
identifying factors that contribute to changing trajectories over time. The analyses show how
specific levels of change function as thresholds for a different direction of
neighbourhood trajectories. It is however unclear to what extent these thresholds can
be used as more than cut-off points. Future research should aim to explore the meaning
of these thresholds for the identification of risk factors for neighbourhood change and
its implications for spatial policy.
A tree-structured discrepancy analysis can be seen as the next step in sequence
analysis, providing a new way of researching neighbourhood dynamics. The
combination between sequence analysis and a tree-structured discrepancy analysis has proven to
be a powerful tool to visualise and understand complex, contextualised patterns of
change over time. These methods could contribute to an understanding of ‘when’, or
‘under what circumstances’, neighbourhood trajectories diverge in a particular
direction, instead of ‘if’. Such research is necessary, because the time-period, frequency
and composition of mechanisms that influence neighbourhood trajectories may be
nonlinear, can be temporary or long-lasting, may vary over time, and might be conditional
on other factors
(Galster 2012; Van Ham and Manley 2012)
Acknowledgments The research leading to these results has received funding from the European Research
Council under the European Union’s Seventh Framework Program (FP/2007-2013) / ERC Grant Agreement
n. 615159 (ERC Consolidator Grant DEPRIVEDHOODS, Socio-spatial inequality, deprived neighbourhoods,
and neighbourhood effects) and from the Marie Curie program under the European Union’s Seventh
Framework Program (FP/2007-2013) / Career Integration Grant n. PCIG10-GA-2011-303728 (CIG Grant
NBHCHOICE, Neighbourhood choice, neighbourhood sorting, and neighbourhood effects).
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
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