Translocations: Migration and Social Change
An Inter-Disciplinary Open Access E-Journal
ISSN Number: 2009-0420
_____________________________________________________________________
Mapping Migrants in Ireland: The Limits of Cartography
Mary Gilmartin1 and Gerald Mills2
1 Department
of Geography, National University of Ireland Maynooth (Email: mary.gilmartin@nuim.ie)
School of Geography, Planning and Environmental Policy, University College Dublin (Email:
Gerald.mills@ucd.ie)
2
______________________________________________________________________________________
Abstract
This paper discusses the practices and politics of mapping migrants in contemporary
Ireland. Using a case study of Polish nationals in Ireland, it illustrates the various ways in
which the same data – in this instance, SAPS data from the 2006 Census – may be used
to create very different maps. In doing so, it highlights the limitations of SAPS data, as
well as the processes of generalisation and selection that underpin any map. The paper
argues that map users need to be aware of these processes, and need to approach all maps
from a critical perspective. Similarly, map-makers need to be aware of the politics of
map-making, particularly in relation to sensitive topics such as migration.
Key Words: cartography, migration, Ireland
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Introduction
In April 2006, the Census collected detailed information, at the level of the household,
about the resident population of Ireland. Each household should have completed a census
form, which asked questions about household composition, and about social, cultural and
economic characteristics. The Central Statistics Office (CSO), since July 2006, has been
publishing reports on the Census, ranging from preliminary overviews to detailed
accounts. In June 2007, the CSO published Small Area Population Statistics (SAPS). The
SAPS provide aggregate information at the level of Electoral Division (ED), of which
there are over 3,400 in the country. EDs vary significantly in terms of areal extent and
population size. Nevertheless, they currently represent the smallest spatial scale at which
census information is made available, and thus SAPS provide the most spatially detailed
information about population characteristics and change; information that describes the
population on the night of the Census, and provides a basis for planning and policy
decision-making.
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Gilmartin and Mills: Mapping Migrants in Ireland
We were particularly interested in the insights that SAPS could provide in relation to
migration. Census 2006 had gathered information about migration and ethnicity,
including place of birth, nationality, ethnic or cultural background, residence a year
earlier, and religion. In response to the release of the SAPS, we produced maps of
settlement patterns of Ireland’s two largest migrant national groups, UK and Polish
nationals, using readily available mapping software. The maps generated were printed by
a national newspaper (O’Brien 2007), and following their publication a range of
journalists, commercial organisations and researchers got in touch with queries, questions
and observations (these maps are now available for download at
http://www.ucd.ie/mcri/). The ease with which these maps were produced is the result of
both the availability of software packages that make mapping quicker and easier than
hitherto possible and of detailed census data made accessible by the Central Statistics
Office. Taken together, census data can be easily mapped by anyone with a modicum of
computer skill. Others have been quick to avail of this opportunity, and other maps of
immigrants in Ireland have been published in national newspapers (Kelly 2007).
However, maps are not naive representations of reality and it is surprising that relatively
little attention is given to their meaning and message within current public discourse in
Ireland. This is in sharp contrast to the critical attention paid to the mapping process
during the colonial period (for example, see Andrews 1975; Friel et al 1983; Smyth
2006), where mapping is seen as a means by which the powerful surveyed, recorded,
named and claimed territory in Ireland. While the link between cartography and
colonialism may seem obvious, our current use of mapping to assess migration to Ireland
should also be assessed critically.
The politics of mapping migrants in contemporary Ireland takes on an added significance
as Irish society struggles to make sense of rapid change. If we are to understand and plan
for migration, it is important to have an understanding of where migrants live, and the
implications of these settlement patterns for the provision of services, the processes of
integration, and broader questions about cohesion and conflict. However, where migrants
live is just one aspect of their daily lives. Equally important is knowledge about where
migrants work or study, their social interactions, and their ability to participate in the
communities that they are part of. The Census provides little in the way of insight into
the broader patterns of everyday life for residents of Ireland. Since it provides definite
information on where people live, this often leads to assumptions about everyday life on
the basis of place of residence. In other words, place of residence is used as a surrogate in
the absence of other, relevant information about migrants in Ireland.
In this paper, we posit that the ease of access to data and software packages has allowed
maps to be created and read without due consideration of the practices and the politics of
cartography (taken here as referring to both map-making and map-reading). Using the
example of Polish migrants to Ireland, we discuss the practices and politics of
cartography. We illustrate the processes of selection and generalisation that underpin
cartography, and highlight the uses and limitations of the maps we produced. In doing so,
we argue that the process of mapping migrants in contemporary Ireland requires critical
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scrutiny, and caution against the use of maps without due regard to their construction and
interpretation.
The Census and Cartography
When the CSO released the SAPS, we paid particular attention to questions of migration
and ethnicity. In particular, we were curious to see if there were any identifiable clusters
of settlement of migrant groups in Ireland. In order to do so, it was important to pay
attention to two broad questions about data gathering. The first relates to demographic
data, particularly in relation to migration, while the second concerns geographic data and
its representation.
Demographic Data
The 2006 Census obtained information about migration to Ireland through: place of birth,
nationality, and the place of residence a year earlier (Figure 1).
Figure 1: Relevant questions on migration, nationality and ethnicity in the 2006 Census forms (Available
online at http://www.cso.ie)
For the purposes of identifying migrant groups and their settlement patterns, we extracted
information on the usually resident population classified by nationality. This represented
a better general guide to migrant groups than place of birth, for two reasons. First, place
of birth does not always imply nationality of the country of birth. This is illustrated by the
fact that 85.3% of the population was born in Ireland, yet 88.9% recorded Irish
nationality. Second, Census respondents have more scope to define nationality on their
own terms, in contrast to place of birth, which is a matter of fact (Question 6, see Figure
1).
In relation to national groups, the SAPS provided details under seven different categories.
These were Irish, UK, Polish, Lithuanian, rest of EU-25, rest of world, and not stated.
This is an aggregation of the information provided earlier by the CSO in its report on
Principal Demographic Results. That report identified a range of categories of Irishness,
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including Irish-American, Irish-English 3 , and Irish-European; all EU and some other
European nationalities; some Asian and African nationalities; US, Canadian and
Brazilian nationalities; as well as Australian and New Zealand. In addition to numbers for
those who did not state their nationality, it also included a count of those with no
nationality, multi nationality, or other nationalities. This national complexity is missing
from the SAPS, which deliberately obscures small numbers that may be readily
identifiable from their ED address.
The use of nationality to identify migrant groups should not detract from broader
concerns about census categorisations. In the run-up to the 2006 Census in Ireland,
particular concerns were raised about a question on ‘ethnic and cultural identity’, which
conflated categories of culture, ethnicity and race (O’Toole 2006; King-O’Riain 2007).
Though the question on nationality did not raise similar concerns, probably because it did
not include an exclusive number of possible answers, nationality is also a contested term.
National censuses can serve to reify socially-constructed categories like nationality and
race, particularly in the face of new patterns of immigration.
Geographic data and Cartography
SAPS are provided at the level of Electoral Divisions (EDs), of which there are over
3,400. There is no ‘natural’ or obvious way to divide space for the purposes of gathering
population-based data, particularly over the wide range of characteristics that is examined
during the census. Thus, for example, a division of space that is appropriate for gathering
demographic information may not be suitable for collecting information on employment
or mode of travel. EDs are, in fact, ‘modifiable areal units’ and represent just one
possible division of the country into non-overlapping enumeration districts4. A different
division would generate a different set of statistics that may affect our view of population
characteristics and how they vary spatially. In fact, it is precisely this knowledge that is
employed in the process of ‘gerrymandering’, where boundaries are redrawn to
disenfranchise a specific group of people. On the merit side however, the current ED
geography is durable and much of our census information for small areas can, with some
adjustments, be monitored over time. Finally, it is worth bearing in mind that while
individual data is collected at the household level, it is the counts of individuals within an
ED that fall within a given census category (e.g. mode of travel) that are provided – the
household variation within an ED is obliterated. In summary, the SAPS data consist of
raw counts of persons for EDs that vary in size and in total population.
For geographers, cartography is a fundamental tool (Robinson et al., 1995). The product
of cartography is the map, which has been used in two distinct ways. Firstly, maps are
used as a means of storing geographic information by encoding features using symbols.
The reader is usually provided with a legend to decode the stored information. For many
of us, our ability to ‘read’ maps is now so engrained in our learning systems that we can
automatically decode maps. For example, we assume that darker shades (more ink)
3
Not Irish-British
There is an extensive discussion of modifiable areal units and the ‘modifiable areal unit problem’
(MAUP) within cartography and, more recently, within GIS (see, for example, Openshaw 1984). In an Irish
context, the MAUP is discussed in more detail in Cook et al (2000).
4
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indicate a greater value of something, that blue means water and so on. Secondly, maps
are used in a different way to place information according to a geographical co-ordinate
system so that spatial relationships can be explored. In this way, geographers seek
potential explanations by exploring patterns that may emerge from the mapping process.
In much the same way that the statistician uses graphical techniques to explore
relationships between variables, geographers employ maps to visualise geographical data.
Fundamentally, we expect that geographic patterns are the result of geographic processes.
The map-making process has been greatly simplified since the development of computer
mapping software, often in the form of Geographic Information Systems (see Heywood
et al 2006; Longley et al 2005; Pickles 1995; Slocum 2005; Wilson and Fotheringham
2008). The researcher now has the ability to generate numerous maps from a wealth of
census data. Increasingly, this means that map-making has become a routine part of the
arsenal of data analysis tools, often for exploratory rather than expository purposes. In the
absence of such technology map-making is a very laborious and skilled task embarked
upon only after careful consideration for the value of the exercise. As such, the
production of a physical map was, before software advances, often the end-point of the
research rather than part of the research process itself. Nevertheless, once analysis is
complete, the researcher will generate a ‘final’ map that is designed to communicate with
a map-reader.
A case-study of Polish Nationals
How, then, can we best use spatial data and demographic data to represent the settlement
patterns of migrants in Ireland? We address this question through a focus on Polish
nationals, though the issues we raise have broader relevance.
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Figure 2: The cumulative concentrations of Poles (black) and Irish (red) in Electoral Divisions.
Without using maps, we can glean some basic information on Polish nationals in Ireland
and their settlement patterns from Census 2006. For example, we already know that the
Census counted 62,674 Polish nationals in Ireland, 63.6% of whom are male. We also
know that Polish nationals are more concentrated in urban areas (i.e. areas with
populations greater than 1,500) than Irish or UK nationals. We can also calculate that the
distribution of Polish nationals is highly concentrated (Figure 2) when compared with the
distribution of Irish nationals. According to the census, there are 1917 EDs (64% of the
land area) where no Polish nationals reside. In contrast, more than 50% of Polish
nationals reside in just 133 EDs (or 3.5% of the land area).
Making maps
This textual description of settlement patterns is a good starting point, but it lacks the
visual power and suggestiveness of settlement maps. For this reason, we decided to
construct maps of settlement patterns, using a variety of approaches. There is a great
number of ways to map this information, but most software packages offer a limited
choice of map type, one of the most fundamental selection decisions. We thus have
chosen to map the settlement distribution of Polish nationals in Ireland using dot-density
and choropleth techniques (Figures 3 & 4), among the more commonly available map
types. Through these maps, we illustrate strengths and pitfalls of different kinds of
mapping and different kinds of assumptions. The reader will note that county boundaries
and selected towns and cities, rather than the numerous ED boundaries, provide the
geographical reference system.
Examine the dot-density map (Figure 3) which represents people using the dot symbol.
The cartographer decides on the dot value (the number of persons represented by each
dot) and the dot size. Together these decisions determine how crowded or empty the ED
appears. A rule-of- thumb is that the dots should begin to coalesce in the areas of densest
settlement. Ideally, the dots are individually placed to capture the distribution of the
population precisely. However, in the absence of detailed locational information, the
software can only randomly distribute the dots within enumeration units – hence the
shape of the ED is often apparent from the shape of the cloud of dots on the map. The
great advantage of this map is its graphic simplicity and that the eye of the reader can
assess density easily. However, while this map captures the nature of the distribution, it
merely suggests that most dots occur near towns and cities, where most people live
anyway. Thus, it does not tell us where Polish nationals live in comparison to the resident
population of Ireland. To examine this characteristic, it is necessary to standardise the
data by calculating the ratio of Polish nationals to all residents in each ED. Moreover, it
makes sense to compare this ratio to that between Polish and all residents for the entire
country.
This new variable is a location quotient (LQ),
P / I 
LQi   i i 
 P/I 
where P represents Polish nationals, I represents residents of Ireland, and the subscript i
refers to an individual ED (the overbars refer to the total populations). The LQ value
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indicates how each ED stands in relation to the entire country with regard to the
proportion of Poles living in the area. For example, a value of 1 indicates that the
proportion of Poles in a given ED is exactly the same as the national average. If all the
Polish nationals were distributed in exactly the same manner as total population, the
resulting map would show no variation. So, LQ has the advantage of distinguishing the
concentration of Polish nationals. The disadvantage of this scoring system is that small
numbers can have a dramatic effect on ratios and their interpretation – we will illustrate
this later. As the new variable is based on a relative measure, a dot-density map that
requires enumerated data is no longer appropriate. Although other types of maps can be
made, the most commonly selected map is an area-shaded or choropleth map (Figure 4).
Figure
3:
A
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dot-density
representation
of
the
27
distribution
of
Polish
nationals
in
2006.
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Figure 4: A choropleth representation of the distribution of Polish nationals. In this figure the ratio of Poles
to Irish in a given Electoral Division is compared with the ratio of Poles to Irish overall.
By comparison to the dot-density map, there are a great number of map-making decisions
encoded in the final map. Two decisions in particular require elucidation: those of
classification and symbolisation. With regard to the former, we decided to separate the
LQi scores into three categories (less than 1, between 1 and 2 and, greater than 2). The
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text on the map converts this classification into meaningful statements. This decision has
the effect of highlighting those places where Poles are concentrated. This is further
enhanced by the decision on the area symbol which employs three, progressively darker
shades of blue to identify into which category an ED belongs.
These decisions on the number of categories and their associated shading are critical. In
theory it is possible to create such a map without classifying the data first as the degree of
blue ink in each ED could be modified precisely according to its score. However, such
maps are rarely effective as they do not distil the data to allow for simple and effective
communication. To a considerable extent then, one must accept that in creating a map
(just as when one is creating a summary table) some information is lost for the sake of
clarity of communication.
Finally, it is now worth comparing the two maps we have created. Both maps show that
the distribution of Polish nationals is concentrated. The dot-density map shows numerical
clustering while the choropleth map shows relative clustering. By itself, the choropleth
map identifies areas of concentration that may provide clues as to cause – for example,
note the high LQ values around Killarney. However, for some clusters, notably those on
the western side of Mayo, the high LQ values occur with very small numbers. As migrant
populations can be transient in nature, these values could be a result of seasonal
employment of very few individuals into a sparsely populated community (as may be
inferred from Figure 3).
Reading maps
In describing the processes by which the same sets of data are transformed into very
different maps, we have shown generalisation and selection at work. In this way, we see
that the choices made by cartographers significantly affect the ways in which spatial
relationships are represented. The two maps present very different pictures of the
settlement of Polish nationals across Ireland. The dot-density map gives the impression of
a dense network of Polish nationals spread across the country, while the choropleth map
gives the impression of pockets of intense concentration. Though each representation has
strengths, neither is fully accurate or fully comprehensive. Each map comes with a
number of qualifications and provisos.
Figures 3 and 4 show residential settlement patterns, where people live at a particular
time and date. This data is not wholly accurate: census forms may not have been correctly
completed, and some people may have been omitted from the count. The data is also
static. As soon as it is collected, it is out of date, for reasons ranging from birth and death
to changes in employment, housing tenure and marital status. The maps we created take
this data with errors and locate it onto a geographical framework. Since the maps deal
with mobile and changeable human subjects, at the aggregate level of the ED, they just
capture the general pattern of residential settlement on 23 April 2006.
Both of the maps demonstrate what the statistics produced by the Central Statistics Office
suggest. The map in Figure 3 shows clusters of Polish nationals living in and around the
major cities – Dublin, Cork, Limerick, Galway – as well as in smaller urban settlements
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such as Killarney, Athlone and Monaghan. Figure 4 accentuates this pattern, creating the
sense of a variety of areas of residential concentration that are in some way disconnected
5
. In this way, the choropleth map shows particular concentrations which replicate the
general patterns of the dot-density map, but provide a more nuanced understanding of
settlement patterns in the larger urban areas. However, as we mentioned before, the
simplicity of the choropleth map also contains distortions, particularly in areas with low
population numbers. It would therefore be inadvisable to use the choropleth map to make
specific statements about residential concentration, in the absence of other supporting
data.
Both maps allow for the visualisation of data, and the readers of the maps can identify
patterns in a way that is not so easily possible from tables of statistics. However, the
maps are not a measure of patterns. Rather, the patterns that we see in maps are a product
of the choices made by both the cartographer and the reader, both of whom have specific
perspectives or agendas. In other words, neither making nor reading a map is politically
neutral, and map readers need to be able to understand the choices made by the
cartographer and the implications of those choices for reading and understanding the
map. For example, in producing our maps we were concerned that we would not add to a
growing and problematic discourse around immigrant ‘ghettos’. The dot size in the dotdensity map is deliberately small: a larger dot size would have suggested a more
ubiquitous presence of Polish nationals in the country. Similarly, the choropleth map uses
Location Quotient rather than the percentage of Polish nationals in an ED, and it contains
just three categories. This serves to level out differences between EDs, and does not
provide a basis for inflammatory comments about concentrations that may be of limited
significance. We also chose colours carefully, using more neutral colours to indicate
concentrations in the choropleth map. If we had used red, for example, we would have
been tapping into a history of the use of red in cartography to signify danger and threat.
This could have further added to an impression of ‘immigrant ghettos’ surrounding, and
taking over, ‘native’ settlements.
In making choices about reading maps, we also want to highlight the importance of good
cartographical and geographical knowledge. A careful reader will study the scale of the
map as well as its legend; will interrogate the source, reliability and accuracy of the data;
and will seek for omissions or obfuscations in the production of the map. In other words,
the map will raise more questions than it answers. Some of those questions will relate to
the technicalities of map production, such as the choice of dot-density and choropleth
maps. Others will relate to the manipulation of data: the use of nationality rather than
place of birth, or the calculation and use of the Location Quotient. Other questions relate
to the patterns in the maps, evident to people with good geographical knowledge, that
might suggest other lines of enquiry. For example, both maps – but particularly the
choropleth map – suggest linear settlement patterns that could well be associated with
road networks and associated building activity. Clusters of settlement around urban areas
such as Killarney and Tralee may well be associated with tourism activities, while
clusters in areas such as Monaghan may be associated with industrial agriculture. These
5
This is also true of a recent map of Polish settlement produced by the CSO (CSO 2008, 28)
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are geographically informed speculations on the patterns that emerge from the maps:
representing data in this way allows us to observe correlations and infer processes. This
is, however, a starting point for investigation rather than a conclusion. In observing these
relationships, we need to also be aware of ways in which our speculations may be
misleading and/or wrong.
The maps are also characterised by their omissions and exclusions. For example, both of
these maps focus on the residential settlement of Polish nationals. They do not indicate
the extent to which other national groups live in the same ED, so that similar LQ scores
could mask the extent to which an ED is multicultural or bicultural. On their own, they
provide no insight into age profile, types of housing tenure, marital status, employment
status, or family types or sizes. The maps would need to incorporate or be associated with
additional information in order to make informed decisions about service needs, and in
order to provide evidence for planning and policy. Similarly, the maps do not provide
insight into social interactions. As such, in this form they are of little use in measuring or
assessing levels of integration. What the maps show us, in different guises, is simply
where Polish nationals live. All other conclusions from these maps are, at best, informed
speculation.
Discussion
What we have pointed out is not new. Cartographers and historians of cartography have
long highlighted the ways in which maps, as forms of representation, simplify the world
(see, for example, Monmonier 1991; Keates 1996; MacEachren 1995; Dent 1990;
Dorling and Fairbairn 1997). At the most basic level, the translation of a threedimensional object into a two-dimensional image will inevitably involve distortion,
whether of shape, size or direction. However, cartographic distortions are not just related
to differing dimensions. Maps are also important weapons of the powerful – whether in
charting and claiming ownership of territory, suggesting or implying risk or threat, or
simplifying complex situations in a manner that suggests the map as truth (see Harley
1989). In short, maps are not innocent representations of a pre-given reality. They are
implicated in systems of power and control, and they help to reinforce as well as
challenge those systems.
In this paper, we have explored our process of map-making, making explicit what is often
implicit. We recognise that once we create maps and make those maps available to
readers, we have to a large extent lost control over the map and its interpretation. At this
point, this is the responsibility of the reader, who brings his/her own knowledge,
intentions and prejudices to the process of map-reading without regard to the intentions
of the map-maker. However, this does not absolve the map-reader from a level of
responsibility in reading the map, and from recognising the difficulties and challenges
that arise. The first difficulty occurs within individual Electoral Divisions (ED). For
example, when we map the Location Quotient for a particular ED, we are representing
aggregate data rather than individual or household data. Therefore, the map will not tell
us where individuals or households are located within an ED: if Polish nationals in a
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particular ED are equally distributed throughout, or if they are concentrated in a
particular area. The second difficulty arises when geographic proximity is used to infer
geographic processes in a way that is not really possible with tabular data or with other
graphical data. Maps imply dynamism and change. For example, when we see a group of
adjacent EDs that fall into the same category, we infer processes of ‘invasion’ and
‘succession’ – immigrants moving into an area, indigenous Irish moving out, as well as
the pathways along which this movement occurs.
Despite the obvious limitations of maps such as those we have produced, it has not
prevented the use of maps in Irish media outlets to make claims about immigrant ghettos,
segregation and white flight. The power of these claims lies in the widely-held belief that
maps represent reality, rather than the realisation that maps are a social product that rely
on processes of selection and generalisation. For example, a newspaper report in August
2007 claimed that over a quarter of the population of parts of Blanchardstown,
Clondalkin and Tallaght are immigrants, a claim supported by maps that highlighted such
areas in a bold red colour (Kelly 2007). Yet, on examination, it was clear that the maps
were based on the place of birth rather than the nationality of Census respondents, and
that many of its sensationalised claims about immigrant settlement patterns were
misleading. For example, one of the areas of high immigrant concentration highlighted in
the map and article includes the Balseskin reception centre. Asylum seekers, under direct
provision, are required to live in such facilities; yet the article and map did not highlight
this structural issue and instead inferred that immigrants made a deliberate choice to live
in the area. This map echoed earlier claims by the Minister for Integration, Conor
Lenihan, that immigrants were beginning to concentrate in some Dublin suburbs: Lenihan
said this could lead to ghettos (Flanagan 2007). The maps reinforced Lenihan’s message
in a visually arresting way, and the idea of ghettos and ‘white flight’ have since been
taken up by other newspapers (for example, the Sunday Tribune in November 2007). As
Mark Ellis has pointed out in the context of the United States, immigrant settlement
concentrations have often been taken
as an indicator of an unwillingness to assimilate to established norms of national
belonging. The corollary to this flawed logic is the tagging of places where
immigrants and the descendents concentrate as un-American, or less American,
than locations in which native-born whites retain demographic dominance (Ellis
2006, 56-7)
The timeline is clear: places identified as immigrant ‘ghettos’ then become problematic
sites because of their identification as disturbingly different. Maps can be effective tools
that enable this process.
When we produced the settlement maps in Summer 2007, we were surprised at how
quickly newspapers published them and requested specific maps of local areas. We were
also surprised at the level of response the maps generated, much of it relatively uncritical.
As one person emailed us, ‘things always look better in maps’. Yet, despite their obvious
visual appeal, all maps must come with a cautionary note. We quickly realised that these
maps had a power beyond tables to confirm existing views, suggest patterns of behaviour,
and, for some, identify nascent ghettos. In other words, when dealing with emotive topics
such as immigration, maps are amenable to interpretation in ways that the map-maker
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might never have considered. Maps speak to where people live and places they know in a
way that graphs and tables do not. However, maps also help to create those places, and
those who make and use make maps should always be alert to this reality.
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