Data Visualisation Narrative


This assignment compared two datasets of homeless figures from census 2011 and 2016, to analyse trends of rising homelessness. The object was to experiment with different visualisation tools in the hope of pulling out a picture of different trends in data.


Homeless by region

Dublin with the highest over 3,000 with low numbers for areas around rest of country while the 16-24 age group could be explained as first time homeless, leaving home or runaways although this section accounts for under 1,000, then there is a sharp rise to nearly 3,000 25-44 age group, then back down to under 1,000 for age group 45-64. The graph does not show where people are going. Are councils housing them as long term homeless? surprising that the 65+ numbers of homeless shrink dramatically, meaning either council has to accommodate them due to age or they are entering nursing homes or just dying on the streets.


Adults and dependants in emergency accommodation

Supported temporary homes same as children

State provides accommodation for families with children, while the high number of adults could comprise of single adults and couples with no children.

While private temporary has the same number as supported temporary homes although it’s not clear what a private temporary home is.


Homeless by region and age 2011

By percentage Dublin has over 60% followed by the S.E. and S.W. with under 10% each and similar for 2017 with Dublin still the highest for homeless as opposed to lower for rest of country and age 25-44 still being the most significant part of the population to be homeless.


Education attainments and occupations of the homeless

It is not clear where statistics are coming from for example 7% no education implies that these people never went to any form of school and similarly with primary school, considering that the minimum age for leaving school in Ireland under the Education Welfare Act 2000 is 16. Similarly with primary school although secondary school is misleading as it groups all together, should be two groups junior and leaving cert i.e. does not compensate for students leaving after junior cert or during leaving cert, it also seems a high and surprising figure that 20% from third level education are homeless.


Nationality of homeless by sex

For Irish 3064 men and 1,007 women while for other nationalities were all less than 100, highlights a severe problem with homelessness but also raises a question about access to homeless services or the state operating an out-dated system which can not cope with or even attempt to alleviate homelessness, in contrast it appears the armature of the state is causing homelessness through banks foreclosing on mortgages, landlords pushing up private rents and pursuing evictions for people falling behind on rent. Other factors such as separation and divorce also contribute to people becoming homeless, low numbers of E.U. homeless could be explained by sharing rents, staying with friends on arrival in country until they secure jobs and own accommodation


General health by sex

Majority good and very good, should really be the opposite, this gives a misleading impression that living on the streets is healthy, when really people on streets are more susceptible to any illness becoming serious and life threatening, due to continued exposure to elements and constant threat of attack, also people on streets have problems accessing medical care.



Psychological or emotional issues is a vague category and one that further stigmatises people i.e. inciting that people are homeless because of a psychological condition.

What would reveal more would be to question and graph how people developed psychological problems for example:

Long term unemployment and no proper programs to combat this and create proper paying jobs.

Disability slow medical system makes it nearly impossible to access any services.

Breakup of marriage and court orders, which plunge families into poverty

Poverty: cost of living increasing while wages and benefits are not.

Disability with pain, breathing, chronic illness being the second highest group, this would reflect a health system which does not care about peoples health and does not provide services to people who need them, and has obviously failed when people with chronic illness’s are ending up homeless other factors include taking away medical cards from elderly, while at same time medial services move at great speed to fast track elderly people into private nursing homes and selling their house even if their family are living there.

Also not included on graphs are people who gave up work to become a carer for a relative.




While Palladio was one of the only tools that managed to create a visualisation form date, it seemed limited to only creating a scatter graph using numbers and therefore losing the context of where they were from or what they related to.



While Raw claimed to be the missing link between data and spreadsheets, there appeared to be a lot of its operation missing also since importing spread sheets into Raw that was as far as it went nothing else worked after as data would not go into either the x or y axis’s for any of the different visualisations.



I downloaded Tableau, when I went to open it got a message that it was not compatible.



Plotify would not let me import or drag and drop spread sheet into in, even when I copied full spread sheet it would come up with message that it only supported excel or CSV files.


Practically all the tools I used had some problems either would not download or were incompatible when downloaded, while others did not do much or in the case of Raw just stopped, while these tools promised a lot especially Raw with all the graphs it said it could visualise (but then did not work). What all the tools suffered from was a lack of clear and step by step information on setting them up and using them, due to the complexity of operation and lack of consistency with their functions it would have been of benefit to have some classes dedicated to setting up and using these tools rather than spending most of the time trying to find tools that worked during the assignment time.



While the graphs and datasets tell a story of rising homelessness predominantly in Dublin, what they don’t establish is the full breakdown of groups of homeless, but instead use vague generalised categories, based upon examples in data such as “homeless because of psychological or emotional issues”, is misleading and implies that people became homeless due to their mental health.

What the census questions don’t address and is then reflected in the dataset generated, is how and why people become homeless, i.e. losing jobs, repossessions, unable to pay rent/ rising rents and how this affects peoples mental health or what local authorities are doing to combat homelessness.



“Homeless Persons in Ireland: A Special Census Report”, 2011, Central Statistics Office


The Department of Housing, Planning, Community & Local Government Homelessness Report, January 2017:



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