Northern Ireland census data visualization

The AIRO team have produced an interactive data visualization of the initial results of the Northern Ireland census 2011.  The data visualization shows the results at district and province level for religion, economic status, national identity, country of birth, and age groups.

With respect to religion the headline statistics was that the percentage of the population who self-declared themselves Catholic has risen to 45.1%, just three percent less than self-declared Protestants (48.4%).  5.6% declared no religion and 0.9% other.  However, it one looks at the data at district level it is clear that very few districts have such a near 50/50 ratio of Catholics/Protestants.  Rather, most districts have a clear religious majority.

The economic status shows that 467,805 people are in employment, but also that 10,957 people who are unemployed have never worked and 29,324 are classed as long term unemployed.  Worryingly, of those unemployed over 40 percent in all districts are long term unemployed, illustrating the difficulties of re-entering the labour force after job loss in the present recession.

38.9% of the population of Northern Ireland declare themselves to be British, 25.3% Irish, 20.9% as Northern Irish, 6.1% as both British and Irish, and 5% as other.  Clearly the declaration of British maps somewhat imperfectly onto Protestant and the relationship between religion and nationality is by no means synonymous.

More than ten percent of the population were not born in Northern Ireland. 3.6% were born in England, 2.1% in the Republic of Ireland, 2% in EU Accession countries, 2% other, 0.85% Scotland, 0.54% elsewhere in Europe, 0.14% in Wales.

The population is quite youthful with 20.9% of people aged 0-15 and 12.6% aged 16-24.  27.5% are aged 25-44 and 24.4% aged 45-64.  14.6% of the population is at retirement age or older (65+) (the EU average is 16%).

Rob Kitchin and Eoghan McCarthy

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Travel to Work Catchments – 2011 POWSCAR results

Since the launch of Census 2011 the AIRO mapping team have developed a series of interactive mapping tools to visualise the results on a national and local authority/regional authority level (see here). This has been a joint project with the Central Statistics Office (CSO) and to date has been very successful with a high number of users viewing and interacting with this publically funded dataset. The aim of this collaboration was to improve access to the results of Census 2011 and thereby make a contribution towards improving evidence informed planning in Ireland. Over the last couple of weeks the team have been working on the recently released Place of Work, School or College Census of Anonymised Records (POWSCAR) dataset.

This dataset contains 2.78 million records where the location of the place of work, school or college was coded for each person on the basis of the reply that was given to Question 34 on  the census form: “What is the Full Name and Address of your place of work, school or college”.

Using this information the CSO matched the employer/school name and address against addresses on the An Post GeoDirectory. In the case of workers, where the coder could not find an exact match they coded to a near match if they could find a GeoDirectory address on the same street or in the same town as the address stated on the form. In the case of students, an exact match was only accepted for the school or college address. The coordinates retrieved from the GeoDirectory match were then linked back to the place of work, school or college Electoral District (ED) and Town and Small Area by superimposing digital boundaries. In some cases it was not possible to match the destination of the worker/student with GeoDirectory, this was a result of a very poor return of address information or the workers destination was classed as being ‘mobile’.  The final dataset is effectively an origin-destination matrix that links the place of residence of the worker/student, either at Electoral Division (ED) or Small Area (SA) level, to the work/school/college destination of the worker/student at the ED, SA and 250m grid level. The dataset contain a wealth of information about each work trip such as age, gender, industry of employment, education level, mode of transport, household occupancy status, one-off housing indicator, socio-economic group etc. Similar data is available about those attending schools/college although not as detailed and much of the data is compressed for disclosure reasons – we will do a further piece on this in a couple of weeks.

In 2011, places of work, school or college with an address in Northern Ireland were also coded in the same way by utilising the NI Pointer address database. NI County, Ward, Towns (2001) and 2001 Census output areas were derived by superimposing digital boundaries. This is a big step forward in understanding the cross-border travel to work catchments that exist in Ireland and the CSO should be commended for realising the benefits of going this extra step in the development of this dataset. Where the person indicated a work, school or college address abroad these records were coded to a specific code to indicate that the person was working abroad i.e. outside Ireland or Northern Ireland.

Due to the level of detail available within the dataset it is not as ‘open’ as the rest of the Census 2011. As there are some minor risks to data disclosure, use of the dataset is restricted and is only available to bone fide researchers who are approved by CSO and signed up as Officers of Statistics for the duration of the research they propose to undertake. A key point on all of this is that All material published from POWSCAR must be approved in advance by CSO.

The following table gives a summary of the POWSCAR address coding process:

	Students	Workers	Total	%
Persons in private households or establishmentsenumerated and resident in Ireland	1,013,292	1,770,644	2,783,936	100.0
Place of work, school or college address (Q34)was matched to a GeoDirectory address point	929,154	1,362,742	2,291,896	82.3
Place of work, school or college address (Q34)blank or uncodeable	78,956	147,251	226,207	8.1
No fixed place of work indicated at Q34	‐	148,177	148,177	5.3
Works from home indicated in Q34	‐	106,055	106,055	3.8
Place of work, school or college address (Q34)was matched to a NI Pointer database addresspoint	3,117	6,419	9,536	0.3
Place of work, school or college address (Q34)overseas	1,447	‐		0.1
Home school indicated at Q34	618	‐		0.0

AIRO POWCAR Mapping:

To get started on work with the POWSCAR dataset the AIRO team have developed travel to work catchments for all 22 Gateways and Hubs and made these accessible via an interactive mapping tool. Each map is based on the percentage of workers within each ED that work within the selected settlement boundary (boundaries based on CSO Settlements). Our analysis here is only based on the workfore where we have information on the destination of workers and therefore excludes those classed as Mobile workers (148,177 or 10% of workers) and workers with an uncodable or Blank destination (147,251 or 9.9%). Users can select a settlement to view the extent of the catchment and then click on an ED to get information on the number/percentage of workers employed within the selected settlement.

The map below details the extent of the travel to work catchment for the Dublin City settlement boundary, an area of about 317 sqkm and including all major employment locations in the local authorities of Dublin City, South County Dublin and DLR. The settlement boundary does not however include many of the large employment locations in south Fingal such as Dublin Airport, Swords, Malahide and Portmarnock. In total there are 457,046 people with a work destination in the Dublin Settlement boundary. Of these, 74% also reside within the Dublin City settlement and 26% reside outside the settlement boundary highlighting the high levels of inward commuting. Of the workers who reside within the Dublin City settlement boundary a total of 12.5% (48,801) commute out of the settlement to employmant destinations.

Red areas on the map highlight where >50% of the workers in an ED are employed within the settlement boundary. The orange band represents areas where over 30% of workers in an ED commute to the Dublin settlement boundary and extend to towns such as Drogheda, Ashbourne, Maynooth, Newbridge, Blessington and Wicklow town. The pale orange band is  based on 10% to 30% of workers and extends much further towards the Mid East, Midlands and parts of the Border region with towns such as Kells, Navan, Mullingar, Portarlington, Portlaoise, Gorey and Dundalk.

To access the tool and view the different catchments for all Gateways and Hubs please click here.  In the map viewer, click on an area to get specific information on that ED.

A link to the tool is also available on our census mapping home page on the AIRO site where you can also access other mapping tools developed over the last number of months. View Census Mapping home page

This is the first step at mapping the travel to work catchments for the main settlements in Ireland. We’re hoping to do some additional work on job’s desnity within settlements and also roll out catchments for other towns. Happy to take suggestions on what is useful for planners, policy makers and general public who would find this information of use.

Justin Gleeson & Eoghan McCarthy

 

Small Area census mapping: 130 maps for areas of 80-100 households

Having generated interactive mapping tools at Electoral Division level on the 31st of July, AIRO has expanded the CSO Census 2011 mapping toolkit to include data using the latest Small Areas boundary set.  In total 130 maps and 975 variables are available across 15 themes.

Small Area boundaries (created by the National Centre of Geocomputation at NUI Maynooth for Ordnance Survey Ireland) are considerably smaller than Electoral Divisions and offer a significantly better level of detail in terms of analysing data spatially. There are approximately 18,488 Small Area units in comparison to 3,409 Electoral Divisions. A Small Area boundary is usually comprised of approximately 80-100 households per unit and have an average size of 3.5km2. In comparison, an Electoral Division can has an average size of over 20km2. When analysing data spatially at Electoral Division level much of the detail is lost across the larger boundary area. With the Small Areas data, the user is now in a position to analyse data that in certain areas can be viewed at housing estate level.

Compare the two maps in the image below. They both contain the same data (% Population Unemployed 2011) but one is mapped at Electoral Division level and the other at Small Area. Note the increase level of detail and the differing distributions across the Dublin area. The increased level of detail allows users to identify trends and patterns in local areas that previously would have been overlooked.

 

How to explore Census 2011 data at Small Areas Level:

On the AIRO site go to the Census mapping module section and select the “Local Authority Module”. Choose which local authority you wish to analyse and within the map window use the “Data” button to select what Census data you wish to view and the “Change Geography” button to select Electoral Division or Small Area level geography.

Aoife Dowling and Eoghan McCarthy

 

 

Launch of 2011 Small Area Population Statistics (SAPS) & new AIRO Mapping Tools

The Central Statistics Office (CSO) has today released the small area population statistics (SAPS) from the 2011 census. For the first time users will now have access to the full set of census variables at the Electoral Division (ED) and new Small Area (SA) level across Ireland. Over the last couple of weeks the All-Island Research Observatory (AIRO) has been working closely with the CSO to provide the public with a new set of mapping tools that will allow users take full advantage of the incredible amount of census data now available. This is a major step forward for evidence informed planning in Ireland and users (general public, public sector and private sector) now have access to a free and fully interactive set of on-line tools to get a better understanding of areas and regions across the country. Through AIRO we have developed a National Census Mapping Viewer and a set of individual census mapping tools for every Local and Regional Authority in the country. To get access to the main AIRO census home page use the following link: http://www.airo.ie/mapping-module/census

National Census Mapping Viewer

On the National Census Mapping Viewer (airomaps.nuim.ie/census2011) we have prepared maps for over 130 variables and have grouped them into the following 14 themes: Population, Religion, Nationality, Education, Social Class, Principal Economic Status, Industry of Employment, Occupation, Housing, Cars per Households, Transport, Communications, Health and Disability. For this mapping tool we are using ArcGIS Viewer for Flex from ESRI, a really useful mapping technology when you are dealing with a very large number of geographical boundaries (3,406 EDs and approx 18k SAs). At present we have just included the mapping at electoral division (ED) level on the national viewer, this will be updated with the full set of small area (SA) data in the coming weeks. We have, however, added unemployment data at SA level for today’s launch and so is the first time that we actually see the full scale of the unemployment problem at the very local level.

To use the tool users simply turn on a theme on the left hand panel and then ‘check’ the map of interest. Remember that you can only show one map at a time with the top checked layer being the one on display – it might take a few moments to get the hang of it but it’s fairly straight forward. To get more information about an area just click on an ED and a pop-up window will provide a very short and basic commentary and a graphic providing more information on the variable. Let’s have a look at some examples:

% Population Aged > 65 plus: This map provides a useful visualization of the distribution of the elderly population across Ireland. As expected we are seeing much higher proportions of elderly population within EDs in rural and peripheral parts of the country.

% population UK by Nationality: The nationality data available at the ED and SA level is broken down into six groupings, users can choose from Irish, UK, Polish, Lithuanian, Other EU 27 or Rest of the World. Each map provides interesting trends and certainly shows some fascinating patterns within urban areas. The map below details the distribution of those whose nationality is classed as UK. What’s striking about this map is the clear pattern of high percentages in south-west cork, north-east Clare/south Galway and a wider area of higher percentages in the Roscommon/Leitrim/Mayo area.

% of Households with Central Heating powered by Peat: There are more than 25 different variables within the housing theme on our national viewer. Maps are available on:

• type of housing unit (detached, semi-d, flat/apartment etc)
• age of housing unit (only 2000 to 2005 and post 2006 included at the moment – let us know if you’d like more)
• tenure (owner occupied, rented etc)
• type of water supply (group scheme, private scheme etc)
• type of sewage system (public scheme, individual septic tank etc)
• and type of fuel used for central heating system.

This last category provides some really interesting maps and shows very clear patterns throughout the country for particular types of fuel. The map below shows the distribution of households that use Peat (including turf) as the primary source of fuel for central heating systems with higher proportions in the midlands and along the western seaboard and then an almost complete absence of use in much of the rest of the country.

Local and Regional Authority Mapping Modules:

As part of the AIRO project and our growing infrastructure of free mapping tools we have now updated all of our Local Authority and Regional Authority mapping modules with the 2011 Census data for Electoral Divisions. The data within each mapping tool mimic the themes that are available for download from the CSO. In total, each mapping module now has 975 individual variables (raw counts and pre calculated percentages and ratios) and includes data from 2006 where possible. Over the next week we will start to build in the Small Area data for each LA/RA, all going well this will be done by Thursday 9th of August. We are also hoping to update all of the mapping modules for Local Partnerships but this may take some time.

To access the mapping modules go to the main AIRO census page (click here) and choose from the drop down list for either LA or RA. Just click View once you’ve made your selection. Once it’s loaded you simply just click on ‘data’ and choose your indicator and away you go.

We hope you enjoy the new tools and they prove to be useful for the work that you do. We’re happy to take comments and suggestions on additional datasets that should also be included. We’re also planning to run a number of training sessions in the coming weeks and months, again please get in touch if you or your organisation are interested.

For further information please contact AIRO at the following: email – airo@nuim.ie, phone – 353 1 7086688

Links:

AIRO National Census Mapping Viewer: airomaps.nuim.ie/census2011

Local and Regional Authority Mapping Modules: http://www.airo.ie/mapping-module/census

CSO SAPSMap data download site: http://census.cso.ie/sapmap

Justin Gleeson & Aoife Dowling

 

Census 2011: A Healthiness Index?

My colleague, Adrian Kavanagh, has designed a very simple but clever metric to explore the recently published data on general health from the 2011 Census. Calculated at local authority, traditional county, provincial and state level, it shows an overall weighted score* based on the levels of self reported health in each area. The results are presented in the table below and seem to make intuitive sense.

The national average for the state is 149.92. We then see that the range of scores that in essence give us a score for healthicity or healthiness. The area with the highest score and therefore the least healthy, is Limerick City with 164.03. At the other end of the scale, the area with the lowest score and by extension, the most healthy, is Dún Laoghaire-Rathdown. Looking at the other local authorities with high scores/low healthicity, we can see a good mix of urban and rural areas which makes sense in terms of extremes of urban and rural poverty. By comparison, Galway City fares better than the other urban areas while the bulk of the counties in the low score/high healthicity categories seem to be the suburban and urban region counties around Dublin and Cork which typically have younger and relatively affluent age-income profiles.

Longer term it will be a very interesting process to see how robust this measure is in relation to other associated measures of poverty and deprivation. Recalculating this data at ED level and comparing the ranked results with deprivation scores may confirm this relationship, though issues of age-standardisation also have to be considered and applied. In addition, there will be some pockets of poverty in affluent counties like Dún-Laoghaire Rathdown and Fingal which will only emerge from more fine-scaled geographical analysis.  

Area	Health Status (% of respondents)
	Very Good	Good	Fair	Bad	Very Bad	Healthicity Index
Dún Laoghaire-Rathdown	66.71	25.50	6.53	1.02	0.23	142.55
Fingal	65.63	27.21	6.01	0.95	0.20	142.89
Meath	65.09	27.23	6.54	0.94	0.21	143.93
Cork County	64.97	27.01	6.88	0.93	0.20	144.38
Kildare	64.91	27.09	6.67	1.10	0.22	144.62
Wicklow	64.16	27.07	7.46	1.09	0.22	146.16
South Dublin	63.30	27.94	7.29	1.20	0.27	147.20
Waterford County	63.08	27.80	7.86	1.06	0.20	147.51
Cork (City & County)	63.09	27.80	7.73	1.14	0.25	147.65
Kilkenny	62.88	27.81	7.90	1.15	0.26	148.09
Cavan	62.85	27.43	8.40	1.11	0.20	148.39
Leinster	62.62	28.07	7.76	1.27	0.28	148.52
Dublin (City & Counties)	62.64	28.03	7.70	1.32	0.30	148.61
Limerick County	61.97	28.89	7.81	1.10	0.22	148.72
Laois	61.58	28.82	8.07	1.21	0.32	149.86
State	61.66	28.58	8.20	1.28	0.28	149.92
Galway City	60.64	30.12	7.72	1.25	0.27	150.38
Munster	61.15	28.99	8.35	1.25	0.26	150.49
Waterford (City & County)	61.22	28.83	8.40	1.31	0.25	150.55
Galway (City & County)	60.93	29.34	8.24	1.23	0.27	150.58
Galway County	61.05	29.00	8.46	1.22	0.26	150.66
Wexford	61.21	28.54	8.59	1.37	0.28	150.98
Monaghan	61.02	28.47	9.17	1.11	0.22	151.04
Louth	61.23	28.32	8.74	1.41	0.30	151.23
Clare	60.05	30.14	8.28	1.25	0.26	151.53
Westmeath	60.46	29.42	8.47	1.34	0.32	151.63
Kerry	59.48	30.28	8.79	1.17	0.27	152.46
North Tipperary	59.88	29.46	9.07	1.34	0.25	152.63
Ulster (part of)	60.24	28.73	9.47	1.31	0.26	152.63
Offaly	59.86	29.63	8.82	1.38	0.31	152.66
Carlow	59.63	29.73	9.01	1.38	0.25	152.88
Limerick	59.42	29.93	8.93	1.43	0.30	153.26
Connacht	59.21	29.94	9.20	1.35	0.29	153.58
Sligo	58.99	29.71	9.59	1.44	0.26	154.28
Leitrim	58.34	30.42	9.80	1.20	0.24	154.59
South Tipperary	58.65	30.11	9.46	1.49	0.31	154.70
Dublin City	59.11	29.51	9.26	1.71	0.40	154.77
Waterford City	58.52	30.32	9.17	1.66	0.33	154.97
Donegal	58.76	29.40	10.05	1.48	0.30	155.15
Roscommon	58.52	29.66	10.01	1.48	0.33	155.44
Longford	57.95	30.28	9.96	1.46	0.34	155.95
Mayo	56.59	31.24	10.29	1.52	0.36	157.82
Cork City	56.71	30.45	10.61	1.84	0.39	158.73
Limerick City	53.36	32.40	11.57	2.20	0.48	164.03

 Ronan Foley, Centre for Health GeoInformatics, NUI Maynooth.

* The score is calculated on the basis of weighting the percentage for ‘very good’ by 1, the percentage for ‘good’ by 2 and all the way through to weighting the percentage for ‘very bad’ by 5. This ends up by balancing out the scores across all responses but giving a higher level of impact, in poor health terms, to the ‘very bad’ and ‘bad’ responses. It also gives a sense of levels of variation from the national average.

 

Census 2011: A Healthy Nation?

Context.

 The results for the newly introduced general health question from the 2011 Census have just been released (June 28^th, 2012). The decision to ask such a question is a laudable one and the first ever attempt to ask a question of this type. It is a self-reported question with census respondents asked to tick a box as to the health status of head of household as well as all household members. Five categories were presented to allow respondents to say if their health was; very good/good/fair/bad/very bad. Such questions are common to census outputs in other jurisdictions and have generally been found to be useful for two broad reasons. Firstly, the value of collecting this data within a census allows for much deeper spatial analysis of the patterns of good and poor health across the country. Knowing something about these deeper geographies of health will be vitally important for a number of state, semi-state and private organizations and agencies. Secondly, asking a general health question within the Census allows analysts to also explore and cross-tabulate relationships, both numeric and geographical, between health status and associated variables in the Census such as social class, education, disability and informal caring. In this way, one can analyse the potential explanatory causes of good/poor health and relate these to wider casual factors and provide good empirical material for wider social and economic profiling.

 Overall Results

As the first time a general health question was asked in the census, the response suggests a generally good level of self reported health for all respondents. The results show that 60.3% reported they were in ‘very good’ health and a further 28% in ‘good health’. Just over 8% recorded their health as ‘fair’, with a small number, 1.3% recording their health as ‘bad’ and an even smaller number, only 0.3%, recording their health as ‘very bad’.  In addition, 2.1% of the population did not answer the question. The total proportion of the population who answered Very Good or Good accounted for 88.3% of the population, which tallies well with recent survey responses of 84% for the same question as reported by the OECD Health at a Glance Report from 2009[1]. Comparisons with country level reporting across the rest of the British Isles suggests the Irish population to consider themselves much more healthy than populations in those other countries. As Table 1 below shows, when compared with the most recently available data from 2001 for Northern Ireland, Scotland and England/Wales, the rates of good health are much higher in Ireland. This is likely to be for two reasons. One is that the phrasing of the question is slightly different, but more importantly, the Irish question provided 5 optional categories whereas the UK questions asked only whether health was Good, Fair and Not Good. This is likely to have thrown up significantly different responses. Given the broad level of agreement across the UK in 2001, it is interesting to speculate that recoding and re-aggregating the Irish data into three categories, as Very Good, Good and combining Fair/Bad/Very Bad as a sort of generic ‘not good’ response, shows a closer match (see adjusted figures in Table 1 below). The use of ‘fair’ in both categorizations makes such an aggregation problematic, but makes for an interesting comparison nonetheless.

 Table 1 General Health Results across British Isles, 2001-2011

General Health
	2001/2	2011
	%	%
Ireland*			Adjusted
Good/Very Good	n/a	88.28	60.32
Fair	n/a	8.02	27.96
Bad/Very Bad	n/a	1.52	9.54
Northern Ireland
Good	70.00	n/a
Fair	19.34	n/a
Not Good	10.66	n/a
Scotland
Good	67.91	n/a
Fair	21.94	n/a
Not Good	10.15	n/a
England&Wales
Good	68.55	n/a
Fair	22.23	n/a
Not Good	9.22	n/a
* 5 point scale (VG-G-F-B-VB)	UK 3 point (G-FG-NG)

As the CSO’s preliminary analysis suggests, there is a strong relationship for example, between social class and health, with the affluent Professional Class reporting levels of Very Good Health at 75.2%, that are significantly higher than those for Unskilled Workers at 45.3%. With the release of more detailed data at electoral division (ED) and small area (SA) level, it will also be important to compare the health question against deprivation scores to further deeper our understanding of relationships between health, poverty and inequality. If results are comparable to other countries, the inverse relationship between deprivation and good health should be both apparent, and consistently identifiable down to neighbourhood level.

Geographical Patterns

As the CSO identify in their preliminary reporting[2], it is possible to map the rates of good and poor health by local authority and county across the country but with some important caveats at this early stage. Clearly counties with significantly older populations come out with poorer health status so to get a more accurate measure, it will be necessary to perform age-standardisation adjustments to take this into account. In addition, it is also clear that in general urban areas have lower levels of very good health than some of the ‘younger’ counties in their hinterlands. The CSO preliminary report provides a map of ‘very good’ health. But a map of a cumulative ‘bad/very bad’ health status, Figure 1b below, shows us a pattern of relatively high rates in the cities of Limerick, Dublin, Waterford and Cork as well as rural parts of Central Connacht, Longford and Tipperary South. Having this information makes it possible for example to compare with other regularly used indicators of mortality and morbidity, though few of these are available at any meaningful spatial scale. One example, drawn from preliminary results from a HRB-funded project being carried out at the Centre for Health GeoInformatics at NUI Maynooth, shows age-standardised mortality rates for 2006 (Figure 1a). The rates are calculated for the population under 75, a proxy measure of premature mortality. While this is a 26 county, rather than a 34 local authority map, the results are quite different. Updating the map to include the cities would be likely to improve the match, but it may be that the inclusion of the ‘fair’ category in Figure 1b might see a better correlation. In addition, deeper analysis on the disability data, as a proxy of limiting long-term conditions, might also throw up a sharper correlation.

 Figure 1.     a) ASMs (per 100k), 2006                

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

b) % with Bad/Very Bad Health, 2011  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

Future directions.

It is very exciting to have this new data available for visual and statistical analysis. As well as cross-tabulations and comparisons with other Census data, it may also be used to compare with proxy measure of potential health care demand, such as the % of residents in each county in receipt of a medical card and other primary care supports. In addition, the mapping of disability data may also enable analysts to uncover and verify relationships between disability and health status and explore this geographically. In the different UK censuses, there is an additional question on limiting-long term illness which typically throws up much higher rates (20.4% for NI, 18.2% for England & Wales) than forIreland.  Given the better match between the new data for people reporting a disability, 13% in Census 2011, this suggests that the re-phrasing of the question is moving a little closer to the type of question used elsewhere and acts as a proxy for chronic illness levels. One advantage perhaps of the Irish census question in its use of five rather than three categories, is an ability to really pick out those with genuinely poor health, which may also prove useful in predicting potential future demand for health services. Indeed, given the paucity of detailed spatial information on the utilization of both hospital and general practitioner services inIreland, this may act as a subtle push to produce better spatial forecasting of demand on health services. Finally, the provision of a general health question is not unproblematic. All censuses are self-reported and therefore are, to an extent, unverifiable. The introduction of a new question, especially one as relatively subjective as health status, will always have associated teething problems. As noted above, the way in which the question is posed will also affect the results. But the data is likely to be useful in a whole variety of fields and will be used at a range of scales from local partnership work up to cross-border and international comparative scales. As a medical/health geographer, for whom any sort of health data, especially those collected at a range of meaningful geographical scales, is crucial, the CSO are to be lauded for suggesting and collecting this valuable dataset.

 Ronan Foley, Centre for Health GeoInformatics.

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[1]  OECD (2011), Health at a Glance 2011: OECD Indicators, OECD Publishing. http://dx.doi.org/10.1787/health_glance-2011-en

[2] CSO (2012) This isIreland: Highlights from Census 2011, Part. 2. Government of Ireland.

 

Census 2011: Post-15 education, broad trends

The census shows that between 2006-2011, the total number of post 15 year old students in the country has risen by 16.9% from 349,596 to 408,838 (figure 1).  This rise has occured despite the decline in the age cohort presently finishing school (there are 55,865 seventeen year olds, as opposed to 82,614 thirty year olds).  Given the present baby boom, numbers are set to grow even more strongly in the coming years which in turn will place enormous stress onto the third level sector to provide additional places, which will require a capital building programme and additional staffing given already very high staff-student ratios.

Figure 1

The data also shows a very clear relationship between the level of education attained and the employment status of individuals, with the higher the qualifications obtained the more likely the person is to be in employment (figure 2).  For example, the unemployment rate for people who had attained at most a primary education was 33.7% as opposed to 7.8% for those with a third level degree or higher.  This is reflective of the changing nature of the Irish economy as it becomes more dependent on high skilled manufacturing and services, and FDI investment, but is also reflective of the unemployment fallout of the present crisis with most jobs being lost in construction and the services sectors that require fewer qualifications.

Figure 2

In Census 2011, a new question on the main field of study of the highest qualification completed to date (excluding secondary school qualifications) was asked for the first time, extending a question that used to be directed only at third level graduates.  The data reveals the educational background and skills of people in the labour market.  Social sciences, business and law dominate, roughly three times the size of science, maths and computing.  Given the difficulties of recruiting in some sectors of the economy, such as IT, the latter seems to be one area that needs to grow.

Figure 3

The data in the three graphics above also provides some detail on gender.  Balance on the overall participation post-15 has improved slightly, with a growth in male participation.  Women in the labour force are more likely to be employed than males, regardless of qualification level.  There are notable differences in the fields of study taken by males and females, with males dominating engineering, manufacturing and construction, agricuture and veterinary.  Women dominate health and welfare, education, social sciences, business and law, and services.  This domination for some sub-areas of work is very stark, for example, women dominate child care and youth services (97.3%), secretarial and office work (96.7%) and hair and beauty services (96.3%).

Rob Kitchin

 

Census 2011: Socio-economic group and class

This is Ireland, Part 2, was released this morning.  It provides macro-level (national and county) results for a broad set of socio-economic data: labour force, occupation, education, health, social class, travel pattern. There are some maps at ED level for unemployment and a couple of occupational sectors, but the full ED and Small Area data set will not be released until later in the summer.  At that stage, we’ll be able to get a much more detailed sense of how the economic crash has played out socio-economically at a local scale.

In this post, I’m just going to concentrate on the socio-economic group and class results.  Analysing these at a county level is problematic because aggregation effects mask the highly variable way in which these play out locally, nevertheless we can see the broad pattern changes.

Socio-economic grouping classifies the entire population into one of ten categories based on the level of skill and educational attainment of their occupation (inc. those at work, unemployed or retired, with dependents classed on the basis of whom they are deemed to be dependent).  There has been growth between 2006-11 (see Figure 1) at the higher, more skilled end of this classification, with increases in employers/managers, higher professional, lower professional and non-manual, whilst those dependent on manual work have declined (in line with job losses in related sectors such as construction).  Moreover, there is a broad spatial pattern to the data with a greater proportion of the highest two categories in and around Dublin reflecting the higher proportion of FDI and public sector jobs around the capital (see Figure 2).

There are also some marked gender contrasts in socio-economic group (Figure 3), with strong differences in the gender profile of some classes.  For example, men make up a much stronger proportion of manual, farmers, agricultural workers, self-employed, and are marginally more likely to be employers/managers, higher professional, semi-skilled and unskilled.  Women make up a strong proportion of lower professional, non-manual and other.  Whilst the balance of the top two classes of employers/managers and higher professional are getting better, it seems that a glass ceiling does still exist.

 

Figure 1

Figure 2

Figure 3

The socio-economic group data is used as the basis for assigning households into a social class, based on almagmating occupations with similar skill sets together to produce seven classes: professional workers, managerial and technical, non-manual, skilled manual, semi-skilled, unskilled and other.  Figure 4 shows the distribution of social class by local authority.  Clearly the standout LAs are in the cities.  DLR has a disproportionate number of professional and managerial/technical classes, whereas Cork City, Waterford City and Limerick City have low rates compared to their surrounding hinterlands, reflecting the suburbanisation of professional/managerial labour.

Figure 4

Rob Kitchin

 

 

Census 2011: Age profiles

The CSO have released the age profile data from Census 2011.  They have produced a nice booklet providing some summary analysis.  We have produced a few interactive data visualisations of the data on AIRO.  Here are a summary of some of the trends.

The population as a whole is ageing and all age cohorts increased in size with the exception of 19-24 year olds.  This is partly to do with recent emigration but is more reflective of a low birth rate in the late 1980s and early 1990s.  The birth rate in 1980 was 74,064.  In 1994, the lowest rate and presently aged 17-18, it was 48,255.  In 2010 it was 76,762.  In other words, this is a small cohort working its way up the population pyramid.

This pattern is not universal.  Cork city, Galway city and Limerick city all have quite high populations aged 18-30, reflective of high student numbers.  There is a noticeable drop age 30+ as people move out the city at family formation age.  This is also evident in the relatively low rates of children in these areas.

There has been a large increase of 17.9% (2006-09) to 356,329 in children aged 0-4.  This increase was experienced everywhere, but was particularly high in the suburbs and commuting counties.  For example, there has been a 72% increase in 0-4 aged children in Fingal between 2002-2011.  Similarly, there has been a growth (12%) in 5-12 year olds.  However, this age group dropped in number in Cork City and Limerick City, and the other cities for secondary school age children.  This is partly due to the lower birth rates in the late 1990s/early 2000s working its way through, but also migration of families out of the city centres.  Interestingly, there has been a 50% increase in the number of 0-4 year old children living in apartments (just over 20,000 overall).  There has also been a slight drop in the rate of 0-4 year old children living in one parent families to 15.4% (19.1% for 5-12).

There was a 14.4% increase to 535,393 in the number of people over the age of 65 in the state, with 389 over the age of 100.  The over 65s constitute 11.7% of the population (one of the lowest rates in the EU – average is 16%).

Despite the strong growth in children, the average age in the state has increased slightly to 36.1.  There is a slight variation around the country, with the average age being 38.7 in Cork City and 32.9 in Fingal, reflective of the large number of family units in the latter.  The west has a slightly higher average age than the east, and rural areas are slightly higher than urban areas.  There are just three counties with falling average ages – Laois, Cavan and Longford, due to strong in-migration and natural increase.

Given the number of births and the declining death rate, the age dependency ratio (the ratio of children under the age of 14 and adults over 65 to the working age population of 15-64) has risen from 45.8% in 2006 to 49.3%.  Given that children are for the most part dependent until at least 18, it is clear that the dependency ratio is for all intents and purposes well over 50%.  In other words, over 50% of the population are largely dependent on the remaining population for some level of support.  The youth dependency rate is 31.9% and the old age rate is 17.4%.  Rural counties tend to have higher old age dependency rates, for example, Mayo, Leitrim and Cavan, due to younger migration to urban areas.  Meath and Laois have high youth dependency rates, with Cork city and Dublin city having the lowest.

Finally, there are slightly more women in the state then men, with the lowest ratio on record of 981 men/1000 women.  The profile varies across the country, with slightly more men in rural areas between the ages of 20 and 70 and in urban areas under the age of 20.  After the age of 20 there are slightly more women in urban areas due to migration patterns.  After 70, women outnumber men in both rural and urban areas.

Rob Kitchin

 

Housing vacancy at the new Small Area level

For the first time, the housing stock and vacancy data from the Census has been released at the new Small Area (SA) level.  This new statistical geography, developed by the National Centre for Geocomputation at NUI Maynooth for Ordnance Survey Ireland, consists of 18,488 areas, typically consisting of 80-130 households.