There are striking differences in the age and gender composition across Cork City, with an abundance of male, young and single people in the city centre. The absence of children, women and older people leads to several intersecting inequalities in access to education, work and housing that are effectively a tax on girls, women, disabled and old people. The increasing focus on single and small apartments – which may seem oppressive or even dangerous to women and disabled people – is transforming the city and the centre into a population of transients. The lack of family, own-front-door and accessible dwellings means that most of life’s transitions – forming a relationship, having a child, unemployment, retirement, disablement or long-term illness – necessitate a move, and the only available and appropriate dwellings are either in poor repair, or increasingly further away from the centre.
The renewal of small parts of Cork City disguises an ongoing decline and dereliction of large swathes of the city, with poor maintenance and high rents increasing the volume of substandard single occupancy dwellings in divided former family dwellings, high levels of neglected vacant property, and indeed physical collapse of older buildings.
Girls, women, disabled and older people experience a double burden of excess journey times to education, work and essential services, as a direct consequence of moving out of a centre and out of a city that increasingly lacks appropriate and secure housing.
A change in decision-making to more adequately include all parts of the community would change the direction of planning. The much-parroted term ‘sustainability’ is a nonsense when the city is unable to sustain its own residents across changes in family structure, sickness and employment.
As the prospect grows closer of a continuous cycle and walking route from the Inniscarra Dam to the harbour, this post assesses how many people and schools are within a car-free catchment area around the route. Two boundaries are displayed, a 2 km zone in which most people will be within a 15-minute walk and a 6 km zone in which most people are within a 15-minute cycle ride of the route. The number of post-primary schools and total pupil roll are separately counted.
These figures matter greatly because car park provision will be immediately raised, with the potential to induce additional motorised traffic to and around the route. In reality, large numbers of people, Bed & Breakfast, hostels and restaurants already lie directly on this resource, with large volumes of existing on-street and business parking space.
The proposed Lee to Sea route provides 46 km of mixed woodland, lake, river and seaside greenway, with an elevation range of just 45 m;
157,000 people live within 2 km of the route and 229,000 within 6 km of the route – most could walk or cycle from home within 15 minutes;
60 primary schools with 16,922 pupils lie within 2km and 82 primary schools with 24,012 within 6km;
34 secondary schools with a total of 16,072 pupils lie within 2 km of the Lee to Sea route, and further 3 schools (Scoil Mhuire, St Aidan’s Community College and Douglas Community School) with a further 1,711 pupils lie within 6 km of the route;
The route directly connects 37 of Cork County’s 85 secondary schools, with one another, and with the city and sea;
19 hotels offering 2,212 rooms lie within 2km of the route.
In Cork City + County
All analysis was performed with open source software using publicly available data, and all software and data sources are provided in the links at the end – also annotated R code used to generate these outputs. The technical description may be a helpful tutorial in using public data and mapping sources. This analysis was proposed by Orla Burke and Pedestrian Cork.
Two contrasting approaches to predicting (guessing) the outcome of an epidemic are 1) projecting data from similar situations observed in the past; and 2) modelling from varying degrees of first principles. Models must match reality for any reasonable usefulness, but are often extremely sensitive to intitial (unknown) conditions and the slightest variation in input parameters.
Here are both approaches, in broad outline, to generate boundaries around expected outcome.
NB1: Code in R (requires population and death or case time series)
Urban space is changing fast and despite the potential to increase urban space through growth, technology and social progress, the reality is often increasing exclusion and isolation. My own experience is one of a city increasingly paved over, squared off, noisier and lacking in calm spaces. Traffic, busy people and blank commercial facades have replaced more welcoming districts, because accessibility and family-friendly features are not a developer priority – they maximise borrowings, ramp up local property prices, take the increase in plot value and move on. Sustainable community is not a short-term money-spinner.
My perspective is very much the social exclusion and sensory impact of unsympathetic development. This post includes some images of Cork City and data maps of changing city demographics, at the level of the 74 electoral districts, to outline how the city is changing.
The Diagnostic and Statistical Manual of Mental Disorders (DSM), published by the American Psychiatric Association, is a definitive document for many professionals assessing, diagnosing and providing services related to autism. The DSM has been slow to recognise of Hans Asperger’s work (see also Historical context of Asperger’s first (1938) autism paper), of Asperger syndrome and Lorna Wing’s contribution to the wider autism/autistic spectrum .
Professionals inform parents, carers, teachers and others about the meaning of ‘autism’ and are often held in awe. The identity of autistic people has been impacted by the ebb and flow of ideas and consensus in the DSM.
This is a description of some images I have been creating of the definition of autism in the full text of every version of the DSM, from 1952 to the present.
I love statistics and numerical analysis, a love that many people do not share — statistics is one of quickest ways to halt a dinner conversation. Statistics is a style of argument that is neither right nor wrong, as useful as any other logical process and has a beauty in summarising or visualizing the subjects under examination in ways that allow two or more things to be compared.
In the case of film, it can be hard to communicate the incredible experience of sitting for an hour or two, absorbed in action, dramatic tension and emotion. Critics reviews and plot summaries (like those on IMDb) are one method of side-by-side comparison, or even more briefly in the star-ratings (e.g. 8.5 out of 10 for “Psycho”). This post describes some numerical and sampling techniques that I use to create single-image summaries of films and books. These images make stunning wall posters and I have had a few printed as big as 30″ by 20″ to display.
I have written before about the major topics that appear in newspaper articles that are “about autism”*, with their bias towards articles that mention boys, children, mothering and negative words. Autism is more often written about as a disorder, of a child, in the context of a parent (usually the mother) and as a sufferer, victim or burden. In this post I am looking at how newspapers write about autism itself, the choice of wording and phrasing that surround the words ‘autism’, ‘autistic’ or ‘Asperger’. Trying to visualise the use of words, in large volumes of text, is a very exciting topic and the results here are well worth studying in detail.
My own position on the use of words is to try to accurately reflect the terms that people choose themselves, or in the sources that I am referencing. The images here are convincing evidence that some word choices have a significant effect on positive reporting. In particular, the (identity-first language) adjective ‘autistic’ favours thoughts about personhood and the (person-first language) noun ‘autism’ is associated with negative, dehumanised phrasing. This is consistent with the findings of the survey “Which terms should be used to describe autism? Perspectives from the UK autism community”.
There are some technical notes at the end for anyone interested in the computer methods used to produce the images.
The Office for National Statistics (ONS) produces comprehensive data tables about the UK, including tables of special needs provision in schools across 152 local authorities in England. Special needs are categorised within this data by primary need, including ASD. The overall rate is 1.26% of all state-funded school pupils, ranging from about 0.5% in the local authorities with the lowest rates to about 3.5% in the authorities with the highest rates. This provides a good example of what may possible when the Autism Bill is enacted. Mapping the distribution raises many interesting questions about the reasons for regional variation, changing rates of ASD and whether there is inequality in provision.
If autism was assumed to affect all people at all ages approximately equally, there are somewhere between 15,000 (1 in 300) and 100,000 (1 in 43) autistic people of all ages in the Irish population of 4.6 million. It should be noted that 75% of the Irish population is older than 18 years and most will not have been assessed or diagnosed, so many autistic people are unrecognized. Recognition is widening from “severe autism in childhood” to “mild autism” and to older ages, which is probably the largest factor driving increased autism prevalence. We have also moved from recognizing approximately one autistic pupil per school to recognizing one autistic child per class in the space of ten or fifteen years.
Fitness and other tracking systems have the potential to measure some aspects of quality of life and possibly suggest changes that will improve future quality of life. I have worn a simple fitness tracker (a pedometer-style step-counting watch) for almost two years and have now combined the fitness tracker data with other sources – my real-life and online social activity – to look at how quality of life might be assessed.
The fitness tracker tells me my total steps and primitive measures of sleep. The real-life activities are social and work activities from my diary and photographs from my camera (which fill in gaps about where I was). The online activity is taken from the number of email and Twitter messages that I have sent per day. Some of the data is noisy and I have averaged over a week.
The take-home message is that physical activity improves quality of life. Twitter (or at least the way I have been using Twitter) has a negative impact. Doing more to be outside the house, even simply taking my activity to another place, would improve my fitness and happiness. Continue reading Fitness tracking the quality of my life→