Clearly, regression and other supervised, predictor and predicted relationships, have their fixed roles in all of data analytics. In unsupervised analytics, information discovery is at issue, from what can be multi-faceted data sourcing. It is this latter, unsupervised analytics, which is our focus, our interest, and our motivation. We begin with a most general introduction to how context is sourced from, or accompanying, our measured data. Our measured, and therefore quantitative data may be in some framework or other which is assessed qualitatively. Perhaps our measured observations are to be contrasted or baselined against what might be termed secondary data sources, or supplementary data sources.
It may be a case of dynamic (evolving) data and also data of heterogeneous precision (sensitivity, trustworthiness). Also, at issue may be both qualitative and quantitative data.
Examples of such multi-faceted data sources are the increasing prevalence of secondary data sources in the domains of Big Data, and Internet of Things. Such secondary data sources may well count as contextual data.
Given multi-faceted data, it is, or it can be, open to the data scientist to consider different contextualization strategies. Consider e.g. how mobile communications, and their monitoring, provide locational data on all device bearers. So, for example, transport analytics can be contextually based on such secondary data and information sources.
In this article, it is sought to explore general strategies for contextualizing analytics. Technically, the main attributes are aided, for analytical interpretation, i.e. extracting information from data, and consolidating that information into knowledge, by the supplementary, contextual, attributes.
The general discussion of Wessel (2016)
points to the need for focus in benefits to be drawn from data analytics. In a general sense, we may regard dimensionality reduction as a form of focused data analytics, since typically (in Correspondence Analysis, or Principal Components Analysis, PCA, or multidimensional scaling, etc.) the percentage variance or inertia explained by the reduced dimensionality space becomes the focus. Similarly feature selection is often used, to focus on the selected variables. Often the clustering of data is with the objective of retaining the clusters for the analytics. Therefore summarization is associated with focusing the analysis. Summarization is a most major theme in data analytics, through information space dimensionality reduction, through visualization, and through basic and core statistics.
Thus focus is a key practical consideration in data analysis. Then, though, Jean-Paul Benzécri’s principle of what we are analysing being both homogeneous and exhaustive should be very central also, from a practical perspective (Le Roux (2014), p. 9). Just in this practical sense, we begin by regarding contextualization as the association of two or more foci, i.e. two or more directions of analytical interest. This association is asymmetric. One focal point is located with reference to the other. This can be generalized to multiple focal directions in our analyses.
We begin with such contextualization in general. Then we take contextualization further in the context of Big Data. In the former, applications related to health and lifestyle analytics are at issue. In the latter, a central theme is the use of secondary data, whether or not with Big Data characteristics, so as to direct and focus the analytics.
2 Short Introduction to General Contextualization
Correspondence Analysis, CA, is most appropriate for latent semantic analysis, with input data being a cross-tabulation of observations or individuals, and variables or attributes. The cross-tabulation is usually frequency of occurrence data, encompassing presence-absence values. There can be considered also recoded quantitative data. Quantitative data, without recoding, can be used. Therefore CA is very appropriate for mixed qualitative and quantitative data. Categorical data is quite central here. Contrasted with PCA, where attribute centering to zero mean, and attribute reduction to unit standard deviation, termed standardization when both carried out, and contrasted with the TF-IDF, term frequency, inverse document frequency that is used in Latent Semantic Analysis of document-term cross-tabulation data, CA uses the following. The chi-squared distribution is defined on both rows and on columns. This is a weighted Euclidean distance of, respectively, row and column profiles. A profile is defined as the row or column vector being divided, respectively, by the row or column total. This is what results from frequencies in the input cross-tabulation matrix, i.e. each value divided by the grand total.
, CA is characterized as “a tale of three metrics”, these being the chi-squared metric for the dual clouds of observations and attributes, the factor space endowed with the Euclidean distance, and from that, hierarchical clustering that is a mapping into an ultrametric topology.
Multiple Correspondence Analysis, MCA, typically works on the set of modalities of the variables. When resulting from a questionaire’s questions, and with just one response for each respondent from among the question’s modalities, then an interesting result of that is that the row (i.e. respondent or individual) masses, defined from the row totals, are constant. It then results that the chi-squared metric between row profiles becomes a weighted Euclidean distance.
2.1 Baselining or Contextualizing Analysis
In Solène and Le Roux (2016), there is an important methodological development, concerning statistical inference in Geometric Data Analysis, i.e. based on MCA. At issue is statistical “typicality of a subcloud with respect to the overall cloud of individuals”. Following an excellent review of permutation tests, the data is introduced: 6 numerical variables relating to gait, body movement, related to the following; a reference group of 45 healthy subjects; and a group of 15 Parkinsons illness patients, each before and after drug treatment. Le Roux (2014) (section 11.1) relates to this analysis, of the, in total, 45 + 15 + 15 observation vectors, of subjects between the ages of 60 and 92, of average age 74.
First there is correlation analysis carried out, so that when PCA of standardized variables is carried out, it is the case that the first two axes explain 97% of the variance. Axis 1 is characterized as “performance”, and axis 2 is characterized as “style”. Then the two sets of, before treatment, and after treatment, 15 Parkinsons patients are input into the analysis as supplementary individuals. Solène and Le Roux (2016) is directly addressing statistically the question of effect of treatment. Just as in Le Roux (2014)
, the healthy subjects are the main individuals, and the treated patients, before and after treatment, are the supplementary individuals. This allows to discuss the subclouds of the before, and of the after treatment individuals, relative to the first, performance, axis, and the second, style, axis. The test statistic, that assesses statistically the effect of medical treatment here, is a permutation-based distributional evaluation of the following statistic. The subcloud’s deviations relative to samples of the reference cloud are at issue. The Mahalanobis distance based on covariance structure of the reference cloud is used. The test statistic is the Mahalanobis norm of deviations between subcloud points and the mean point of the reference cloud.
In summary, this exemplifies in a most important way, how supplementary elements and the principal elements are selected and used in practice. The medical treatment context is so very clear in regard to such baselining, i.e. contextualizing, against healthy reference subjects.
3 Data: Mental Health: Adult Psychiatric Morbidity Survey, England, 2007
In Coenders et al. (2002), there is this motivation for MCA, in the Abstract: “Most of the relevant factual and subjective items in quality of life questionnaires are qualitative and call for a multiple correspondence analysis type of analysis.” This is an important vantage point on the analytics: “Subjective well-being questions play the role of active variables and objective well-being questions that of illustrative variables.” It is noted how MCA is able to handle non-responses and also interviewer effects. Concluded is: “The analysis was carried out without accounting for non-response and interview effects and the interpretation of the axes became much less clear.” The first two axes of factors are found to account for a polarisation between high quality of life, and low quality of life. The data at issue in this work, Coenders et al. (2002), is a survey of elderly people, over 65 and over 75, carried out in Girona, Spain, in 1999. There were 2000 subjects. The questions comprised background variables (location, age, gender, last job, family), factual health questions, and a subjective (perception) health question. Also included were questions on activity, movement, mobility; and housework, shopping, managing one’s income. Modalities were specified for each question. Social satisfaction questions had Likert scale modalities.
Is is noted in Coenders et al. (2002) how missing data may be “substantively interpretable”, and will require a large number of presences in the missing value modalities. The overall presence of a sufficiently large number of missing values is so as not to overly impact the analysis through factors being found for the rarely occurring missing values. It is also noted how impactful, in the analysis, missing values will be if they are common to clusters of variables. Ways to address this include doubling the data variables, and perhaps even modalities of the variables, see Murtagh (2005); Coenders et al. (2002)
, doubling the column set, so that it results that row sums remain constant. It is even possible under such circumstances to modify the “deflated” and sufficiently large eigenvalues (Benzécri (1979); Greenacre (1993); Coenders et al. (2002)).
Quite a unique analytics approach is pursued by Coenders et al. (2002)
, firstly availing of MCA, with appropriate data coding, being equivalent to PCA, with appropriate data normalization. Secondly, correlation analysis, and analysis of variance, are carried out on the principal factor plane, accounting for most of the variance, and also k-means clustering. In summary, on the given data, the work ofCoenders et al. (2002) is well accomplished.
In the following, now, we will take as a case study, survey data that is heterogeneous and diverse. We note that for the present initial study of this data, we will place “Don’t know” responses, with very low response totals, as supplementary response modalities, that, thus, will not influence detrimentally the essential aspects of our analytics.
A periodic survey of mental health is used. This is a continuing survey of adult psychiatric morbidity in England, carried out as a household survey, NHS (2009). The number of respondents is 7403. There are 1704 variables, including questioning of the subjects about symptoms and disorders, psychoses and depression characteristics, anti-social behaviours, eating characteristics and alcohol consumption, drug use, and socio-demographics, including gender, age, educational level, marital status, employment status, and region lived in.
We begin with a general, visualization-based, presentation of the outcome of this quite directed analysis. Following the description of the principal factor plane display, there will be a brief presentation that justifies the visual, i.e. display, findings.
As a first analysis, the following variables were selected: 14 questions, hence 14 categorical variables, relating to “Neurotic symptoms and common mental disorders”. These are described inNHS (2009) (Appendix C). Almost all of these variables had as question responses, whether or not there were symptoms or disorders in the past week, one question related to one’s lifetime, and one question related to the age of 16 onwards. Another question set was selected, relating to socio-demographic variables, noted in the foregoing paragraph. In this set of socio-demographic variables, there were 9 variables.
An initial display of the neurotic symptoms and common mental disorders, see Figure 1, sought to have socio-demographic variables as supplementary. But these were projected close to the origin, therefore showing very little differentiation or explanatory relevance for the symptoms and disorders data.
In Figure 1, it is found that factor 1 is explained as PTSDcom, “Trauma screening questionnaire total score” versus all other variables. Factor 2 is explained as “CISRFOUR” versus “nosymp”. These are, respectively, “CIS-R score in four groups, 0-5, 6-11, 12-17, 18 and over. (CIS-R = Common Mental Disorders questionnaire)”; and no neurotic symptoms in the past week.
What has been stated about Figure 1, from the visual interpretation is fully supported by the following. The contribution of “CISRFOU” to factor 2 is 79.09%, and its correlation, viz. cosine squared, with factor 2 is 0.96. The contribution of “PTSDcom” to factor 1 is 89.65%, and its cosine squared with factor 1 is 0.99. The cosines squared of “PTSDever” with factor 1 is 0.26; that of “MajorT16” with factor 1 is 0.21; and that of “PTSDPos” with factor 1 is 0.71. These last three questions are close to the origin, i.e. very average and unexceptional. Finally, in this figure, “nosymp” has this contribution to factor 2: 13.39%, and its cosine squared with factor 2 is 0.40.
To summarize the analysis at issue here, the following are the data characteristics: 7403 individual respondents; 23 questions with 107 response modalities; of these questions, 1 to 14 are active, and 15 to 23 are supplementary. The 14 active questions have 52 response modalities, and the 9 supplementary questions have 55 response modalities. As noted above, the 14 active questions were relating to “Neurotic symptoms and common mental disorders”, and the 9 supplementary questions were socio-demographic variables.
Next, Figure 2, it was sought to characterize the socio-demographic data, and then to see if the neurotic symptoms and common mental disorders data could be explanatory and contextual for the socio-demographic data. But no differentiation was found for these supplementary variables, indicating no particular explanatory capability in this particular instance.
It may be just noted how, in Figures 1 and 2, the main and supplementary variables were interchanged. Respectively, the symptoms and demographic variables were main and supplementary; then the main and supplementary variables were the demographic variables and symptoms. This was done in order to explore the data.
In Figure 2, factor 1 is seen to have age and education level counterposed to home region. Factor 2 is seen to have educational level counterposed to: employment status, gender, marital status, ethnicity. (In the paragraph to follow, the names of some of these variables are explained.)
Just to complete and confirm, numerically, the contributions to the axes, then validated by the cosine squared, this is as follows for these questions. We note once again, that this analysis is carried out on the 9 socio-demographic questions at issue here, and as 14 supplementary questions, these were questions related to neurotic symptoms and common mental disorders. On the negative half-axis of factor 1, “newsha”, “gor06” (see the next paragraph for the names of these questions) had contributions, respectively, of 35.5% and 27.8%. Their cosines squared were, respectively, 0.96 and 0.97. The main contributions for the positive half-axis of factor 1 were: “EDQUAL5” and “Age10Yr”, with contributions of 16.2% and 10.4%. Their cosines squared were 0.38 and 0.40. The next contribution to factor 1, less than all these values, was 5.24% and its cosine squared was 0.42. This was the question “Age20yr”. (The questions “Age10Yr” and “Age20Yr” were both querying the individual’s age, with 10-year and with 20-year intervals for age.)
For factor 2, in Figure 2, the highest contributions are not at all as high as for factor 1, and nor are the cosines squared. The contribution to factor 2, of “EDQUAL5”, negatively positioned for its coordinate on factor 2, is 45.8%. The cosine squared of “EDQUAL5” with factor 2 is 0.43. Smaller than these values of contributions, and cosines squared is the collection of active variables, displayed in Figure 2, with coordinates on the positive half axis of factor 2.
Finally, it was checked whether neurotic symptoms and common mental disorders data should be jointly analysed with the socio-demographic data. Figure 3 shows this outcome. On the positive first factor, what is particularly important, from the contribution to the inertia, is PTSDcom, “TSQ (Trauma Screening Questionnaire) total score”. The negative second factor, is highly influenced by these two variables: “gor06”, “newsha”, respectively: “Government office region” and “Strategic Health Authorities”. These were both sets of geographic regions in England, respectively 9 and 10. (The 7th such region in each of these variables was London.)
One aim arising out of this analysis is to see if we can formulate forms and expressions of “mental capital”. This concept is noted in section 5.1 where an example of it was going for a walk, therefore taking exercise, in the countryside or parkland.
A summary, and preliminary, interpretation derived from Figure 3 is how factor 1 accounts for recorded trauma, and factor 2 accounts for region of the respondent.
4 Analysis of Activities, Contextualized for General Health, Depression and Demographics
Our primary aim in this initial analysis of one year’s data is to seek insightful properties in the data, and also to become aware of where there is a lack of impressionable outcomes.
We base our analysis of (i) general health, (ii) activity, (iii) depression, and (iv) demographics, on the following: activity. We do so for two reasons, firstly that in the future sensor systems, related to the surveillance or to the Internet of Things, such systems will give rise to a very great amount of free and flexible data, that is secondary and contextual in its essential nature. The second reason is to pursue observation, associated with a line of enquiry, towards determining forms and expressions of mental capital. As background and definition of mental capital, the following may be considered, from Cooper et al. (2016): “… paths to wellbeing – being active, taking notice, connecting with others, giving and learning – can be achieved in the outdoors and can lead to further resilience, enhance self-esteem, improve learning and develop emotional intelligence, thus building mental capital.” This short account, Cooper et al. (2016), entitled “The ‘mental capital’ value of the outdoors”, begins as follows: “It is now well known that mental wellbeing is linked with people using outdoor environments and feeling connected to nature.” Here we seek to pursue what can be forms of mental capital at the elementary stages of motion and movement. A further short essay which is after that of Cooper et al. (2016) is entitled “Walking improves mental wellbeing”.
We have 7403 respondents, or individuals. Now, from the 57 questions, furnishing variables, these are as follows: general health: 19, activities: 7, depression: 24, and demographics, 7. These questions are, in the set of 57, and in succession, as follows: 1 to 19, 20 to 26, 27 to 50, and 51 to 57. The numbers of modalities for these question sets, viz. for respectively general health, activities, depression and demographics, are: 101, 25, 154 and 44. In total, the number of modalities is 324.
For activities, as noted there are 7 questions, i.e. variables. The respective number of modalities are: 3, 4, 3, 4, 4, 3, 4. In total, there are 25 modalities. Let us look in turn at these modalities: “No, no difficulty at all”, “Yes, a lot of difficulty”, “Yes, some difficulty”; “Don’t know”, “No, no difficulty at all”, “Yes, a lot of difficulty”, “Yes, some difficulty”; “No, no difficulty at all”, “Yes, a lot of difficulty”, “Yes, some difficulty”; “Don’t know”, “No, no difficulty at all”, “Yes, a lot of difficulty”, “Yes, some difficulty”; “Don’t know”, “No, no difficulty at all”, “Yes, a lot of difficulty”, “Yes, some difficulty”; “No, no difficulty at all”, “Yes, a lot of difficulty”, “Yes, some difficulty”; and “Don’t know”, “No, no difficulty at all”, “Yes, a lot of difficulty”, “Yes, some difficulty”. Due to very low total frequencies, we find that the four cases of “Don’t know” have very few respondents. Given hundreds or thousands of respondents responding to the modalities of the other questions, for these modalities, the 4th, 11th, 15th and 22nd, we find the number of respondents to them to be, respectively, 1, 2, 16 and 2. Very small numbers of respondents, like we have here, leads to exceptionality and to over-influence on the Correspondence Analysis determining of new axes. Hence we can make them (the four modalities, the 4th, 11th, 15th and 22nd) as supplementary in the analytics that crosses respondents by retained modalities.
In brief, these activity questions are related to the following summary forms:
AcDif1: personal care such as dressing, washing oneself.
AcDif2: using transport.
AcDif3: medical care.
AcDif4: housekeeping, shopping.
AcDif5: gardening, household repair.
AcDif7: managing money.
The activities data, itself to begin with, crosses 7403 respondents and 21 response modalities for the 7 questions. Of the 25 response modalities, 21 are main attributes and 4, as explained above, are supplementary. The eigenvalues as rates of inertia explain 26.8% of the first factor’s, or new axis’s, inertia, and 14.1 % of the second factor’s, new axis’s, inertia. Modifying the rates of inertia is well based on such reasons as the following: emphasizing relative importance of the axes; have an index of deviation from sphericity of the factor space cloud; and adjusting for the important role in the defining of factors that is played by the faint and minor information present in sparse input data, as is the case here. Cf. Le Roux (2014), page 252. Having questions, here , the average eigenvalue is . For the set of eigenvalues greater than the average, , these are modified as follows: . For the activities data being mapped into the Correspondence Analysis factor space, we find the modified rate of inertia associated with the first and second factorial axes to be 88.7 % and 11.1 %. Therefore in relative terms, the first two axes account for essentially all the information content in this activities data.
|Question||Factor 1||Factor 1|
From Table 1, the first axis is characterized, in regard to the positive half axis, by “Yes, a lot of difficulty”, denoted by “YESPLUS” in the modality naming here. This is counterposed, in regard to the negative half axis, by the “No, no difficulty at all” modalities.
From Table 2, the second axis is characterized, in regard to the positive half axis, by “Yes”, here denoting “Yes, some difficulty” modalities. The negative half axis is characterized by the “YESPLUS”, i.e. “Yes, a lot of difficulty”. The modalities labelled “NO” here, i.e. “No, no difficulty at all”, are near zero in value on this second factorial axis.
|Question||Factor 2||Factor 2|
It follows from Tables 1 and 2 that the lower right quadrant is associated with a lot of difficulty indicated in the responses, contrasted with the negative first axis that is associated with no difficulty. In the sense then of the sourcing of mental capital, it is the negative, but near zero, first factor that is most relevant and demonstrably important.
Figure 4 displays the principal factor plane with respondents represented by an “o” point. It would be easy to see which respondents had most difficulty in the activities at issue here, i.e. those who are located towards the bottom of the bottom right quadrant.
The modalities of all other questions can be taken as supplementary elements and mapped into the factor space. Considering these supplementary modalities, “Economically inactive”, “In employment” and “Unemployed”, these were found to be to the left half axis, near zero, of factor 1. Cf. Figure 4. This same outcome was found for race attributes, “South Asian”, “White”, “Black”. For age, Figure 5 shows, to some extent, a worsening of activities with age . Just to note are the numbers of respondents of the age intervals: 16–34, 1603; 35–54, 2543; 55–74, 2307; and 75+, 950. Given the theme of all of this work, psychiatric morbidity, we see that most ages are quite average and neutral, in being close to the origin, with the exception, to some extent of those aged 75+. The coordinates of the 75+ mean projection, on axes 1 and 2 are 0.69 and 0.33, that are higher in value compared to the nearly all negatively values coordinates of the other age intervals. The cosine squared of this 75+ mean projection are, for axes 1 and 2, 0.07 and 0.02, and these very small values are nonetheless greater than all other ages.
For all the modalities of questions, their cosines with the very important and informative first factor were looked at, and the top ten cosines listed. These modalities of questions are as follows: 4 that were supplementary (being “Don’t know” responses) for the Activities question set; 19 questions relating to General Health, with 101 associated modalities of these questions; 24 questions relating to Depression, with 154 associated modalities of these questions; and 7 questions relating to Demographics, with 44 associated modalities of these questions.
Figure 6 is a display that is very helpful for our interpretation. It does use the original, unmodified, inertias, relative to the two previous displays of this principal factor plane. As noted above, question AcDif5 relates to “gardening, household repair”. In Figure 6, the modality AcDif5YESPLUS, i.e. the “Yes, a lot of difficulty” modality is close, in location, to all the modalities of questions that indicated “Yes, some difficulty”. For the present, let us group together that modality, AcDif5YESPLUS, with the AcDif2YES, i.e. “Yes, some difficulty”, modalities. Figure 7 displays Figure 6 a little differently. It may be noted that in Figure 6, attention should be directed only at cosine squared values that are greater than 0.5. That is to say, there is some non-negligible correlation with the axes. The practical cosine squared, hence correlation, with the first axis, consists of three “NO” and two “YESPLUS” response modalities. For the second axis, the highest cosine squared value is 0.4. The contributions also need to be seen, in relation to Figure 6. For axis 1, the highest contributions, greater than 8.6%, are all “YESPLUS” modalities. For axis 2, the highest valued contributions are for three of the “YES” modalities that are greater than 10.9%. Figure 7 further illustrates these findings.
Next, in Figure 8, just as in previous figures, but more explicitly shown here is the display of the three groupings, label NO, “No, No difficulty at all” in relation to movements and activities, and the means of ages 16–34, 34–54, and 54–74 are all close to the origin, and the NO, “No, no difficulty at all” response. The very aged respondents tend towards the YES, “Yes, some difficulty” response.
We recall that we are dealing here with these variables: 19 General Health questions, comprising modalities 1 to 101; 7 Activities questions, comprising modalities 102 to 126; 24 Depression questions, comprising modalities 127 to 280; and 7 Demographics questions, comprising modalities 281 to 324. Now, of these, the supplementary modalities include four modalities from the first two question sets (i.e., General Health, Activities), taken as supplementary modalities because of their sparsity – with total numbers of responses from the 7403 respondents being 1, 2, 16, 2. Other supplementary modalities are all modalities from the General Health, Depression and Demographics questions, comprising 19, 24, and 7 questions, and these then comprise, respectively, 101, 154 and 44 modalities. Adding the latter gives 299 modalities, and then with 4 (the sparse ones) with them, this leads to 303 modalities here being supplementary modalities. The main variables here are the Activities, with 7 questions, comprising 25 modalities. In the following we direct attention to the supplementary modalities.
It was found that only the General Health questions were relevant for the top ranked cosines of all these modalities, in all 303, with the first factor. The positive (tending towards: “A lot of difficulty”) first factor projections were the case for the 1st, 2nd, 5th, 7th and 10th of the top ranked cosines. The negative (tending towards: “No difficulty at all”) first factor projections were the case for the 3rd, 4th, 6th, 8th and 9th of the top ranked cosines.
The question for the 1st and 4th response modalities in the list of top ranked cosines with the first axis: “And how much does your health now limit you with these activities… [activities you might be doing during a typical day]”.
The question with modality responses that was the case for the 2nd, 5th and 8th of the top ranked cosines: “And how much does your health now limit you with this activity [in climbing several flights of stairs]”.
The question for the 6th and 7th of the top ranked cosines is related to climbing several flights of stairs.
For the 3rd of the top ranked cosines, there was this question: “Does your health now limit you in moderate activities such as moving a table, pushing a vacuum cleaner, bowling or playing golf”.
Finally, for the 9th and 10th of the ten top ranked cosines with the first factor, there was this question: “And during the past 4 weeks, were you limited in the kind of work or other activities as a result of your physical health”.
In summary, we can conclude the following here. Firstly, we have a planar, 2-dimensional mapping that explains essentially all of the information content in the data. Secondly, having focused attention here on action and activities, it is found that the first axis, hence the alignment associated with it, is the predominant interpretative outcome. As noted, this focusing on activities was both to be open to the increasing flow of secondary, contextual data that we can expect, arising out of modern technology. Such technology is often labelled Internet of Things or smarter cities. It is wanted also, to consider a “No, no difficulty at all” outlook in relation to activities and physical action, movement and motion. That has been seen in Figure 4. Directly from this there can follow the clustering or characterizing of the respondents. Finally, the most influential and relevant questions in the surveying that underlies the data source were listed, and it was then noted which these were, what question set they were related to, and their ranking.
What we also consider an important outcome is this: baselining, i.e. contextualizing, against the “No, No difficulty at all” activity modality, cf. section 2. In the “No, No difficulty at all” perspective, we have a clear and coherent reference group for what we can label as mental capital.
5 The Challenges and Opportunity in Big Data Analytics: Analytics Focus and Contextualization
Let us consider practical aspects of accounting for contextual relationships. The context could provide the baseline or benchmark for what the data represents. Perhaps one could present a case for the domain from which the data comes, being contrasted with some other domain. That latter can be quite formal, and algorithmically well specified, for example, let us consider, in the long established methodology of canonical correlation analysis. Below, however, we will just look at meaningful structural relationships between more than one focus of interest, and relate these, quite briefly, to “field” as a focus of interest, and “homology” as an association linkage between certain properties in these fields. Those terms, field and homology, come from the work of eminent social scientist, Pierre Bourdieu.
In Lebaron and Roux (2015), p. 43, there is this citation of Bourdieu: “I make extensive use of correspondence analysis, in preference to multivariate regression, for instance, it is because correspondence analysis is a relational technique of data analysis whose philosophy corresponds exactly to what, in my view, the reality of the social world is. It is a technique which ‘thinks’ in terms of relation, as I try to do precisely in terms of field.”
5.1 Towards Behaviour and Activity Analytics, for Mental Health
It is noted in Kleinman et al. (2016) how relevant and important mental health is, given the integral association with physical health. From Kleinman et al. (2016) there is the following: “… parity between mental and physical health conditions remains a distant ideal”. “The global economy loses about $1 trillion every year in productivity due to depression and anxiety”. “Next steps include … integration of mental health into other health and development sectors”.
In Cooper et al. (2016), page 4, under the heading of “Five Ways to Wellbeing”, reference is made to “mental capital and wellbeing”. On page 14, a section is entitled “The ‘mental capital’ values of the outdoors”.
In Murtagh (2010), and further described in Murtagh (2017), an ontology is assumed or created for the domain of application. This leads to the main variables in the analysis being taken from a high level in the taxonomic hierarchy, and then using lower level variables (in the taxonomic hierarchy) as supplementary variables. Such variables can be discipline-related, so that there is quite a natural taxonomic hierarchy at issue. This is at issue and to be further pursued in this work, relating to content-based and qualitative analytics of published research literature: Murtagh et al. (2017). Because higher order concepts are being employed for the primary and main attributes, relative to lower order concepts that enter as supplementary attributes, it directly follows from the smaller number of higher order concept attributes that the inherent factor space dimensionality is reduced. In this sense, therefore, following from the metaphor of a microscope, there is the ability to helpfully focus in on the interpretation of the data.
5.2 Employing Big Data to Contextualize Sampled Data, with a Proposal for Lifestyle Analytics
In the main analyses carried out in this article, it was noted that a motivation was to assume quantifiable action- and activity-related data to have a central and baseline role in the analytics. Arising out of this perspective, let us consider contexts where data is voluminous and varied.
Consider the essential requirements for contextualizing Big Data analytics. A major challenge in Big Data analytics is the bias due to self selection. The consequences and repercussions for statistical sampling in Big Data analytics are discussed in Keiding and Louis (2016). In the contribution to the discussion in Keiding and Louis (2016), we point to field and homology analytics, following the work of eminent social scientist Pierre Bourdieu. We also point to the calibrating potential of Big Data. In general, and in the work presented here in this article, in practical settings, the aim is to determine the most revealing coupling of mainstream data and context. This is technically processed in Correspondence Analysis through use of the main and the supplementary data elements, i.e., individuals or objects, attributes and modalities.
We propose that analytics based on Bourdieu’s work, based on MCA, (encompassing e.g. field, homology, habitus, etc.) should be a main analytics approach in many current areas of work, including smarter cities, analytics of Internet of Things, security and forensics (including trust and identity), Big Data, etc.
5.3 Lifestyle Analytics Requires Contextualization
In Zhang et al. (2012)
relating to activities of daily living (using the acronym, ADLs) of the elderly and those in poor health, there is use made of “contextual information from uncertain sensor data”. There is this: “… our algorithm learns directly from incomplete data, and inhabitants’ behavioural patterns are characterized using the learned probability distribution over various activities. The model is used to infer the activities, and the inhabitants who have carried them out.” For example, the monitoring of a kettle boiling water and perhaps some other sensor, these sensor data are used to model and predict some aspects of “making a tea with milk and sugar”, “making coffee with milk”, etc. Further predictive models are at issue inZhang et al. (2014), leading to a mobile app that will trigger personalized reminders.
The foregoing examples are such that sensor systems provide contextual data, as also do movement and other types of activities, with the main attention given to health and mental well-being.
5.4 Other Vantage Points: Social Activity in the Context of Health, and Health in the Context of Social Activity, and Analogously for Social Media.
An objective of this article has been to contextualize the analysis of large and multi-faceted data sources. For example, there may be health research in the context of social characteristics. Also there may be social research in the context of health characteristics. In Murtagh (2016a), and as reported above in section 4, there is use of an adult psychiatric morbidity survey, 2007 data in England, encompassing a household survey, neurotic symptoms, common mental disorders, and socio-demographics. Another large application to Twitter sources, with approximately 55 million tweets per annum, is at issue in the work of Murtagh (2016b). This can be social media, i.e. Twitter, research in the context of social and individual characteristics, and it can be social and individual activity research in the context of social media. The context of social media implies being well expressed and well represented by this form of social media.
In this work, we have sought to provide perspectives and focus for the analytics of behaviours and activities, that can be related to the determining of forms of mental capital. We discovered in our analyses how a few forms of activity play a very central role in such contexts as health and depression, and demographic characteristics.
This work was based on one year’s surveying, and it is planned to be further pursued by having a pre-prepared approach to investigate trends over time and other patterns that may be discovered in the data.
In line with our discussion in Keiding and Louis (2016), additionally we will be responding to the problem of bias in big data sampling and related representativity. We also seek to bridge the data with decision-making information. In the sense of the latter, we are bridging data analytics with position-taking, i.e. decision-making.
One final point is to note that factor space mapping methods can render interpretation difficult, when the number of variables or of modalities of the variables are very large. One reason for this is just how mapping and display becomes when there is a lot to be analysed, interpreted and understood simultaneously. However it may be the case that Multiple Correspondence Analysis or any related methods can be taken as a preliminary or intermediate technique in order to produce, or set up the data for, other methods. Examples of the latter often include clustering, and statistical modelling or other methods may be the case. That simply leads to the conclusion, that Multiple Correspondence Analysis or any related methods can be very appropriate as an early stage in the analytics carried out. Such methods may be a means of having valuable, relevant and new perspectives and vantage points on the data.
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