Sentiment Analysis of Social Survey Data for Local City Councils

Big data analytics can be used by smart cities to improve their citizens’ liveability, health, and wellbeing. Social surveys and also social media can be employed to engage with their communities, and these can require sophisticated analysis techniques. This research was focused on carrying out a sentiment analysis from social surveys. Data analysis techniques using RStudio and Python were applied to several open-source datasets, which included the 2018 Social Indicators Survey dataset published by the City of Melbourne (CoM) and the Casey Next short survey 2016 dataset published by the City of Casey (CoC). The qualitative nature of the CoC dataset responses could produce rich insights using sentiment analysis, unlike the quantitative CoM dataset. RStudio analysis created word cloud visualizations and bar charts for sentiment values. These were then used to inform social media analysis via the Twitter application programming interface. The R codes were all integrated within a Shiny application to create a set of user-friendly interactive web apps that generate sentiment analysis both from the historic survey data and more immediately from the Twitter feeds. The web apps were embedded within a website that provides a customisable solution to estimate sentiment for key issues. Global sentiment was also compared between the social media approach and the 2016 survey dataset analysis and showed some correlation, although there are caveats on the use of social media for sentiment analysis. Further refinement of the methodology is required to improve the social media app and to calibrate it against analysis of recent survey data.

Agricultural Transformation and Its Impact on Ecosystem Services and Human Well-Being in Peri-Urban Areas: The Case of Xi’an, China

There is a gap in understanding the relationships between the transformation of agricultural landscapes, ecosystem services and human well-being in the peri-urban fringe of major cities worldwide. In this paper, we use semi-structured interviews, perception surveys, social surveys and field mapping to examine linkages between agricultural and landscape transition, ecosystem services and human well-being in five sample villages in Xi’an metropolitan zone, China. The results indicate that: (1) Agricultural change has increased landscape fragmentation, with a shift from grain to more profitable horticulture and nursery production. The farming system is more diversified and exhibits a multifunctional character. (2) This transformation has had a significant impact on the character of the agroecosystem. (3) The agricultural transformation towards greater multifunctionality has increased the supply of ecosystem services, including tourism-related activities, potentially improving human well-being. (4) Different combinations of activities in the sample villages were evaluated with respect to a well-being index, indicating the importance of combining horticulture and tourism. (5) Linkages identified between agricultural transformation, ecosystem services and human well-being may have significant implications for potential approaches within future studies.

The political consequences of housing (un)affordability

The enormous growth in house prices in Europe since the 1990s has led to increasing concerns about the affordability of housing for ordinary citizens. This article explores the relationship between housing affordability – house prices relative to incomes – and the demand for redistributive and housing policy, using data drawn from European and British social surveys and an analysis of British elections. It shows that, as unaffordability rises, citizens appear in aggregate to become less supportive of redistribution, interventionist housing policy and left-wing parties. However, this aggregate rise, driven by the predominance of homeowners in most European countries, masks a growing polarization in preferences between renters and owners in less affordable regions.

Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data

<p>The Food Nutrition Environment Survey (FNES) is a survey of New Zealand early childhood centres and schools and the food and nutritional services that they provide for their pupils. The 2007 and 2009 FNES surveys were managed by the Ministry of Health. Like all the other social surveys, the FNES has the common problem of unit and item non-responses. In other words, the FNES has missing data. In this thesis, we have surveyed a wide variety of missing data handling techniques and applied most of them to the FNES datasets. This thesis can be roughly divided into two parts. In the first part, we have studied and investigated the different nature of missing data (i.e. missing data mechanisms), and all the common and popular imputation methods, using the Synthetic Unit Record File (SURF) which has been developed by the Statistics New Zealand for educational purposes. By comparing all those different imputation methods, Bayesian Multiple Imputation (MI) method is the preferred option to impute missing data in terms of reducing non-response bias and properly propagating imputation uncertainty. Due to the overlaps in the samples selected for the 2007 and 2009 FNES surveys, we have discovered that the Bayesian MI can be improved by incorporating the matched dataset. Hence, we have proposed a couple of new approaches to utilize the extra information from the matched dataset. We believe that adapting the Bayesian MI to use the extra information from the matched dataset is a preferable imputation strategy for imputing the FNES missing data. This is because the use of the matched dataset provides more prediction power to the imputation model.</p>

Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data

<p>The Food Nutrition Environment Survey (FNES) is a survey of New Zealand early childhood centres and schools and the food and nutritional services that they provide for their pupils. The 2007 and 2009 FNES surveys were managed by the Ministry of Health. Like all the other social surveys, the FNES has the common problem of unit and item non-responses. In other words, the FNES has missing data. In this thesis, we have surveyed a wide variety of missing data handling techniques and applied most of them to the FNES datasets. This thesis can be roughly divided into two parts. In the first part, we have studied and investigated the different nature of missing data (i.e. missing data mechanisms), and all the common and popular imputation methods, using the Synthetic Unit Record File (SURF) which has been developed by the Statistics New Zealand for educational purposes. By comparing all those different imputation methods, Bayesian Multiple Imputation (MI) method is the preferred option to impute missing data in terms of reducing non-response bias and properly propagating imputation uncertainty. Due to the overlaps in the samples selected for the 2007 and 2009 FNES surveys, we have discovered that the Bayesian MI can be improved by incorporating the matched dataset. Hence, we have proposed a couple of new approaches to utilize the extra information from the matched dataset. We believe that adapting the Bayesian MI to use the extra information from the matched dataset is a preferable imputation strategy for imputing the FNES missing data. This is because the use of the matched dataset provides more prediction power to the imputation model.</p>

Exploring Chinese folk religion: Popularity, diffuseness, and diversities

Folk religion, as the basis of the religious landscape in traditional China, is a highly syncretic system which includes elements from Buddhism, Daoism, and other traditional religious beliefs. Due to the shortcomings of denomination-based measurement, most previous social surveys have documented a very low percentage of folk religion adherents in China, and found almost no overlapping among religious beliefs. This study offers a quantitative portrait of the popularity, the diffuseness, and the diversity of Chinese folk religion. With the improved instruments in the 2018 China Family Panel Studies, we first observe that nearly 50% of respondents claim to have multiple (two or even more than three) religious beliefs and the believers of folk religion account for about 70% of the population. By using latent class analysis, this article explores the pattern of inter-belief mixing and identifies four typical classes of religious believers: “non-believers and single-belief believers”, “believers of geomancy”, “believers of diffused Buddhism and Daoism”, and “believers embracing all beliefs”. Finally, we find that the degree of commitment varies across these religious classes. Believers of folk religion are found to be less committed than believers of Western institutional religions, but as committed as believers of Eastern institutional religions.

Family Values in South Korean Society: Marital and Sexual Values in Gender, Age, and Protestantism Contexts

The purpose of this study is to examine differences in marital and sexual values according to gender, age, and Protestantism contexts using the 2012 and 2018 Korea General Social Surveys. Samples for marital values were from the 2012 survey (N=797 adults aged 20 and over), and those for sexual values were from the 2018 survey (N=550 adults aged 20 and over). There were four domains in marital values (happiness, child, cohabitation, and divorce) and three domains in sexual values (premarital intercourse, extramarital intercourse, and same-sex intercourse). We applied ANCOVA and post hoc analysis to examine the differences in each domain via gender, age, and Protestantism contexts. Results on marital values did not show significant interactions between gender, age group, and Protestantism contexts, while age differences were consistently significant in all domains of marital values. In results about sexual values, there were significant interactions between gender, age, and Protestantism contexts in all domains of sexual values. The findings of this study may promote an understanding of the dynamics and diversity of Korean contexts on marital and sexual values.

Refining the Short Social Dominance Orientation scale (SSDO): A validation in seven European countries

People and societies differ in their tendency to justify inequalities and group hierarchies, a motivation that has been labelled social dominance orientation (SDO). In order to efficiently measure this motivational tendency, Pratto and colleagues (2013, https://doi.org/10.1177/1948550612473663) proposed the four-item Short Social Dominance Orientation (SSDO) scale. The present study comprehensively assesses the SSDO scale’s psychometric properties in seven European countries (Austria, Czech Republic, Germany, France, Hungary, Italy, and Poland). Using large and diverse samples from these countries, we propose a measurement model to assess the scale’s structural validity and we assess measurement invariance (MI), reliability, and convergent validity. Results suggest that the scale is sufficiently reliable, shows theoretically predictable and consistent correlations with external criteria across countries, it exhibits at least partial scalar and partial uniqueness MI across the seven countries and full MI across gender. These findings offer support for the psychometric quality of the SSDO scale and its usefulness for cross-national and multi-topic social surveys.