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Maps are a primary method of displaying statistical data that comes from a geographical frame. Maps are aesthetically appealing and make it easier to identify geographic patterns in a data set. However, few introductory statistical texts and courses explicitly present maps as a way to display data. In this paper, we will present examples of differe...
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... We note that the functional nature of any spatial data is intrinsically geographic, being capable to reference point-level locations via coordinates (latitude or longitude) or area-level projections via shapefiles to intuitively display patterns, distributions, or trends of data within geographical boundaries that are complemented with descriptive place-based summary geostatistics (Adrian et al., 2020). Such capabilities are projected through maps which visualize real-life stories of a studied problem. ...
Our objective was to study the impact of vaccinations on COVID-19 pandemic indicators across different regions of Malaysia. We collected population-level pandemic data from government open sources from 1 January 2021 until 30 June 2022. The aggregated data was then analysed by rates for vaccinations, infections, hospital admissions, intensive care unit (ICU) admissions, and case fatalities according to five regions in Malaysia. From the cumulative data, a total of 4,456,066 COVID-19 cases that contributed to 489,210 hospital admissions, 292,897 ICU admissions, and 35,378 deaths were operationalized to regional-levels, coherently stratified by pandemic-control measures. Vaccination rates were computed based on the proportion of people within each region who completed their primary doses (27,275,616 people) and booster shots (16,230,989 people). Geographic visualizations, ecological correlations, and ordinary least squares (OLS) regressions for statistically significant effect quantification were synthesized. Region-specific geo-visualization using choropleth maps confirmed that the indicators of the pandemic were effectively controlled with vaccinations. It was observed that a percent increase in vaccination rates resulted in a significant decrease in the rates of infections, hospital admissions, ICU admissions, and case fatalities. This reduction in pandemic indicators was greater in populations with higher booster vaccination rates across the country. However, the magnitude effect of those suppression coefficients as explained by the populations’ vaccination showed different gradients and varying consistencies, indicating the influence of geographical variations and pandemic control measures in different regions. Vaccinations were largely effective in reducing pandemic indicators but were not powered to halt or zero them. Trend reductions varied by regions and by pandemic control measures in place, suggesting that interventions for pandemic control are highly influenced by geographical contexts, coexistent with a certain degree of sustained mitigation strategies.
... Students learned how to create maps so that they could investigate possible geographical patterns and to use a very simple text analysis to explore command themes. As stated in Adrian et al (2020), maps are a common component of our everyday lives and should be a part of the introductory statistics curriculum. Additionally, the exercise gave the students experience with formatting data before it is analyzed. ...
To be prepared for the modern world, students need to learn how to work with multivariate relationships as well as geographical and text data. In this paper, three class activities to investigate geographic data in conjunction with other standard categorical and quantitative data are described. The activities are described using the statistical software JMP, but modifications are given for using R. Modifications for undergraduate and graduate level work are also given. The activities have students explore data at an international level as well as local level. This paper illustrates classroom activities that demonstrate necessary scaffolding to move students beyond univariate and bivariate understandings as the curriculum shifts to keep up with modern data. The results of a ten-question survey to graduate and undergraduate students are given.
As data have become more prevalent in academia, industry, and daily life, it is imperative that undergraduate students are equipped with the skills needed to analyze data in the modern environment. In recent years there has been a lot of work innovating introductory statistics courses and developing introductory data science courses; however, there has been less work beyond the first course. This paper describes innovations to Regression Analysis taught at Duke University, a course focused on application that serves a diverse undergraduate student population of statistics and data science majors along with non-majors. Three principles guiding the modernization of the course are presented with details about how these principles align with the necessary skills of practice outlined in recent statistics and data science curriculum guidelines. The paper includes pedagogical strategies, motivated by the innovations in introductory courses, that make it feasible to implement skills for the practice of modern statistics and data science alongside fundamental statistical concepts. The paper concludes with the impact of these changes, challenges, and next steps for the course. Portions of in-class activities and assignments are included in the paper, with full sample assignments and resources for finding data in the supplemental materials.
As data has become more prevalent in academia, industry, and daily life, it is imperative that undergraduate students are equipped with the skills needed to analyze data in the modern environment. In recent years there has been a lot of work innovating introductory statistics courses and the developing introductory data science courses; however, there has been less work beyond the first course. This paper describes innovations to regression analysis taught at Duke University, a course focused on application that serves a diverse undergraduate student population of statistics majors and non-majors. Three principles guiding the modernization of the course are presented, along with how these principles align with the necessary skills of statistical practice outlined in recent statistics curriculum guidelines. The paper includes pedagogical strategies, motivated by the innovations in introductory courses, that make it feasible to implement skills for modern statistical practice into the curriculum alongside the fundamental statistical concepts. The paper concludes with the impact of these changes, challenges, and next steps for the course. Portions of in-class activities and assignments are included in the paper, with full sample assignments and resources for finding data in the supplemental materials.
