Data Visualization
This graph has a clear topic and content of analysis, we can see each color what it signify, the age group is clear, and the gender separation is clear.
The content is made explicit because we can see in the chart that colors separates the education level and the each gender is on a side as well as the age group is from lowest to highest
- Source of data: there is no source of data; we have no tittle or nothing that indicates for us from where we have this data.
- Indicators of measurement (what is measured and how), we have on the graph the indictors of measurements that are age and thousands.
- Timeframe and scales, for the number of women and men it is in multiple of 5s starting from 0 and for the age is intervals of 5 starting from the age of 20.
- Type of data and Sample size, the type of education that each gender had, this data is done on more than 40 thousands people (male and female)
- Color, chart types and visual arrangement of text, numbers and charts, we can see that the type of education is separated by color; it is a bar chart where number of men/women in thousands is in horizontal and the age in vertical.
The visualization expand our understanding of the topic/problem, because we can clearly see the evolution and how it changes/shifted.it is achieved by coloring the type of education and by choosing the bar chart. The tittle and from where the data came from is missing from this graph. It influence what can be learned or known because we do not have the full data visualization, we do not have a proof that this is correct.
The data visualization speak from a epistemological perspective because the chart is taking the data from a population that is not specific and from different genders.
These visibilities are produced by the type of data collected and the indicators or the visual arrangement and design because we can clearly understand the graph. The invisibilities are shown with the data source because we do not know form where this data is taken and for what country.
The colors and visual design of the charts plays a major role form contribute or shape how the graphic is read because without them we won’t understand the graph and we won’t be able to separate between the types of education for each gender
This graph has a clear topic and content of analysis, we can see each color what it signify, the age group is clear, and the gender separation is clear.
The content is made explicit because we can see in the chart that colors separates each opinion about gender attitude by % from 1997–2012
- Source of data: there is a source of data written in the buttom; we have a tittle but nothing that indicates for us from where we have this data or from which country.
- Indicators of measurement (what is measured and how), we have on the graph the indictors of measurements that are percentage and years. The opinion os measured in % and the years.
- Timeframe and scales, the percentage is in scale of 10% and the years do not have a specific scale.
- Type of data and Sample size, the gender attitude, we do not know on how many people this data is made.
- Color, chart types and visual arrangement of text, numbers and charts, we can see that the different opinion is separated by color; it is a line chart where years are in horizontal and the percentage of opinion is in vertical.
The visualization expand our understanding of the topic/problem, because we can clearly see the the difference in opinion and how it changes throughout the years .It is achieved by coloring the lines and choosing this type of chart for this subject. The population chosen and from what country is missing from this graph. It influence what can be learned or known because we do not have the full data visualization, we do not have a proof that this is correct.
The data visualization speak from a particular epistemological perspective because the chart is taking the data from a population that is not specific and from different genders.
These visibilities are produced by the type of data collected, the indicators the data source, the visual arrangement and design because we can clearly understand the graph and know from where the data is taken from. The invisibilities are shown with the data source because we do not know from what country this data is taken and in the data collection because we do not have the gender separated.
The colors and visual design of the charts plays a major role form contribute or shape how the graphic is read because without them we won’t understand the graph and we won’t be able to separate between the types opinions that are taken and in which year.
This graph has not a clear topic and content of analysis, we can see each color what it signify, but the name of the countries that this data is tested on does not appear.
The content is made explicit because we can see in the chart that colors separates each gender and if they are joint with colors and the percentage of the land use certificate holders.
- Source of data: there is no source of data; we have nothing that indicates for us from where we have this data.
- Indicators of measurement (what is measured and how), we have on the graph the indictors of measurements that is percentage and the gender by color
- Timeframe and scales, there’s no timeframe and scales.
- Type of data and Sample size, the sex of the land use certificate holders, the sample size is unknown.
- Color, chart types and visual arrangement of text, numbers and charts, we can see that the sex of the land use certificate holders is separated by color; it is a pie chart where number of the % of land owner is shown with the gender in color
The visualization narrow our understanding of the topic/problem, because we can not clearly understand the chart. The source of data is missing from this graph. It influence what can be learned or known because we do not have the full data visualization, we do not have a proof that this is correct.
The data visualization speak from a epistemological perspective because the chart is taking the data from a population that is not specific and from different genders.
These visibilities are produced by the type of data collected and the indicators or the visual arrangement and design because we can kind of understand the graph. The invisibilities are shown with the data source because we do not know form where this data is taken from.
The colors and visual design of the charts plays a major role form contribute or shape how the graphic is read because without them we won’t understand the graph.
