Description
Answer 1 :
Interactivity means communication with new information transmitted between two or more parties. Interactive data visualization is different compared to traditional static data visualization from two directions. From the viewers direction, audiences can trigger requests and change the views of how data are visualized. From the other direction, data can be kept updating and represented the changes in graphs. Both of these two directions can be considered as interactive data visualization, which has various benefits in contrast to static graphs.
Firstly, interactive data visualization is easier to explore as it supports changing parameters, plots and colors. Secondly, interactive data visualization can be manipulated instantly. Also, it usually provides the (near) real-time update. Last but not least, it is much easier to understand, which supports better decision-making and provides more important insights.
With saying all of the advantages, interactive data visualization also introduces much more challenges compared to static graphs and charts. Obviously, interactive approaches cost more than static data visualization. First of all, compared to static graphs, interactive data visualization asks for much more designs and advanced UIs, which skill sets may not have in the team already. Secondly, most interactive data visualization requires connecting to diverse of data sources for (near) real-time data and keep refresh graphs. Those skill sets may ask some engineering experience. Thirdly, static graphs can be treated as images that are easy to save and store. In contrast, saving interactive data visualization is not that straightforward. The tasks to achieve this goal includes preserving multiple layers, from data extraction, integration to the representation layer. Last but also important is, it is not an easy task anymore to represent nice job and keep interactivity function on different devices (mobile, laptop, and other bigger screens) for an interactive data visualization compared to present a static graph (Belorkar et al., 2020, pp. 13). Lets take a look at this example, Manhattan Population Explore Map. Well known as the densest city in the U.S, Manhattan resides 1.6 million people according to 2010 US census, and actually, there are nearly 4 million people during workdays. Thats why it is called sleepless Manhattan. On the other hand, Manhattan also has the highest ratio of daytime-to-nighttime population around 2 to 1. But are we clear how the population distributed block-by-block and hour-by-hour? Here comes this Manhattan Population Explore interactive map. Apparently, it is a cool map from the first of the view. You can filter by district and drag the day and time bar to view the population distributions. You can even switch between tags to retrieve more background information. Actually, as different viewers, urban planners, public safety managers, and tourists, they use this map very differently. As a powerful piece of data visualization, it is armed with several sharp techniques, such as visualization design, data modeling, map engine and graph engine.
Manhattan Population Explore Map. http://manpopex.us/
References:
Belorkar, A., Guntuku, S. C., Hora, S., & Kumar, A. (2020). Interactive Data Visualization with Python: Present your data as an effective and compelling story, 2nd Edition (Illustrated ed.). Packt Publishing.Manhattan Population Explorer. (2019).
Manhattan Population Explore Map. http://manpopex.us/
