Statistics: Final Project
Course number and name
Background and Deliverables
As a data scientist, I was contracted to create visualizations for Massachusetts state’s government. The state government asked that three specific data visualization solutions be developed. The state budget office has asked for a PowerPoint presentation regarding storm activity to establish a monetary reserve and allocate standby emergency resources in areas where they are needed most within the state. The state’s emergency management agency (EMA) group has asked for an interactive dashboard they can utilize to analyze storm damage data for risk assessment, allocating standby emergency equipment/resources, and responding to media inquiries regarding damage estimates during and after weather events; the interactive dashboard’s underlying data is automatically updated as new events are entered. Lastly, the state would also like to draft a pamphlet/web page with storm safety information for public members. This pamphlet/web page should include some interesting visualizations and statistics to engage the intended audience and reinforce the importance of preparedness in severe weather events.
Audience Analysis and Data Visualization
The need to collect data is to present information; information presentation is useful if it is done visually. Data visualization plays an intrinsic part in how information is understood by those who need it to make a critical decision (Ward et al.,2010). The evolution of most data into Big Data calls for means to represent the complex data that aids decision making. As the “time of Big Data” gets going, perception is an inexorably key instrument to figure out the trillions of columns of information created each day (Bikakis,2018). Information perception helps recount stories by curating information into a structure clearer, featuring the patterns and anomalies. A decent perception recounts a story, eliminating the clamor from data and featuring valuable data (Ali et al.,2016).
Based on the three-target audience, there will be a need to represent the information collected visually but with different sophistication and granularity since decision-making varies. That means the information presented should aid the process of decision making for the three audiences. Understanding different perspectives will reduce the distance opposing views and maybe the key to efficient audience targeting (Zapata & Katz, 2014);
Target audience 1: The state budget office of Massachusetts
The budget office is a sophisticated entity, and the decision made affects millions of lives. The difference in education levels, understanding, ethnicity, and race affects how the data should be presented (Bikakis, 2018). The general characteristic of this audience is educated, informed with available information but not with the specifics. Setting up a monetary reserve relies on many factors such as population, most affected regions in terms of damage and displacement, and the available money to implement the financial reserve decision. The appropriate format of information presentation should be accessible to take care of these varying factors hence the use of a time series graphs with colors to show the degree of severity of past storms then with the integration of QlikView to give the granularity needed and also the large volumes associated with the state’s budgetary office. The QlikView presents the Qlik Sense that can aid the future approximation of costs so that allocations can be done in advance before the storm. The software’s ability to handle larger volumes of data and present the information visually in a manner that can be easily understood by the decision-makers through the PowerPoint presentation using slides with analytics of the past allocation and future allocation estimations and the scale of the damage for each county of Massachusetts.
Target audience: Massachusetts’s state emergency management agency (EMA) group
This target group has a vast interest in risk assessment, resource allocation, and data used by third parties like the media. The general characteristic of this target audience is educated, well informed with all specifics, and well conversant with emergency planning requirements. The use of spatial data mapping, indicating high-risk and low-risk areas, will play a vital role in the visualization of the data needed by the EMA group. The functionality of Tableau which allows for mapping by inputting the latitude and longitude that allows for connection with other geospatial files to demonstrate custom geography. Tableau’s use can be integrated into the dashboard requested to highlight the needed information, which will aid the decision-making process. The dashboard can also present a platform that can be used to address the media’s queries by showing which are the most at-risk areas and even advise the residents of such regions appropriately on how they can seek help (Ginis et al.,2018).
Tableau’s use for the EMA group will allow for a more analytical approach since a decision made is likely to have a massive impact on risk assessment and the lives of the Massachusetts residents (Eisemann et al., 2018). The data used in this case for higher sophistication and granularity data should be rich in value to be analytical enough for the excellent decision-making process. The data needs to be more granular, and it should be elemental sufficient to answer the queries of parties such as the media. Tableau can be incorporated with other forms of data presentation to enable the EMA team to use visuals to digest the fast information presented; care needs to be taken to avoid flooding or overwhelming the audience with information that is considered as not being necessary. The
Target audience: The residents of Massachusetts
This target audience has little use of data analytics. They are interested in the actions taken due to the data presented. Still, they are more interested in visualization, which then drives their decision-making to prepare a severe storm. This audience has the most significant variation of all the three audiences; the audience’s age varies from 10-100, diverse ethnicity, multiple languages, and numerous cultures. The use of colors, charts, and white spaces will satisfy this audience’s needs (Bak et al.,2017). The use of Excel to present this data visually will be sufficient for this group. The use of categorical data presented in terms of charts and graphs will go along way to help the public’s preparedness for the storm. The granularity and sophistication for this audience should be low as possible; the main objective is to make the information easy to understand and digest and command a quick and best reaction possible from the data and information presented.
The formatting of the visuals for the three audiences will empower incredible agreement or great misconception and misunderstanding. For every deliverable, the target audience must be considered all in all, for their merits and shortcomings. Proper organization helps the undertaking lead to making visuals focused on lucidity and exact comprehension of the depicted data. For all audiences, the font style should be one that is anything but difficult to peruse.
For all the perceptions, I suggest utilizing the Times New Romans, Calibri, or Arial text style families, as these text styles are straightforward, simple to-peruse typeface. For headers and titles in all expectations, the textual style should be intense, and all CAPS, focused or left-adjusted, and 12—16 point text dimension, contingent upon the size of the visual this should complement the data significantly. For all keys, legends, and any axis marking in any visuals, the text style should be 9-10-point text style, focused and aligned on the left side, with no different augmentations. This textual style type emphasizes only the required data without overwhelming a visual with words. All visuals for the three audiences should avoid over-marking information and focus on the fundamental idea presented.
Possible Feedback Methods for Each Audience That Will Uncover Possible Comprehension or Technical Problems
Center gatherings are an extraordinary method to guarantee that the client comprehends the information introduced and can give territories that could be improved. Another criticism technique is to send delegates to help prepare and watch the second objective crowd (Patel). This will permit them to get input and give close to ongoing answers for the client. For this scenario;
Ali, S. M., Gupta, N., Nayak, G. K., & Lenka, R. K. (2016, December). Big data visualization: Tools and challenges. In 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I) (pp. 656-660). IEEE.
Bak, S. H., Kim, J. H., & You, H. B. (2017). Implementation of public data contents using Big data Visualization technology-Map visualization technique. Journal of Digital Contents Society, 18(7), 1427-1434.
Bikakis, N. (2018). Big data visualization tools. arXiv preprint arXiv:1801.08336.
Eisemann, E. R., Wallace, D. J., Buijsman, M. C., & Pierce, T. (2018). The response of a vulnerable barrier island to multi-year storm impacts: LiDAR-data-inferred morphodynamic changes on Ship Island, Mississippi, USA. Geomorphology, 313, 58-71.
Ginis, I., Ullman, D. S., Hara, T., Huang, W., Becker, A., & Luettich, R. (2018). Developing a Coastal and Inland Hazard and Impact Prediction System for Extreme Weather Events in the Northeastern United States. AGUFM, 2018, NH53A-01.
Joy, A., Chheda, H., Chy, J., & Sun, Y. Visualization of Tornado Data.
Murphy, S. A. (2013). Data visualization and rapid analytics: Applying Tableau desktop to support library decision-making. Journal of Web Librarianship, 7(4), 465-476.
Patel, Neil. The five best ways to get feedback from your customers. Retrieved on 24 October 2020 from https://neilpatel.com/blog/best-ways-to-get-feedback/
Ward, M. O., Grinstein, G., & Keim, D. (2010). Interactive data visualization: foundations, techniques, and applications. CRC Press.
Wiggins, G. (2012). Seven keys to effective feedback. Feedback, 70(1), 10-16.
Zapata-Rivera, J. D., & Katz, I. R. (2014). Keeping your audience in mind: Applying audience analysis to the design of interactive score reports. Assessment in Education: Principles, Policy & Practice, 21(4), 442-463.
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