In this module, you will be creating a 10-minute presentation to “pitch” your project proposal to a potential stakeholder. A stakeholder may be a hypothetical (or real) potential funder for your project and/or a boss/manager for requesting approval to begin the project. Either way, a stakeholder will be interested in understanding every component of your project, therefore, you should follow the organization outlined in your proposal.

You will also be completing an ESRI GeoAI training of your choice to understand how you can use ESRI machine learning models. Machine learning models can be used with a variety of data including population data, climate change, demographics, crime, and many other datasets. Therefore, it has applications to virtually any geospatial discipline.

Assignments

Creating PowerPoint (or other presentation software) slides is an art and it is important to make your presentation slides "catching" so your viewers stay engaged throughout the whole presentation. As you design slides, consider these factors (also see Style for Students).

• Use Images, Not Text: Keep your slide simple by using graphics, videos, figures, and maps instead of text. This also allows you to create an engaging narrative instead of relying on slide text. You can use images when presenting data, demonstrating trends, simplifying complex issues, and visualizing concepts/ideas.
• Simple: Keep your slides as simple as possible. If you have complex slides, use transitions and animations to direct your viewer's eye to focus on the point being emphasized
• Titles: Generally, it is recommended to avoid using titles, since the large font size directs the user's eyes from the primary content of the slide. However, there are several ways to guide your research through the sections of your slides with titles.
  1. Don't use titles, but explain each slide appropriately.
  2. Create a "work plan" at the bottom and highlight each section as you move through each slide (e.g. Background > METHODS Results).
• Rule of 4s: If you are using text, only include bullet points. The rule of 4 says to include no more than 4 works per line and no more than 4 lines per slide.
  • Bullets: If you use bullet points, consider adding animation/transitions so each point appears as you discuss it, to keep your audience engaged
• Color: Make sure the colors are readable, particularly based on the background color you choose (light background, use dark text and/or dark borders around images if necessary)
• Proofread: Make sure you don't have any spelling errors!

References:

ESRI offers several machine learning models that can be implemented to evaluate patterns, predictions, and hotspot analysis. Several different machine learning models can be used, including regression analysis, clustering, hot spot analysis, classification, temporal trends, and prediction.

Time series analysis has applications for many different geospatial disciplines. Machine learning has been applied to emergency response and disaster management by analyzing wildfires, flooding events, earthquakes, pandemics, and other human impacts including infrastructure, economy, and the environment. It has been applied to geospatial intelligence and crime modelling to track criminal activity, human trafficking, terrorist activities, location prediction of criminal activities, and many more. Additionally, it has been applied to track environmental phenomenon including wildfire prediction, flooding/stream flow prediction, climate change, invasive species modelling, endangered and vulnerable species population prediction, and many more.

A review of the machine learning models that can be applied to emergency management disciplines. Disaster events have common characteristics, such as spatiotemporal evolution and cascading effects. For example, fires, earthquakes, and floods can all impact road accessibility and power outage, while dynamics used in modelling fires could be adapted to also model disease spread during pandemics [10]. Machine learning can play a key part in the development of novel tools to identify useful characteristics of disasters in due time, provide means for situational awareness, facilitate modelling of various phenomena, and alert for potential cascading effects.

Source Kyrkou et al, 2015.

In short, machine learning models have an innumerable amount of applications and research continues to apply the models in new, innovative ways. Therefore, it is valuable for you to understand how it works and how to apply it to projects of interest to you. ESRI offers low/no code options that make machine learning and GeoAI accessible to new and novice AI users.

References:

The proposal presentation is a short description of your body of work that explains the effort in a way such that any listener can understand it in a short period of time.

To create an appealing presentation, be sure to include a lot of images and avoid (at all costs) long paragraphs of text. You may use some bullet points to guide your presentation, but don't use them to read your slides. Be sure to practice your presentation beforehand several times before you begin recording. You want to create a cohesive narrative that sounds like an organic conversation. The more you practice, the more you will sounds like an expert on your topic (which you are!).

Project Presentation

1. This week you will prepare a PowerPoint presentation with a voiceover that includes all the elements of your project proposal including:
   • The background
   • needs assessment
   • user analysis
   • concept selection
   • prototype and cognitive walkthrough
   • system requirements
   • data
   • heuristic evaluation
   • cost benefit analysis
   • Gantt chart
   • UML design.

You will upload your presentation to the discussion post and to the “My Media” mini conference in Canvas (see the assignment for more details).

Once you are ready, move on to the  Lesson 10 Mini-Conference Presentation Assignment for assignment description and submission instructions.