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Data Vis

Data Visualization, Processing (Python), Interactive System, Database, OpenCV

SUMMARY

The uniqueness of this project lies in its ability to clearly pinpoint the best locations to reach specific target audience(s) at specific times over a period of 30 days. By tracking clusters of target groups, we only display data where we are certain that the selected audience is strongly represented at the given location(s). 

Interactive Target Audience Map

TOUCH POINTS

Since I chose the center of Utrecht as the focus, my visualization is intended for entrepreneurs and businesses operating in this area, as well as advertisers looking to place advertisements or media content in this region.

Due to my interest in Facial Recognition Technology and the discussions regarding privacy policy during the feedback session, I aimed to focus my concept on a positive application of facial recognition that ensures the privacy of end-users while providing them with value for the application.

 

FOR NEW BUSINESSES

As an entrepreneur, it is essential to know where your target audience is located on the map. Therefore, I intend to map the people in the city center of Utrecht, grouping their diversity. This will provide insights to entrepreneurs and make it easier for them to reach their target audience. Whether it’s for a pop-up store, street vendors, market stalls, or placing advertising boards, a better location means a larger reach.

This tool can offer valuable insights for those seeking to improve their market position. It can help generate more leads or contribute to running a campaign. I have chosen the city center of Utrecht as the location, simply because it holds the largest gathering of people here in Utrecht.

To achieve this, I will collect anonymous data from passersby in the most generic way. This approach respects privacy and ensures the accessibility of the data to every entrepreneur.

DESIGN STRATEGY

To lay the foundation for my visualization, I aimed to create a clear distinction between men and women using a slider to demonstrate changes over time. The insight I intended to provide did not focus on the exact location of each city, but rather on anchor points where specific target groups, such as men between 20 and 25 years old, frequently pass by or gather. These clusters of target groups were monitored to eventually link locations to their respective audiences.

With this data, it would become evident where a particular target group can be found most frequently at specific times and locations. For instance, we could plan the most effective approach to reach women under 30 with a clothing advertisement during weekends.

EXPANDING TAGS

In my ideal vision, I also wanted to collect additional attributes of city-goers, such as colors, patterns, and other visual variables to establish a style or archetype. Using this information, I would have added unique symbols for different archetypes to represent them on the map. Examples of these archetypes could be “Urban,” “Artsy,” “Trendy,” “Vintage,” “Punk,” etc. Due to technical limitations, I limited the visualization to age group and gender.

 

DATA MAPPING

To realize this technically, I employed a grid of cells placed over the map. I then used mock data to fill the grid with random values corresponding to the actual longitude and latitude coordinates within the center of Utrecht. A scripting tool helped determine the extreme coordinates, and a random value generator created values between these extremes, which were then mapped over the grid cells. To make the data more understandable, I had to scale the ellipses or targets, depending on the age group.

 

UNDER THE RADAR

For the visualization, I drew inspiration from a sonar radar known from submarines. I chose this design because of its clarity, contrast, and simplicity, which easily draw focus. Combined with a map of the city center, it becomes a tangible “target map.”

INTERACTION

The initial design only featured a time slider to determine the time of day. Based on feedback from my instructor, I made several important adjustments. He suggested that multiple options should be available to compare the data for better insights.

By adding an extra slider, users can now compare both the time and days with each other. For example, you can now determine that different hotspots exist for a particular age group on weekends compared to weekdays.

To clearly represent the age groups, I displayed an indication number for each target, clarifying the corresponding age groups. Numbers 1 to 6 respectively represent their corresponding groups, as indicated in the legend at the bottom.

GHOSTING

To illustrate the difference compared to the previous hours, I experimented with adding ghosting effects. This leaves a trail of the previously displayed data. Additionally, I made adjustments to the targets on the map. Using the “PGraphic” data-type, I added a glow effect scaled based on the age group.

version 01

Preview of the first functional prototype.

Final Prototype

NOTEBOOK

© 2025 Yassine Raddahi - All Rights Reserved.