One of the important aspects when dealing with high-dimensionality data is to separate the different dimensions according to their data type (Ward et al., 2015, p. 46). I would propose the following categorization:
city: nominal (but don’t forget that a city is also a geographic place)
phone number: nominal (technically ordinal, but doesn’t make much sense)
Most visualizations are meant to be helping the decision-making process (Ware, 2013, p. 18). I think in this case – especially with this being a business case – one should ask themselves what relationships of the data one wants to analyze:
- Are you looking for geographic trends?
- Or maybe for age related trends, e.g. change of the amount of debts depending on the age.
Exemplary one could group entries by city and calculate the mean of the debt. This mean could then be displayed on the map using a color encoding to mark more or less attractive potential markets. When choosing colors for the illustration, one should specially understand the target audience, as meaning of color is different in different cultures (Madden et al., 2000, p. 91).
As always when creating visual representations, it is important to understand the target audience. To iterate on and improve on the visualization an approach such as human-centered design might be useful (Munzner, 2015, p. 69).
As for interaction with the visualization, one should follow the “Overview first, zoom and filter, then details-on-demand” (Shneiderman, 1996, p. 2) guideline. This could include allowing for filter by certain data point, for example age (or age-group) in this case.
I think the two exclusively nominal attributes are probably best shown as text, perhaps in an interactive form such as a hover query (Ware, 2013, p. 348).
I hope these inputs are a good enough starting point to get you off the ground. The sources I have provided at the bottom include many more
Madden, T. J., Hewett, K., & Roth, M. S. (2000). Managing images in different cultures: A cross-national study of color meanings and preferences. Journal of International Marketing , 8 (4), 90–107. https://doi.org/10.1509/jimk.126.96.36.19995
Munzner, T. (2015). Visualization analysis & design . CRC Press.
Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. Proceedings 1996 IEEE Symposium on Visual Languages , 336–343. https://doi.org/10.1109/VL.1996.545307
Ward, M., Grinstein, G. G., & Keim, D. (2015). Interactive data visualization: Foundations, techniques, and applications (Second edition). CRC Press, Taylor & Francis Group.
Ware, C. (2013). Information visualization: Perception for design (Third edition). Morgan Kaufmann.
Full transparency: This answer has been written as part of a university course on data visualization concepts.