This challenge tackled a less discussed problem in cities: visual pollution. Many cities are flooded by countless outdoor advertising panels. It is known that visual aspects are crucial in the urban planning process since each modification can generate obstruction to urban elements. Besides the city aesthetics, it is also possible to argue that too much outdoor advertising can decrease well-being and increase social inequality. It is clear that reducing outdoor advertisements can create a positive impact on the city. The goal of this challenge is to do so with a minimum impact on the reduction of the audience.
Create a model that optimizes the number and location of the outdoor advertising positions to minimize the visual impact in urban environments. Better dimensioning and integrating outdoor advertising positions in cities should also be considered.
GOAL 11: Sustainable Cities and Communities
The following datasets were provided to the participants:
Using domain knowledge, it was noted that some panels were missing in the dataset. It was also recommended to include in the dataset the type of outdoor advertisement, advertisement cost, and the direction the advertisement panel is facing.
More concretely, on the dataset, it would also be essential to have the time frame to which the dataset is related (e.g., does it include the COVID period?) and how the different metrics are calculated (e.g., daily views and max visibility).
Other interesting data to be considered in the future can be the socio-demographic information of each of the parishes and the companies that run the billboards to minimize the problem of the company.
Due to the nature of this challenge, very different approaches were proposed by the competing teams. One proposal was to use a clustering algorithm (K-Means and K-ProtoTypes) to cluster the location of advertisement placement and to remove the advertisements with fewer views from the cluster.
Other teams decided on a metaheuristics approach. One approach was based on a local or neighborhood search that optimizes for the spread of advertisement panels while simultaneously maintaining the number of views high. Another approach was to train a regressor with a Gradient Boosting model to predict the number of views at a particular location, generate random geographical points, and for those points predict the number of views. Later on, optimize on a set of parameters the points to be selected (e.g., spread from concentration points or a number of views).
One team proposed three methodologies: removing panels with less contribution, spreading out panels by selecting areas with the biggest viewership, and a genetic algorithm.
As expected, the biggest urban centers have the highest concentration of outdoor advertisements (see Figure 1). But also the viewership distribution was not equal: 56.7% of panels produced 80.6% of total views, and 71.4% of panels produced 90.4% of total views. This shows the potential for better distribution of the outdoor advertisement.
It was noted that if it were possible to predict the number of unique viewership for each geographical point (e.g., by using mobility data), it would be possible to create an optimization model which could be used for optimizing the distribution of outdoor panels while also minimizing the number of panels.
