Educational & Group project
April 2023 – June 2023
Overview
Breda University of Applied Sciences approached the municipality of Breda with an offer to have the students perform analysis and create a dashboard using both publicly available data and an internal database, hoping to answer a problem statement that can bring value and change to the city of Breda.
The students were split in multiple groups of 5 where we had to create a problem statement, consider the value it could bring and the complexity of the proposed solution and develop an interactive dashboard to showcase our findings, culminating in a conference where multiple people of interest from the municipality and from the University can complete a survey and choose which 3 teams provided the best solutions.
The top 3 teams were then chosen to present their finding directly to members from the city’s council.
My team: me, Panna Pfandler, Wojciech Stachowiak and Thijn Van Oort; chose to take on the problem of housing within the city. We created a dashboard/tool which can be used to rank a person’s needs for safety, affordability, proximity to store, public services and other key factors which would then rank the neighborhoods. Our tool took into account both rent and purchase prices to determine the affordability. Our presentation was chosen as one of the three to present directly to stakeholders.
Dataset
As mentioned previously we used both publicly available data and an internal database to perform analysis on. We cannot disclose or show the contents of the internal database but the nature of it was a such: The city is divided into square grids, each with a unique identifier and belonging to neighborhoods, geo-data is stored and updated early about some aspects of the city. In addition there is increased focus on the city center and the foot traffic entering and leaving by means of sensors.
The publicly available data was sourced from three websites:
Approach
After processing we had a complete overview of the city, however choosing neighborhoods as the base level of our dataset meant that creating a machine learning model was not feasible. Initial analysis showed that there were multiple correlations between the indicators and the quality of life, which led us to creating a weighted system which would take into the account the relationship between all indicators and the quality of life metric we defined.
A big part of this project was ensuring we adhere to multiple Ethical Frameworks such as DEDA, ALTai and GDPR. Exploring all the possible ways our analysis could be biased, or have effects on the world at large. We were graded on the completion of multiple documents such as the Data Management report and Data Quality.
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