In this project, we aimed to identify one or more optimal locations to open a new brewery in the twin cities, Minneapolis and St. Paul, Minnesota. As there already exists a vibrant community of small, independent breweries in the area, we looked for locations that do not already have breweries nearby.
In this project, we aimed to identify one or more optimal locations to open a new brewery in the twin cities, Minneapolis and St. Paul, Minnesota. As there already exists a vibrant community of small, independent breweries in the area, we looked for locations that do not already have breweries nearby. Additionally, we thought it to be advantageous for breweries to be in close proximity to restaurants, as a possible destination for diners to meet before or after a meal. Hence we also analyzed restaurant density in proximity to the brewery locations and attempted to identify areas with few breweries but high restaurant density. Our conclusions will be based primarily on proximity to restaurants and existing breweries. We sought to identify areas distant from the nearest breweries, with many restaurants nearby. Our approach and results were data driven, and we concluded with suggestions for the best possible areas to open a new brewery or taproom in the twin cities.
We gathered json files with neighborhood boundary data for the twin cities, and we called the Foursquare API to gather data about restaurant and brewery venues in the twin cities. The data was cleaned and formatted into a dataframe, and several new features were introduced, such as the number of restaurants/breweries in a fixed radius from each venue and the distance to the nearest brewery. With this information, we created choropleth maps of the restaurant and brewery densities by neighborhood, and we applied machine learning to cluster the data.
We used two unsupervised clustering algorithms to group the restaurants and breweries, k-means *and *DBSCAN. Each method has advantages and disadvantages. K-means is an iterative algorithm that puts each venue (observation) into a cluster. In this algorithm, each observation is a part of a cluster; there are no outliers. The number of clusters is determined before running the algorithm. On the other hand, DBSCAN (density-based spatial clustering of applications with noise) looks for density-based clusters; i.e. clusters where observations within the cluster are ‘close’ to one another with regards to some metric. This algorithm does not cluster all observations; some are left as outliers. Additionally, the number of clusters is an output of the algorithm. Two key parameters are specified before running the algorithm, eps and min_samples. The eps parameter represents a radius centered about each venue, and min_samples represents the minimum number of observations that must be contained within the epsilon ball in order for that observation to be considered a core point. Neighboring core points and their neighbors are then grouped as clusters.
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The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
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