Challenge 25 - Zurich Clustermap

<p><strong>Challenge 25: </strong>Zurich Clustermap</p><p><strong>Level:</strong> Medium</p><p><strong>Description:</strong> You have been called upon by Zurich’s city council to interpret a growing dataset of citizen-submitted service reports. To ensure equitable and efficient resource distribution, the council wants to break down the city into smaller, more manageable clusters. Can you pinpoint a systematic method to group Zurich’s neighborhoods based on these incoming reports?</p><p></p><p><em>Beginner-friendly objective:</em><strong> </strong>Read and preprocess the dataset.</p><p><em>Intermediate-friendly objectives: </em>Extract features that can differentiate and quantify areas of the city; cluster the city into sections and visualize the clusters.<br><br><strong>Solution Summary:</strong> The workflow begins with reading and preprocessing geospatial data, followed by setting up language translation components using Google Translate. The solution then moves on to geospatial transformations, including creating H3 grids for aggregating reports into Polygons. Data normalization and aggregation are performed to create appropriate features for the analysis. Finally, the workflow clusters the grids based on their similarity, and visualizes the clusters with connecting the same cluster grids.<br><br><strong>Solution Details:</strong> The workflow begins with the "GeoFile Reader" node, which imports geospatial data from a GeoPackage file. This data is then processed through a series of "Table Creator" nodes to establish reference tables for language codes. The "Single Selection Configuration" nodes allow users to select input and output languages for translation tasks. The "Create H3 Grid" node generates an H3 grid based on the geospatial data, while the "Projection" node reprojects the data into the Swiss coordinate system (EPSG:2056). Data normalization is achieved using the "Normalizer" node, which applies Min-Max normalization to selected columns. The "GroupBy" nodes aggregate data based on specific criteria, such as service names and H3 cell indices. The "Joiner" nodes perform inner joins to combine datasets based on matching criteria, while the "Column Filter" nodes manage the inclusion and exclusion of specific columns. The "Webpage Retriever" and "XPath" nodes handle the language translation process by retrieving and extracting translated text from Google Translate. The "AZP" node performs clustering on the geospatial data, using the "geometry" column for spatial information and "Grid Count" as a boundary parameter. The "Dissolve" node merges geometries based on the "Cluster ID" column, simplifying the spatial data. Finally, the "Geospatial View" node visualizes the results on a map, with features colored according to the "Cluster ID" column. The workflow concludes with the "Component Output" node, which outputs the translated text and geospatial data for further analysis or reporting.</p>