Auctus Search¶
This notebook introduces you to searching and loading urban datasets using Auctus, a data discovery tool integrated with UrbanMapper. We’ll guide you through finding datasets, profiling them, and loading them for analysis.
Further readings:
Let’s dive in! 🚀
import urban_mapper as um
# Start up UrbanMapper
pluto_buildings = um.UrbanMapper()
Searching for Datasets with Auctus¶
Let’s begin by searching for datasets related to 'PLUTO' using Auctus. This step queries the Auctus system and displays initial results so you can see what datasets are available.
collection = pluto_buildings.auctus.search_datasets(search_query="PLUTO", display_initial_results=True)
Profiling the Selected Dataset¶
Next, we’ll profile the dataset you’ve selected from the search results. This gives you a closer look at its structure and contents to ensure it’s the right fit for your analysis.
pluto_buildings.auctus.profile_selected_dataset()
Loading the Dataset from Auctus¶
Once you’re happy with the dataset, this step loads it directly from Auctus into UrbanMapper, making it ready for further use.
dataset = pluto_buildings.auctus.load_dataset_from_auctus()
Integrating the Dataset with UrbanMapper’s Loader¶
Finally, we’ll take the loaded dataset and integrate it into UrbanMapper’s loader. By specifying the latitude and longitude columns, we prepare the data for geospatial analysis.
pluto_data_loader = pluto_buildings.loader.from_dataframe(dataset).with_columns("latitude", "longitude")
pluto_data = pluto_data_loader.load()
pluto_data
Wrapping Up¶
That’s it! 🎈 You’ve successfully searched for a dataset with Auctus, profiled it, and loaded it into UrbanMapper. You’re now ready to explore more features, like creating urban layers or enriching your data.