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Sydney OpenStreetMaps data analysis using Python and MongoDB. Covers transforming, auditing, cleaning and wrangling data. Specifically focusing on cleaning street names and types, street speed restrictions, cycleway tags.

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Sydney OpenStreetMap data wrangling

The aim of this project is to analyse a metro extract of Sydney OpenStreetMap data, which includes the following steps:

  1. Auditing the xml data.
  2. Defining functions to clean problematic tags, street names and types, maximum speed values, and cycling-related tags.
  3. Transformation of data to Python dictionary and exporting to JSON file.
  4. Connecting to MongoDB and importing data.
  5. Additional examination of data in MongoDB using queries requested from Python.
  6. Concluding on the data wrangling process, benefits, and potential issues for future use.

Results

The results are saved in the Jupyter notebook file in the repository.

Files

The following auxiliary files can be found in the files folder in the repository:

  • sydney.zip: Zipped OSM XML file.
  • 2.1.Auditing_data.py: Code used for data auditing.
  • 2.1.Transform_data.py: Used for data transformation from xml to json.
  • 2.3.Overview_import.py: Overview of the files, import to MongoDB.
  • 2.4.Examine.py: Examination of imported data in MongoDB.

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Sydney OpenStreetMaps data analysis using Python and MongoDB. Covers transforming, auditing, cleaning and wrangling data. Specifically focusing on cleaning street names and types, street speed restrictions, cycleway tags.

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