An introduction on spatial analysis with python.
This project uses a study case to introduce basic geometry operations and fundemental spatial analysis skills for newcomers.
The crime index is calculated for every postcode, similar to the burglarystatistics at racv website.
The datasets are publicly available from following websites:
| Geometry with Shapely | 01_It-starts-with-a-point.ipynb |
| Tabular analysis with Pandas | 02_Tabular-analysis-with-pandas.ipynb |
| Spatial analysis with Geopandas | 03_Spatial-analysis-with-geopandas.ipynb |
| Victoria household info | 04_Melbourne_households.ipynb |
| Victoria crime index | 05_Melbourne-crime-index.ipynb |
| Visualising spatial data | 06_Visualise_spatial_data.ipynb |
Following this tutorial will require recent installations of:
- Python >= 3.6
- requests
- numpy
- shapely
- matplotlib
- pandas
- geopandas >= 0.3.0
- matplotlib
- rtree
- PySAL
- xlrd
- openpyxl
- folium
- seaborn
- mapclassify
- Jupyter Notebook
If you do not yet have these packages installed, it is recommended to use the conda package manager to install all the requirements (you can install miniconda or install the (larger) Anaconda distribution, found at https://www.anaconda.com/download/).
Once this is installed, the following command will install all required packages in your Python environment:
conda create -n spatial python=3.7
conda activate spatial
pip install -r requirements.txt
!pip install xlrd !pip install openpyxl
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