nmarchio commented 2 years ago

kblock

Python tools for creating residential blocks, estimating population, and detecting informal settlements using geospatial data.

Repo Structure

/kblock

/cli

/deploy

Highest level code that contains bash scripts for deploying the CLI functions in /cli in a HPC environment.

0-ingest-data
1-prepare-blocks
2-centroid-buildings
3-model-population
4-compute-k
5-analysis

/data

/example

Data Sources

The method relies on three kinds of data:

Building footprints
Street and administrative delineations
- OpenStreetMaps from Geofabrik
- Database of Global Administrative Areas
Gridded population rasters
- LandScan 1x1 km rasters
- WorldPop 100 x 100 m rasters

Env Setup

conda create --name geospatial python=3.9.7 --yes
source activate geospatial
conda install -c conda-forge pygeos=0.10.2 --yes
conda install -c conda-forge geopandas=0.10.2 --yes 
conda install -c conda-forge urlpath --yes
conda install -c conda-forge requests --yes
conda install -c conda-forge dask --yes
conda install -c conda-forge dask-geopandas --yes
conda install -c conda-forge pyarrow --yes
conda install -c conda-forge rasterio --yes
conda install -c conda-forge xarray --yes
conda install -c conda-forge rioxarray --yes

nmarchio commented 2 years ago

Add midway cheat sheet: https://github.com/mansueto-institute/prclz-proto/tree/master/prclz/parcelization

nmarchio commented 1 year ago

https://github.com/mansueto-institute/kblock/issues/48