Bringing open data to affordable housing decision makers in Washington DC. A D3/Javascript based website to visualize data related to affordable housing in Washington DC. Data processing with Python.
the DCHousing and DHCD data sets are being merged into the projects table; any time a filed is missing, it is given a Null value.
For our geographic zones, we want to encode this data instead of using nulls, so that filtering can work properly for these new buildings. We are already connecting to the MAR to get the mar address ID; the MAR has this information. We just need the cleaner to add it to the appropriate columns.
This should be done for all our zones:
ward
neighborhood_cluster (e.g. 'cluster 1'
neighborhood_cluster_desc (eg. 'woodley park and zoo')
zip
anc
census_tract
latitude
longitude
per additional notes in PR #371 will attempt to encode an additional field that has nulls based on available data resources and information I can gather.
the DCHousing and DHCD data sets are being merged into the projects table; any time a filed is missing, it is given a Null value.
For our geographic zones, we want to encode this data instead of using nulls, so that filtering can work properly for these new buildings. We are already connecting to the MAR to get the mar address ID; the MAR has this information. We just need the cleaner to add it to the appropriate columns.
This should be done for all our zones: ward neighborhood_cluster (e.g. 'cluster 1' neighborhood_cluster_desc (eg. 'woodley park and zoo') zip anc census_tract latitude longitude