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.
As part of the logic for deduping projects from our multiple sources we sometimes make a call to mar api to get the missing mar id. Currently, that information is used to identify any duplicate project but not persisted in the output csv files (_project.csv & _addre.csv). We then make another mar api call in the cleaner code where we attempt to clean up any missing geocode for a given project.
To minimize unnecessary calls to mar api during the loading process, especially since we have a mar table, we should update the code to persist the resulting mar id during create_project_subsidy_csv() method.
As part of the logic for deduping projects from our multiple sources we sometimes make a call to mar api to get the missing mar id. Currently, that information is used to identify any duplicate project but not persisted in the output csv files (_project.csv & _addre.csv). We then make another mar api call in the cleaner code where we attempt to clean up any missing geocode for a given project.
To minimize unnecessary calls to mar api during the loading process, especially since we have a mar table, we should update the code to persist the resulting mar id during create_project_subsidy_csv() method.