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.
For the Preservation the Preservation Network meeting on Tuesday 11/7, we will be asking potential users about their data priorities - which outside data sources are most important to them. To spur thinking about the type of insights they could get by connecting external data sources, we want to show them 2 example visualizations that combine the Preservation Catalog with at least one external data source.
This visualization will combine all entries from the project table with matching entries from the dc_tax table (must use intermediate table of parcel). It will then group these by neighborhood cluster to show the sum total of the assessed land value of the affordable properties in each neighborhood cluster.
Recommended presentation of this data is a side-by-side map-based heatmap and bar chart.
Horizontal Bar:
[ ] Same data,
[ ] Sorted from highest to lowest total taxable land value
[ ] Bar labeled with neighborhood cluster short name (not just cluster #). Need to create a short name (e.g. "Chinatown/N.Capital" instead of "Downtown, Chinatown, Penn Quarters, Mount Vernon Square, North Capital Street". Choose either most prevalent neighborhood and use "etc." or the two prominent neighborhoods on the outer bounds of the cluster.
[ ] Stretch goal 1: Link bar and map via hover (hover over bar chart or map section adds highlighting color to both entries).
[ ] Stretch goal 2: Tool tip with Neighborhood Cluster full name, total land value, total assessed value, total number of properties, and total assisted units (project.proj_units_assist_max). Average land value per unit calculation.
Map:
[ ] Neighborhood clusters
[ ] Heatmap value indicates sum of taxable land value.
[ ] Stretch goal 3: If time allows, alternate toggleable view is taxable total value, and/or 'past' vs 'current' value also in toggle.
This will be a PROTOTYPE graph, so choose your favorite graphing tool and keep in mind that the code will eventually be replaced by D3. Quick and dirty is the way to go. If you already know D3 and think we can get it done in time, that will be the most long term friendly, but a mockup for Tuesday is more important.
Tableau Public (drag-and-drop interface, but custom shapefiles is difficult and might just be in pro? Custom shapefile guide
Resources
\scripts\analysis\join_tables_exploration.sql has start of script for joining the data (note, column names have been updated to no longer be MiXedCase so script needs editing)
-\scripts\small_data\Neighborhood_Clusters_shapefile contains neighborhood cluster boundaries.
Work can be split between someone on SQL/data extraction and formatting and a visualization team.
NealHumphrey
commented
7 years ago
For the Preservation the Preservation Network meeting on Tuesday 11/7, we will be asking potential users about their data priorities - which outside data sources are most important to them. To spur thinking about the type of insights they could get by connecting external data sources, we want to show them 2 example visualizations that combine the Preservation Catalog with at least one external data source.
This visualization will combine all entries from the
projecttable with matching entries from thedc_taxtable (must use intermediate table ofparcel). It will then group these by neighborhood cluster to show the sum total of the assessed land value of the affordable properties in each neighborhood cluster.Recommended presentation of this data is a side-by-side map-based heatmap and bar chart.
Horizontal Bar:
project.proj_units_assist_max). Average land value per unit calculation.Map:
This will be a PROTOTYPE graph, so choose your favorite graphing tool and keep in mind that the code will eventually be replaced by D3. Quick and dirty is the way to go. If you already know D3 and think we can get it done in time, that will be the most long term friendly, but a mockup for Tuesday is more important.
Options:
Resources
\scripts\analysis\join_tables_exploration.sqlhas start of script for joining the data (note, column names have been updated to no longer be MiXedCase so script needs editing) -\scripts\small_data\Neighborhood_Clusters_shapefilecontains neighborhood cluster boundaries.Work can be split between someone on SQL/data extraction and formatting and a visualization team.