NACIS 2020: Mapping COVID-19 Coast to Coast, and Around the World

In developing an interactive map for the COVID-19 Mobility Network, we developed a unique set of map projections and dynamic map labels for the clearest and most effective communication of our data. In this session I’ll walk through the stages of developing and refining several iterations of our map, starting with a US-only Albers projection customized to include all US territories and possessions, to a global map combining hierarchical administrative data from many different sources. I explain how we used a variety of open source tools (from D3, Dirty Reprojectors, GDAL/OGR, MapShaper, Makefiles, and numerous Mapbox command-line tools and APIs) to create a repeatable and highly customizable workflow.

Video recording: https://www.youtube.com/watch?v=DeDYDql4O3Q

Transcript

1. Mapping COVID-19 Coast to Coast, and Around the World Alan McConchie // Stamen Design NACIS Practical Cartography Day October 14, 2020 Slides: sta.mn/8vj Video: youtu.be/DeDYDql4O3Q
2. COVID-19 Mobility Data Network Working with Facebook’s Data for Good and the COVID-19 Mobility Data Network, we mapped movement in regions around the world during the pandemic. The COVID-19 Movement Trends tool seeks to help policy makers answer critical questions: How well have physical distancing interventions worked? Where do communities need the most support with their distancing efforts? How and when, and how quickly, should we re-open different cities, states and countries? visualization.covid19mobility.org
3. Mobility trends as small multiples visualization.covid19mobility.org Stay-at-home percentage Relative change in mobility
4. Mobility trends in the wild Governor’s COVID-19 press conference with Secretary of the California Health and Human Services Dr. Mark Ghaly, April 13, 2020
5. The mobility dashboard: version 1 (R.I.P.)
6. Wanted: map projections for the US (the whole US)
7. Daniel Immerwahr “How to Hide an Empire: A History of the Greater United States”
8. Custom map projections in TileMill and Leaflet (aka “Lying to Leaflet”) oceanplanning.org climate.audubon.org natgeo.com/climate-change/explore-amazonia more info (NACIS PCD 2015) >
9. Custom map projections in TileMill and Leaflet (aka “Lying to Leaflet”) more info (NACIS PCD 2015) > ogr2ogr -f GeoJSON -t_srs "EPSG:2163" -s_srs "EPSG:4326" /vsistdout/ unprojected.geojson | ogr2ogr -f GeoJSON -t_srs "EPSG:4326" -s_srs "EPSG:3857" projected.geojson /vsistdin/
10. Custom map projections in PostGIS and CARTO dsl.richmond.edu/panorama/overlandtrails dsl.richmond.edu/panorama/forcedmigration more info (NACIS 2015) >
11. Custom map projections in PostGIS and CARTO more info (NACIS 2015) > SELECT cartodb_id, name, ST_Transform(ST_SetSRID(ST_Transform(the_geom,2163),3857),4326) as the_geom FROM my_table
12. Reprojection with Dirty Reprojectors using D3.js under the hood more info (dirty-reprojectors github) > cat original.geojson | dirty-reproject --forward patterson > projected_patterson.geojson
13. Lo Bénichou Mapping the US elections: Guide to Albers USA projection in Studio
16. Albers USA + Territories now available: github.com/stamen/geo-albers-usa-territories npmjs.com/package/geo-albers-usa-territories
17. Labels Label data must be reprojected too. Note how county labels only appear for the currently-active state.
18. Make & makefiles ❤ Reproducible command-line workflows Learn more from Seth Fitzsimmons at NACIS PCD 2016 >
19. Makefile morsels just a taste
20. International mobility data for 15 countries visualization.covid19mobility.org Stay-at-home percentage Relative change in mobility
23. FIPS, NUTS, and GADM
24. FIPS: Federal Information Processing Standard (US only) NUTS: Nomenclature des unités territoriales statistiques (Europe only) GADM: Database of Global Administrative Areas (worldwide)
30. Thank you! Alan McConchie @mappingmashups alan@stamen.com Slides: sta.mn/8vj Video: youtu.be/DeDYDql4O3Q Explore the map and let us know what you think! Visualization.covid19mobility.org Thanks to the COVID-19 Mobility Data Network, coordinated by Direct Relief and researchers from the Harvard T.H. Chan School of Public Health. Thanks also to Facebook’s Data for Good program.
31. #exhaust
32. #exhaust
33. #exhaust
34. Miller vs Patterson
35. Tried pasting a Wikipedia table into LibreOffice, because what's the worst that could happen? Oh look, some SVG images came along for the ride!
36. #exhaust