NACIS 2016 Presentation Michael Page, Emory University Matthew Pierce, Emory University Alan Pike, Emory University Jason Yang, Emory University The Digital Lab of Emory's Center for Digital Scholarship produced a 3D geodatabase and geocoder of circa 1930's Atlanta, Georgia as part of its Atlanta Explorer Project which seeks to transform city directories and historical spatial data into geospatial tools and immersive visualizations for exploring the history of the city. This presentation discusses the methods used and lessons learned from the first phase of the project and how it has informed our strategy to produce geocoders for the years 1867-1930.

1. Atlanta Explorer
An Example in Historical Geocoding & the City
Michael Page, Geographer
Emory Center for Digital Scholarship & Department of Environmental Science

4. Atlanta
Explorer
Project
(modified)
1. Construct a circa 1930 historical geodatabase of
Atlanta – 1878 now in production
2. Provide the community with a historical
geodatabase model and methodologies – We are
now partnered with University of Sao Paulo,
Brazil to help map their city
3. Create historical batch geocoders of Atlanta to build
new datasets and facilitate research in Atlanta
Studies - expanded the geocoder years from just
1928 to prior 1878 - 1930
4. Create an online search tool for the public –
developed a 3D/VR prototype and now retooling
our buildings database and models
5. Produce example data sets -- started with racial
segregation now includes historical epidemiology
and several other geospatial layers
6. Provide a base map data of historic Atlanta for
cartographers to use – will be expanding available
years to 1878 to 1930
Atlanta ~1930 Topo
Sao Paulo ~1930
Topo Mosaic and
Historical Overlay

6. HYDROGRAPHY
ADMINISTRATIVE
BOUNDARIES
Data Extraction

7. Absolute (exact buildings are matched) vs. Relative (uses street network for address
interpolation) Geocoders
Map showing new house numbering systems,
courtesy University of Chicago
Student effort at Emory to digitize
places and match with addresses
(structures)

10. Students use machine learning in the
Center for Digital Scholarship to fix
errors in OCR’ed text
Opportunity: Speeds the process of creating
address databases that can be matched to
geography; working through years sequentially
reduces the amount of manual corrections
Problems: Python code must be modified for each
year because directories were structured
differently and different typewriters were used; in
some years there were major changes to street
names or the numbering system

11. Atlanta Street Network Historical Morphology
Orange = streets added since 1928
Red = streets that have been removed
Blue = streets that exist both now and in 1928
Green = unpaved roads, alleyways, paths

13. RULES OF HISTORICAL GEOCODER UNIQUE IDENTIFIERS
• The unique identifier is meant to always equate to a specific structure as situated
in a specific location.
• What might change year to year with a record is the box number and/or street
name, and/or the person who is listed as owner or occupant.
• New “structures” are always given the next identifier in sequence. If a place no
longer has a building or if a street is not there (this will occur frequently as we
work backwards in time) then the identifier (and record) is removed from that
specific year.
• It is possible that more than one unique identifier shares the same geographic
coordinates across time if one structure has been removed and another erected.
• These unique identifiers reflect an record (entry) in the city directories from
construction to removal of the structure.

15. Original prototype of a few city blocks centered on
Atlanta’s Flatiron Building

16. Incorporating
VR/AR in future
prototypes

17. New questions arise : How do we reconstruct the historical topography of cities?

18. Collective Access AWS

19. Michael Page
ECDS & ENVS
michael.page@emory.edu
Extent area of 3D Buildings and Terrain in Unity