Then
The massive earthquake hit early morning on Wednesday, April 18th. Houses toppled, gas mains ruptured causing fires that destroyed neighborhoods made mostly out of wood frame buildings(Rumsey, 2017). The devastation was widespread with more than 28,000 buildings destroyed including residences, workplaces, and public infrastructure (Casualties and Damage after the 1906 Earthquake, 2022). Map 1 shows the area affected by the fire from April 18th-21st. Businesses large and small were impacted, losing critical access their customers needed to reach them. For many people who called San Francisco home, this event turned their daily lives upside down.
Today
To assess the public opinion of the socio-economic landscape of San Francisco’s South Market in the 21st century, I turned to Nomadlist - a website that collects user-generated data regarding cities around the world and the quality of life in them for nomads. The Neighborhoods tab on Nomadlist is a rich example of public deep maps - maps that show people’s opinions, feelings, and emotions regarding a place. Screenshots 1 and 2 show my favorite titles for the parts of South Market(Best Neighborhoods in San Francisco, 2020). The blue background corresponds to a “Suits” category and the yellow corresponds to “Hip.” The main point of categorization is to show the similarity between parts of San Francisco. This also symbolizes a way of pointing at the socio-economic and potentially cultural flattening of the South Market. In this analysis, “flattening” is used to refer to the decrease in the diversity of residents’ occupations and the variety of building uses in the neighborhood. This leads to the appearance of the neighborhood as comprised of homogenous and monolith communities.
Some curious titles from Screenshot 1 are:
- “Land of suits and hotels”
- “Lines for $15 salads”
- “Techies and game design students”
Some titles worth noting from Screenshot 2 (the area I am focusing on) are:
- “More expensive tiny apartments”
- “ ‘artist’ ”
- “Skid Row of SF” (referring to a neighborhood of Los Angeles with one of the largest homeless populations in the US)
- “Worse Skid Row”
- “5 lattes and other snacks beyond reach”
A historical question sparks from this: Could the earthquake of 1906 be one of the reasons for the "flattening" of the socio-economic landscape of San Francisco's South Market neighborhood during the last century?
Onto QGIS
Sources
To address this tentative historical question, I use the US Census records(US Census Records - SF - Google Drive, 2013) and the Sanborn insurance map from 1900. These are primary sources of different types - textual and visual. To make the textual source usable, it needs to be processed using OCR but since I don’t have experience with it, I transcribed the Census sheets by hand into a Google Sheet. To make the visual source usable, you need to locate it on the current map of San Francisco. The difference in primary source types also signifies a difference in the processes used to map them. Thus, I will use georeferencing for the Sanborn map and geocoding for the US Census. It is also crucial to note that my primary sources are public transcripts, easily accessible online. If the sources I needed for this project came from hidden transcripts, accessing them in the first place would be a challenge. The impact of power structures of the time is visible in the Census records. Information is practical, concise and in some places, it seemed that enumerators finished their work just to go home sooner. There is a lot of missing data, such as surnames and occupations. The collection of Census data was done to keep track of things and thus, they are not the most useful resource for qualitative understanding of how people actually lived.
Process: Georeferencing and Geocoding
I decided to work with block 142 due to the diversity of building use I noticed in it. I georeferenced a Sanborn map onto the OpenStreet Layer in QGIS by selecting about 10 control points to match the location of the block on current-day streets. For clarity, I rotated the map 45 degrees to have Market and Mission Streets in a horizontal plane instead of a diagonal one.
I used the Census sheets from blocks 141 and 142. From block 142 I retrieved information for the occupation category on Market Street and from block 141 information on Mission Street. After I transferred the essential data into a Google Sheet, I created occupation categories that I would plot on the map later on. I took agency in this task and created 4 main occupation categories: business, creative, service, and technical. The missing data and occupations such as “student” are grouped into the “All other” category. Then, I used the Geocoding tool in Google Sheets to create coordinates for the data entries from Census(QGIS Tutorial #4, 2022). After plotting the geocoded data, the locations were not on block 142. It meant that I needed to tinker with the longitude and latitude for proper mapping(see Longtitude2 and Latitude2 in Screenshot 3). After some 60 attempts of tinkering with the coordinates in the Google Sheet, I got Map 3.
From Map 3, it seems that there are a few points plotted. It is due to the assignment of many people under the same house number. When zoomed in, you can see that the points are stacked, revealing more occupation diversity.
Since this block seemed to have a variety of building uses, I plotted polygons that are color-coded to correspond to the colors of occupation categories. I used the third QGIS tutorial(QGIS Tutorial #3, 2022) and the written entries from the Sanborn map to plot the polygons(Map 4).
South Market’s Socio-Economic Landscape 1900
By combining the georeferenced Sanborn map layer, the occupation category layer from the US Census, and the building use layer from the Sanborn map, the final result is Map 5.
Food for Thought
The South Market’s Socio-Economic Landscape map helps us understand the diversity of the social geography of the South Market neighborhood through the occupation types and the building usage. Thus, this map shows the physical manifestation of San Francisco’s social landscape. We see a variety of building types and jobs and when visually comparing it with the user-generated Nomadlist maps, we witness signs of a potential flattening of the features of the socio-economic landscape(decreased diversity of occupation types and building usage categories). The next step for this analysis would be comparing the 1900 census records with the ones from 1910(after the 1906 earthquake) using the occupation category as a unit of comparison. Plotting these data points and analyzing the differences in the diversity of occupations should follow.
I have faced some practical difficulties geocoding historical data. The longitude and latitude were incorrectly geocoded, requiring me to fix the coordinates with the Google Sheets functions. For example, increase longitude to move east and decrease it to move west; increase latitude to move north and decrease to move south.
A conceptual difficulty was considering the different ways to show the manifestation of the socio-economic landscape without providing too much information for the audience. I found the combination of polygons and data points to deliver the right amount of important information. The decision to make the polygons transparent was a deliberate effort since I wanted to show as much of the Sanborn map as possible, including the writing and different markings.
QGIS provides a very generous interface with a multitude of editing options, styles, and ways to modify your maps. While this is helpful to a professional mapper, for a beginner like me amount of options was sometimes overwhelming. Another benefit of QGIS is the ability to effectively georeference and geocode data, which often aids you in deepening your analysis. It also allows comparing the current state to the studied period to assess change over time. A drawback of digital tools like QGIS is the knowledgable “feel” they give to historical questions drawn from maps and Census records. This should be addressed with transparency in mind and could be implemented by adding screenshots and a description of your process so that the audience understands where the data came from and why the final map looks the way it does.
Something that stood out to me about the potential of digital tools for humanities inquiry is the ability to connect the past and the present while avoiding presentism. QGIS allows merging historical maps with the existing city fabric without portraying them as the same, thus allowing us to keep some distance from the past. Nonetheless, too much reliance on digital tools might stripe digital humanities from their ability to be human-centered. When working with digital tools and loads of data, we tend to overlook the people behind data points. This danger is something a historian needs to always be aware of.
Bibliography
1906 Map of San Francisco, California. Showing limits of the Burned Area destroyed by the Fire of April 18th-21st, 1906, following the Earthquake of April 18th, 1906. (2017). New World Cartographic. https://nwcartographic.com/products/antique-map-sanfranciscofire-harts-1906?variant=40292723728
Best Neighborhoods in San Francisco. (2020, June 27). Nomad List. https://nomadlist.com/neighborhoods/san-francisco
Casualties and damage after the 1906 Earthquake. (2022). Usgs.gov. https://earthquake.usgs.gov/earthquakes/events/1906calif/18april/casualties.php
Rumsey, D. (2017). David Rumsey Historical Map Collection | Pre-Earthquake San Francisco 1905 Sanborn Insurance Atlas. Davidrumsey.com. https://www.davidrumsey.com/blog/2011/6/27/pre-earthquake-san-francisco-1905-sanborn-insurance-atlas
QGIS Tutorial. (2021, January 23). At These Coordinates; At These Coordinates. https://atcoordinates.info/qgis-tutorial/ (not cited in-text, but used for making the map legend)
QGIS Tutorial #3. (2022). QGIS Tutorial #3. Google Docs. https://docs.google.com/document/d/1Y9pO1q_sXiPC-wxSrrPsFOO8DGOtR9KCRVgFkNUWi4E/edit
QGIS Tutorial #4. (2022). QGIS Tutorial #4. Google Docs. https://docs.google.com/document/d/1l2oENerkFbKKkUpMfZk244vHXUpzJH6eY0NhusNY-JU/edit
US Census records - SF - Google Drive. (2013). Google.com. https://drive.google.com/drive/folders/1Kea24Ej78WIu4eylvXYrccGe-HIJvVJZ