Making Spatial Arguments - 1880 and 1910 Manhattan

For this week’s blog, I focused on the New York City dataset. I initially attempted to use the tornado dataset but ran into technical issues with the tools, so I decided to shift my focus. My goal was to examine population, race, parental birthplace (foreign or native born), and the two most common occupations, domestic servant and clerk, to identify potential patterns. I used the ArcGIS Online datasets for the 1880 and 1910 Manhattan Dwelling records.

From my initial maps, it’s immediately clear that there was a dramatic population shift between 1880 and 1910. During this thirty-year period, New York City’s population doubled, driven largely by massive waves of immigration; a trend clearly reflected in the visualizations below.




In 1880, the population was relatively sparse, with most residents concentrated around 123rd and 125th Streets.

In the unweighted 1880 map, only three Black residents are shown living within the dwellings. When the data is weighted, the Black population becomes nearly invisible. In contrast, the white population clearly dominates, as shown by the close similarity between the total population map and the white population map.

   

By 1910, the population had grown dramatically, with notable density around 122nd Street and Madison Avenue. The population was almost entirely white; a fact made evident when the black population was weighted equally with the white population, resulting in minimal visible color on the map.

The 1910 density maps further illustrate that the majority of residents who could afford to live in buildings surrounding the park were white. Similar to the 1880map, once weighted, the Black population nearly disappears from view. To better represent their presence, I used directional distribution to show where Black residents lived, even though they were sparsely depicted on the map. I attempted to perform the same analysis for 1880, but there were too few data points for ArcGIS to complete the calculation.

Next, I examined the birthplace of residents’ parents, comparing those who were foreign-born to those who were native-born. Given the scale of immigration during this period, I expected to find more foreign-born parents in 1880 and 1910.

Given the limited population coverage in 1880, it’s unsurprising that the areas of density appear in similar locations across the maps. The visualizations indicate a higher density of residents with foreign-born parents compared to those with native-born parents. This makes sense in light of the city’s rapid growth, though I was still somewhat surprised by the extent of the difference.

By 1910, I expected to see an even higher concentration of foreign-born parents, and the maps confirmed this trend.

Next, I examined changes in the two most common occupations found in these dwellings, domestic servant and clerk. In 1880, domestic servant was the predominant job title. I would be interested in exploring how common this occupation was among immigrants during this period.

The 1910 data shows a nearly identical pattern, with domestic servant again emerging as the most frequent occupation. Interestingly, clerks appeared to live in close proximity to one another, suggesting possible occupational clustering. It would be interesting to compare these findings with data from 2025 to see which jobs are now dominant in similar dwellings.

I also used the mean center analysis to determine whether the spatial locations of the categories I mapped earlier corresponded with the density maps.

The results were largely consistent with expectations. In 1880, the overall population, white residents, and clerks were concentrated near the central, high-density areas. Domestic servants and residents with foreign-born parents were more centrally located as well, suggesting that populations to the west of the main cluster also tended to have foreign-born parents. Black residents appeared only in the limited areas shown on the map.

The 1910 maps reveal a similar pattern. Population density, white residents, foreign-born parents, and clerks are all situated near the denser areas surrounding the park. The mean centers often fall near the park because the dwellings encircle it, and the central point lies outside the block dwellings themselves. Native-born parents appear more evenly distributed across the blocks, placing their mean center almost directly in the middle. As seen in the earlier maps, the Black population was very small and primarily located in the western portion of the blocks, represented by a single point on the map.

Finally, I attempted to analyze spatial proximity between residents with native-born parents and those with foreign-born parents. Unfortunately, I encountered repeated errors when using the 1880 dataset. I suspect this issue stemmed from the dataset being locked and lacking an Object ID or another variable to match with the Near Table I created. As a result, I only have maps for 1910, which appear nearly identical, likely due to an error in the process. I plan to continue refining this analysis and will update the blog if I am able to produce more accurate results.

To answer our blog questions:

What are meaningful time bins?
I think using 5 or 10 year intervals (such as 1880, 1885, 1890, 1895, 1900, 1905, and 1910) would provide meaningful insight into population changes over this thirty-year period. The population more than doubled within the blocks I studied, which is remarkable. Breaking this timeframe into smaller intervals would make it possible to see how quickly that increase occurred and whether it followed a steady or rapid growth pattern.

How does the periodicity change the results of the analysis?
For my research, the data confirmed that the majority of residents were immigrants or first-generation Americans, with a large portion working in domestic service. It also showed that there were very few Black immigrants, likely because most migration to New York at this time came from Europe. Analyzing the same categories in five-year intervals could reveal fluctuations that the thirty-year comparison might obscure. For instance, examining shorter periods might show increases or decreases in immigration that, in turn, influenced every category I studied, from occupation to racial and parental demographics.

What evidence do we get from the results of the spatial analysis that could inform historical questions?
The results raise several interesting historical questions: Why was domestic service such a common occupation? Was it primarily held by women or men? Were these neighborhoods considered working-class areas, and if so, how long did they remain that way? Did residents settle near others with similar backgrounds, such as Irish or German immigrants, and did those community ties influence the types of jobs they held?