Who is the Public in our Public Libraries?

Abstract

Considering the privatization of space within urban landscapes and growing disparities in equity, libraries have incredible potential to shed light on community needs and the power of public space. This paper not only investigates the ways in which library services/uses vary across space but also the ways in which they map onto the diverse socioeconomic and demographic characteristics of the neighborhoods that they serve. In this study, I map socio-demographic variables from the 2021 American Community Survey onto library service areas generated using Thiessen polygons. I surveyed Chicago Public Library web profiles to collect library service data, which was then analyzed alongside the socio-demographic variables. The results of this research reveal a relationship between socio-demographic variables and library services among library service areas, with noticeable variability across the city — which is reflective of Chicago’s longstanding history of racial and economic segregation. Socio-economic and demographic characteristics significantly relate to the services, programming, and amenities found in Chicago libraries. Rather than using libraries in their “traditional” book lending sense, disadvantaged areas seem to rely more on non-traditional services that libraries offer, like internet access and social supports. Considering challenges that these neighborhoods might face, like lower incomes and educational attainment, and limited access to the internet, these patterns may be due to targeted efforts of local libraries to meet the needs of their constituents. However, this study also reveals gaps in access, specifically related to libraries that do not offer mental health and citizenship support services but are situated in communities that would benefit from them.

Methods

Collect and Process Socio-Demographic Variables

The social and demographic variables (see Table 1) used in this study were obtained from the 2021 American Community Survey (ACS) 5-year estimates and 2020 decennial United States Census. Socio-demographic variables from the ACS were obtained at the Census tract level. Population counts were obtained both at the tract and block level from the US Census.

Table 1: Neighborhood characteristic variables considered in this study

Define Library Service Areas

Libraries do not have formally defined geographic service areas and it was necessary to create spatial units for this analysis. The Chicago Data Portal maintains a data set of all Chicago Public Library Locations, contact information and usual hours of operation. The locations of the 81 libraries were plotted as points in GIS using their latitude and longitude coordinates from this data set. GIS was used to create proximal zones by generating Thiessen polygons from the library points.

# Generate Thiessen/Voronoi polygons from library points

vorpoly <- st_union(points)%>%
  st_voronoi()%>%
  st_collection_extract("POLYGON")%>%
  st_sf %>%
  st_cast


# Because I had to union the points prior to st_voronoi() function the library attributes were lost
# Rejoin attributes from library points data to voronoi polygons 

vorjoin <- st_join(vorpoly, points)

# Clip Thiessen polygons by Chicago boundary 
chi_vor <- st_intersection(vorjoin, chi)%>%
  st_set_crs(st_crs("+proj=utm +zone=16 +datum=WGS84"))
  

Population Weighted Reaggregation (PWR)

The Thiessen Polygons representing LSAS, unfortunately, do not map neatly onto the Census geographies of blocks that contain the socioeconomic data. To address this, I apportion census data to service areas using a population weighted reaggregation technique.

In order to increase the granularity of this data I transformed the sociodemographic data from one scale (tract) to another (block). I created a weighting factor that was the proportion of the census tract population in the individual block. This proportion was representative of the population contribution of an individual block towards the total population of the census tract that it belongs to.

# Calculate a  weighting factor that is the proportion of the census tract population in a every individual census block.This proportion is representative of the population contribution of an individual block towards the total population of the census tract that it belongs to.

weights_chi <- cook_blocks_tbl %>% 
  mutate(tract=substr(GEOID, 1, 11)) %>% 
  arrange(tract) %>% 
  merge(cook_tracts_tbl, by.x="tract", by.y="GEOID", all.x=T) %>% 
  mutate(block=substr(GEOID, 1, 16)) %>% 
  mutate(pop_wt=block_pop/tract_pop, hu_wt=block_hu/tract_hu)

The proportion weight was then applied to the social demographic variables at the tract level so the result would be a weighted count by its population contribution.

Apportioning the social demographic data to the LSAs consisted of generating centroids from the blocks and doing a centroid assignment to the LSAs. Sociodemographic data was aggregated by the name of the library service area and summed. Apportioning the data in this way allowed me to profile the sociodemographic characteristics and be confident in the quality of the data outputs. Apportioning the data in this way maintained an acceptable degree of data precision and accuracy. From this step I could normalize the data by the population in the LSA and further aggregate by whether a library provided a service or not.

Surveying Library Use, Services, and Programming

Libraries’ increased web presence has afforded us the possibility to explore the kinds of services they offer other than print material circulation. The Chicago Public Library has a library profile for its 81 neighborhood branches on their official website (www.chipublib.org). An initial scan of 3 library profiles revealed that the profiles were conveniently formatted to be more or less identical to each other. This initial scan also allowed me to create a list of variables (Table 2) for my survey that I planned to look out for and include in my study. Data on the non-traditional programming and features (see Table 2) was then collected over a period of two days by visiting each library profile with an eye for the variables in my list. The presence of said features was manually coded as a Boolean feature into an Excel spreadsheet.

Table 2. Library features as listed on the CPL website

Results

Descriptive statistics were used to summarize the distribution of certain socioeconomic and demographic variables across Chicago and are displayed in Table 3. One of the main takeaways from these results is the large range of values between most of the socioeconomic and demographic variables. For example, consider the foreign-born population in Chicago. On average, a library service area may contain around 5,299 foreign born persons. However, one library service area in Chicago contains as few as 57 foreign born persons while another contains as many as 25,840. This large range (25,783 persons) in values is indicative of high variability within the data which reflects the considerable heterogeneity of Chicago’s neighborhoods. This high variability is observed in the other variables, as well, particularly among the racial and economic variables.

Table 3. Summary table of descriptive statistics for demographic variables

Mapping the percentage of a certain racial and ethnic groups onto the library service areas, the lines of racial segregation become particularly apparent. Racial groups are highly concentrated in different parts of the city with some library service areas having as much as 80-100% of a certain racial group. The Black population is primarily located in LSAs on the South and West Sides of the city. The white population is primarily located on the North Side of the city however you can also see a handful LSAs on the South Side exhibiting high white populations. The Latinx population is predominantly concentrated on the Southwest and Northwest Sides of Chicago. This corresponds to established and well known Latinx neighborhoods: Pilsen and Little Village (in the Southwest Side) and growing populations in the Northwest Side: Hermosa and Belmont-Cragin. Chicago’s Asian population is the fastest growing in the city but predominantly concentrated in small clusters in and around the Near South Side of Chicago as well as a part of the Far North Side. Concentrations of the Asian population around the Near South Side are no surprise considering the region’s proximity to the cultural hub that is Chicago’s Chinatown and long-established immigrant communities in the area. The distribution of race that we see among the LSAs affirm the patterns of segregation that have long been documented in Chicago’s urban landscape (Taeuber & Taeuber, 1964; Erbe, 1975; Acs et al., 2017) and demonstrate that these patterns also influence the demographic makeup of the LSAs. Libraries in different parts of the city will be serving communities made up of different racial and ethnic majorities.