Archive for February, 2016

Average Distance to Public Libraries in the US

Monday, February 22nd, 2016

A few months ago I had a new article published in LISR, but given the absurd restrictions of academic journal publishing I’m not allowed to publicly post the article, and have to wait 12 months before sharing my post-print copy. It is available via your local library if they have a subscription to the Science Direct database (you can also email me to request a copy). I am sharing some of the un-published state-level data that was generated for the project here.

Citation and Abstract

Regional variations in average distance to public libraries in the United States
F. Donnelly
Library & Information Science Research
Volume 37, Issue 4, October 2015, Pages 280–289
http://dx.doi.org/10.1016/j.lisr.2015.11.008

Abstract

“There are substantive regional variations in public library accessibility in the United States, which is a concern considering the civic and educational roles that libraries play in communities. Average population-weighted distances and the total population living within one mile segments of the nearest public library were calculated at a regional level for metropolitan and non-metropolitan areas, and at a state level. The findings demonstrate significant regional variations in accessibility that have been persistent over time and cannot be explained by simple population distribution measures alone. Distances to the nearest public library are higher in the South compared to other regions, with statistically significant clusters of states with lower accessibility than average. The national average population-weighted distance to the nearest public library is 2.1 miles. While this supports the use of a two-mile buffer employed in many LIS studies to measure library service areas, the degree of variation that exists between regions and states suggests that local measures should be applied to local areas.”

Purpose

I’m not going to repeat all the findings, but will provide some context.

As a follow-up to my earlier work, I was interested in trying an alternate approach for measuring public library spatial equity. I previously used the standard container approach – draw a buffer at some fixed distance around a library and count whether people are in or out, and as an approximation for individuals I used population centroids for census tracts. In my second approach, I used straight-line distance measurements from census block groups (smaller than tracts) to the nearest public library so I could compare average distances for regions and states; I also summed populations for these areas by calculating the percentage of people that lived within one-mile rings of the nearest library. I weighted the distances by population, to account for the fact that census areas vary in population size (tracts and block groups are designed to fall within an ideal population range – for block groups it’s between 600 and 3000 people).

Despite the difference in approach, the outcome was similar. Using the earlier approach (census tract centroids that fell within a library buffer that varied from 1 to 3 miles based on urban or rural setting), two-thirds of Americans fell within a “library service area”, which means that they lived within a reasonable distance to a library based on standard practices in LIS research. Using the latest approach (using block group centroids and measuring the distance to the nearest library) two-thirds of Americans lived within two miles of a public library – the average population weighted distance was 2.1 miles. Both studies illustrate that there is a great deal of variation by geographic region – people in the South consistently lived further away from public libraries compared to the national average, while people in the Northeast lived closer. Spatial Autocorrelation (LISA) revealed a cluster of states in the South with high distances and a cluster in the Northeast with low distances.

The idea in doing this research was not to model actual travel behavior to measure accessibility. People in rural areas may be accustomed to traveling greater distances, public transportation can be a factor, people may not visit the library that’s close to their home for several reasons, measuring distance along a network is more precise than Euclidean distance, etc. The point is that libraries are a public good that provide tangible benefits to communities. People that live in close proximity to a public library are more likely to reap the benefits that it provides relative to those living further away. Communities that have libraries will benefit more than communities that don’t. The distance measurements serve as a basic metric for evaluating spatial equity. So, if someone lives more than six miles away from a library that does not mean that they don’t have access; it does means they are less likely to utilize it or realize it’s benefits compared to someone who lives a mile or two away.

Data

I used the 2010 Census at the block group level, and obtained the location of public libraries from the 2010 IMLS. I improved the latter by geocoding libraries that did not have address-level coordinates, so that I had street matches for 95% of the 16,720 libraries in the dataset. The tables that I’m providing here were not published in the original article, but were tacked on as supplementary material in appendices. I wanted to share them so others could incorporate them into local studies. In most LIS research the prevailing approach for measuring library service areas is to use a buffer of 1 to 2 miles for all locations. Given the variation between states, if you wanted to use the state-average for library planning in your own state you can consider using these figures.

To provide some context, the image below shows public libraries (red stars) in relation to census block group centroids (white circles) for northern Delaware (primarily suburban) and surrounding areas (mix of suburban and rural). The line drawn between the Swedesboro and Woodstown libraries in New Jersey is 5-miles in length. I used QGIS and Spatialite for most of the work, along with Python for processing the data and Geoda for the spatial autocorrelation.

Map Example - Northern Delaware

The three tables I’m posting on the resources page are for states: one counts the 2010 Census population within one to six mile rings of the nearest public library, the second is the percentage of the total state population that falls within that ring, and the third is a summary table that calculates the mean and population-weighted distance to the nearest library by state. One set of tables is formatted text (for printing or just looking up numbers) while the other set are CSV files that you can use in a spreadsheet. I’ve included a metadata record with some methodological info, but you can read the full details in the article.

In the article itself I tabulated and examined data at a broader, regional level (Northeast, Midwest, South, and West), and also broke it down into metropolitan and non-metropolitan areas for the regions. Naturally people that live in non-metropolitan areas lived further away, but the same regional patterns existed: more people in the South in both metro and non-metro areas lived further away compared to their counterparts in other parts of the country. This weekend I stumbled across this article in the Washington Post about troubles in the Deep South, and was struck by how these maps mirrored the low library accessibility maps in my past two articles.

Review of The Census Reporter

Monday, February 8th, 2016

Picking up where I left off from my previous post (gee – welcome to 2016!) I thought I’d give a brief review of another census resource, The Census Reporter at http://censusreporter.org/.

The Census Reporter was created to make it easier for journalists to write stories using census data. To that end, they’ve created a really slick and easy to use web site that makes the data accessible and fun to explore. From the homepage you have three ways of diving into the data: you can pull up a profile by typing in the name of a place, you can enter an address and explore places that contain that address, or you can explore tables by topic.

Census Reporter Homepage

First, the place-based approach. You can type in a named place, like a state, county, or a census place (incorporated cities and towns, or census designated places) to get started. This will give you a selection of data from the most recent release of the American Community Survey. For larger areas where the data is available, it gives you 1-year ACS data by default; otherwise you get the latest 5-year data.

You’re presented with a map of the location at the top, and a series of attractive looking graphs and charts sorted by the demographic profile table source – social, economic, housing, and demographic. If you hover over a data point in a table it gives you some geographic context by comparing this place’s value with that of larger places where it’s contained. For example, if I search for Philadelphia I can hover over the chart to get the value for the Philly metro area and the State of Pennsylvania. I can click a link below each chart to open the full table, which includes both estimates and margins of error. There are small links for viewing the table by itself on a separate page (which also gives you the ability to download it) and for embedding the chart in a website.

Census Reporter Chart and Table

Viewing the table gives you additional options, like adding additional places for comparison, or subdividing the place into smaller areas for comparison. So if I’m looking at Philadelphia, I can break it down into tracts, block groups, or ZIP Codes. From there I can toggle away from the table view to view a map or a distribution bar to explore that variable by individual geographies.

View Data by Sub-Areas, Distribution Bars

The place-based search is great at allowing you to drill down either by topic or by these smaller geographies. But if you wanted to access a fuller range of geographies like congressional districts or PUMAs, it seems easier to do an address-based search. Back on the homepage, selecting the address button and typing in an address brings you to a map with the address pin-pointed, and on the left you can choose any geography that encloses that address. Once you do that, you get a profile for that geography and can start doing the same sorts of operations for changing the topics or tables, or adding or subdividing geographies for comparison.

Address Search

The topic-based search lets you search just by topic and then figure out the geography piece later. Of the three types of searches this one is the toughest, given the sheer number of tables and cross-tabulations. You can click on a link for a general topic to narrow things down a bit before beginning a search.

In downloading the data you have a variety of useful options: CSV, Excel, GeoJSON, KML, and shapefiles. So in theory you can download data that’s readily mappable – in practice I wasn’t able to download a shapefile, but could grab a KML or GeoJson and was able to visualize it in QGIS. One challenge in downloading any of the files is that the column names use the identifier codes, and the actual names of the variables aren’t included in the download format you choose – they’re included in a json file. So you can use that for reference, but it can’t be readily incorporated into the table.

So – where would this resource fall within the pantheon of US Census data resources? I think it’s great for accessing and, especially, visualizing profiles (profile = lots of data for one place) from the most recent ACS releases. It’s easy to use and succeeds at making the data interesting; for that reason I certainly would incorporate it into undergraduate courses where I’m introducing data. The ability to embed the charts into websites is certainly a bonus, and they deserve a big thumbs up for incorporating the margin of error data, rather than hiding or discarding it like other resources do.

The ability to create and view comparison tables (comparison = one piece of data for many places) is good – select an area and then break it down – but not as strong as the profile options. If you want to get a profile for a non-named place like a tract, ZIP Code, or PUMA you can’t do that from the profile search. You can do an address search and back out (if you know an address for that you’re interested in) or you can drill down by topic, which lets you search by summary area in addition to named places.

For users who need to download a lot of data, or for folks who need datasets that aren’t the most recent ACS release, this resource isn’t the place to go. The focus here is on providing the data in an easy and compelling way, as is. In viewing the profiles, it’s not clear if you can choose 5-year data over 1-year data for places where both datasets are available – even for large geographic areas with high population, sometimes it’s preferable to use the 5-year data to take advantage of the smaller margins of error. I also didn’t see an option for choosing decennial census data.

In short, this resource is well-designed and definitely worth exploring. It seems clear why this would be a go-to source for journalists, but it can be for many others as well.


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