spatial analysis

Washington DC street

Using the ACS to Calculate Daytime Population

I’m in the home stretch for getting the last chapter of the first draft of my census book completed. The next to last chapter of the book provides an overview of a number of derivatives that you can create from census data, and one of them is the daytime population.

There are countless examples of using census data for site selection analysis and for comparing and ranking places for locating new businesses, providing new public services, and generally measuring potential activity or population in a given area. People tend to forget that census data measures people where they live. If you were trying to measure service or business potential for residents, the census is a good source.

Counts of residents are less meaningful if you wanted to gauge how crowded or busy a place was during the day. The population of an area changes during the day as people leave their homes to go to work or school, or go shopping or participate in social activities. Given the sharp divisions in the US between residential, commercial, and industrial uses created by zoning, residential areas empty out during the weekdays as people travel into the other two zones, and then fill up again at night when people return. Some places function as job centers while others serve as bedroom communities, while other places are a mixture of the two.

The Census Bureau provides recommendations for calculating daytime population using a few tables from the American Community Survey (ACS). These tables capture where workers live and work, which is the largest component of the daytime population.

Using these tables from the ACS:

Total resident population
B01003: Total Population
Total workers living in area and Workers who lived and worked in same area
B08007: Sex of Workers by Place of Work–State and County Level (‘Total:’ line and ‘Worked in county of residence’ line)
B08008: Sex of Workers by Place of Work–Place Level (‘Total:’ line and ‘Worked in place of residence’ line)
B08009: Sex of Workers by Place of Work–Minor Civil Division Level (‘Total:’ line and ‘Worked in MCD of residence’ line)
Total workers working in area
B08604: Total Workers for Workplace Geography

They propose two different approaches that lead to the same outcome. The simplest approach: add the total resident population to the total number of workers who work in the area, and then subtract the total resident workforce (workers who live in the area but may work inside or outside the area):

Daytime Population = Total Residents + Total Workers in Area - Total Resident Workers

For example, according to the 2017 ACS Washington DC had an estimated 693,972 residents (from table B01003), 844,345 (+/- 11,107) people who worked in the city (table B08604), and 375,380 (+/- 6,102) workers who lived in the city. We add the total residents and total workers, and subtract the total workers who live in the city. The subtraction allows us to avoid double counting the residents who work in the city (as they are already included in the total resident population) while omitting the residents who work outside the city (who are included in the total resident workers). The result:

693,972 + 844,345 - 375,380 = 1,162,937

And to get the new margin of error:

SQRT(0^2 + 11,107^2 + 6,102^2) = 12,673

So the daytime population of DC is approx 468,965 people (68%) higher than its resident population. The district has a high number of jobs in the government, non-profit, and education sectors, but has a limited amount of expensive real estate where people can live. In contrast, I did the calculation for Philadelphia and its daytime population is only 7% higher than its resident population. Philadelphia has a much higher proportion of resident workers relative to total workers. Geographically the city is larger than DC and has more affordable real estate, and faces stiffer suburban competition for private sector jobs.

The variables in the tables mentioned above are also cross-tabulated in other tables by age, sex, race, Hispanic origin , citizenship status, language, poverty, and tenure, so it’s possible to estimate some characteristics of the daytime population. Margins of error will limit the usefulness of estimates for small population groups, and overall the 5-year period estimates are a better choice for all but the largest areas. Data for workers living in an area who lived and worked in the same area is reported for states, counties, places (incorporated cities and towns), and municipal civil divisions (MCDs) for the states that have them.

Data for the total resident workforce is available for other, smaller geographies but is reported for those larger places, i.e. we know how many people in a census tract live and work in their county or place of residence, but not how many live and work in their tract of residence. In contrast, data on the number of workers from B08604 is not available for smaller geographies, which limits the application of this method to larger areas.

Download or explore these ACS tables from your favorite source: the American Factfinder, the Census Reporter, or the Missouri Census Data Center.

Lying with Maps and Census Data

I was recently working on some examples for my book where I discuss how census geography and maps can be used to intentionally skew research findings. I suddenly remembered Mark Monmonier’s classic How To Lie with Maps. I have the 2nd edition from 1996, and as I was adding it to my bibliography I wondered if there was a revised edition.

To my surprise, a 3rd edition was just published in 2018! This is an excellent book: it’s a fun and easy read that provides excellent insight into cartography and the representation of data with maps. There are concise and understandable explanations of classification, generalization, map projections and more with lots of great examples intended for map readers and creators alike. If you’ve never read it, I’d highly recommend it.

If you have read the previous edition and are thinking about getting the new one… I think the back cover’s tagline about being “fully updated for the digital age” is a little embellished. I found another reviewer who concurs that much of the content is similar to the previous edition. The last three chapters (about thirty pages) are new. One is devoted to web mapping and there is a nice explanation of tiling and the impact of scale and paid results on Google Maps. While the subject matter is pretty timeless, some more updated examples would have been welcome.

There are many to choose from. One of the examples I’m using in my book comes from a story the Washington Post uncovered in June 2017. Jared Kushner’s real estate company was proposing a new luxury tower development in downtown Jersey City, NJ, across the Hudson River from Manhattan. They applied for a program where they could obtain low interest federal financing if they built their development in an area were unemployment was higher than the national average. NJ State officials assisted them with creating a map of the development area, using American Community Survey (ACS) unemployment data at the census tract level to prove that the development qualified for the program.

The creation of this development area defies all logical and reasonable criteria. This affluent part of the city consists of high-rise office buildings, residential towers, and historic brownstones that have been refurbished. The census tract where the development is located is not combined with adjacent tracts to form a compact and contiguous area that functions as a unit, nor does it include surrounding tracts that have similar socio-economic characteristics. The development area does not conform to any local conventions as to what the neighborhoods in Jersey City are based on architecture, land use, demographics, or physical boundaries like major roadways and green space.

Jersey City Real Estate Gerrymandering Map

Census tracts that represent the “area” around a proposed real estate development were selected to concentrate the unemployed population, so the project could qualify for low interest federal loans.

Instead, the area was drawn with the specific purpose of concentrating the city’s unemployed population in order to qualify for the financing. The tract where the development is located has low unemployment, just like the tracts around it (that are excluded). It is connected to areas of high unemployment not by a boundary, but by a single point where it touches another tract diagonally across a busy intersection. The rest of the tracts included in this area have the highest concentration of unemployment and poverty in the city, and consists primarily of low-rise residential buildings, many of which are in poor condition. This area stretches over four miles away from the development site and cuts across several hard physical boundaries, such as an interstate highway that effectively separates neighborhoods from each other.

The differences between this development area and the actual area adjacent but excluded from the project couldn’t be more stark. Gerrymandering usually refers to the manipulation of political and voting district boundaries, but can also be used in other contexts. This is a perfect example of non-political gerrymandering, where areas are created based on limited criteria in order to satisfy a predefined outcome. These areas have no real meaning beyond this purpose, as they don’t function as real places that have shared characteristics, compact and contiguous boundaries, or a social structure that would bind them together.

The maps in the Post article high-lighted the tracts that defined the proposal area and displayed their unemployment rate. In my example I illustrate the rate for all the tracts in the city so you can clearly see the contrast between the areas that are included and excluded. What goes unmentioned here is that these census ACS estimates have moderate to high margins of error that muddy the picture even further. Indeed, there are countless ways to lie with maps!

Average Distance to Public Libraries in the US

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). .

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 here 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.