Interesting reports or articles published

Net Out-Migration from the NY Metro Area to Other Metro Areas 2011-2015

Recent Migration Trends for New York City and Metro

The Baruch GIS lab crew just published a paper: New Yorkers on the Move: Recent Migration Trends for the City and Metro Area. The paper (no. 15 Feb 2018) is part of the Weissman Center for International Business Occasional Paper Series, which focuses on New York City’s role in the international and domestic economy.


We analyzed recent population trends (2010 to 2016) in New York City and the greater metropolitan area using the US Census Bureau’s Population Estimates to study components of population change (births, deaths, domestic and international migration) and the IRS Statistics of Income division’s county to county migration data to study domestic migration flows.

Here are the main findings:

  1. The population of New York City and the New York Metropolitan Area increased significantly between 2010 and 2016, but annually growth has slowed due to greater domestic out-migration.
  2. Compared to other large US cities and metro areas, New York’s population growth depends heavily on foreign immigration and natural increase (the difference between births and deaths) to offset losses from domestic out-migration.
  3. Between 2011 and 2015 the city had few relationships where it was a net receiver of migrants (receiving more migrants than it sends) from other large counties. The New York metro area had no net-receiver relationships with any major metropolitan area.
  4. The city was a net sender (sending more migrants than it received) to all of its surrounding suburban counties and to a number of large urban counties across the US. The metro area was a net sender to metropolitan areas throughout the country.

For the domestic migration portion of the analysis we were interested in seeing the net flows between places. For example, the NYC metro area sends migrants to and receives migrants from the Miami metro. What is the net balance between the two – who receives more versus who sends more?

The answer is: the NYC metro is a net sender to most of the major metropolitan areas in the country, and has no significant net receiver relationships with any other major metropolitan area. For example, for the period from 2011 to 2015 the NYC metro’s largest net sender relationship was with the Miami metro. About 88,000 people left the NYC metro for metro Miami while 58,000 people moved in the opposite direction, resulting in a net gain of 30,000 people for Miami (or in other words, a net loss of 30k people for NYC). The chart below shows the top twenty metros where the NYC metro had a deficit in migration (sending more migrants to these areas than it received). A map of net out-migration from the NYC metro to other metros appears at the top of this post. In contrast, NYC’s largest net receiver relationship (where the NYC metro received more migrants than it sent) was with Ithaca, New York, which lost a mere 300 people to the NYC metro.

All of our summary data is available here.

domestic migration to NYMA 2011-2015: top 20 deficit metro areas


For the IRS data we used the county to county migration SQLite database that Janine meticulously constructed over the course of the last year, which is freely available on the Baruch Geoportal. Anastasia employed her Python and Pandas wizardry to create Jupyter notebooks that we used for doing our analysis and generating our charts, all of which are available on github. I used an alternate approach with Python and the SQLite and prettytable modules to generate estimates independently of Anastasia, so we could compare the two and verify our numbers (we were aggregating migration flows across years and geographies from several tables, and calculating net flows between places).

One of our goals for this project was to use modern tools and avoid the clunky use of email. With the Jupyter notebooks, git and github for storing and syncing our work, and ShareLaTeX for writing the paper, we avoided using email for constantly exchanging revised versions of scripts and papers. Ultimately I had to use latex2rtf to convert the paper to a word processing format that the publisher could use. This post helped me figure out which bibliography packages to choose (in order for latex2rtf to interpret citations and references, you need to use the older natbib & bibtex combo and not biblatex & biber).

If you are doing similar research, Zillow has an excellent post that dicusses the merits of the different datasets. There are also good case studies on Washington DC and Philadelphia that employ the same datasets.

Article on the History of the US Census

I’ve been cranking away on my census book these past few months. I’m almost finished revising the preface and chapter 1 to submit as part of the book proposal review process, and a rough draft of chapter 2 is also complete.

I wrote a short piece on the history of the US census while I was doing background research last fall. It was recently published in Metropolitics, which is an on-line academic journal that specializes in short pieces on cities, urban politics, and urban economics. The journal was originally created in Paris as Metropolitiques, and a separate English-language version with a New York-based editorial board was created more recently. Each version is published independently and a select number of articles are translated from one version and published in the other, giving it a unique international flair in terms of content and contributors. A new article in the English version is published every Tuesday.

I wrote the piece as a lead-in to the 2020 census, which we’re starting to hear more about in the news. After spending a decade of research on creating new and improved categories for the race question, the OMB and White House decided not to accept the Bureau’s proposal and thus we’re keeping the same categories from 2010 and 2000. Meanwhile, the Justice Department is lobbying for a adding a citizenship question, which already appears in the American Community Survey and has not appeared in the short form of the decennial census since 1950. Read the article and follow the links to the references to see what the repercussions could be. The questions and categories must be finalized by March 31st…

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


“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.”


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.


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.