Tag Archives: ACS

Data and code for my columns

I would like to share the original data and code I use in all of the, well, software and code column.  I’m using Figshare to host all of this, and will try to keep it organized by column. Here is the data and original code for the first column, with the column found here and the  second column found here.  Thanks!


Introduction to Software and Code Forum

The purpose of this forum is to highlight the tools of the trade, our methodological toolbox, if you will.  With so many scientists in so many disciplines contributing to the area known as “Spatial Demography”, we all have our old stand by routines, our tricks and our tips for new researchers.  This is how I see this column evolving.

I will try to routinely post how-to guides to various techniques of spatial analysis and spatial statistics using the tools I know.  This should include primarily open source computing applications, with lots of annotated code, but occasionally commercial or proprietary software will be highlighted as well.  The first column for my area, for example, shows how to use the free R software package to read ACS data downloaded from American Factfinder, join it to a shapefile and conduct exploratory spatial data analysis using some standard methods.  Future columns will continue to use R for various other programming and analysis examples and expand into other languages and platforms as time goes by.

While I’ve managed to come into contact with lots of different platforms and software over the past decade, I welcome those among us who make a habit of writing code, developing software, or even like to tinker with every program under the sun that will open a shapefile to contribute to this area.  I welcome brief discussion for the forum, but would also welcome longer pieces of 1,000-3,000 words for publication in the regular column for this area.

I am very excited to be involved in this journal dedicated to our particular area of expertise and interest and personally look forward to growing our shared knowledge base over the next few years.



Public Use Microdata Area Fragmentation: Research and Policy Implications of Polygon Discontiguity

By: Carlos Siordia and Amber Fox

Abstract: The American Community Survey (ACS) is created by the Unites States Census Bureau and contains the most recent and detailed information on the population. The U.S. Census Bureau releases Public Use Microdata Sample (PUMS) ACS files to allow public entities the ability to examine detailed individual-level data. In order to protect the confidentiality of survey participants, the Census only allows individual-level observations to be geographically referenced with Public Use Microdata Areas (PUMAs)—polygons that contain at least 100,000 people. Microdata files offer the unique advantage of creating custom tabulations on the population that are not made available elsewhere. The advantages from using PUMS files are limited by the typology of PUMAs. Our project fills a gap by introducing the reader to how PUMA typology is produced and how it affects research. The specific aim of this project is to highlight the fact that PUMAs can be fragmented—made up of physically non-adjacent/multi-part polygons. This is important because the treatment of fragmented/multi-part polygons as contiguous spatial entities erroneously imposes a false structure of contiguity that challenges theoretical and statistical assumptions in geographically aware research. By providing evidence from three metropolitan areas in Texas (USA) and displaying the degree of polygon discontiguity concentration by state, we outline some of the implications of polygon discontiguity for research and policy. We compliment our descriptive investigation by discussing solutions in closing and by identifying and discussing the three general elements in polygon fragmentation: quantity, distance, and size.