CEC North America LULC

Dataset Roundup: A Summary of Specialized Open Data Sources

I list the top free GIS data sources that I consistently use on my Resources page; these are general, foundational sources that can be used for many applications. In this post I’m going to summarize an eclectic mix of more specialized resources that I’ve used or that have been recommended to me over this past year. I’ve categorized these into GIS datasets, sub-national population data for countries (tabular data that can be joined to GIS vector layers), and historic socio-economic data for countries.

Geospatial Data

North American Land Change Monitoring System

Published by the Commission for Environmental Cooperation, these land use and land cover rasters (see photo at the top of this post) are derived from MODIS imagery at 250 meter resolution for earlier years and either Landsat-7 or RapidEye imagery at 30 meter resolution for later years for Canada, the United States, and Mexico in 2005, 2010, and 2015. There are layers for both land cover and land cover change over a 5-year period. Land cover is classified into 19 categories based on UN FAO standards. It’s easy to download as the layer is unified (no individual tiles to mess with and stitch together) and for the 2015 series you can choose a national file or one for the entire continent.

PRISM Climate Data

Published by the Northwest Alliance for Computational Science & Engineering at Oregon State University, the PRISM Climate Group publishes climate data for the United States. You can generate daily, monthly, or 30-year normal rasters for temperature (min, max, mean), precipitation, dew point, and a few other measures for the continental US. There are also some prepackaged files that were created for special projects that cover Alaska, Hawaii, and some of the US territories. The site is very easy to use (certainly compared to other sites that provide climate data) and beyond its research applications the data is good for teaching purposes, as files are straightforward to create, download, and interpret.

PRISM Mean Temp Map Oct 2020

Marineregions.org Marine Boundaries

I usually help people find vector boundaries for terrestrial features, and the oceans are an afterthought that appear as the absence of land. But what if you specifically needed features that represent oceans and seas? Marineregions.org, maintained by the Flanders Marine Institute, provides many sets of water-based boundaries that include maritime regions (legal sea zones around countries) as well as polygons that represent the boundaries of the oceans and largest seas (IHO Sea Areas, defined by the International Hydrographic Association). See the screenshot of this layer in QGIS below.

IHO Seas Layer in QGIS

GNSS Time Series

Produced by NASA JPL, this dataset can be used for measuring vertical land movement (VLM) and subsistence, primarily due to movement of the earth’s tectonic plates. The dataset contains over 2,000 GPS observation points or stations; the majority are in the US but there are a scattering of points throughout the world. The data file for geodetic positions and velocities contains two records for every station: the POS (position) record provides data for the latitude (N), longitude (E), and elevation (V) in mm. The VEL (velocity) indicates the rate of movement over the time period by direction (N / E) and elevation. The last three columns for both sets of records are margins of error for each value. The data file is in a fixed-width text format. To use it in GIS you need to parse the data into a tabular format and drop the header information. When plotting the coordinates, the CRS for the geodetic file is IGS14 (EPSG code 9019). If your CRS library doesn’t include this system, it is roughly equivalent to ITRF2014 (EPSG code 7789).

Subnational Population Data

IPUMS Terra

Are you looking for population or socio-economic data for the first-level administrative divisions (states, provinces, departments, districts, etc) for many different countries? IPUMS Terra is part of the IPUMS series at the Minnesota Population Center, Univ of Minnesota. The data has been gathered from census and statistical agencies of individual countries, or in some cases from estimates generated by the project. Choose the "Create Your Custom Dataset" option, then on the next screen choose "Start Extract Area Level Output". On the Extract Builder (see pic below) choose variables on the left, like Demographic and Total Population. Then under Datasets on the right you can choose countries and filter by year. Once you move on to the next screen, you can choose to harmonize the output or choose specific years, and choose your administrative level: national, ADM-1, or smallest available. You must register to use the IPUMS data series, but registration is free for educational and non-commercial use (as long as you cite IPUMS as the source).

IPUMS Terra Interface

Subnational Human Development Index

An alternative for first-level admin data is the Subnational Human Development Index published by the GlobalDataLab at the Institute for Management Research at Radboud University. There are far fewer variables and less customization compared to IPUMS Terra, but as such the site is smaller and easier to use. There are several different indices for measuring human development, but you can also access the following indicators: life expectancy, GNI per capita, expected and mean years of schooling, and population size in millions.

Historic Global Population and Economic Data

Maddison Project

Yes, that’s Maddison with two "ds". This project from the Groningen Growth and Development Centre at the University of Groningen generates comparative economic growth, income, and population data for countries over a long historical time span; back to the year AD 1 in a few cases, but for the most part from AD 1500 forward. They provide detailed documentation that explains how the dataset was created, and it’s easy to download in either an Excel or STATA format.

The World Countries Urban Population

This dataset consists of two spreadsheet files – one for the total urban population and another for the urban ratio of the population for countries going back to the year 1500. The dataset was created by Jonathan Fink-Jensen at Utrecht University and is held in the International Institute of Social History’s data repository. The repository contains a variety of other historic socio-economic datasets for many different countries.

NYC and NYMA Pop Change Graph 2000 to 2019

New York’s Population and Migration Trends in the 2010s

The Weissman Center for International Business at Baruch College just published my paper, “New York’s Population and Migration Trends in the 2010s“, as part of their Occasional Paper Series. In the paper I study population trends over the last ten years for both New York City (NYC) and the greater New York Metropolitan Area (NYMA) using annual population estimates from the Census Bureau (vintage 2019), county to county migration data (2011-2018) from the IRS SOI, and the American Community Survey (2014-2018). I compare NYC to the nine counties that are home to the largest cities in the US (cities with population greater than 1 million) and the NYMA to the 13 largest metro areas (population over 4 million) to provide some context. I conclude with a brief discussion of the potential impact of COVID-19 on both the 2020 census count and future population growth. Most of the analysis was conducted using Python and Pandas in Jupyter Notebooks available on my GitHub. I discussed my method for creating rank change grids, which appear in the paper’s appendix and illustrate how the sources and destinations for migrants change each year, in my previous post.

Terminology

  • Natural increase: the difference between births and deaths
  • Domestic migration: moves between two points within the United States
  • Foreign migration: moves between the United States and a US territory or foreign country
  • Net migration: the difference between in-migration and out-migration (measured separately for domestic and foreign)
  • NYC: the five counties / boroughs that comprise New York City
  • NYMA: the New York Metropolitan Area as defined by the Office of Management and Budget in Sept 2018, consists of 10 counties in NY State (including the 5 NYC counties), 12 in New Jersey, and one in Pennsylvania
Map of the New York Metropolitan Area
The New York-Newark-Jersey City, NY-NJ-PA Metropolitan Area

Highlights

  • Population growth in both NYC and the NYMA was driven by positive net foreign migration and natural increase, which offset negative net domestic migration.
  • Population growth for both NYC and the NYMA was strong over the first half of the decade, but population growth slowed as domestic out-migration increased from 2011 to 2017.
  • NYC and the NYMA began experiencing population loss from 2017 forward, as both foreign migration and natural increase began to decelerate. Declines in foreign migration are part of a national trend; between 2016 and 2019 net foreign migration for the US fell by 43% (from 1.05 million to 595 thousand).
  • The city and metro’s experience fit within national trends. Most of the top counties in the US that are home to the largest cities and many of the largest metropolitan areas experienced slower population growth over the decade. In addition to NYC, three counties: Cook (Chicago), Los Angeles, and Santa Clara (San Jose) experienced actual population loss towards the decade’s end. The New York, Los Angeles, and Chicago metro areas also had declining populations by the latter half of the decade.
  • Most of NYC’s domestic out-migrants moved to suburban counties within the NYMA (representing 38% of outflows and 44% of net out-migration), and to Los Angeles County, Philadelphia County, and counties in Florida. Out-migrants from the NYMA moved to other large metros across the country, as well as smaller, neighboring metros like Poughkeepsie NY, Fairfield CT, and Trenton NJ. Metro Miami and Philadelphia were the largest sources of both in-migrants and out-migrants.
  • NYC and the NYMA lack any significant relationships with other counties and metro areas where they are net receivers of domestic migrants, receiving more migrants from those places than they send to those places.
  • NYC and the NYMA are similar to the cities and metros of Los Angeles and Chicago, in that they rely on high levels foreign migration and natural increase to offset high levels of negative domestic migration, and have few substantive relationships where they are net receivers of domestic migrants. Academic research suggests that the absolute largest cities and metros behave this way; attracting both low and high skilled foreign migrants while redistributing middle and working class domestic migrants to suburban areas and smaller metros. This pattern of positive foreign migration offsetting negative domestic migration has characterized population trends in NYC for many decades.
  • During the 2010s, most of the City and Metro’s foreign migrants came from Latin America and Asia. Compared to the US as a whole, NYC and the NYMA have slightly higher levels of Latin American and European migrants and slightly lower levels of Asian and African migrants.
  • Given the Census Bureau’s usual residency concept and the overlap in the onset the of COVID-19 pandemic lock down with the 2020 Census, in theory the pandemic should not alter how most New Yorkers identify their usual residence as of April 1, 2020. In practice, the pandemic has been highly disruptive to the census-taking process, which raises the risk of an under count.
  • The impact of COVID-19 on future domestic migration is difficult to gauge. Many of the pandemic destinations cited in recent cell phone (NYT and WSJ) and mail forwarding (NYT) studies mirror the destinations that New Yorkers have moved to between 2011 and 2018. Foreign migration will undoubtedly decline in the immediate future given pandemic disruptions, border closures, and restrictive immigration policies. The number of COVID-19 deaths will certainly push down natural increase for 2020.

Rank Change Grid

Creating Heatmaps to Show Change in Rank Over Time with Python

In this post I’ll demonstrate how I created annotated heatmaps (or what I’m calling a rank change grid) showing change in rank over time using Python and Matplotlib’s imshow plots. I was writing a report on population trends and internal migration using the IRS county to county migration dataset, and wanted to depict the top origins and destinations of migrants for New York City and the New York Metropolitan Area and how they changed from year to year.

I hit upon this idea based on an example in the Matplotlib documentation using the imshow plot. Imshow was designed for manipulating and creating images, but since images are composed of rows and columns of pixels you can use this function to create grids (for GIS folks, think of a raster). The rows can indicate rank from 1 to N, while the columns could represent time, which in my case is years. I could label each grid cell with the name of a place (i.e. origin or destination), and if a place changes ranks over time I could assign the cell a color indicating increase or decrease; otherwise I’d assign a neutral color indicating no change. The idea is that you could look at place at a given rank in year 1 and follow it across the chart by looking at the label. If a new place appears in a given position, the color change clues you in, and you can quickly scan to see whether a given place went up or down.

The image below shows change in rank for the top metro area destinations for migrants leaving the NYC metro from 2011 to 2018. You can see that metro Miami was the top destination for several years, up until 2016-17 when it flips positions with metro Philadelphia, which had been the number 2 destination. The sudden switch from a neutral color indicates that the place occupying this rank is new. You can also follow how 3rd ranked Bridgeport falls to 4th place in the 2nd year (displaced by Los Angeles), remains in 4th place for a few years, and then falls to 5th place (again bumped by Los Angeles, which falls from 3rd to 4th as it’s bumped by Poughkeepsie).

NYC Metro Outflow Grid
Annual Change in Ranks for Top Destinations for NYC Metro Migrants (Metro Outflows)

I opted for this over a more traditional approach called a bump chart (also referred to a slope chart or graph), with time on the x-axis and ranks on the y-axis, and observations labeled at either the first or last point in time. Each observation is assigned a specific color or symbol, and lines connect each observation to its changing position in rank so you can follow it along the chart. Interpreting these charts can be challenging; if there are frequent changes in rank the whole thing begins to look like spaghetti, and the more observations you have the tougher it gets to interpret. Most of the examples I found depicted a small and finite number of observations. I have hundreds of observations and only want to see the top ten, and if observations fall in and out of the top N ranks you get several discontinuous lines which look odd. Lastly, neither Matplotlib or Pandas have a default function for creating bump charts, although I found a few examples where you could create your own.

Creating the rank change grids was a three-part process that required: taking the existing data and transforming it into an array of the top or bottom N values that you want to show, using that array to generate an array that shows change in ranks over time, and generating a plot using both arrays, one for the value and the other for the labels. I’ll tackle each piece in this post. I’ve embedded the functions at the end of each explanation; you can also look at my GitHub repo that has the Jupyter Notebook I used for the analysis for the paper (to be published in Sept 2020).

Create the Initial Arrays

In the paper I was studying flows between NYC and other counties, and the NYC metro area and other metropolitan statisical areas. I’ll refer just to the metro areas as my example in this post, but my functions were written to handle both types of places, stored in separate dataframes. I began with a large dataframe with every metro that exchanged migrants with the NYC metro. There is a row for each metro where the index is the Census Bureau’s unique FIPS code, and columns that show inflows, outflows, and net flows year by year (see image below). There are some rows that represent aggregates, such as flows to all non-metro areas and the sum of individual metro flows that could not be disclosed due to privacy regulations.

Initial Dataframe
Initial Dataframe

The first step is to create an array that has just the top or bottom N places that I want to depict, just for one flow variable (in, out, or net). Why an array? Arrays are pretty solid structures that allow you to select specific rows and columns, and they mesh nicely with imshow charts as each location in the matrix can correspond with the same location in the chart. Most of the examples I looked at used arrays. It’s possible to use other structures but it’s more tedious; nested Python lists don’t have explicit rows and columns so a lot of looping and slicing is required, and with dataframes there always seems to be some catch with data types, messing with the index versus the values, or something else. I went with NumPy’s array type.

I wrote a function where I pass in the dataframe, the type of variable (in, out, or net flow), the number of places I want, whether they are counties or metro areas, and whether I want the top or bottom N records (true or false). Two arrays are returned: the first shows the FIPS unique ID numbers of each place, while the second returns the labels. You don’t have to do anything to calculate actual ranks, because once the data is sorted the ranks become implicit; each row represents ranks 1 through 10, each column represents a year, and the ID or label for a place that occupies each position indicates its rank for that year.

In my dataframe, the names of the columns are prefixed based on the type of variable (inflow, outflow, or net flow), followed by the year, i.e. inflows_2011_12. In the function, I subset the dataframe by selecting columns that start with the variable I want. I have to deal with different issues based on whether I’m looking at counties or metro areas, and I need to get rid of any IDs that are for summary values like the non-metro areas; these IDS are stored in a list called suppressed, and the ~df.indexisin(suppressed) is pandaesque for taking anything that’s not in this list (the tilde acts as not). Then, I select the top or bottom values for each year, and append them to lists in a nested list (each sub-list represents the top / bottom N places in order for a given year). Next, I get the labels I want by creating a dictionary that relates all ID codes to label names, pull out the labels for the actual N values that I have, and format them before appending them to lists in a nested list. For example, the metro labels are really long and won’t fit in the chart, so I split them and grab just the first piece: Albany-Schenectady-Troy, NY becomes Albany (split using the dash) while Akron, OH becomes Akron (if no dash is present, split at comma). At the end, I use np.array to turn the nested lists into arrays, and transpose (T) them so rows become ranks and years become values. The result is below:

ID Array
Function and Result for Creating Array of IDs Top N Places
# Create array of top N geographies by flow type, with rows as ranks and columns as years
# Returns 2 arrays with values for geographies (id codes) and place names
# Must specify: number of places to rank, counties or metros, or sort by largest or smallest (True or False)
def create_arrays(df,flowtype,nsize,gtype,largest):
    geogs=[]
    cols=[c for c in df if c.startswith(flowtype)]
    for c in cols:
        if gtype=='counties':
            row=df.loc[~df.index.isin(suppressed),[c]]
        elif gtype=='metros':
            row=df.loc[~df.index.isin(msuppressed),[c]]
        if largest is True:
            row=row[c].nlargest(nsize)
        elif largest is False:
            row=row[c].nsmallest(nsize)
        idxs=list(row.index)
        geogs.append(idxs)

    if gtype=='counties':
        fips=df.to_dict()['co_name']
    elif gtype=='metros':
        fips=df.to_dict()['mname']
    labels=[]
    for row in geogs:
        line=[]
        for uid in row:
            if gtype=='counties':
                if fips[uid]=='District of Columbia, DC':
                    line.append('Washington\n DC')
                else:
                    line.append(fips[uid].replace('County, ','\n')) #creates short labels
            elif gtype=='metros':
                if '-' in fips[uid]:
                    line.append(fips[uid].split('-')[0]) #creates short labels
                else:
                    line.append(fips[uid].split(',')[0])
        labels.append(line)

    a_geogs=np.array(geogs).T
    a_labels=np.array(labels).T

    return a_geogs, a_labels

Change in Rank Array

Using the array of geographic ID codes, I can feed this into function number two to create a new array that indicates change in rank over time. It’s better to use the ID code array as we guarantee that the IDs are unique; labels (place names) may not be unique and pose all kinds of formatting issues. All places are assigned a value of 0 for the first year, as there is no previous year to compare them to. Then, for each subsequent year, we look at each value (ID code) and compare it to the value in the same position (rank) in the previous column (year). If the value is the same, that place holds the same rank and is assigned a 0. Otherwise, if it’s different we look at the new value and see what position it was in in the previous year. If it was in a higher position last year, then it has declined and we assign -1. If it was in a lower position last year or was not in the array in that column (i.e. below the top 10 in that year) it has increased and we assign it a value of 1. This result is shown below:

Rank Change Array
Function and Result for Creating Change in Rank Array
# Create array showing how top N geographies have changed ranks over time, with rows as rank changes and
# columns as years. Returns 1 array with values: 0 (no change), 1 (increased rank), and -1 (descreased rank)
def rank_change(geoarray):
    rowcount=geoarray.shape[0]
    colcount=geoarray.shape[1]

    # Create a number of blank lists
    changelist = [[] for _ in range(rowcount)]

    for i in range(colcount):
        if i==0:
            # Rank change for 1st year is 0, as there is no previous year
            for j in range(rowcount):
                changelist[j].append(0)
        else:
            col=geoarray[:,i] #Get all values in this col
            prevcol=geoarray[:,i-1] #Get all values in previous col
            for v in col:
                array_pos=np.where(col == v) #returns array
                current_pos=int(array_pos[0]) #get first array value
                array_pos2=np.where(prevcol == v) #returns array
                if len(array_pos2[0])==0: #if array is empty, because place was not in previous year
                    previous_pos=current_pos+1
                else:
                    previous_pos=int(array_pos2[0]) #get first array value
                if current_pos==previous_pos:
                    changelist[current_pos].append(0)
                    #No change in rank
                elif current_posprevious_pos: #Larger value = smaller rank
                    changelist[current_pos].append(-1)
                    #Rank has decreased
                else:
                    pass

    rankchange=np.array(changelist)
    return rankchange 

Create the Plot

Now we can create the actual chart! The rank change array is what will actually be charted, but we will use the labels array to display the names of each place. The values that occupy the positions in each array pertain to the same place. The chart function takes the names of both these arrays as input. I do some fiddling around at the beginning to get the labels for the x and y axis the way I want them. Matplotlib allows you to modify every iota of your plot, which is in equal measures flexible and overwhelming. I wanted to make sure that I showed all the tick labels, and changed the default grid lines to make them thicker and lighter. It took a great deal of fiddling to get these details right, but there were plenty of examples to look at (Matplotlib docs, cookbook, Stack Overflow, and this example in particular). For the legend, shrinking the colorbar was a nice option so it’s not ridiculously huge, and I assign -1, 0, and 1 to specific colors denoting decrease, no change, and increase. I loop over the data values to get their corresponding labels, and depending on the color that’s assigned I can modify whether the text is dark or light (so you can see it against the background of the cell). The result is what you saw at the beginning of this post for outflows (top destinations for migrants leaving the NY metro). The function call is below:

Function for Creating Rank Change Grid
Function for Creating Rank Change Grid
# Create grid plot based on an array that shows change in ranks and an array of cell labels
def rank_grid(rank_change,labels):
    alabels=labels
    xlabels=[yr.replace('_','-') for yr in years]
    ranklabels=['1st','2nd','3rd','4th','5th','6th','7th','8th','9th','10th',
               '11th','12th','13th','14th','15th','16th','17th','18th','19th','20th']
    nsize=rank_change.shape[0]
    ylabels=ranklabels[:nsize]

    mycolors = colors.ListedColormap(['#de425b','#f7f7f7','#67a9cf'])
    fig, ax = plt.subplots(figsize=(10,10))
    im = ax.imshow(rank_change, cmap=mycolors)

    # Show all ticks...
    ax.set_xticks(np.arange(len(xlabels)))
    ax.set_yticks(np.arange(len(ylabels)))
    # ... and label them with the respective list entries
    ax.set_xticklabels(xlabels)
    ax.set_yticklabels(ylabels)

    # Create white grid.
    ax.set_xticks(np.arange(rank_change.shape[1]+1)-.5, minor=True)
    ax.set_yticks(np.arange(rank_change.shape[0]+1)-.5, minor=True)
    ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
    ax.grid(which="major",visible=False)

    cbar = ax.figure.colorbar(im, ax=ax, ticks=[1,0,-1], shrink=0.5)
    cbar.ax.set_yticklabels(['Increased','No Change','Decreased'])

    # Loop over data dimensions and create text annotations.
    for i in range(len(ylabels)):
        for j in range(len(xlabels)):
            if rank_change[i,j] < 0:
                text = ax.text(j, i, alabels[i, j],
                           ha="center", va="center", color="w", fontsize=10)
            else:
                text = ax.text(j, i, alabels[i, j],
                           ha="center", va="center", color="k", fontsize=10)

    #ax.set_title("Change in Rank Over Time")
    plt.xticks(fontsize=12)
    plt.yticks(fontsize=12)
    fig.tight_layout()
    plt.show()
    return ax 

Conclusions and Alternatives

I found that this approach worked well for my particular circumstances, where I had a limited number of data points to show and the ranks didn’t fluctuate much from year to year. The charts for ten observations displayed over seven points in time fit easily onto standard letter-sized paper; I could even get away with adding two additional observations and an eighth point in time if I modified the size and placement of the legend. However, beyond that you can begin to run into trouble. I generated charts for the top twenty places so I could see the results for my own analysis, but it was much too large to create a publishable graphic (at least in print). If you decrease the dimensions for the chart or reduce the size of the grid cells, the labels start to become unreadable (print that’s too small or overlapping labels).

There are a number of possibilities for circumventing this. One would be to use shorter labels; if we were working with states or provinces we can use the two-letter postal codes, or ISO country codes in the case of countries. Not an option in my example. Alternatively, we could move the place names to the y-axis (sorted alphabetically or by first or final year rank) and then use the rank as the annotation label. This would be a fundamentally different chart; you could see how one place changes in rank over time, but it would be tougher to discern which places were the most important source / destination for the area you’re studying (you’d have to skim through the whole chart). Or, you could keep ranks on the y-axis and assign each place a unique color in the legend, shade the grid cells using that color, and thus follow the changing colors with your eye. But this flops is you have too many places / colors.

A different caveat is this approach doesn’t work so well if there is a lot of fluctuation in ranks from year to year. In this example, the top inflows and outflows were relatively stable from year to year. There were enough places that held the same rank that you could follow the places that changed positions. We saw the example above for outflows, below is an example for inflows (i.e. the top origins or sources of migrants moving to the NY metro):

NYC Metro Inflow Grid
Annual Change in Ranks for Top Origins for NYC Metro Migrants (Metro Inflows)

In contrast, the ranks for net flows were highly variable. There was so much change that the chart appears as a solid block of colors with few neutral (unchanged) values, making it difficult to see what’s going on. An example of this is below, representing net flows for the NYC metro area. This is the difference between inflows and outflows, and the chart represents metros that receive more migrants from New York than they send (i.e. net receivers of NY migrants). While I didn’t use the net flow charts in my paper, it was still worth generating as it made it clear to me that net flow ranks fluctuate quite a bit, which was a fact I could state in the text.

NYC Metro Net Flow Grid
Annual Change in Ranks for Net Receivers of NYC Metro Migrants (Metro Net Flows)

There are also a few alternatives to using imshow. Matplotlib’s pcolor plot can produce similar effects but with rectangles instead of square grid cells. That could allow for more observations and longer labels. I thought it was less visually pleasing than the equal grid, and early on I found that implementing it was clunkier so I went no further. My other idea was to create a table instead of a chart. Pandas has functions for formatting dataframes in a Jupyter Notebook, and there are options for exporting the results out to HTML. Formatting is the downside – if you create a plot as an image, you export it out and can then embed it into any document format you like. When you’re exporting tables out of a notebook, you’re only exporting the content and not the format. With a table, the content and formatting is separate, and the latter is often tightly bound to the publication format (Word, LaTeX, HTML, etc.) You can design with this in mind if you’re self-publishing a blog post or report, but this is not feasible when you’re submitting something for publication where an editor or designer will be doing the layout.

I really wanted to produce something that I could code and run automatically in many different iterations, and was happy with this solution. It was an interesting experiment, as I grappled with taking something that seemed intuitive to do the old-fashioned way (see below) and reproducing it in a digital, repeatable format.

Copybook Chart
atcoordinates YouTube Channel

Video Tutorials for Finding US Census Data

I have recently created an atcoordinates YouTube channel that features a series of how-to videos on finding and accessing US census data using a variety of websites and tools. I explain basic census concepts while demonstrating how to access data. At this point there are four videos:

  1. Exploring US Census Data: Basic Concepts. This is a narrated slide show where I cover the essential choices you need to make and concepts you need to understand in order to access census data, regardless of the tool or platform: data set, time period, subjects or topics, and geography. I discuss the decennial census, American Community Survey, and population estimates. This video is intended as a prerequisite for viewing the others, so I don’t have to explain the same concepts each time and can focus on demonstrating each particular application.
  2. American Community Survey Census Profiles with MCDC Apps. This screencast illustrates how you can quickly and easily access census profiles for any place in the US using the Missouri Census Data Center’s profile applications. It’s also a good introduction to census data in general, if you’re unfamiliar with the scope of data that’s available.
  3. Search Strategies for data.census.gov. I demonstrate how to use the Census Bureau’s primary application for accessing current census data, using the advanced search tool and filters.
  4. Using TIGERweb to Explore US Census Geography. I show you how to use this web map application for viewing census geography, while explaining what some of the small-area census geographies are.

I plan on adding additional videos every month or so. The pandemic lock down and uncertainty over whether classes will be back in session this fall inspired me to do this. While I prefer written tutorials, I find that I’ve been watching YouTube more often for learning how to do certain tasks with particular software, so I thought this would be useful for others. The videos average about 10 to 15 minutes in length, although the introductory one is a bit longer. The length is intentional; I wanted to explain the concepts and describe why you’re making certain choices, instead of simply pointing and clicking without any explanation.

Feel free to spread the news, share and embed the videos in research guides or web pages, and use them in classes or workshops. Of course, for a more in-depth look at US census data, check out my book: Exploring the US Census: Your Guide to America’s Data published by SAGE.