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Susan Smith
Susan Smith
Susan Smith has worked as an editor and writer in the technology industry for over 16 years. As an editor she has been responsible for the launch of a number of technology trade publications, both in print and online. Currently, Susan is the Editor of GISCafe and AECCafe, as well as those sites’ … More »

U.S. Income, Immigration and Impacts Told Through Maps

February 15th, 2018 by Susan Smith

Recently, I began to receive maps pertaining to income, immigration, unemployment and related impacts. It made me consider putting together these maps to show a broader story of what these maps can show us in terms of current as well as historical timelines in terms of income or lack thereof. The following maps also displays communities where the highest number of non-citizen residents and DACA recipients live.

DACA and Non-Citizen Maps

When elected officials talk about changing our immigration system, just who and where are people affected? That’s the question Esri is trying to help answer with a new interactive story map that explores communities with the highest shares of non-citizen residents and DACA recipients. There is also the question of: what is the total estimate of GDP lost when taking non-citizens out of the workforce? (Chicago is used as an example below)

Using data from ACS and other sources, this tabbed story map illustrates the percentage of non-citizen foreign-born residents in each state, county, and city, along with information about sanctuary areas. The map also visualizes this data at the tract level, so you can understand which specific communities within a larger area may be most impacted by changes to the immigration system. Lastly, the map uses data from USC to show which congressional districts have the largest number of DACA recipients, as well as the estimated economic impact lost if those workers are removed.

Putting this in perspective, just for Chicago:

Chicagoland snapshot:

Estimate of DACA eligible for IL congressional districts 1-11, 14:                 64,700

Estimate of DACA recipients for IL congressional districts 1-11, 14:             39,400

Estimate of GDP lost for IL congressional districts 1-11, 14:                            $2,238,900,000

The information from Caliper’s Maptitude on Potential DACA recipients was gathered from the Migration Policy Institute,

DACA Estimates

The information displayed in the maps provided shows the potential Total Estimated Population of DACA recipients by county.  They have also provided ranges and more detailed county information as well. Maptitude is hoping to include this or similar information as part of the 2018 data packages that they will be releasing over the coming weeks.

Top 3 States for Highest Income By Age

For locations of highest income, we can look at things like number of workers (Employed Civilian Population 16+), Household Median/ Mean income and number of Households that make certain income ranges, said Drew Smith, Senior GIS Specialist, Mapping Software, Caliper.  These values are available at the Census Tract, ZIP Code, county and MSA levels, said Using the data included with Maptitude 2017 this map shows the three states that have the highest median income for each of four age groups:

  • Of those in the under 25 age group, half make over $34,000 in the states of NH, HI, AK.
  • Of those in the 25-44 age group, half make over $77,000 in MA, NJ, DC.
  • Of those in the 45-64 age group, half make over $88,000 in CT, NJ, MD.
  • Of those in the over 65 age group, half make over $51,000 in MD, AK, HI.

So the highest median incomes for all age groups lie in the Northeast corridor, Alaska, or Hawaii. Alaska and Hawaii have the highest median incomes for both the oldest and the youngest people.

Most of the data for these maps is provided from the database included with Maptitude with the values coming from the 2016 American Community Survey 5-year estimates.

Unemployment Rate by MSA for December 2016 and 2017

Unemployment Rate by MSA for December 2016 and 2017. The last map looks at the change in the Unemployment Rate between 2016 and 2017. Colors in yellow to blue/purple, indicate areas where the Unemployment Rate decreased between 2016 and 2017. Colors in light orange to dark orange indicate areas where the Unemployment Rate increased between 2016 and 2017.  Customized the legend in the last map with arrows showing good and poor.

Maptitude MSA Unemployment Rate Map 1

Maptitude MSA Unemployment Rate Map 2

Maptitude MSA Unemployment Rate Map 3

Minimum Wage Data from Maptitude

The minimum wage information is very straight-forward and was gathered from this website.

Minimum Wage by State

Minimum Wage Story Maps from Esri:

History of Minimum Wage Map from Esri

Esri history of minimum wage in the U.S.

  • At the highest level, the variability of minimum wage policies from state to state is striking—this ranges from some states in the South that don’t even require a minimum wage, to places like D.C. that have a $12.50 minimum wage (currently the highest for a state or territory).
  • Similarly, the number of cities and counties that have taken it upon themselves to raise wages locally is impressive, and have the most robust plans for raising minimum wages over the next few years.
  • Regardless of an area’s minimum wage, all states fail to guarantee minimum wages that actually match up to the cost of living for their respective areas.
  • As such, there is a growing divide between states that have raised minimum wages and are at least bringing minimum wages closer to the cost of living, versus those states that are slower to raise minimum wages (or don’t raise wages at all) and fall much further below the local cost of living.
  • Underlying all of these points is the profound impact of inflation over the last four decades: Even while minimum wages have nominally increased, inflation has devalued the dollar in such a way that even in 2018 some wages today have less purchasing ability than nominally lesser wages in the 1970s.

Income Inequality

A map showing the Predominant income range in each census tract and county, U.S.

Using data from the American Community Survey at the census tract and county level, Esri has created income predominance maps that better tell the nuanced story of income distribution across the country. Despite assumptions of income segregation at both ends of the spectrum, Esri’s maps reveal more nuance and texture at both the local and regional level. Explore the rural/urban income divide and compare urban cores that are thriving in places like Seattle and Houston to struggling metro areas such as Cleveland and Detroit.

2016 Trends doc (page 5 & 6) outline some income trends over the decade. We are still experiencing the same inequality of growth that is outlined in the paper. Most of the divide is along a metro and rural split. In recent developments, the new tax overhaul will certainly increase the growing gap between the rich and poor.

There are some clear “interesting” patterns.

  1. College Towns. Poor core of students surrounded by wealthier neighborhoods
  2. Columbus OH
  3. College populations are popping out in all the major cities. It would be really interesting to apply a mask for the tracts that fall under Tapestry 14B College Towns and 14C Dorms to Diplomas. These areas really skew the “analysis” given that these places have significant wealth but little to no income.
  4. Racially/Economically segregated cities
  5. Milwaukee, WI
  6. Cincinnati, OH
  7. Boston, MA
  8. San Antonio, TX
  9. Memphis, TN
  10. Cities where the downtown core is thriving/hip
  11. Philadelphia, PA
  12. Seattle, WA
  13. Houston, TX
  14. Cities where the downtown core is dying/struggling
  15. Detroit, MI
  16. Cleveland, OH

Esri maps created by Robby Deming, Media Strategy Manager, Esri

Maptitude maps of DACA, Income and Minimum Wage were created by Drew Smith,
Senior GIS Specialist, Mapping Software, Caliper

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Categories: analytics, ArcGIS, Caliper, cloud, data, election maps, emergency response, Esri, geospatial, GIS, Google Maps Engine, government, lidar, location based sensor fusion, mapping, Maptitude

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