The map uses county data from the 2006-2010 American Community Survey (ACS) and is ultimately aggregated into 10 square mile grid cells.  There were two criteria used in calculating a popular location “score”.  First, we looked at the total number of workers that work from home (telecommuters) in each U.S. county.  Then the data was normalized.  Normalization is the process of ranking the data on a scale of 0 to 1 using the county with the most telecommuters as the base.  The top county gets a score of 1 and all other counties are scored in proportion to the top county.  For example, Los Angeles County, California had 200,450 people working from home; therefore, they received a score of 1 for this category.  Maricopa County, Arizona was second on the list with 88,689 people working from home.  Their normalized score is 0.44 which was calculated by dividing the number of commuters in Marcopa County (88,689) by the top value from Los Angeles County (200,450).  This step was repeated for each county to produce a normalized telecommuting score.

The top ten counties in terms of total number of people working from home are:

1. Los Angeles County, CA – 200,450
2. Maricopa County, AZ – 88,689
3. Cook County, IL – 88,287
4. San Diego County, CA – 86,297
5. Orange County, CA – 66,404
6. Harris County, TX – 57,861
7. King County, WA – 53,621
8. New York County, NY – 52,281
9. Riverside County, CA – 41,753
10. Miami-Dade County, FL – 41,560

The second category we looked at was the number of people working from home as a percentage of all workers in the county.  Analyzing the data in this fashion allows us to pay proper attention to those counties that are not as heavily populated, but yet have a high percentage of workers telecommuting.  The top county in this category is Wheeler County, Nebraska which had 40.45% of their workers working from home.  This data was also normalized.

The counties with the highest percentage of the workforce working from home are:

1. Wheeler County, NE – 40.45%
2. Chattahoochee County, GA – 39.24%
3. Slope County, ND – 38.19%
4. Arthur County, NE – 32.88%
5. Pulaski County, MO – 32.54%
6. Billings County, ND – 30.51%
7. Kidder County, ND – 29.20%
8. Carter County, MT – 28.83%
9. Harding County, SD – 28.11%
10. Loup County, NE – 27.76%

The final score used in our map is  simply the combination of these two scores for each county divided by two.   This allows us to give equal weight to both data categories.  The final top ten counties are thus:

1. Los Angeles County, CA  (normal score = 0.56)
2. Wheeler County, NE (normal score = 0.50)
3. Chatahoochee County, GA (normal score = 0.49)
4. Slope County, ND (normal score = 0.47)
5. Pulaski County, MO (normal score = 0.42)
6. Arthur County, NE (normal score = 0.41)
7. Billings County, ND (normal score = 0.38)
8. Kidder County, ND (normal score = 0.36)
9. Carter County, MT (normal score = 0.36)
10. Harding County, SD (normal score = 0.35)

The final step was to apportion the data into 10 square mile grid cells.  This final step accomplishes a couple of things.  First, it makes it quick and easy to display on a web map.  Second, it ignores political boundaries by considering  data from surrounding counties.  The result is a thematic map that displays the most popular locations for telecommuters.

Urban Decision Group (UDG) is responsible for the creation of this map.

Last week we calculated the average commuting time in minutes per trip.  From this we can calculate the the average number of hours a worker spends commuting per week regardless of the mode of transportation.  We then used two ACS datasets to compute the average hourly wages for a worker that lives within each grid cell (a 20 square mile area).  The  average hourly wage computation is based on a 40 hour work week and 48 weeks of work per year-this assumption factors in holidays and 2 weeks of vacation per year.  We then computed a new hourly wage by adding the number of hours spent commuting per week to 40.  In other words, if the average worker spends 3 hours per week commuting, then they are actually “working” 43 hours per week.  The base hourly wage is then subtracted from the “new” hourly wage (which factors in time spent commuting) to arrive at the decrease in hourly wages after factoring in the time spent commuting.

If you click on any cell in this week’s map, it will display the amount of dollars you can subtract from the average worker’s hourly wage to derive the actual hourly wage after considering the average worker’s commute time.  Why is this important?  It’s important because it considers things like negative externalities and opportunity costs.  The time spent commuting to a job is time that the worker could be spending with friends or family, working on another job, etc.  In other words, a worker may want to consider a lower paying job if the job is closer to hisher home.  This particular exercise is exclusive of gas prices, which is a whole other matter.

Keep in mind, the grid cells represent the location of the worker’s residence, not the location of employment.  The location of employment has already been factored in when we computed the time spent commuting.  When you click on a cell, the field named CHGINWAGES contains the dollar amount you should subtract from the worker’s hourly wage.  If you want to compute your actual hourly wage you first need to compute your base hourly wage by taking your salary, divide it by 40, and then divide that by 48.  Then locate the grid cell that represents your residence, click on it, and then subtract the amount displayed from your hourly wage.