1. Summary of Tract-to-Tract Commuter Flows by Type of Geographic Area. A useful way of comparing the general pattern of tract-to-tract commuter flows across multiple data sets is to summarize them into categories. For this analysis, tract-to-tract worker flow data from the LED and the census are compared by summarizing them into categories of a "flow typology" based on a simple classification scheme of the type of geographic area in which the tracts are located. Tracts located in the central city of a metropolitan statistical area were classified as "urban." Tracts located outside metropolitan central cities but still within the urbanized area were classified as "suburban." Tracts located within urbanized areas that are outside any metropolitan area were classified as “non- metro urbanized”. Finally, tracts located in the non-urbanized remainder of metropolitan areas or in non-metropolitan territory and not within an urbanized are were classified as “rural”. The resulting flow typology comprised intra-urban flows, intra-suburban flows, intra-rural flows, and flows between urban, suburban and rural areas. Flows to, from, and within tracts located in non-metropolitan urbanized areas also were differentiated in the same manner. Origin-Destination Worker Flows at the Census Tract Level Origins Destinations Total Productions UrbanSuburban Non-Metro UrbanizedRural Percent of Trips Census Urban23.47.7--1.832.8 Suburban10.128.4--3.441.9 Non-Metro Urbanized0.1--1.10.92.1 Rural4.94.01.812.523.2 Total Attractions38.340.12.918.7100.0 LED Urban21.68.70.12.032.4 Suburban12.128.20.13.543.9 Non-Metro Urbanized0.20.11.11.02.5 Rural4.84.51.710.221.3 Total Attractions38.741.63.016.8100.0 Note: In the Census data, 3,471 interchanges with a destination tract code of 999999, accounting for 200,280 trips, are not included in the table. These include 2,810 trips with urban origins, 1,876 trips with suburban origins, 23,138 trips with non-metro urbanized origins, and 174,456 trips with rural origins. -- Less than 0.5, so rounds to zero percent. Sources: 2001 LED O-D. Census 2000 Transportation Planning Package, Illinois, part 3. Origins Destinations Average UrbanSuburban Non-Metro UrbanizedRural Census Urban 21 12 8 17 18 Suburban 14 19 9 17 Non-Metro Urbanized 12 9 109 32 41 Rural 20 15 38 37 26 Average 19 17 48 29 19 Urban 5 3 2 3 4 Suburban 4 6 1 4 5 Non-Metro Urbanized 3 2 38 8 9 Rural 5 4 11 7 Average 5 4 10 7 5 LED Average Number of Trips Per Tract-to-Tract Interchange, by Type of Flow Tract-to-Tract Work Trips, by Type of Flow Internal Work Trips Within the Same Tract by Area Type Note: The LED data contained 124 tracts with no internal trips. The Census data contained 102 tracts with no internal trips. n/a = not applicable Sources: 2001 LED O-D File. Census 2000 Transportation Planning Package, Illinois, part 3. Distance Decay Transportation planners have shown that the number of work trips between places is inversely related to distance. That is, all other things being equal, people tend to live close to where they work. So, for any destination tract, we would expect more trips from closer tracts and fewer trips from tracts farther away. A general form of this relationship is: T w = T 0 e bd-cd 2 Where: T w = Total work flows T 0 = Internal trips d = Distance b,c parameters to be estimated When c = 0, the number of trips decreases exponentially with increasing distance. When b = 0, flows decline exponentially with the square of the distance. For each of the four geographic area types, the ten destination tracts that attracted the most work trips in each dataset were analyzed (or 40 total destination tracts from each data set). Great circle distances were calculated between the geometric centers of each origin-destination pair. Flow between tracts greater than 150 miles apart were excluded from the analysis as anomalies. Area Type LEDCensus ModelR2R2 R2R2 Urban ln(T w ) = 10.6 - 0.037d0.74ln(T w ) = 11.3 - 0.072d0.84 ln(T w ) = 12.3 - 0.11d + 0.0005d 2 0.92ln(T w ) = 13.2 - 0.15d + 0.0005d 2 0.92 Suburban ln(T w ) = 9.9 - 0.039d0.73ln(T w ) = 10.5 - 0.070d0.86 ln(T w ) = 11.7 - 0.12d + 0.0005d 2 0.94ln(T w ) = 12.4 - 0.15d + 0.0005d 2 0.94 Non-Metro Urbanized ln(T w ) = 7.9 - 0.028d0.73ln(T w ) = 8.5 - 0.058d0.84 ln(T w ) = 9.2 - 0.08d + 0.0003d 2 0.90ln(T w ) = 10.4 - 0.14d + 0.0005d 2 0.95 Rural ln(T w ) = 9.3 - 0.037d0.75ln(T w ) = 9.8 - 0.077d0.83 ln(T w ) = 11.1 - 0.11d + 0.0005d 2 0.96ln(T w ) = 12.1 - 0.16d + 0.0006d 2 0.95 Regression Results – transformed model ( Ln T w = ln T 0 +bd – cd 2 ) Conclusions This exploratory analysis found that the total counts of workers in the LED data for the State of Illinois and selected Illinois counties, aggregated up from block records on the H- B File, compare favorably with the census and the ACS. However, the LED O-D File contains more than three times the number of tract-to-tract interchanges and nearly 1 million fewer work trips than the census. This results in a much lower average number of trips per interchange in the LED data compared with the census. But when the tract-to- tract interchanges and worker trips are summarized by type of origin-destination interchange or flow (e.g. suburban-to-urban or rural-to-suburban), the proportion of interchanges and trips by type of flow in the LED data is virtually identical to the census. The results of this analysis, particularly the comparability of worker flow patterns between the LED and census data, demonstrate the potential utility of journey-to-work data from the LED Program for use in transportation planning. Because it is national in scope, the LED Program offers many advantages over the piecemeal approach of individual states trying to work with their state employment security agencies to obtain and use the employment data. Many states do not allow access to ES-202 data for research, so the LED Program is a means of accessing the data. The LED methodology is consistent for every state. The file that the LED Program uses to assign residence addresses to workers is consistent for every state and contains extensive confidential information not available outside the Census Bureau. Data from the LED Program are updated quarterly. The data are cost effective, costing much less than a survey or a census special tabulation. The transportation data community has embarked on a coordinated effort to study and adjust to the ACS. It is an opportune time to thoroughly evaluate the utility of the LED data as well. Small-area data from the ACS will be based on multi-year averages. If the accuracy of the worker flow data from the LED Program is improved, the data could be used to evaluate and validate the origin-destination data from the ACS. Annual LED data could be used to measure changes in key worker flows and travel corridors, and provide evidence of new travel patterns caused by shifts in the location of employment or new housing developments. Area Type Number of Tracts Number of Internal Tract Trips Percent of Trips Originating in Area Type Average Internal Trips Per Tract Maximum Internal Trips LED Urban1,087 38,1922.735714 Suburban907 89,0334.798657 Non-Metro Urbanized94 16,96716.1181780 Rural752 129,60714.11721,338 Total2,840 273,7996.496n/a Census Urban1,104 118,0756.81072,115 Suburban910 185,4808.42041,880 Non-Metro Urbanized94 26,42023.42811,010 Rural754 237,65919.43151,890 Total2,862 567,63410.7198n/a