Technical Report: Population

Transcription

1 Cherokee County orecasts Technical Report: An Element of the Joint Comprehensive Plan For Cherokee County and the Cities of Ball Ground, Waleska and Woodstock, Georgia Plan Cherokee Team: ROSS+associates McBride Dale Clarion Day Wilburn Associates Robert Charles Lesser & Company

2 Technical Report: Forecasts Cherokee County and its Cities Table of Contents Introduction... 1 Regressions... 1 TAZ Data for Cities... 3 Cherokee Forecasts: Methodology... 3 City Forecasts... 4 County Forecasts... 5 Analysis... 5 Appendix A: Trend Regressions... 9 Appendix B: City TAZ Data Appendix C: Mobility Forecasts Appendix D: The Data Regression Process Best Fit Regressions Trend Projections List of Tables Cherokee County Regressions against Estimated Growth Rates for Key Cities...3 City Forecasts:...7 County Forecasts:...8 Estimates: U.S. Bureau of the...10 Cherokee County Regressions against Cherokee County Regressions against...11 Ball Ground City Regressions against January i

3 Technical Report: Forecasts Cherokee County and its cities Ball Ground City Regressions against...12 Canton City Regressions against Canton City Regressions against...13 Holly Springs City Regressions against Holly Springs City Regressions against...14 Nelson City (pt) Regressions against Nelson City (pt) Regressions against...15 Waleska City Regressions against Waleska City Regressions against...16 Woodstock City Regressions against Woodstock City Regressions against...17 Unincorporated Cherokee County Regressions against Unincorporated Cherokee County Regressions against...18 Socioeconomic Data by TAZ...20 Socioeconomic Data by TAZ...21 January ii

4 Technical Report: Forecasts Cherokee County and its Cities Introduction This report presents the methodology used in preparing population forecasts for Cherokee County and its cities, and contains recommendations for use in the Joint Comprehensive Plan 10thYear Update. The selected population forecasts will become the basis for household and housing unit forecasts, and for other populationrelated tables in the Assessment Report (age breakdowns, etc.). They will also be influential in making employment forecasts. This report contains the following text that presents the methodology and resulting forecasts, and three appendices that contain the population trend line regression data and tables, the Traffic Analysis Zone data tables for the cities, and a brief explanation of the methodology used by the Atlanta Regional Commission in preparing their Mobility Forecasts for the Regional Transportation Plan. Regressions As a first step, 1 st, 2 nd and 3 rd order regressions were prepared for the county as a whole, each of its cities and the unincorporated area against two sets of historic trend data annual population figures for the longer range 1993 period and for the more recent period. In addition, regressions were prepared for the county as a whole covering the 1970 base line period (presented below). The 1 st, 2 nd and 3 rd order regressions produce straight line, parabola and ess curve functions, respectively, projecting the trend line data out to. As noted, both the longer view and the affect of the more immediate past are considered. The first page in the Trend Regressions section (Appendix A, starting on page 9) presents the methodology used to rectify the Bureau's annual population estimates for 1993 through 1999 with the actual census count. As the years go by, Bureau estimates become ever more inaccurate until, when the next decennial census is taken, surprises occur. The top table on the first page of Appendix A (page 10) presents the annual July 1 estimates for all years between 1993 and 1999 (published in October of 1999) and for through (published in July, ). The middle table on the page projects what the census count would have been, had those annual estimates for been correct. This figure is compared to the actual census figure 1 and the variance between them is determined. This variance is then applied to the estimates (as reported) to modify them to the actual census. The bottom table on the page presents the annual data, as rectified to the census. 1 All figures are the Bureau's estimates of population as of July 1 each year, including for, for data consistency. January 1

5 Technical Report: Forecasts Cherokee County and its cities The annual population data on that bottom table are used as the historic trend line data in calculating the mathematical regressions and projecting the trends to. The 1993 period was chosen instead of 1990 because it was noticed that a dip in annual estimates occurred across the board between 1992 and 1993, which reflects the recession of the early `90s. This dip would have thrown the regression curves off trying to account for the noncontinuous data stream. Pages are then presented in the Trend Regressions section that show the regression data for each jurisdiction along with a graph of the projections; each jurisdiction's page contains projections against both the 1993 period and the period. As a comparison, countywide data in 5year increments for were also prepared to. The results are: Cherokee County Regressions against 1970 Trend Projections* ,059 30, ,073 40, ,699 52, ,600 68, ,204 88, , , , , , , , , , , , , , , , , , , , , ,018 Correlation Coefficients , , , The data point was drawn from the nd order regression for the county. January 2

6 Technical Report: Forecasts Cherokee County and its cities TAZ Data for Cities Two tables have been drawn from the forecast data prepared by the Atlanta Regional Commission for the Regional Transportation Plan. The data on the tables are shown for each Traffic Analysis Zone (TAZ) within which a city is located or partially located. The city TAZ data is combined for each of the key cities 3 in the county. The two tables present data for the years and, respectively. (See Appendix B, the City TAZ Data Section, starting on page 19 for the data tables.) After the City TAZ Data Section, the Atlanta Regional Commission s summary of their Mobility forecasting methodology is included as Appendix C. This is the methodology that was used by ARC to produce the TAZ estimates and forecasts. Cherokee Forecasts: Methodology Since Mobility does not provide forecasts for cities, the first step is to consider the expected share of TAZ population that will be located within each city in the future. The following table shows the calculation of growth rates that have been used to forecast population in the county s key cities. (Note that the population figures on the following table are used only in estimating the growth rate; the actual forecasts are discussed in the next section City Forecasts.) Estimated Growth Rates for Key Cities TAZ * % of TAZ in % of TAZ in (est**) TAZ * City at % City at % Annual Rate of Increase at % Ball Ground 3, % 80.00% 10,727 2,573 8, % Canton 13,531 7, % 80.00% 49,179 28,019 39, % Holly Springs 7,577 3, % 66.67% 23,328 9,837 15, % Waleska 3, % 25.00% 8,979 1,767 2, % Woodstock 27,101 10, % 85.00% 51,653 19,155 43, % * Source: Atlanta Regional Commission, Regional Transportation Plan, for those TAZs including and surrounding each city indicated. ** percentage empiracally chosen relative to "most likely" regression projection. 3 The city forecast methodology differs from all of the other cities for the portions of Mt. Park and Nelson in the county, so these cities are not shown on the tables. January 3

7 Technical Report: Forecasts Cherokee County and its cities figures for, taken from the data tables for the TAZs that include and surround each city, are shown on the above table, along with each city s population count from the census. 4 This yields the percentage of the population in each TAZ grouping that actually lived in each city at that time. The fourth column on the table above shows the estimated percentage of the city TAZ population that is expected to be living within each city in. These two percentages ( actual and projected) are applied to the city TAZ population that is forecast to, yielding first an estimated city population if the city s share of the TAZ population does not increase (the % ), and secondly the population anticipated within the city assuming an increase in share (the % ), at least within the TAZs identified as currently including and surrounding each city. The last column contains the average annual growth rate that each city will have experienced between and in population increase within those identified city TAZs, assuming an increase in city share of the total population over time (the % ). 5 The increase in share results from continued annexation as well as attraction of infill development from within the city TAZ groupings. As will be seen below, the forecast methodologies for the cities of Canton, Holly Springs and Woodstock anticipate further expansion of the cities beyond the identified city TAZs such that the total forecast for each city is greater than the city population shown on the table above. Ball Ground and Waleska are located within large city TAZ groups such that expansion into neighboring TAZs is not expected. City Forecasts The first table on the page following this methodology text (see page 7) presents the calculations that represent the recommended population forecasts for each city (or portion of a city) in the county. For the key cities Ball Ground, Canton, Holly Springs, Waleska and Woodstock the annual rate of increase using the % from the table above is applied to the population (as estimated by the Bureau) and to the resulting population each subsequent year, ending in. 6 Mt. Park and Nelson, which have only minor portions of their city limits in the county, are treated somewhat differently. In the case of Mt. Park, estimates have shown a consistent population of 13 every year since ; we have assumed that this will continue into the foreseeable future. For Nelson, we selected the 2 nd order (parabolic) trend line projection calculated against the estimates; 7 the average annual rate of population increase shown on the table on page 7 was calculated from the and projected figures for comparison to the other cities. 4 TAZ data reflect actual counts from the, not the July 1 estimates. 5 The percentages were estimated, in part, with reference to most likely projections generated by the regressions, presented later in this report. 6 Over the forecast period, of course, individual years will experience a higher or lower result than that forecast because an average rate is applied to each year. By, however, the forecast will be accurate in the aggregate. 7 The same total for all cities is used in both methodologies because the city forecasts were prepared independently from the countywide forecasts and as such stand on their own. January 4

8 Technical Report: Forecasts Cherokee County and its cities County Forecasts County population forecasts are shown on the table and graphs on the page following the city forecasts (see page 8). Three methodologies are illustrated on the page: use of the ARC Mobility figures for the county as a whole for the low forecast, and consideration of medium and high countywide figures drawn from the trend line regressions. In each methodology, the total population forecast for the cities is subtracted from the county total to estimate the population each year in the unincorporated area. The Atlanta Regional Commission s Mobility forecasts a population for Cherokee County of 362, This forecast is combined with the Bureau s population estimate to identify the resulting average annual rate of increase for the county as a whole. Applying the average annual rate to each year produces an annual population for the county. By subtracting the annual population forecast for the cities as a group, the remaining population will be located within unincorporated areas of the county. The average annual rate of increase is calculated for the unincorporated population growth () and shown for comparison on the table. The ARC Mobility forecast is roughly consistent with several of the trend line projections calculated for the county as a whole, and included in the Trend Regressions section of this report (Appendix A). For the medium forecast shown on the County table below, the 3 rd order ( ess curve) trend line projection calculated against the historic population estimates from 1993 is used as a most likely forecast. The high population forecast represents the 2 nd order (parabola) trend line projection and is essentially an unconstrained forecast. As in the first methodology, the city population totals are subtracted from the county total to yield the population forecast in the unincorporated area. Analysis All of the methodologies used for the countywide population forecasts reflect an increasing share of the population located in one or another city most notably Canton, Holly Springs and Woodstock each of which is anticipated to roughly quadruple their population. Using the ARC Mobility forecast for the county (the low forecast), by the share of the countywide population living within all of the cities combined will have grown from 22% in to 41% in. If the medium countywide population forecast is considered, the total share among the cities still grows to 36%, while the high forecast results in an allcities share of 29% (still an increase over ). Differences, however, can be seen between the low and the high forecasts that are most notably evident in the growth rate and pattern for the unincorporated area. Under the Mobility scenario, the countywide population continues to increase at an increasing rate (note the upward curve in the line on the first graph). With the comparably higher rate of growth in population located within the cities, the unincorporated population grows at a much lower, but steady, rate (note the flatness of the line on the first graph). 8 A summary of ARC s methodology is attached as Appendix C. January 5

9 Technical Report: Forecasts Cherokee County and its cities Considering the medium forecast, however, the countywide total increases at a steady rate (a function of using an average annual rate of growth), but the unincorporated population exhibits a much stronger ess curve shape, suggesting a continuation of an increasing growth rate over the next 10 years or so, then dropping off in the latter part of the forecast period still growing but at a decreasing annual rate. Ultimately, under the medium scenario, the unincorporated population in will be higher than under the low scenario, but the pattern of that increase could be characterized as a higher rate of growth in the coming decade that drops off in time, compared to a steady state of growth throughout the forecast period, respectively. In the "high" forecast, growth in the unincorporated area continues at a steady but everincreasing rate. This forecast is considered "unconstrained" by the naturalgrowth effects of dwindling land resources, increasing land prices, and market forces. The reoccurring theme among all of these regressions, based on the 1993 base period, is a medium population forecast in the low 400,000s and a high forecast in the low 500,000s. Although we believe the 500,000s will not be achieved by because of all the natural growth processes that are evident in the medium forecast, such a high estimate is presented for discussion and may represent the initial buildout of the county after. January 6

10 City Forecasts: Ball Ground Canton Holly Springs Mt. Park (pt) Nelson (pt) Waleska Woodstock All Cities Total Rate: 8.561% 5.583% 5.417% n/a 4.288% 4.405% 5.038% n/a 70, ,094 4, ,214 38, ,937 4, ,081 40, ,827 5, ,992 43,119 1,013 17,767 5, ,949 45,427 1,100 18,759 5, ,954 47,860 1,194 19,806 6, ,010 50,425 1,296 20,912 6, ,119 53,130 1,407 22,080 6, ,284 55,982 1,527 23,313 7, ,024 25,507 58,989 1,658 24,615 7, ,069 26,792 62,161 1,800 25,989 7, ,116 28,142 65,505 1,954 27,440 8, ,165 29,560 69,032 2,121 28,972 8, ,216 31,049 72,751 2,303 30,590 9, ,270 32,613 76,674 2,500 32,298 9, ,326 34,256 80,810 2,714 34,101 10, ,384 35,982 85,173 2,946 36,005 10, ,445 37,795 89,774 3,198 38,015 11, ,509 39,699 94,627 3,472 40,138 12, ,575 41,699 99,747 3,769 42,379 12, ,644 43, ,147 4,092 44,745 13, ,716 46, ,842 4,442 47,243 14, ,792 48, ,851 4,822 49,881 14, ,871 50, ,191 5,235 52,666 15, ,953 53, ,879 5,683 55,607 16, ,039 56, ,936 6,170 58,712 17, ,129 58, ,384 6,698 61,990 18, ,015 2,223 61, ,242 60,000 40,000 30,000 Notes: population figures: U.S. Bureau of the annual estimates, as of July 1 each year. population figures for Ball Ground, Canton, Holly Springs, Waleska and Woodstock based on average annual rate of increase for each city. 20,000 population figures for the portion of Mt. Park in Cherokee County assumes a continuation of current population. population figures for the portion of Nelson in Cherokee County based on 2nd order (parabolic) trend line regression against historic data. 10,000 Ball Ground Canton Holly Springs Waleska Woodstock January 7

11 County Forecasts: All Cities Total LOW FORECAST County Uninc. Total County MEDIUM FORECAST County Uninc. Total County HIGH FORECAST County Uninc. Total County n/a 2.847% 1.693% 3.410% 2.610% 4.288% 3.910% 5 38, , , , , , ,824 40, , , , , , ,976 43, , , , , , ,870 45, , , , , , ,008 47, , , , , , ,386 50, , , , , , ,998 53, , , , , , ,834 55, , , , , , ,887 58, , , , , , ,151 62, , , , , , ,615 65, , , , , , ,272 69, , , , , , ,111 72, , , , , , ,122 76, , , , , , ,295 80, , , , , , ,619 85, , , , , , ,081 89, , , , , , ,670 94, , , , , , ,372 99, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,175 Notes: Low Forecast based on ARC Mobility, Atlanta Regional Commission, forecast for Cherokee County. Calculated annual rate of increase for applied to intervening years. Medium Forecast reflects 3rd order ("ess" curve") trend line regression for Cherokee County against 1993 historic data. Medium Forecast Low Forecast 500, , , , , , County Total Uninc. County All Cities Total County Total Uninc. County All Cities Total High Forecast reflects 2nd order (parabola) trend line regression for Cherokee County against 1993 historic data. All figures for Unincorporated Cherokee County calculated by subtracting All City from Total County ,000 High Forecast 4 400, , County Total Uninc. County All Cities Total January 8

12 Appendix A: Trend Regressions January 9

13 Estimates: U.S. Bureau of the Cherokee County and Its Cities: 1993 Bureau Annual Estimates (7/1 of each year) Estimated (by ) Area Name Cherokee County, GA 103, , , , , , , , , , , ,680 Ball Ground city, GA 1,034 1,095 1,159 1,227 1,288 1,353 1, Canton city, GA 5,826 5,842 5,881 5,889 5,974 6,178 6,786 8,185 9,564 11,372 13,249 15,094 Holly Springs city, GA 2,734 2,940 3,173 3,299 3,338 3,409 3,489 3,600 4,090 4,201 4,409 4,699 Mountain Park city (pt.), GA Nelson city (pt.), GA Waleska city, GA Woodstock city, GA 5,431 5,618 5,854 6,133 6,552 7,621 8,629 10,342 11,502 13,166 14,852 17,214 Balance of Cherokee County, GA 87,430 92,638 97, , , , , , , , , , Annual Estimates Projected to Yr Divergence Cherokee County, GA 103, , , , , , , , Ball Ground city, GA 1,040 1,096 1,155 1,218 1,284 1,353 1,426 1, Canton city, GA 5,678 5,798 5,921 6,046 6,173 6,304 6,437 6, Holly Springs city, GA 2,839 2,951 3,067 3,187 3,312 3,442 3,577 3, Mountain Park city (pt.), GA Nelson city (pt.), GA Waleska city, GA Woodstock city, GA 5,156 5,560 5,995 6,465 6,971 7,517 8,106 8, Balance of Cherokee County, GA 87,804 92,664 97, , , , , , Annual Estimates Rectified to Estimated (by ) Cherokee County, GA 103, , , , , , , , , , , ,680 Ball Ground city, GA 1,034 1, Canton city, GA 5,826 6,001 6,336 6,681 7,039 7,408 7,790 8,185 9,564 11,372 13,249 15,094 Holly Springs city, GA 2,734 2,927 3,144 3,254 3,278 3,332 3,394 3,600 4,090 4,201 4,409 4,699 Mountain Park city (pt.), GA Nelson city (pt.), GA Waleska city, GA Woodstock city, GA 5,431 5,765 6,160 6,614 7,238 8,618 9,984 10,342 11,502 13,166 14,852 17,214 Balance of Cherokee County, GA 87,430 91,867 96, , , , , , , , , ,824 Sources: U.S. Bureau of the, Annual Estimates and ; projections and rectification to by ROSS+associates. January 10

14 Cherokee County Regressions against 1993 Trend Projections* , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Correlation Coefficients * Projections based on 1st, 2nd and 3rd order regressions against 1993 estimates, as rectified to the by ROSS+associates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. Cherokee County Regressions against Trend Projections* 143, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,320 1,025,274 Correlation Coefficients , 1,000, , , ,000 * Projections based on 1st, 2nd and 3rd order regressions against estimates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. January 11

15 Ball Ground City Regressions against 1993 Trend Projections* ,034 1, ,015 1, , , , , , , ,013 2, ,071 3, ,136 3, ,206 4, ,283 5, ,366 6, ,455 7, ,551 8, ,652 9, ,760 10, ,873 12, ,993 13, ,119 15, ,252 17, ,390 19, ,535 21, ,685 23, ,842 25,670 (14) 3,005 28,189 30,000 25,000 20,000 15,000 10,000 5,000 (5,000) Correlation Coefficients * Projections based on 1st, 2nd and 3rd order regressions against 1993 estimates, as rectified to the by ROSS+associates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. Ball Ground City Regressions against Trend Projections* , ,074 1, ,123 1, ,179 1, ,241 1, ,311 1, ,389 1, ,476 1, ,571 1, ,676 1, ,791 1, ,917 1, ,053 1, ,201 2,500 2,000 1,500 1, Correlation Coefficients * Projections based on 1st, 2nd and 3rd order regressions against estimates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. January 12

16 Canton City Regressions against 1993 Trend Projections* ,826 6, ,001 6, ,336 6, ,681 6, ,039 6, ,408 7, ,790 7,932 8,185 8,904 9,564 10,075 11,372 11,446 13,249 13,017 15,094 14,787 13,727 16,757 18,204 14,499 18,927 21,709 15,270 21,297 25,890 16,042 23,867 30,812 16,813 26,636 36,538 17,585 29,605 43,131 18,357 32,774 50,655 19,128 36,143 59,173 19,900 39,712 68,750 20,671 43,480 79,447 21,443 47,448 91,331 22,214 51, ,463 22,986 55, ,907 23,758 60, ,727 24,529 65, ,987 25,301 70, ,750 26,072 75, ,079 26,844 80, ,039 27,615 86, ,692 28,387 92, ,103 29,159 98, ,335 29, , ,451 30, , ,516 31, , ,592 32, , ,743 33, , , , , , Correlation Coefficients * Projections based on 1st, 2nd and 3rd order regressions against 1993 estimates, as rectified to the by ROSS+associates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. Canton City Regressions against Trend Projections* 8,185 7,992 9,564 9,743 11,372 11,493 13,249 13,243 15,094 14,993 16,744 17,244 16,599 18,494 19,495 17,559 20,244 21,889 17,740 21,995 24,426 16,911 23,745 27,105 14,843 25,495 29,928 11,304 27,246 32,894 6,064 28,996 36,003 (1,108) 30,746 39,255 (10,441) 32,496 42,649 (22,167) 34,247 46,187 (36,516) 35,997 49,868 (53,719) 37,747 53,692 (74,005) 39,498 57,659 (97,606) 41,248 61,768 (124,752) 42,998 66,021 (155,674) 44,749 70,417 (190,601) 46,499 74,956 (229,765) 48,249 79,638 (273,396) 49,999 84,462 (321,725) 51,750 89,430 (374,981) 53,500 94,541 (433,396) 55,250 99,795 (497,200) 57, ,192 (566,624) 58, ,731 (641,897) 60, ,414 (723,251) Correlation Coefficients () () (300,000) (400,000) (500,000) (600,000) (700,000) (800,000) * Projections based on 1st, 2nd and 3rd order regressions against estimates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. January 13

17 Holly Springs City Regressions against 1993 Trend Projections* ,734 2, ,927 2, ,144 3, ,254 3, ,278 3, ,332 3, ,394 3,556 3,600 3,742 4,090 3,948 4,201 4,174 4,409 4,420 4,699 4,686 4,669 4,971 5,161 4,836 5,277 5,642 5,002 5,602 6,206 5,168 5,948 6,860 5,335 6,313 7,614 5,501 6,698 8,475 5,667 7,103 9,453 5,833 7,528 10,554 6,000 7,973 11,788 6,166 8,438 13,164 6,332 8,923 14,688 6,499 9,427 16,371 6,665 9,952 18,219 6,831 10,496 20,242 6,998 11,060 22,447 7,164 11,645 24,844 7,330 12,249 27,440 7,496 12,873 30,244 7,663 13,517 33,265 7,829 14,180 36,509 7,995 14,864 39,987 8,162 15,568 43,706 8,328 16,291 47,675 8,494 17,035 51,901 8,660 17,798 56,394 8,827 18,581 61,161 70,000 60,000 40,000 30,000 20,000 10, Correlation Coefficients * Projections based on 1st, 2nd and 3rd order regressions against 1993 estimates, as rectified to the by ROSS+associates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. Holly Springs City Regressions against Trend Projections* 3,600 3,696 4,090 3,948 4,201 4,200 4,409 4,452 4,699 4,703 4,955 4,803 5,449 5,207 4,904 6,840 5,458 4,961 9,110 5,710 4,974 12,488 5,962 4,944 17,207 6,213 4,872 23,496 6,465 4,755 31,586 6,717 4,596 41,706 6,969 4,393 54,089 7,220 4,147 68,964 7,472 3,858 86,561 7,724 3, ,112 7,975 3, ,846 8,227 2, ,995 8,479 2, ,788 8,730 1, ,456 8,982 1, ,231 9, ,341 9,486 (16) 353,018 9,737 (695) 405,492 9,989 (1,417) 462,995 10,241 (2,182) 525,755 10,492 (2,991) 594,004 10,744 (3,843) 667,972 10,996 (4,739) 747,890 11,247 (5,677) 833,988 Correlation Coefficients , , , , , , ,000 0 * Projections based on 1st, 2nd and 3rd order regressions against estimates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. January 14

18 Nelson City (pt) Regressions against 1993 Trend Projections* (18) (141) (287) (458) (657) (884) (1,142) (1,432) (1,756) (2,116) (2,513) (2,948) (3,425) (3,944) (4,507) 857 (14) (5,116) 882 (58) (5,772) 906 (105) (6,478) 930 (154) (7,234) 954 (205) (8,043) 978 (259) (8,906) 2,000 (2,000) (4,000) (6,000) (8,000) (10,000) Correlation Coefficients * Projections based on 1st, 2nd and 3rd order regressions against 1993 estimates, as rectified to the by ROSS+associates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. Nelson City (pt) Regressions against Trend Projections* , , , , , , , , , , , , , , , , , ,015 10,122 12,000 10,000 8,000 6,000 4,000 2,000 0 Correlation Coefficients * Projections based on 1st, 2nd and 3rd order regressions against estimates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. January 15

19 Waleska City Regressions against 1993 Trend Projections* , , ,167 1, ,235 1, ,307 1, ,384 1, ,465 1, ,550 1, ,639 1, ,733 1, ,830 1, ,932 1, ,039 1, ,149 1, ,263 1, ,382 1, ,505 1, ,633 1, ,764 1, ,900 1,066 3,500 3,000 2,500 2,000 1,500 1, Correlation Coefficients * Projections based on 1st, 2nd and 3rd order regressions against 1993 estimates, as rectified to the by ROSS+associates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. Waleska City Regressions against Trend Projections* , , , , , (139) 7, (365) 10, (616) 13, (894) 17, (1,197) 22, (1,526) 28,359 1,003 (1,881) 34,960 1,023 (2,261) 42,533 1,043 (2,668) 51,144 1,063 (3,100) 60,860 1,083 (3,558) 71,746 1,103 (4,043) 83,870 1,123 (4,553) 97,299 1,143 (5,088) 112,098 1,163 (5,650) 128,334 1,183 (6,238) 146,074 1,204 (6,851) 165,384 1,224 (7,490) 186,331 1,244 (8,155) 208,980 1,264 (8,846) 233,400 Correlation Coefficients () * Projections based on 1st, 2nd and 3rd order regressions against estimates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. January 16

20 Woodstock City Regressions against 1993 Trend Projections* ,431 5, ,765 5, ,160 6, ,614 6, ,238 7, ,618 8, ,984 9,350 10,342 10,539 11,502 11,883 13,166 13,383 14,852 15,039 17,214 16,850 16,458 18,816 19,236 17,491 20,939 21,745 18,525 23,216 24,547 19,558 25,649 27,662 20,591 28,238 31,108 21,625 30,982 34,902 22,658 33,882 39,064 23,692 36,937 43,612 24,725 40,148 48,564 25,758 43,514 53,939 26,792 47,036 59,755 27,825 50,714 66,030 28,859 54,546 72,783 29,892 58,535 80,033 30,925 62,679 87,798 31,959 66,978 96,096 32,992 71, ,945 34,026 76, ,365 35,059 80, ,373 36,093 85, ,988 37,126 90, ,229 38,159 96, ,113 39, , ,660 40, , ,887 41, , ,813 42, , , Correlation Coefficients * Projections based on 1st, 2nd and 3rd order regressions against 1993 estimates, as rectified to the by ROSS+associates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. Woodstock City Regressions against Trend Projections* 10,342 10,343 11,502 11,533 13,166 13,069 14,852 14,951 17,214 17,181 18,543 19,756 19,997 20,253 22,679 23,401 21,962 25,948 27,496 23,672 29,563 32,367 25,381 33,525 38,101 27,090 37,834 44,783 28,800 42,489 52,500 30,509 47,491 61,337 32,219 52,840 71,381 33,928 58,535 82,718 35,637 64,576 95,433 37,347 70, ,613 39,056 77, ,343 40,766 84, ,710 42,475 92, ,799 44,184 99, ,697 45, , ,490 47, , ,263 49, , ,103 51, , ,095 52, , ,326 54, , ,881 56, , ,847 57, , ,310 59, , ,355 61, , ,069 Correlation Coefficients , , , ,000 0 * Projections based on 1st, 2nd and 3rd order regressions against estimates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. January 17

21 Unincorporated Cherokee County Regressions against 1993 Trend Projections* ,430 86, ,867 91, ,114 96, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,179 90, , ,785 77, , ,342 61, , ,850 43, , ,310 23, , ,720 1, , ,081 (23,066) 228, ,393 (50,095) 232, ,656 (79,676) 237, ,870 (111,909) 241, ,035 (146,891) 246, ,151 (184,720) 250, ,218 (225,495) 255, ,236 (269,314) 300,000 () () (300,000) Correlation Coefficients * Projections based on 1st, 2nd and 3rd order regressions against 1993 estimates, as rectified to the by ROSS+associates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. Unincorporated Cherokee County Regressions against Trend Projections* 120, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,296 94, , ,241 87, , ,186 79, , ,131 70, , ,076 61, , ,021 52, , ,966 41, , ,911 31, ,732 Correlation Coefficients , * Projections based on 1st, 2nd and 3rd order regressions against estimates. Sources: U.S. Bureau of the, Annual Estimates and ; projections by ROSS+associates. January 18

22 Appendix B: City TAZ Data January 19

23 Socioeconomic Data by TAZ Construction Manufacturing TCU Wholesale Retail Fire Service Total Private Government Government Total Households University Enrollment Acre City TAZ Ball Ground ,916 Ball Ground , ,757 Ball Ground ,043 1,100 25,673 Canton ,325 Canton , ,148 2, ,635 2,871 1,062 5,398 Canton Canton Canton Canton , ,243 Canton , ,004 Canton ,194 1,370 1,370 2,564 2, ,123 Canton ,229 1, ,745 Canton , ,125 Canton , ,470 7,777 2,340 2,340 10,117 13,531 4,871 22,672 Holly Springs , ,310 Holly Springs , ,556 Holly Springs , ,021 Holly Springs , ,371 Holly Springs 418 1, , ,152 7,577 2,630 6,258 Waleska ,104 2,689 Waleska ,119 Waleska , ,855 Waleska , ,125 Waleska ,130 1,094 1,104 22,788 Woodstock Woodstock Woodstock , Woodstock , Woodstock Woodstock , Woodstock Woodstock , Woodstock , , ,323 Woodstock , , Woodstock Woodstock , ,450 2, Woodstock ,030 2,020 2,418 Woodstock ,344 7,024 2,557 1,811 Woodstock , ,811 8, ,422 27,101 9,865 13,042 Source: Atlanta Regional Commission. January 20

24 Socioeconomic Data by TAZ Construction Manufacturing TCU Wholesale Retail Fire Service Total Private Government Government Total Households University Enrollment Acre City TAZ Ball Ground ,838 1,019 4,916 Ball Ground , ,446 7,889 3,089 20,757 Ball Ground , ,024 10,727 4,108 25,673 Canton , ,325 Canton , ,823 7, ,991 9,795 3,599 5,398 Canton , ,646 1, Canton , Canton , , ,180 3,279 1, Canton , ,566 3,882 1,633 1,243 Canton , ,038 6,268 2,428 3,004 Canton ,080 3,008 1,874 1,874 4,882 8,681 3,170 2,123 Canton , ,984 6,579 2,464 2,745 Canton , ,656 4,767 1,834 2,125 Canton 1,186 1, ,225 9,942 1,433 6,782 22,715 3,074 3,074 25,789 49,179 18,459 22,672 Holly Springs ,470 2,104 2,310 Holly Springs , ,806 5,649 2,227 1,556 Holly Springs ,483 1,531 1,021 Holly Springs , ,724 7,726 2,850 1,371 Holly Springs 611 1, ,334 4, ,020 23,328 8,712 6,258 Waleska , ,243 2,689 Waleska ,076 1, ,119 Waleska ,339 1,210 10,855 Waleska ,780 1,060 6,125 Waleska , ,995 8,979 3,338 1,243 22,788 Woodstock , Woodstock , ,409 2, Woodstock ,337 2, Woodstock ,877 1, Woodstock ,005 2, Woodstock , ,307 2,166 1, Woodstock Woodstock ,843 1, Woodstock , ,143 2,841 1,264 1,323 Woodstock ,258 2, ,974 2, Woodstock , ,510 1, Woodstock , ,725 3,122 1, Woodstock ,168 2, ,416 8,814 3,163 2,418 Woodstock ,298 1,156 1,156 2,454 8,440 3,325 1,811 Woodstock 1,074 1, ,493 5,337 1,375 7,100 18,307 2,330 2,330 20,637 51,653 20,635 13,042 Source: Atlanta Regional Commission. January 21

25 A TLANTA R EGIONAL C OMMISSION 40 COURTLAND S TREET, NE A TLANTA, GEORGIA Appendix C: Mobility Forecasts Forecast Study Area The Mobility forecasts are for the13 county area that comprises the Atlanta Air Quality Nonattainment area. It includes the 10 ARC counties (Cherokee, Clayton, Cobb, DeKalb, Douglas, Fayette, Fulton, Gwinnett, Henry and Rockdale), plus Coweta, Forsyth and Paulding. Forecasting Methodology ARC produces longrange forecasts using a twostep method. The first step produces forecasts for the entire 13county study area as a whole. The second step disaggregates the studyarea forecast to census tracts and traffic analysis zones (TAZs). Study Area Forecasts The forecast for the entire 13county study area is produced using a mixed econometric and cohort component model called IPEF (Interactive and Econometric Forecasting). This model divides the population of the region into small groups (cohorts) whose members are of the same race and sex and nearly the same age. Operating in 5year iterations, the model assesses the effects of the three ways population can change, births, deaths and migration, on each cohort. The model uses birth and death rates forecast by the U. S. Bureau of the. Migration is determined as the population movement required to create a labor force sufficient to meet the needs of the econometric forecast of jobs. The econometric module of the model is calibrated against historical employment and population data. Its inputs include an industry specific forecast of national employment and forecasts of future laborforce participation rates. Because population is partly a function of employment and vice versa, the model is iterative. It cycles until the employment and population are in balance. ARC staff produce the studyarea forecasts with the assistance of a team of local experts in economics and demography. This technical advisory committee advises on the calibration of the model and assists in the development of assumptions affecting the forecasts. When complete, the committee recommended the study area forecasts to the Commission. ARC Methodology 22

26 A TLANTA R EGIONAL C OMMISSION 40 COURTLAND S TREET, NE A TLANTA, GEORGIA Small Area Forecasts The primary tool used to disaggregate the studyarea forecasts to smaller areas (census tracts in the Atlanta application) is a model called DRAM/EMPAL (Disaggregated Residential Allocation Model/EMPloyment ALlocation model). Calibrated against historical data for the study area, this model attempts to replicate observed growth patterns. Like the IPEF model, it operates in fiveyear cycles. Each successive forecast becomes the base for the next. Inputs for each census tract include baseyear households by income, employment by industry, land use by type and mathematical measures of accessibility to each of the other census tracts in the study area. The accessibility measure is based on anticipated changes in the regional transportation system. ARC staff held extensive meetings with local government planning staffs to determine how local policies would affect population and employment distribution at the smallarea level (designation of a substantial area as greenspace or the ongoing development at Atlantic Station, for example). Where appropriate the findings from these discussions were incorporated into the model. ARC s process for developing local government input to the forecasting process is discussed in detail in Appendix A. The accessibility measures used in the DRAM/EMPAL model are produced using the TP+ travel demand model. TP+ is a mathematical model of the study area s transportation system. Futureyear transportation networks are developed in a complex process that considers regional mobility, regional equity, air quality, and financial constraints on future improvements. TP+ requires input at the traffic analysis zone (TAZ) level. TAZs are subdivisions of census tracts (The 589 census tracts in the forecast study area are divided into 1,683 TAZs.) A relatively simple Zonal Allocation Procedure (ZAP) is used to further disaggregate the DRAM/EMPAL forecast for each censustract to its constituent TAZs. ARC Methodology 23

27 Appendix D: The Data Regression Process The term regression when used in projecting historical data into the future is a mathematical expression for a method of finding trends in the known data on which the projections can be based. Some refer to this as curvefitting because the process attempts to find the mathematical line 9 that best fits the known data points; continuing this line into the future produces the projection. The best fit line is the line that has the highest correlation to the data that is, the line with data points that are, collectively, the closest to reproducing the historic data points. In some cases, of course, the best fit is not the most realistic projection, as discussed below. Demographic data is highly complex and rarely fits neatly along a simple line. On the other hand, demographic data regarding population and employment most often reflect a progression from the past into the future as change occurs. Some years may show a much greater change than others, but trends in these changes over time are usually evident. Regression analysis, then, attempts to fit a straight line (1 st order regression), a parabolic line (2 nd order, which assumes a steady change that is constantly increasing or decreasing), and an ess curved line (3 rd order, which assumes that the trend is to go up for awhile and then down, or vice versa) to best define the trend in the data. Ultimately, fitting trend lines to historic data must be viewed as an approximation at best, and extending these lines into the future is useful as an analytical tool, an indicator of the future, but not necessarily a prediction of reality. Best Fit Regressions To illustrate the regression analysis process, particularly when the historic data is relatively continuous such as population counts, the Data Table on the right has been created for use as an example. The table shows the historic data that has been created, as well as the 1 st, 2 nd and 3 rd order regressions that have been calculated against the historic data (the straight line, the parabola and the ess curve, respectively). The correlations indicate the fit to the data, with a 1.0 being a perfect fit and 0.0 being no fit at all. First, we ll look at how a regression might treat apparently unrelated, noncontinuous data, in contrast to the example considered here. Then, we ll discuss how well the regressions in the Example Data Table fit the historic data itself to illustrate our example. EXAMPLE DATA TABLE Historic ,021 22,357 28,976 30, ,998 31,200 32,146 30, ,524 40,043 37,206 35, ,105 48,886 44,158 43, ,876 57,729 53,001 53, ,520 66,572 63,735 65,588 79,361 75,415 76,361 77,684 88,056 84,258 90,878 89,025 93, ,286 98, , , , , , , , , , , ,829 97,366 Correlations: In mathematical terms, a line may be straight or curved. A curved line usually traces a mathematical formula, such as a parabola or ess curve function, when used in regression analyses. January 24