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Mapping & analysis objectives

Population & employment projection maps: Salt Lake County & Kennecott Land Dec 2006 prepared by Scott Bridwell DIGIT Lab, University of Utah scott.bridwell@geog.utah.edu. Mapping & analysis objectives. Create population and employment maps for Salt Lake County that seamlessly integrate:

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Mapping & analysis objectives

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  1. Population & employment projection maps:Salt Lake County & Kennecott LandDec 2006prepared by Scott BridwellDIGIT Lab, University of Utahscott.bridwell@geog.utah.edu

  2. Mapping & analysis objectives • Create population and employment maps for Salt Lake County that seamlessly integrate: • Projections for population and employment counts generated by Wasatch Front Regional Council at the TAZ level for 2010-2030 (based on the 2003 baseline) • State projections for anticipated employment and population counts for the Kennecott Land development area as a whole for 2010-2059 (based on the 2005 baseline) • Kennecott Land master plan detailing the distribution of anticipated land-uses as well as the time phases in which specific areas are expected to be developed

  3. Mapping & analysis procedures • Step 1: Spatially allocate GOPB prescribed population & employment within w/in the Kennecott development • For specified window years: 2010, 2020, 2030, 2040, 2050, 2059 • Cumulative population/employment allocated must comply with GOPB Kennecott totals • Allocated proportionally based on attributes w/in the master plan (total dwelling units for population; total square footage for employment) • Map population & employment at the resolution of the master plan • Step 2: Reconcile the Kennecott totals with WFRC small area projections • For specified window years: 2005, 2010, 2020, 2030 • Scale the small area totals to match 2005 GOPB baseline • Spatially join Kennecott totals w/ the TAZ projections • Re-scale TAZ totals such that cumulative population/employment comply w/ GOPB county totals while maintaining Kennecott totals • Map the results for Salt Lake County & populated places at TAZ level

  4. This map provides a reference of the Kennecott land development for later maps to come… Source: West Bench Master Plan from Kennecott

  5. Allocating GOPB projections among Kennecott planned development • Kennecott planned units • Cell resolution: 150 x 150 meters • Attributes: • Planned use: commercial, industrial, mixed use, mixed use – low, open space, public, residential • Dwelling units at build-out • Square footage at build-out • Development phasing • Spatial allocation method • Allocate projected population and employment according to the cell attributes, timing of development phasing & GOPB projection trends • Population determined from the # of dwelling units in a development cell • Employment determined from the # of total square feet and planned land-use of a development cell Kennecott master plan

  6. This is the spatial distribution of development start and end times. This produces 5 distinct phases…

  7. Kennecott development phasing • Phase 1: 2010 – 2019 • Dwelling units: 19,789; square footage: 12,946,879 • Phase 2: 2010 - 2029 • Dwelling units: 15,318; square footage: 6,046,356 • Phase 3: 2025 – 2039 • Dwelling units: 54,314; square footage: 4,240,224 • Phase 4: 2025 – 2059 • Dwelling units: 3913; square footage: 389,595 • Phase 5: 2040 – 2059 • Dwelling units: 72,579; square footage: 37,232,910

  8. Note: This is the distribution of dwelling units and square footage @ 2059 when the project completes The following slides discuss how these units are distributed in time according to development phasing and GOPB projection controls

  9. The temporal distribution of the Kennecott population by year provides the starting points for allocating the population spatially. Source: GOPB 2005 baseline

  10. Allocating Kennecott population • Step 1: for each year and each phase: • Determine the total number of dwelling units that are likely to be built • Essentially we are allocating the number of dwelling units that will be built at a location at a given year • Conducted w/in a spreadsheet • Step 2: for each year and location • Determine the total population that is likely to be at a given location • Based on the total # of units built at the location (provided by Step 1) for the given year and the aggregate population total for the given year • Conducted w/in ArcGIS

  11. The following shows the % of population growth contribution for each year. If there was a single development phase then we could allocate the number of units built for a given year based simply on this (see the notes below). However, since there are multiple, overlapping phases…

  12. Phase specific growth contributions • Required to interpolate / allocate development units to years in between start and completion times • Attempt to improve upon linear allocations (that produce irregular people per dwelling densities) by fitting the allocation to the population change distribution • Growth contributions generated according to a specific interval of time, with the total population change indicating the total change within the temporal interval • Intervals created by ‘intersecting’ overlapping development phases (e.g. 2010-2029 & 2025-2039) • Intervals created • 2010 - 2019 (phases 1 & 2); population change: 63,565 • 2020 - 2024 (phase 2); population change: 56,076 • 2025 - 2029 (phases 2, 3 & 4); population change: 60,665 • 2030 - 2039 (phases 3 & 4); population change: 112,600 • 2040 - 2059 (phases 4 & 5); population change: 175, 951

  13. Interval 1 63,565 Interval 2 56,076 Interval 3 60,665 Interval 4 112,600 Interval 5 175,951 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 *See the notes below for more info

  14. Applying the interval specific growth contributions to the total dwelling units that will be built yields the following (see notes)…

  15. This following illustrates the differences between allocating the units linearly/uniformly with the fitting allocation. The differences seem negligible until looking @ the derived population densities…

  16. This use of dwelling/household density provides a kind of the validation of what allocation approach provides the most realistic fit with the population, since we know there are reasonable upper and lower bounds. The growth contribution allocation is far from perfect but provides a little bit better fit than just allocating units linearly. This is especially true for our window years of 2010, 2020 & 2030. Also, the lower densities previous to 2020 (over estimate of housing) followed by the spike in the early to mid 20s (under estimate in housing) probably is an artifact of the phasing and suggests that some of the development in phases are out of sync with the expected population.

  17. Allocating population changes spatially This table was summarized for the window years from the growth contribution allocation. It will be joined with the Kennecott development units feature class to allocate the total dwelling units at a location for each window year. The tables are joined based on the ‘StartEnd’ attribute which uniquely defines a development phase. The number of dwelling units at a location at a given time is obtained by multiplying the location’s total build-out units (field du_total in the attribute table) by the ratio of the total units allocated by a phase at the given time to the total units that will be allocated by the phase at build-out. For example, if we have a location that has 10 dwelling units, is a member of phase 2, and we want to know the total units built by 2020 then we would do the following: 10 * (6022 / 15318) = 3.93 So 3.93 units are allocated to this specific location for the year 2020.

  18. Allocating population changes spatially (2) Now that we have assigned the total dwelling units at a location that will be built for the window years we can allocate the population by multiplying the total dwelling units at the desired time by the ratio of the total population at the time to the total number of dwelling units built at that time. So for our location from the last slide with 3.93 dwelling units at time 2020, the total population @ the location at 2020 is: 3.96 * (73633 / 25812) = 11.21 So there are 11.21 people at this location during 2020

  19. Resulting population distribution for 2010.

  20. Resulting population distribution for 2020.

  21. Resulting population distribution for 2030.

  22. Kennecott population by geographic sector

  23. The same process was applied to employment. Except here we are interested in allocating the amount of square footage that will be built and then using this to allocate employment. Again we begin w/ the yearly distribution of projected employment…

  24. Next we consider the % employment growth contribution by year…

  25. Employment intervals • 2010 – 2019 (phases 1 and 2) • Employment change: 11,379 • 2020 – 2024 (phase 2) • Employment change: 3,963 • 2025 – 2029 (phases 2, 3, & 4) • Employment change: 3,625 • 2030 – 2039 (phases 3, & 4) • Employment change: 10,282 • 2040 – 2059 (phases 4 & 5) • Employment change: 51,261

  26. Interval 1 11,379 Interval 3 3,625 Interval 4 10,282 Interval 5 51,261 Interval 2 3,963 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 The employment intervals yield the following growth contributions…

  27. This is the resulting allocated square footage by phase and year…

  28. Attempting to validate the resulting employment distribution is a little less intuitive than that of using population density as discussed above. However, it seems somewhat obvious in comparing the derived square feet per employee obtained here with the standard estimates of the Calthorpe associates (1000 industrial, 350 office, 750 retail) that there is somewhat of a discrepancy here w/ the number of planned square footage far exceeding those estimates. Industrial Retail Office

  29. Allocating employment spatially This table was summarized for the window years from the growth contribution allocation. It will be joined with the Kennecott development units feature class to allocate the total square footage at a location for each window year. The tables are joined based on the ‘StartEnd’ attribute which uniquely defines a development phase. The amount of square footage at a location at a given time is obtained by multiplying the location’s total build-out square footage (field sf_total in the attribute table) by the employee to square foot ratio of the square footage allocated by a phase at the given time to the total square footage that will be allocated by the phase at build-out. For example, if we have a location that has 15,000 total square feet, is a member of phase 2, and we want to know the total square footage built by 2020 then we would do the following: 15000 * (2297706.82 / 6046356) = 5700.23 So 5700.23 square feet are allocated to this specific location for the year 2020.

  30. Allocating employment changes spatially (2) Now that we have assigned the total square footage at a location that will be built for the window years we can allocate the employment by multiplying the total square footage at the desired time by the ratio of the total employment at the time to the total amount of square footage built at that time. So for our location from the last slide with 5700 square feet at time 2020, the total employment @ the location at 2020 is: 5700 * (11934 / 15244586) = 4.46 So there are 4.46 jobs at this location during 2020 CAVEATS: - As noted before, the derived square feet per employee seems a bit a high. - Given the mixed-use component of the majority of the Kennecott plan, no attempt was made to explicitly incorporate land-use. Instead our strategy implicitly estimates this by fitting phase square footage to aggregate employment projections.

  31. Resulting employment distribution for 2010.

  32. Resulting employment distribution for 2020.

  33. Resulting employment distribution for 2030.

  34. Derived GIS attributes for Kennecott master plan • GIS feature class: kennLandUse.shp • Derived attributes: • Dwelling units allocated by year: DU2010, DU2020, DU2030, DU2040, DU2050, DU2059 • Population by year: POP2010, POP2020, POP2030, POP2040, POP2050, POP2059 • Square footage allocated by year: SF2010, SF2020, SF2030, SF2040, SF2050, SF2059 • Employment by year: EMP2010, EMP2020, EMP2030, EMP2040, EMP2050, EMP2059

  35. Reconciling WFRC TAZs & Kennecott counts • Issue 1: WFRC TAZs controlled to 2003 baseline whereas Kennecott figures controlled to 2005 baseline • Resolution: Scale the WFRC TAZs (based on the 2003 Baseline) to meet the GOPB 2005 baseline • Scaled as follows: count(taz, t) = wfrcCount(taz, t) * (gopbSumCount(t) / wfrcSumCount(t)) Where: - count(taz, t) = count (employment or population) at a given TAZ for the given year scaled to the 2005 baseline - wfrcCount(taz, t) = count at a given TAZ for the given year as provided by the WFRC projections - gopbSumCount(t) = total count for GOPB 2005 baseline for the entire county for the given year - wfrcSumCount(t) = total count for all WFRC TAZ projections for the given year

  36. Reconciling WFRC TAZs & Kennecott counts (2) • Issue 2: How to assign the Kennecott count to the TAZs? • Resolution: • For each Kennecott development cell: • Obtain the cell’s centroid • Use a spatial join to assign the centroid to the closest TAZ polygon • Summarize the joined class based on the TAZ attribute to obtain a sum of the Kennecott population for each TAZ • Join the TAZ polygon class to the Kennecott summary table and assign the total Kennecott counts for each year

  37. Reconciling WFRC TAZs & Kennecott counts (3) • Issue 3: How to reconcile the count w/in a given TAZ with the Kennecott summary count assigned to that TAZ? • Two cases: • Kennecott summary count is greater than the TAZ count • Kennecott summary count is less than the TAZ count • Resolution: • For each TAZ: • Determine the amount of the population that is not part of the Kennecott development • Assign the reconciled population as the sum of the Kennecott population and the scaled not-Kennecott population

  38. Determining the not-Kennecott population • For each TAZ at a given time t: • If the Kennecott population in the TAZ at time t is greater than the TAZ population at time t: • Not-Kennecott population at time t = not-Kennecott population at time t – 1 • For example if Kennecott population in 2030 is greater than the TAZ then the amount of non-Kennecott population in 2030 is equal to the non-Kennecott population in 2020 • This assumes that all the new development taking place during this time is attributed to Kennecott • This allows us to retain populations/employments that existed in the TAZ previous to the Kennecott development

  39. Determining the not-Kennecott population (2) • For each TAZ at a given time t: • If the Kennecott population in the TAZ at time t is less than the TAZ population at time t: • Not-Kennecott population at time t = TAZ population at time t minus the Kennecott population at time t • This assumes that the growth occurring w/in the TAZ is a function of developments both within and outside of the Kennecott development

  40. Script for determining the not-Kennecott population for 2020 For use with the ArcGIS field calculator: out = 0 if [kenn2020] > [gopb2020] then out = [nkenn2010] else out = [gopb2020] - [kenn2020] end if __esri_field_calculator_splitter__ out kenn2020: attribute containing the Kennecott population from the master plan for 2020 for the given TAZ nkenn2010: attribute containing non-Kennecott population in the for 2010 for the given TAZ gopb2020: attribute containing the scaled WFRC population for the given TAZ

  41. Deriving the reconciled TAZ count • The total count at a given TAZ for a given time t is then: count(taz, t) = kenn(taz, t) + [scaling(taz, t) * gopbNotKenn(t) ] To determine the scaling factor: scaling (taz, t) = notKenn(taz, t) / gopbNotKenn(t) Where: -count(taz, t): final count for a given TAZ for the given time -kenn(taz, t): Kennecott population within a given TAZ for the given time -notKenn(t): total (sum of all TAZs) not-Kennecott for the given time -gopbNotKenn(t): total (sum of all TAZs) not-Kennecott population based on the GOPB baseline

  42. Script for determining the final Kennecott population for 2020 For use with the ArcGIS field calculator: notKenn = 1340337.305244 scaling = [nkenn2030] / notKenn notKennGopb = 1189528 out = [kenn2030] + (scaling * notKennGopb) __esri_field_calculator_splitter__ out notKenn: total not Kennecott population obtained by summing the nkenn attribute described in slides (37-40); for 2020 this value is 1,340,337 nkenn2030: attribute containing the not-Kennecott population for the given TAZ notKennGopb: total not-Kennecott population based on the GOPB baseline; for 2020 this value is 1,189,528

  43. GIS attributes for Salt Lake County • Population attributes (tazPopulation class) • Original WFRC population: wfrc2005, wfrc2010, wfrc2020, wfrc2030 • WFRC population scaled to GOPB 2005 baseline: gopb2005, gopb2010, gopb2020, gopb2030 • Kennecott population obtained from master plan: kenn2010, kenn2020, kenn2030 • Not Kennecott population (slides 37 - 40): nkenn2010, nkenn2020, nkenn2030 • Final population: pop2005, pop2010, pop2020, pop2030 • Final population change: chg05_10, chg10_20, chg20_30, chg_05_30

  44. GIS attributes for Salt Lake County • Employment attributes (tazEmployment class) • Original WFRC employment: wfrc2005, wfrc2010, wfrc2020, wfrc2030 • WFRC employment scaled to GOPB 2005 baseline: gopb2005, gopb2010, gopb2020, gopb2030 • Kennecott employment obtained from master plan: kenn2010, kenn2020, kenn2030 • Not Kennecott employment (slides 37 - 40): nkenn2010, nkenn2020, nkenn2030 • Final employment: emp2005, emp2010, emp2020, emp2030 • Final employment change: chg05_10, chg10_20, chg20_30, chg_05_30

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