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HomeMy WebLinkAbout2024-11-1 Chuck Flaherty Testimony GP 2045 1 of 2 From:Chuck Flaherty To:WPCtestimony Subject:Re: GP2045, late written testimony plus attachments Date:Friday, November 1, 2024 12:00:18 PM Attachments:GP2040, Aug. 2019 draft, Public Access thru Cultural&Historic Preservation.pdf Scenario Planning - Trend Scenario Land Use Allocation Technical Report - Placeways (2016).pdf Technical Methodology for General Plan Land Use (2020-24), 8-23-24.pdf Aloha Mr. Chair and members of the Commission, Kala mai for not getting my written testimony to you prior to the meeting. Please see testimony below and attached documents to which I was referring in my testimony that have been a part of the General Plan Comprehensive Review process and should have been provided to the Commission, as well as the public. Documents attached to this e-mail: Draft General Plan 2040, pages 102-125 Trend Scenario and Land Use Allocation Technical Report Technical Methodology for General Plan Land Use (2020-2024) Because of the large size of the documents, I will have to send the Scenic Resources Inventory and Mapping Project report and an e-mail to immediately follow this one. Mahalo, Chuck Flaherty November 1, 2024 Windward Planning Commission 101 Pauahi St, Ste 103 Hilo HI 96720 Re: Final draft General Plan 2045 Aloha Mr. Chair and members of the Windward Planning Commission, My name is Chuck Flaherty. I am speaking on my personal behalf today. Mahalo nui loa for your volunteer service on this Commission. This special meeting and agenda item should have been given more time. If you have read the draft GP2045, then we now have had a shared experience of attempting to compare it to the current General Plan. The feedback that I’ve gotten from other members of the public is that this comparison has been an almost impossible task because GP2045 is a complete gut-and-replace of the current General Plan. Also, placing the GP2045 online in two different software platforms has disenfranchised a large number of people who have made sincere efforts to make informed comments, but are technologically- challenged. As a general statement, the draft GP2045 does not contain sufficient imperative and directive language to provide reasonable assurance that it will be implemented. That being said, there are documents that are relevant to the draft General Plan that should have been provided to the Commission. It may be that these documents are the ones that have been submitted today, but I will review the list with you to make sure. I've also attached them to this e-mail so they may be forwarded to the Commission's members. The most important of the documents not provided to the Commission is the draft General Plan 2040 that was released in August 2019 by then-Planning Director Michael Yee. The reason GP2040 is so important is because it shows the extent to which public access and cultural, historic, and scenic resource preservation policies and actions contained within GP2040 were removed and not included in GP2045. For instance, the Sustainability Objective on page 108 states, “Native Hawaiian language, values, and practices are integrated into all County processes.” This objective has been cleansed from the draft GP now before the Commission. Another document is a report titled “Hawai’i County Scenic Resources Inventory and Mapping Project, Scenic Resource Protection Programs and Strategies” prepared for the Planning Department in August 2016. The Policies and Actions in GP2040 included the programs and strategies recommended by this report. However, GP2045 removed these policies and actions and now GP2045 only contains a reference to the report. Another report not in the Commission’s initial board packet is the Trend Scenario and Land Use Allocation Technical Report, published in April 2016. This report contains important information, such as how many dwellings can be built with existing zoning, subdivision approvals, and building permits. Another critical report not included in the Commission’s initial board packet is the Technical Methodology for General Plan Land Use (2020-2024). This report was not released until August 2024. It should have been released a year earlier because it contains information that the public has been requesting since last September 2023, such as explanations of how the GP2045 land use maps were developed. I would like to give an example of why General Plan land use designations are so important. Let’s use the example of a property owner who wants to rezone and subdivide 300 acres in the state land use Agriculture district. If the property owner applies to rezone and subdivide for Ag-1 lots, the owner could be granted approvals. Now let’s say the property owner wants to subsequently have the subdivision moved to the state land use Rural district, so that amenities, such as a members’ lodge, could be built. The owner would have to file a petition with the state Land Use Commission (LUC) for boundary amendment and the petition would go before the LUC for consideration. And let’s say the owner asks the county to use the general plan comprehensive review process to redesignate the subdivision from Agricultural to Rural in the GP land use map and let’s say the county makes the change in the GP land use map. Because the individual lots in the subdivision are Ag-1, that is, less than 15 acres, the state LUC would no longer have jurisdiction and individual land owners could then petition the county to have their lot(s) redesignated from the state land use Agriculture district to Rural and also apply for county rezoning to Rural as well. The bottom line is that while the project owner cannot request a state land use boundary amendment involving more than 15 acres because the Planning Director would have redesignated the GP land use map to Rural for all lots in the project’s subdivision, the individual lot owner(s) can apply to the county have their 1 acre lots redesignated to the state land use Rural district and apply for county rezoning to Rural in order to accrue the ability to have expanded property rights and uses on their lots. Presently, the Planning Director is refusing to provide the reason that he is proposing such changes to the GP land use map. Mahalo, Chuck Flaherty Captain Cook PROJECT DOCUMENT Date: April 18, 2016 Authors: Amy DeBay, Ian Varley, Doug Walker Introduction Task S2 (Land Use Allocation) setsup a framework for estimating future development patterns (amounts and location) based on a set of rules. Task E (Trend Scenario) uses an initial, calibrated run of the allocation model to estimate future development patterns based on historical trends. These tasks go hand-in-hand and are combined in this technical report. Allocation Concepts and Approach Future development patterns (amounts and location) are estimated using an algorithm-driven process called allocation. Allocation models the interplay between market demand for development in certain locations (“desirability”) and amount of development allowed according to current regulations or by future land use patterns suggested by alternate scenarios (“capacity”). Given a pre-determined amount of growth expected in the given time frame (here, new growth between 2015 and 2040), the allocation process estimates where each incremental unit of new development will go, following the basic presumption that the most desirable areas will be developed first, capacity allowing. Thus highly desirable areas are assigned growth first, and then slightly less desirable areas get developed next, etc., until all the estimated growth amount has been accommodated. Numerous refinements to the basic principle are used to produce the final estimates. For example, parcels aren’t always filled all the way to capacity, a certain amount of controlled “randomness” is often applied to the growth pattern, etc. For mixed use areas, both residential and non-residential growth can be assigned. In this study, a CommunityViz tool called Allocator 5 is used. The methods combined with the algorithms in Allocator 5 provide a well-reasoned analysis that will be helpful for this and myriad other planning studies, but it is recognized that the results have limitations in terms of modelling precision and confidence. The CommunityViz allocation method is sometimes described as “light-weight” to “medium-weight,” differentiating it from the “heavy-weight” algorithms such as UrbanSim or PECAS that are considerably more sophisticated but are more difficult and expensive to implement. In contrast, the Allocator 5 method is easier to use and lends itself well to “what-if” type scenario planning. At the highest level, the allocation algorithm takes 3 inputs—growth amount, capacity, and desirability—and generates 1 output—a pattern of future development. Our methods for each of these are described next. Growth Amount As a 2015 baseline for housing unit data, Placeways used data from the County’s Real Property Tax (RPT) office to identify the number of housing units and non-residential square feet. The procedure began with a database file from RPT that, unfortunately, lacked metadata, so the fields were interpreted manually. For each TMK, buildings were converted to housing units where appropriate Trend Scenario and Land Use Allocation Technical Report 1 (buildings and dwelling units were tracked separately). Single family homes and ohana units were readily identified in the RPT data. Multifamily housing required some additional steps. In cases where there were multiple records per TMK (as with condos), the records required consolidation to identify the total number of dwelling units per TMK. Separate analyses were performed for single family, multifamily, and commercial properties. In RPT data, housing units that are treated as commercial property (e.g., apartments and timeshares) were counted as residential only when the RPT data showed them as such. Once this basic processing was complete, Placeways used the RPT online tool, Google Street View, digital air photos and other tools to verify the number of existing units with the goal of establishing an accurate baseline and using the RPT database to its maximum extent. For the trend scenario, growth projections are provided by SMS, a Hawaii-based research and consulting company (see their report “General Plan Comprehensive Review Trends and Forecast Analysis Final Report (2015)”). These projections are broken out by 13 geographic areas called “forecast analysis zones” or FAZs (see Figure 1) and by use type (residential dwelling units and non-residential square feet). In order to add additional land use information to the allocation, the SMS forecasts were further broken down into four categories: single family dwelling units, multifamily dwelling units, commercial square feet, and industrial square feet. The ratio of single family to multifamily was found using the mean of the ratio from three dates in the recent past (2000, 2010, 2015) for which the ratio was known. This ratio was then applied to the combined residential allocation amounts to produce the single family/multifamily splits seen in Tables 1, 3, and 6. Similarly, growth amounts for non-residential development were developed as a single forecast and had to be split into amounts for commercial and industrial uses. The 2013 ACS Employment by Occupation Type data were used to identify the ratio of industrial employment to commercial employment and to produce the splits seen in Tables 2, 3, and 7. While this method assumes no change in the ratio of single family to multifamily units and commercial to industrial space, it reflects the recent development patterns in the Trend Scenario and can easily be adjusted for use in alternate scenarios. Trend Scenario and Land Use Allocation Technical Report 2 Figure 1. Map of Forecast Analysis Zone (FAZ) Areas (source: SMS) In contrast to the RPT-based method for establishing a baseline, the housing forecast data produced by SMS rely on US Census and Hawaii DBEDT as sources. The methods used to collect Census and DBEDT data are quite different from RPT, resulting in differing 2015 baseline quantities. In addition, SMS did not suggest a 2015 baseline, instead using decadal increments for forecasting. In order to establish an SMS 2015 baseline, Placeways used the average of SMS’s 2010 and the first forecast year of 2020. This results in a 2015 baseline difference of 11,558 housing units (RPT 2015: 75,100; SMS/Census: 86,658). The reasons that the RPT and Census derived baseline amounts are different are due to the sources’ two distinct methods, and no attempt was made to reconcile them. To calculate the amount of new residential growth, Placeways used a method to find the relative amount of net new amount of growth per FAZ. This method finds the percent change, per SMS, between 2015 and 2040 and applies that to the RPT 2015 baseline. This forecast results in fewer net new units (35,750) than the SMS forecast (40,160 new units), but its rates of change match SMS. The SMS non-residential forecasts were already reconciled with the RPT data, and their forecast was calibrated to closely match the 2015 RPT baseline square footage. Therefore, in the case of non- residential growth, there was no need to rectify the forecast numbers as was the case with residential growth. Non-residential square feet were rounded and translated from square feet to 1,000 square feet Trend Scenario and Land Use Allocation Technical Report 3 for the purposes of allocation. This ensured that allocation amounts are in whole increments and not in very small portions of square feet. Table 1. Residential Forecast by FAZ FAZ20152040 Change 2040 Total Single Multi-TotalSingle Multi-Single Multi-Total Family family Family family Family family Hilo 14,713 1,138 15,851 2,953 833 17,666 1,971 19,636 North Hilo - 2,822 12 2,834 721 71 3,543 83 3,626 Coast Villages a-Paauilo 2,399142,413871 56 3,270 70 3,340 Waimea 3,212 98 3,310 1,420 158 4,632 256 4,887 North Kohala 2,499 17 2,516 785 59 3,284 76 3,360 Kawaihae--2,610 3,390 6,000 1,337 1,337 3,947 4,727 8,675 - Resorts North Kona 11,181 5,989 17,170 4,418 2,708 15,599 8,697 24,295 South Kona Villages 3,437 73 3,510 1,129 125 4,566 198 4,765 Ka 3,397 76 3,473 2,135 112 5,532 188 5,720 Keaau -Kurtistown 1,640 10 1,650 834 35 2,474 45 2,518 Upper Puna 4,884 0 4,884 3,373 104 8,257 104 8,361 HPP-Orchidland 6,654 0 6,654 7,431 152 14,085 152 14,237 Lower Puna 4,835 0 4,835 2,515 78 7,350 78 7,428 64,283 10,817 75,100 29,922 5,828 94,205 16,645 110,850 Total Trend Scenario and Land Use Allocation Technical Report 4 Table 2. Non-Residential Forecast by FAZ (in 1,000 square feet) FAZ 2015 2040 Change 2040 Total ustrial CommercialIndTotalCommercialIndustrialCommercialIndustrialTotal Hilo 9,1873,762 12,9493,979 34613,1664,10817,274 North Hilo -34730 37798 844538483 Coast Villages a-Paauilo 43838476130756845613 Waimea 1,30375 1,379421 371,7241121,837 North Kohala 290255 545181 12471267738 Kawaihae--5,406635,4701,608857,0141487,163 - Resorts North Kona 6,5125,135 11,6483,400 2969,9125,43115,344 South Kona Villages 86816 884235 181,103341,137 Ka 3030 303110 1141311424 Keaau-Kurtistown 760902 1,662454 511,2149532,167 Upper Puna 2012 20354 32555260 HPP-Orchidland 1200 12057 41774181 Lower Puna 4130 413114 1452714541 Total 26,15010,279 36,42810,841 89236,99111,17148,161 Capacity Capacity values used in this report are based primarily on the results of Task S1, covered in a separate report. The focus is on net capacity, which is gross (or total) capacity minus existing development. Numeric capacity is assigned to every parcel on the island for residential dwelling units, and a separate numeric capacity for non-residential square feet. The majority of visitor units, existing and projected, are within 3 of the 13 FAZ areas. More information on visitor units and how they are addressed can be found in the Indicator Modeling technical report (Task G). During the initial calibration of the allocation model, it was decided to place caps on the capacity of individual parcels that limited the amount of development that could occur on any single parcel. Caps for both residential and non-residential development were identified by looking at the County’s subdivision records and the 2004-2015 building permit data. This information revealed what the recent historical maximums were for each kind of development: 600 units for residential development and 306,000 square feet for non-residential development. (The historical maximums and their place in the model are also discussed below in the section on additional calibration factors.) The final numbers below represent capped net capacity. Later in the project, these capacity values may be adjusted to model different potential planning and policy decisions. Trend Scenario and Land Use Allocation Technical Report 5 Table 3. Net Capacity (Capped) by FAZ Residential Residential Commercial Industrial Capacity FAZ Name Single Family Multifamily Capacity (sq ft) Capacity (DU) Capacity (DU) (sq ft) 22,299 7,037,091 6,766,170 Hilo 1,183 8,721 1,173,273 894,155 North Hilo - Villages 50 9,691 579,692 133,073 - 39 6,807 1,242,247 577,334 Waimea 827 9,950 1,660,320 1,499,805 North Kohala 231 15,2371,944,304310,342 Kawaihae-- - Resorts 3,815 21,855 9,831,956 12,956,188 North Kona 5,376 21,445 539,714 0 South Kona Villages 190 25,088 768,304 620,075 1,971 5,518 1,481,939 4,484,680 -Kurtistown 75 25,390 135,975 0 Upper Puna 31 9,996 0 0 HPP-Orchidland 0 25,928 656,727 74,451 Lower Puna 151 207,925 13,939 27,051,542 28,316,273 Total Desirability Desirability is a complex topic and represented a large proportion of the effort for this task. The desirability of a given parcel for a given use (residential or commercial) is represented by a score from 0 (least desirable) to 100 (most desirable). A parcel that is not eligible for a given use is assigned a desirability score of -1. On a map, the pattern of desirability scores is sometimes called a “desirability surface” because one can picture a lumpy blanket covering the island with high points in areas of high desirability and low points in areas of low desirability. Desirability was calculated all at once for the entire island. Given more time and resources, it would be possible to perform separate calculations for subareas such as east/west or individual FAZs. However, the additional effort would make little difference because growth amounts are constrained to meet FAZ-specific totals and thus growth is allocated based on relative desirability scores within a FAZ, not between FAZs. Trend Scenario and Land Use Allocation Technical Report 6 The detailed procedure for creating the initial desirability surface, used to create the Trend Scenario, follows: 1. Spatial and non-spatial factors affecting location desirability were hypothesized. These factors, such as proximity to infrastructure or coastline, likely affected development desirability in the past and could be quantified for all parcels on the island given available information. While a complete list of factors tested is included at the end of this report, all hypothesized factors could fit into the following categories: Distance to infrastructure, geographical features, town and commercial centers Parcel shape/size Terrain/climate characteristics Neighbor (10 ft) and neighborhood (1000 ft) context: the number of nearby parcels and development proximity (the number of nearby parcels that are built) Current land use and build status Geographic location (District, FAZ, etc.) Property and building valuation. Using CommunityViz, these factors were calculated for each parcel on the island and exported to a table for use in SPSS statistical analysis software. 2. Statistical regression analysis in SPSS was used to calculate how well each factor correlates with new development in a given time frame. Three timeframes were initially considered: All development since the beginning of RPT (County of Hawaii Real Property Tax Office) recordkeeping (1880s) All development since 1995 All development between 1975 and 1995. However, because the goal of this statistical regression was to capture the principal factors influencing urban growth in the recent past, a cutoff date of 1995 was established. This year was selected to encompass a full cycle of real estate development and to capture the “highs and lows” in development activity. Development during this period followed this approximate pattern: Trough: 1996 Peak: 2005-2006 Trough: 2009-2011 Recent uptick: 2015. The SMS CoH 2016 General Plan Final Report (Figure 1, Resident Population) illustrates this pattern. The statistical analysis models the relationship between dependent variables – Commercial Square Feet (COM_SQFT) and Number of Residential Buildings (NumberOfResidences) – and the hypothesized list of independent variables (see Appendix 1, Table 12. Factors Tested for Historical Growth Trends). As a result, a step-wise Multiple Linear Regression model was chosen to create coefficients associated with each of the variables in order to represent the independent contributions of each independent variable to the prediction of the dependent variable after controlling for all other independent variables. Trend Scenario and Land Use Allocation Technical Report 7 The initial analysis included an overall view of development, an earlier era of development (1975-95), and post-1995 recent development patterns for both of the dependent variables (see Appendix 1: Comparing Post 1995 Regression Factors with the 1975-1995 Regression Factors for a discussion of these results). The results show the top 10 variables—that is, the top 10 of the hypothesized desirability factors—that influence each of the analyses, along with the absolute value of each of the standardized coefficients. The coefficient values allow ranking the variables from most to least influential. Detailed analysis information is included at the end of this report. The standardized regression coefficients with the 10 highest absolute beta values for the post- 1995 period were converted into CommunityViz weighting factors normalized to the scale 0 – 10, where 0 is no correlation and 10 is the highest correlation of any factor (though less than 1). Candidate factors with lower beta coefficients, below the top ten, were ignored for the desirability score. A cutoff of 10 factors was chosen for a few reasons. One was to keep the most significant factors in the mix. The top 10 account for the majority of the causal influence of all factors tested. Additionally, there was a benefit to limiting the number of movable parts for testing. Even with 10, it is challenging to understand the interplay of all inputs and the individual effect on the overall score. The goal here is to incorporate both sophistication and manageable interactive parts, and 10 is a reasonable number for that. Some factors are negatively correlated, and some factors are inversely correlated. For instance, distance values that correlate to growth are often inverse: nearer, smaller distance values are more desirable and further, larger distance values are less desirable. In the table below, negative and inverse correlations are indicated by a negative beta value score. Understanding this, many factors below make intuitive sense. The slope factor for example suggests that as land gets steeper (slope increases), the likelihood of development decreases. Some results are not always intuitive, however. Statistically, for example, it is found that parcels that are closer to old lava flows are more desirable for non-residential development than those far away (i.e. Distance2LavaFlow1790). Some positively correlated cases also benefit from some explanation. For example, the strongest factor for residential development is Distance2VolcanoHazard. This is a positively correlated factor meaning that as distance increases away from volcano hazards, the likelihood of development also increases. Trend Scenario and Land Use Allocation Technical Report 8 Table 4. Residential Factors (Top 10) for Growth Desirability Model (Post 1995 Development) Normalized Beta Absolute Factor Description Value Value Beta Value (Weighting) Distance2VolcanoHazard Distance to high volcano risk area, 0.08080.080810 defined as areas classified as category 1 or 2 on the risk layer Distance2LavaFlow1250 Distance to older lava flow, -0.0575 0.0575 7.1 deposited between the year 1250 and the present Slope Mean slope of the parcel in percent -0.0494 0.0494 6.1 rise Intersections1miDensity Road intersection density: the -0.0439 0.0439 5.4 number of intersections on major roads within ½ mi of the parcel divided by area of the parcel Rainfall Annual average rainfall, in inches -0.0429 0.0429 5.3 Distance2CenterAdjusted Network distance to commercial -0.0411 0.0411 5.1 center. Commercial center is defined by RPT land use "commercial." This adjusted value uses straight line distance for those features not picked up by the patchy network. RESpost1995ProximitySum1000ft Total number of dwelling units on -0.0394 0.0394 4.9 parcels within 1000 ft of a parcel built after 1995 RESpost1995ProximitySum10ft Total number of dwelling units on -0.0321 0.0321 4 parcels within 10 ft of a parcel built after 1995 ParcelPerimeter2DivArea Lot shape: perimeter squared 0.0305 0.0305 3.8 divided by area Distance2Coastline Distance to the island’s coastline -0.0301 -0.0301 3.73 Trend Scenario and Land Use Allocation Technical Report 9 Table 5. Non-Residential Factors (Top 10) for Growth Desirability Model (Post 1995 Development) Absolute Beta Normalized Factor Description Beta Value Value Value Distance2LavaFlow1790 Distance to recent lava flow, -0.34100.341010 deposited since 1790 Intersections1miDensity Road intersection density: the -0.2560 0.2560 7.5 number of intersections on major roads within ½ mi of the parcel divided by area of the parcel Distance2Airports Distance to nearest major airport0.2246 0.2246 6.6 Proximity1000ftParcelDensity The number of parcels within 1000ft 0.1723 0.1723 5 divided by the area of the parcel Distance2ExistingMF1 Distance to nearest multifamily -0.1380 0.1380 4 residential development Distance2SewerService Distance to nearest wastewater -0.1207 0.1207 3.6 service line Distance2WaterService Distance to nearest potable water 0.1089 0.1089 3.2 service line Proximity10ftParcelDensity The number of parcels within 10 ft -0.1006 0.1006 3 divided by the area of the parcel ParcelPerimeter2DivArea Lot shape: perimeter squared -0.0935 0.0935 2.7 divided by area Distance2VolcanoHazard Distance to high volcano risk area, 0.0885 0.0885 2.6 defined as areas classified as category 1 or 2 on the risk layer 3. The CommunityViz Suitability Wizard was then used to create a suitability analysis using the parcels’ normalized factor values (such as distance to roads) as inputs. Since these values were pre-calculated for the statistical analysis, Suitability Wizard was pointed directly at the numeric values, improving processing performance. The wizard default is set to 5 (on a scale of 0 to 10) for each factor weighting. After the suitability analysis is run with defaults, the weighting assumption defaults are set for each factor according to the values calculated in the previous step to calibrate the score to historical trends. 4. The suitability (desirability) score for each parcel is displayed using the parcels layer symbolized by the suitability scores. The factor weightings are adjustable, so each factor that contributes to the overall score can be given a level of priority appropriate to the goals of the scenario. Trend Scenario and Land Use Allocation Technical Report 10 Trend ScenarioAllocation The allocation of forecasted development in the “Trend Scenario” is based on the desirability surface representing historic development trends. Later in the project, it will be possible to develop alternate desirability surfaces for scenario planning in which planners test planning strategies that could encourage growth to evolve in particular ways. Allocation is performed using Allocator 5 with the inputs above. Again, allocation distributes predetermined growth amounts for each FAZ to parcels within that FAZ, developing the most desirable parcels first and proceeding until all growth has been accommodated. No parcel receives more development than it has capacity for, and many parcels receive little or no development even though they have capacity available. For the initial run, used to develop the Trend Scenario, randomness is set at 1 out of 10. Results for each FAZ are as follows: Table 6. Allocated Residential Growth by FAZ Residential RemainingResidential Remaining FAZ Name Single Family Single Family Multifamily Multifamily Dwelling Units Capacity Dwelling Units Capacity Hilo 2,952 19,347 833 350 North Hilo - Coast Villages 721 8,000 50 0 - 871 8,820 39 0 Waimea 1,419 5,388 158 669 North Kohala 785 9,165 59 172 Kawaihae---1,338 13,899 1,338 2,477 North Kona 4,418 17,437 2,708 2,668 South Kona Villages 1,130 20,315 126 64 2,135 22,953 112 1,859 -Kurtistown 833 4,685 35 40 Upper Puna 3,373 22,017 31 0 HPP-Orchidland 7,431 2,5650 0 Lower Puna 2,515 23,413 78 73 Total 29,921 178,004 5,567 8,372 Trend Scenario and Land Use Allocation Technical Report 11 Table 7. Allocated Non-Residential Growth by FAZ in 1,000 square feet Remaining Remaining Commercial Industrial FAZ Name Commercial Industrial Allocated Allocated Capacity Capacity Hilo 3,9793,0393466,424 North Hilo - Coast Villages 98 1,074 8 887 - 130 450 7 125 Waimea 421 814 37 541 North Kohala 181 1,479 12 1,487 Kawaihae---1,608 336 85 224 North Kona 3,400 6,411 296 12,656 South Kona Villages 235 303 0 0 11065511609 -Kurtistown 454 1,023 51 4,434 Upper Puna 54 80 0 0 HPP-Orchidland 0 0 0 0 Lower Puna 114 542 14 60 Total10,784 16,206 867 27,447 Trend Calibration Factors In order to better replicate the patterns of the previous 20 years, additional calibration factors were developed to supplement the factors used in the statistical analysis. The objective of the Trend Scenario is to reflect a continuation of recent patterns, and these factors help reflect the recent development characteristics. While the statistical analysis provides an impartial and “data-driven” perspective on the patterns of recent development, its results cannot provide a complete picture because of a number of limitations: Data limitations. Data were not available on all factors and across all historic time scales. In particular, the model lacked in-depth real estate market data, including market conditions and consumer preferences for both residential and non-residential development. In addition, historic building data were not available in appropriate forms. Geographic biases that resulted from using parcel data as the unit of analysis. For example, large parcels tended to score very well in the statistical analysis. These features tend to show advantages that small parcels do not: they have more capacity, they have more neighboring parcels, and they can be closer to more desirable locations simply because of their size. A grid- based analysis would reduce these issues, but it would introduce many other issues (e.g., mismatch between the grids and the available parcel data) in their place. Difficulty reflecting all patterns and preferences for all the island at a local scale (e.g., what drives growth in North Kona is somewhat different from what drives growth in Lower Puna). It was outside the scope of this project to run individual analyses for smaller areas (e.g., FAZs). Trend Scenario and Land Use Allocation Technical Report 12 No forecast model is perfect, and it is expected that any algorithmic results will require some degree of adjustment to match observed results. Because of these limitations on factors derived solely from statistical analysis, additional calibration factors based on expert human knowledge were included in the analysis as well. The combination resulted in a hybrid system that carefully combines both statistical modeling and expert judgement. Each calibration factor works in a different way to help fine-tune the model by what is believed as common knowledge by County of Hawaii planners and by comparing values observed in the recent development data to the results of the draft versions of the Trend Scenario allocation. Some of these factors (Redevelopment Friction, Residential Subdivision Friction and Non-residential Size Friction) help steer growth towards areas that have received consistent development pressure in the previous 20 years. The Pipeline Projects Factor prioritizes parcels that currently have development proposals pending. While these factors help calibrate the Trend Scenario to reflect recent patterns, it is difficult—and not necessarily desirable—to exactly replicate rates and patterns of the recent past with what occurs in the Trend Scenario’s modeled future. The future is unknown and many external factors and unforeseeable conditions will affect how growth actually occurs. The goal of the Trend Scenario is to provide a plausible and useful reference for comparing alternative scenarios to help inform policy and the contents of the General Plan; it is not intended as a detailed forecast. Recent Lava Factor While a significant amount of development in the County of Hawaii occurs on geologically recent lava flow (often on lava less than a few hundred years old), lava that has been deposited very recently is a significant obstacle to development. Lava flows that are more recent than 1990 were identified, and desirability of parcels that overlapped post-1990 lava flows was reduced. Pipeline Projects Factor This factor adds a bonus to the desirability of parcels that are currently under development or that County planning staff assume are likely to develop in the near future. The pipeline projects are divided into two groups. Group one consists of two projects, Kamakana Villages and Kealakehe Homesteads, which are very likely to develop or are in the process of development. The second group includes five projects that are less advanced in the planning process but have greater than average chances of developing. The project pipeline projects are mostly located in North Kona and primarily entail residential development. Table 8 identifies the pipeline projects and the number of units allocated to them. Trend Scenario and Land Use Allocation Technical Report 13 Table 8. Pipeline projects Allocated Project NameApproximate Location Residential Units Villages lole Highway, North of 600 Kailua Kona North of Kailua-Kona, Corner of 184 Keanalehu & Manawalea UH PlamanuiUH West Hawaiicampus, just east of 300 airport Pualani South of Kailua-Kona along Queen 178 Ka‘ahumanu highway uokalani Various parcels, north of Kailua-Kona 48 Trust downtown Redevelopment Friction The uncalibrated allocation model orients development towards areas of greater desirability and available capacity. Many parcels already have existing residential or non-residential structures on them but according to the capacity analysis have some additional capacity (these parcels are considered “underbuilt” for purposes of the model). The Redevelopment Friction factor considers redevelopment in the sense of any new development on a parcel that has an existing residential or non-residential structure (according to RPT). The uncalibrated model does not distinguish the nature of this new development: whether it is a physical addition to a structure, a new structure on an undeveloped portion of the parcel, or the wholesale redevelopment of an existing structure. Redevelopment areas can sometimes be more difficult and costly to develop but they may be highly desirable because of their location or other amenities. The 2004-2015 building permit data reveal that 34% of residential development occurred on parcels that already had a residential or non-residential structure. This figure is lower for commercial development, where 15% of growth occurred on parcels with existing structures. The Redevelopment Friction factor applies a penalty to the desirability score for both residential and non-residential development to parcels that already have some development. Residential Subdivision Friction An analysis of residential growth since 1995 revealed that over 75% of new dwelling units were built on parcels that were subdivided to sizes of less than 5 acres. Subdivisions yielding parcels larger than 25 acres were not as common and did not contain large amounts of residential development in the recent past. The County’s subdivision data revealed that in the last fifteen years, the largest subdivision to be approved was 590 parcels. During initial runs of the allocation model, it was observed that the tool tended to select larger parcels over smaller parcels, a pattern that was not consistent with the recent development patterns. The County’s subdivisions layer depicts all of the island’s major subdivisions and using this data as a guide, subdivided parcels were flagged and received higher desirability scores than Trend Scenario and Land Use Allocation Technical Report 14 non-subdivided parcels. This factor actually consists of two factors, one factor that penalizes large parcels by size and another that caps residential development at 600 units, similar to the historic maximum. Non-residential Footprint Friction Similar to residential subdivision friction, recent non-residential development had certain characteristics related to building footprint size (i.e., the square footage of the non-residential development) that were not well captured by the regression analysis. Early runs of the model revealed some extremely large non-residential developments that seemed uncharacteristic with the recent development patterns. According to RPT records, the largest commercial development since 1995 was 306,000 square feet and the median size 6,510 square feet. For the Trend Scenario, non-residential capacity was capped at 300,000 and desirability was boosted for smaller capacity parcels. Comparing Recent Development Trends and the Trend Scenario After calibration, the Trend Scenario matches recent development patterns fairly well, as shown in the table below. Table 9. Comparing recent development with the trend scenario Recent Development* 2004- Trend Scenario 2015 Residential Redevelopment 11% of recent growth occurred 4% of growth occurs on built on built parcels parcels Commercial Redevelopment 15% of recent growth occurred 16% of growth occurs on built on built parcels parcels Residential Subdivision 75% of recent growth occurred 73% of recent growth occurs in Development in existing subdivisions; median existing subdivisions; median parcel size is 0.7 acres parcel size is 0.9 acres Non-residential Footprint Median development sizewas Median development size is Friction 6,510 square feet 5,000 square feet * Recent redevelopment is based on RPT and building permit data; subdivision and parcel size factors use RPT data. Trend Scenario and Land Use Allocation Technical Report 15 Appendix 1. Comparing Post 1995 Regression Factors with the 1975-1995 Regression Factors The regression analysis conducted for all development and development post-1995 was also run for development that occurred between 1975 and 1995. Development in this era appeared to manifest some similar patterns (Distance to Volcano Hazards, Lava Flow, and Intersection Density) as the post 1995 development. Distance to centralized services such as water and sewer systems was more strongly correlated in the 1975-95 development, perhaps reflecting the closer proximity of development in that era to those services. In a more extreme case, the correlation for Distance2Airport reverses: it is negative for 1975-95 development and positive for post 1995 development. In other words, as distance to airports diminishes the likelihood of development increases in the 1975-95 era. The opposite is true for development in the post 1995 era. Valuation factors (building, land values) were poorly correlated in the 1975-95 era for residential development. Valuation factors were more strongly correlated with non-residential square footage. However, the valuation factors used reflect present-day conditions. In general, care should be taken when comparing the results of the 1975-95 era beta values with the post 1995 beta values because this analysis relies on data that represents conditions as they are now, not as conditions were during the 1975-95 phase of the island’s development. Some factors rely on data that has remained relatively consistent through time; for example the location of the island’s coastline, slope, rainfall patterns, etc. are very similar now to how they were 40 years ago. Other factors have changed considerably since the 1975-95 development occurred (distance to commercial development, development proximity, land and building valuations, etc.). The 1975-95 development patterns would be better compared to the post 1995 development by factoring in the conditions as they existed in that 1975-95 era, not conditions as they exist in the present day. Unfortunately that level of analysis was not feasible within the scope of the current project. Below are two tables comparing the 1975-95 and the post 1995 beta values for residential and nonresidential development. Table 10. Comparing post 1995 and 1975-95 non-residential beta values FactorComPost95BetaCom75-95Beta Difference Distance2LavaFlow1790 -.341 .006 -0.3474 Intersections1miDensity -.256 -.100 -0.1564 Distance2Airports.225 -.145 0.3700 Proximity1000ftParcelDensity .172 -.042 0.2140 Distance2ExistingMF1 -.138 -.126 -0.0120 Distance2SewerService -.121 .161 -0.2813 Distance2WaterService .109 .013 0.0958 Proximity10ftParcelDensity -.101 .019 -0.1196 ParcelPerimeter2DivArea -.093 .077 -0.1703 Distance2VolcanoHazard.088 .090 -0.0016 Trend Scenario and Land Use Allocation Technical Report 16 Table 11. Comparing post 1995 and 1975-95 residential beta values ResPost95Beta ResPost75-95Beta Difference Factor Distance2VolcanoHazard.081 .079 0.0017 Distance2LavaFlow1250-.057-.1730.1151 Slope -.049 -.049 0.0000 ValueBuilding.049.0100.0389 Intersections1miDensity -.044 -.078 0.0338 Rainfall-.043-.007-0.0359 Distance24CenterAdjusted-.041 -.028 -0.0132 RESpost1995ProximitySum1000ft*-.039#N/A-0.0419 RESpost1995ProximitySum10ft**-.032#N/A-0.0273 ParcelPerimeter2DivArea .031 .037 -0.0067 * The 1975-95 results used the factor, RESpost7595ProximitySum1000ft instead of the post 1995 proximity sums, yielding the beta value of 0.002. ** The 1975-95 results used the factor, RESpost7595ProximitySum10ft instead of the post 1995 proximity sums, yielding the beta value of -0.005. Table 12. Factors Tested for Historical Growth Trends Factor Name Description Unit Source Acres Size of the parcel in acres acres Distance2Airports Distance to nearest major airport feet County GIS Distance2Belt10 Distance to the island's belt road feet County GIS Distance2Coastline Distance to coastline feet County GIS Network distance to commercial center. Commercial center is defined by RPT land use "commercial,” this Distance24CenterAdj adjusted value used straight line distance for those features not picked up by the patchy network. feet RPT and county GIS Distance to nearest electric utility Distance2ElectricService pole feet County GIS Distance to nearest multifamily Distance2ExistingMF1 residential development feet RPT and county GIS Distance to nearest single family Distance2ExistingSF1 residential development feet RPT and county GIS Distance2Hospitals Distance to hospitals feet County GIS Distance to older lava flow, USGS Distance2LavaFlow1250 deposited since 1250feet Geologic Map Distance to recent lava flow, USGS Distance2LavaFlow1790 deposited since 1790feet Geologic Map Distance to nearest major, arterial- Distance2Major10 type road feet County GIS Trend Scenario and Land Use Allocation Technical Report 17 Factor Name Description Unit Source Distance2Schools Distance to nearest public school feet County GIS Distance to nearest wastewater Distance2SewerService service linefeetCounty GIS Distance to nearest major town, Distance2Towns towns defined by county provided "towns" layerfeet County GIS Distance to high volcano risk area, Distance2VolcanoHazard defined as areas classified as USGS, via county category 1 or 2 on the risk layer feet GIS Distance to nearest potable water Distance2WaterService service linefeet County GIS Mean slope of the parcel in percent Slope risepercent riseUSGS DEM Land value in dollars per the ValueLand County's RPT records dollars RPT University of Rainfall Annual average rainfall, in inches inches District Planning district County GIS FAZ Forecast analysis zone SMS Uses 1 or makai (outside the belt) MaukaMakaiNum and 2 for mauka (inside the belt) Placeways COM_YRBLT_YN If built 1, if not 0. RPT RES_YRBLT_YN If built 1, if not 0. RPT isCom If commercial, 1; if not, 0 RPT isSFIf single family residential, 1;if not,0 RPT isMF If multifamily residential, 1;if not,0 RPT Total commercial square feet on parcels within 10ft of any parcel with COMAllYearsProximitySum10ft commercial square footage RPT Total commercial square feet on parcels within 1000ft of any parcel COMAllYearsProximitySum1000ft with commercial square footage RPT Total commercial square feet on parcels within 1000ft of any parcel COMpost1995PS1000ft with a structure built after 1995RPT Total commercial square feet on parcels within 10ft of any parcel with COMpost1995PS10ft a structure built after 1995 RPT Total number of dwelling units on parcels within 10ft of any parcel with RESAllYearsProximitySum10ft a residential structure RPT Total number of dwelling units on parcels within 1000ft of any parcel RESAllYearsProximitySum1000ft with residential structure RPT Total number of dwelling units on parcels within 10 ft of any parcel DUpost1995PS10ft with a structure built after 1995RPT DUpost1995PS1000ft Total number of dwelling units on RPT Trend Scenario and Land Use Allocation Technical Report 18 Factor Name Description Unit Source parcelswithin 1000 ft of any parcel with a structure built after 1995 Lot shape: perimeter squared square ParcelPerimeter2DivAreadivided by area feet/feet County GIS Road intersection density: the number of intersections on major roads within ½ mi of the parcel intersection Intersections1/2miDensitydivided by area of the parcel per acre County GIS Proximity10ftParcelDensity The number of parcels within 10ft Parcels per divided by the area of the parcel acre County GIS Proximity1000ftParcelDensity The number of parcels within 1000ft Parcels per divided by the area of the parcel acre County GIS Trend Scenario and Land Use Allocation Technical Report 19 Appendix 2. Detailed Statistical Methods Commercial All Data The first multiple linear regression was calculated to predict COM_SQFT for all possible parcels based on ParcelPerimeter2DivArea, Intersections1miDensity, Slope, Rainfall, Distance2Major10, Distance2WaterService, Distance2SewerService, Distance2Schools, Distance2Coastline, Distance2VolcanoHazard, Distance2Airports, Distance2LavaFlow1250, Distance2LavaFlow1790, Distance2Towns, Distance2ElectricService, Distance2Hospitals, Distance24CenterAdjusted, Distance2ExistingSF1, Distance2ExistingMF1, Distance2Belt10, COMAllYearsProximitySum10ft, COMAllYearsProximitySum1000ft, ValueLand, Proximity10ftParcelDensity, Proximity1000ftParcelDensity with n= 132,842. A significant regression equation was found (F(28,132814) = 161.888, p <0.001 ) with 2 an Rof 0.033. After the analysis the 12 coefficients that influence Commercial square feet are listed below. Table 13. Twelve most influential variables for commercial square feet, all years VariablesBetaValue Coefficient COMAllYearsProximitySum10ft .125 COMAllYearsProximitySum1000ft .056 Distance2VolcanoHazard .054 Intersections1miDensity -.051 Distance2Hospitals .037 Distance2LavaFlow1790 .032 Distance2ExistingSF1 .025 Distance2ElectricService-.023 Distance2ExistingMF1-.020 Slope -.019 Distance2SewerService -.018 Distance2LavaFlow1250 -.018 Commercial Post 1995 The first multiple linear regression was calculated to predict COM_SQFT commercial buildings built on or after 1995 based on ParcelPerimeter2DivArea, Intersections1miDensity, Slope, Rainfall, Distance2Major10, Distance2WaterService, Distance2SewerService, Distance2Schools, Distance2Coastline, Distance2VolcanoHazard, Distance2Airports, Distance2LavaFlow1250, Distance2LavaFlow1790, Distance2Towns, Distance2ElectricService, Distance2Hospitals, Distance24CenterAdjusted, Distance2ExistingSF1, Distance2ExistingMF1, Distance2Belt10, COMpost1995ProximitySum1000ft, COMpost1995ProximitySum10ft, ValueLand, Proximity10ftParcelDensity, Proximity1000ftParcelDensity with n= 451. A significant regression 2 equation was found (F(27,424) = 2.259, p <0.001 ) with an Rof 0.126. After the analysis the 12 coefficients that influence Commercial square feet after 1995 are listed below. Trend Scenario and Land Use Allocation Technical Report 20 Table 14. Twelve most influential variables for commercial square feet, post 1995 VariablesAbsolute Value Coefficient Distance2LavaFlow1790-.341 Intersections1miDensity -.256 Distance2Airports.225 Proximity1000ftParcelDensity.172 Distance2ExistingMF1-.138 Distance2SewerService -.121 Distance2WaterService.109 Proximity10ftParcelDensity-.101 ParcelPerimeter2DivArea -.093 Distance2VolcanoHazard .088 ValueLand.082 Distance2Towns -.067 Commercial Post 1975-95 The first multiple linear regression was calculated to predict COM_SQFT commercial buildings built on or after 1975 and before 1995 based on ParcelPerimeter2DivArea, Intersections1miDensity, Slope, Rainfall, Distance2Major10, Distance2WaterService, Distance2SewerService, Distance2Schools, Distance2Coastline, Distance2VolcanoHazard, Distance2Airports, Distance2LavaFlow1250, Distance2LavaFlow1790, Distance2Towns, Distance2ElectricService, Distance2Hospitals, Distance24CenterAdjusted, Distance2ExistingSF1, Distance2ExistingMF1, Distance2Belt10, COMpost1995ProximitySum1000ft, COMpost1995ProximitySum10ft, ValueLand, Proximity10ftParcelDensity, Proximity1000ftParcelDensity with n= 687. A significant regression 2 equation was found (F(27,660) = 3.634, p <0.001 ) with an R of 0.129. After the analysis the 12 coefficients that influence Commercial square feet between 1975-95 are listed below. Table 15. Twelve most influential variables for Commercial Square Feet > 1975 and < 1995 VariablesBetaValue Coefficient Distance2SewerService .161 Distance2Airports-.145 ValueLand-.144 Distance2ExistingMF1-.126 Distance2ExistingSF1 -.126 Distance2Schools.109 Intersections1miDensity -.100 Distance2VolcanoHazard .090 ParcelPerimeter2DivArea .077 Distance24CenterAdjusted-.072 Distance2Coastline -.070 Distance2Belt10 -.059 Trend Scenario and Land Use Allocation Technical Report 21 Residential All Data The first multiple linear regression was calculated to predict NumberOfResidences for residential buildings built after 1995 based on ParcelPerimeter2DivArea, Intersections1miDensity, Slope, Rainfall, Distance2Major10, Distance2WaterService, Distance2SewerService, Distance2Schools, Distance2Coastline, Distance2VolcanoHazard, Distance2Airports, Distance2LavaFlow1250, Distance2LavaFlow1790, Distance2Towns, Distance2ElectricService, Distance2Hospitals, Distance24CenterAdjusted, Distance2ExistingSF1, Distance2ExistingMF1, Distance2Belt10, RESAllYearsProximitySum10ft, RESAllYearsProximitySum1000ft, ValueLand, Proximity10ftParcelDensity, Proximity1000ftParcelDensity with n=132,842. A significant regression equation was found 2 (F(27,132,815) = 73.875, p < 0.001 ) with an Rof 0.015. After the analysis the 12 coefficients that influence Number of Residences the most are listed in the following table. Table 16. Twelve most influential variables for residential, all years Variables Beta Value Coefficient Distance2LavaFlow1250 -.062 Distance2VolcanoHazard.060 Distance2ExistingMF1 -.050 Distance2SewerService -.048 Intersections1miDensity -.043 Distance2WaterService.035 Distance2Coastline -.028 Distance2Airports .028 Rainfall -.025 Distance2Towns-.024 RESAllYearsProximitySum1000ft.022 Slope -.021 Residential Post 1995 The first multiple linear regression was calculated to predict NumberOfResidences for residential buildings built on or after 1995 based on ParcelPerimeter2DivArea, Intersections1miDensity, Slope, Rainfall, Distance2Major10, Distance2WaterService, Distance2SewerService, Distance2Schools, Distance2Coastline, Distance2VolcanoHazard, Distance2Airports, Distance2LavaFlow1250, Distance2LavaFlow1790, Distance2Towns, Distance2ElectricService, Distance2Hospitals, Distance24CenterAdjusted, Distance2ExistingSF1, Distance2ExistingMF1, Distance2Belt10, DUpost1995ProximitySum10ft, DUpost1995ProximitySum1000ft, ValueLand, Proximity10ftParcelDensity, Proximity1000ftParcelDensity with n=18,819. A significant regression 2 equation was found (F(27,18,792) = 11.549, p < 0.001 ) with an Rof 0.016. After the analysis the 12 coefficients that influence Number of Residences built after 1995 the most are found in the table below. Trend Scenario and Land Use Allocation Technical Report 22 Table 17. Twelve most influential variables for residential, post 1995 VariablesBetaValue Coefficient Distance2VolcanoHazard.081 Distance2LavaFlow1250 -.057 Slope -.049 Intersections1miDensity-.044 Rainfall -.043 Distance24CenterAdjusted-.041 RESpost1995ProximitySum1000ft-.039 RESpost1995ProximitySum10ft-.032 ParcelPerimeter2DivArea .031 Distance2Coastline -.030 Distance2Schools-.029 Distance2Hospitals 0.23 Residential 1975-95 The first multiple linear regression was calculated to predict NumberOfResidences for residential buildings built on or after 1975 and before 1995 based on ParcelPerimeter2DivArea, Intersections1miDensity, Slope, Rainfall, Distance2Major10, Distance2WaterService, Distance2SewerService, Distance2Schools, Distance2Coastline, Distance2VolcanoHazard, Distance2Airports, Distance2LavaFlow1250, Distance2LavaFlow1790, Distance2Towns, Distance2ElectricService, Distance2Hospitals, Distance24CenterAdjusted, Distance2ExistingSF1, Distance2ExistingMF1, Distance2Belt10, DUpost1995ProximitySum10ft, DUpost1995ProximitySum1000ft, ValueLand, Proximity10ftParcelDensity, Proximity1000ftParcelDensity 2 with n=23,822. A significant regression equation was found (F(27,23,795) = 17.956, p < 0.001 ) with an R of 0.020. After the analysis the 12 coefficients that influence Number of Residences built between 1975-95 the most are found in the table below. Table 18. Twelve most influential variables for Residential > 1975 and < 1995 VariablesAbsolute Value Coefficient Distance2LavaFlow1250 -.173 Distance2ExistingMF1-.137 Distance2Airports.123 Distance2SewerService -.106 Distance2VolcanoHazard .079 Intersections1miDensity -.078 Distance2Schools.073 Distance2WaterService .073 Proximity10ftParcelDensity .064 Proximity1000ftParcelDensity -.059 Distance2LavaFlow1790 .051 Slope -.049 Trend Scenario and Land Use Allocation Technical Report 23 1 1 IğǞğźź/ƚǒƓƷǤ (approximately 2,578,828acres) 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use /ƚǒƓƷǤ Breakdown of Other Land Use Types 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use (143,033acres) (146,802acres)(144,604acres) (approximately 404,337acres) 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use Breakdown of Other Land Use Types 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use (2,763acres) (1,602acres)(1,715acres) bƚƩƷŷ Iźƌƚ (approximately 167,230acres) 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use bƚƩƷŷ Iźƌƚ Breakdown of Other Land Use Types 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use (853acres) (659acres)(681acres) {ƚǒƷŷ Iźƌƚ (approximately 255,518acres) 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use {ƚǒƷŷ Iźƌƚ Breakdown of Other Land Use Types 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use (20,292acres) (19,936 acres)(21,289acres) tǒƓğ (approximately 321,604acres) 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use tǒƓğ Breakdown of Other Land Use Types 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use (43,854acres) (53,607acres)(39,017acres) (approximately 661,461acres) 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use Breakdown of Other Land Use Types 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use (15,360acres) (23,103acres)(22,599acres) {ƚǒƷŷ YƚƓğ (approximately 146,964acres) 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use {ƚǒƷŷ YƚƓğ Breakdown of Other Land Use Types 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use (1,994acres) (1,601acres)(2,966acres) bƚƩƷŷ YƚƓğ (approximately 365,585acres) 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use bƚƩƷŷ YƚƓğ Breakdown of Other Land Use Types 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use (28,646acres) (26,755acres)(25,485acres) {ƚǒƷŷ Yƚŷğƌğ (approximately 176,135 acres) 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use {ƚǒƷŷ Yƚŷğƌğ Breakdown of Other Land Use Types 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use (25,645acres) (15,811acres)(23,533acres) bƚƩƷŷ Yƚŷğƌğ (approximately 79,993acres) 2019 Proposed 2024 Proposed 2005 LUPAG Land UseLand Use bƚƩƷŷ Yƚŷğƌğ Breakdown of Other Land Use Types 2019 Proposed 2024Proposed 2005 LUPAG Land UseLand Use (3,625acres) (4,267acres)(7,319 acres)