Land Use Conflict Identification Strategy (LUCIS ...heartland2060.org/download/scenario_modeling/lucis_technical... · LUCIS Technical Report 1 Land Use Conflict Identification Strategy

LUCIS Technical Report

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Land Use Conflict Identification Strategy

(LUCIS) Technical Report

Prepared by the University of Florida

For the Florida Heartland Consisting of DeSoto, Glades, Hendry, Highlands and Okeechobee Counties

555 E Church St, Bartow, FL 33827

www.cfrpc.org

“The work that provided the basis for this publication was supported by funding under an award with the U.U. Department of Housing and Urban Development. The substance and findings of the work are dedicated to the public. The author and publisher are solely responsible for the accuracy of the statements and interpretations contained in this publication. Such interpretations do not necessarily reflect the views of the Government.”

http://www.cfrpc.org/


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Table of Contents

LUCIS Modeling Methodology ................................................................................................. 6

Background ............................................................................................................................................... 6

Land Use Suitability Analysis ............................................................................................................ 6

LUCIS – Land Use Conflict Identification Strategy .................................................................... 7

LUCIS in the Heartland 2060 Sustainable Communities Regional Planning Grant 12

Utility Assignment for Suitability Rasters ................................................................................. 12

Weighting Suitability Rasters for Objectives and Goals ....................................................... 14

Community Weighting and AHP .............................................................................................. 15

Assigning Preference and Creation of the Land Use Conflict Raster ......................... 15

Scenario Development ...................................................................................................................... 16

The Criteria Evaluation Matrix (CEM) ....................................................................................... 16

Data Included in the Employment CEM ..................................................................................... 17

Suitability and Conflict Rasters ................................................................................................ 17

Masks .................................................................................................................................................. 18

Geographic Identifiers ................................................................................................................. 20

Data included in Population CEM ................................................................................................. 21

Suitability and Conflict Rasters ................................................................................................ 21

Masks .................................................................................................................................................. 21

Geographic identifiers .................................................................................................................. 22

The Allocation Process ...................................................................................................................... 22

Data Used in the Allocation Process ............................................................................................ 26

Population and Employment Projections ............................................................................. 26


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Table of Contents

Population and Employment Densities ................................................................................. 26

Allocation Outputs .............................................................................................................................. 29

References ..................................................................................................................................... 30

Appendix: ...................................................................................................................................... 31

Agriculture Models Toolbox and Weights ................................................................................ 31

Goal 1: ...................................................................................................................................................... 37

Goal 2 ....................................................................................................................................................... 37

Goal 3 ....................................................................................................................................................... 38

Goal 4 ....................................................................................................................................................... 38

Goal 5 ....................................................................................................................................................... 38

Community Weights for Combining Agriculture Goal .......................................................... 39

Agriculture Models - Data ................................................................................................................ 39

Urban Models Toolbox and Weights ........................................................................................... 40

Goal 1a ..................................................................................................................................................... 40

Goal 1b .................................................................................................................................................... 41

Goal 2 ....................................................................................................................................................... 42

Goal 3 ....................................................................................................................................................... 43

Goal 4 ....................................................................................................................................................... 44

Goal 5 ....................................................................................................................................................... 44

Goal 6 ....................................................................................................................................................... 45

Community Weights for Combining Urban Goals .................................................................. 45

Urban Models - Data .......................................................................................................................... 46


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List of Figures

Figure 1: Simplified raster-based land use suitability analysis ............................... 7

Figure 2: Urban Suitability Toolbox example .................................................................. 9

Figure 3: Combination of preference rasters ................................................................ 10

Figure 4: Understanding existing relationships .......................................................... 13

Figure 5: Assigning utility to create suitability raster .............................................. 14

Figure 6: The ‘combine’ process used to create the CEM ........................................ 17

Figure 7: Polk County Population Allocation Densities (for year 2060) ........... 28

Map A-1: Today’s Population and Distribution ............................................................. 31

Map A-2: Future Population and Distribution (2060) ............................................... 32

Map A-3: Today’s Employment and Distribution ......................................................... 33

Map A-4: Future “Current Economy” Employment and Distribution ................. 34

Map A-5: Energy-Focused Economy Employment and Distribution ................... 35

Map A-6: Trade-Focused Economy Employment and Distribution ..................... 36


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List of Tables

Table 1: The six-step LUCIS process .................................................................................... 8

Table 2: Description of urban goals, objectives & sub-objectives (abridged) ... 9

Table 3: Combination of preference rasters .................................................................. 11

Table 4: Draft Scenario Current Economy Future Employment Allocation .... 23

Table 5: Sample Scenario Table for Institutional Employment Allocation ...... 25

Table 6: Population Projections – 2040 to 2060 ......................................................... 26

Table 7: Population Density (people per acre) ............................................................ 27

Table 8: Employment Density (jobs per acre in year 2060) .................................. 29

Table A-1: Row Crops ............................................................................................................... 37

Table A-2: Livestock ................................................................................................................. 37

Table A-3: Specialty Farms .................................................................................................... 38

Table A-4: Nurseries ................................................................................................................. 38

Table A-5: Timber Production .............................................................................................. 38

Table A-6: Community Weights for Combining Agriculture Goal ......................... 39

Table B-1a: Multi-Family Residential ................................................................................ 40

Table B-1b: Single Family Residential .............................................................................. 41

Table B-2: Commercial ............................................................................................................ 42

Table B-3: Retail ......................................................................................................................... 43

Table B-4: Industrial ................................................................................................................. 44

Table B-5: Service ...................................................................................................................... 44

Table B-6: Institutional............................................................................................................ 45

Table B-7: Community Weights for Combining Urban Goals .................................. 45


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LUCIS Modeling Methodology This section outlines the methodology that creates land use pattern information for each

of the modeled Heartland future scenarios, or Futures. The methodology, a land use suitability analysis approach, was adopted from LUCIS, the Land Use Conflict Identification Strategy, developed at the University of Florida’s Department of Urban and Regional Planning. The LUCIS model identifies conflicts between certain characteristics associated with individual areas of land and balances those conflicts according to user-defined criteria.

Background

Land Use Suitability Analysis Land use suitability analysis is a process of identifying the most appropriate location

and distribution of future land uses (Collins, 2001, p. 611; Malczewski, 2004, p. 4). Various layers of spatially referenced, superimposed information about selected characteristics of a region can be analyzed collectively. This provides a cumulative assessment of a location’s qualities that is useful in making decisions about its future use. Land use suitability analysis is used in a wide variety of settings; examples include ecology for defining habitat suitability, agriculture for crop suitability, landscape architecture for site analysis, and environmental sciences where impact assessments may be required. (Malczewski, 2004, p. 4). Its applicability to planning related fields stems from its ability to assist decision makers in formulating policies about site-specific development proposals, their environmental impacts and their potential cumulative effects over time (Collins et al., 2001, p. 611).

The use of computer models to perform land use suitability analysis has become more common in the past two decades as off-the-shelf geographic information system (GIS) software programs have become more readily available, affordable and user-friendly. The development of raster-based GIS programs has dramatically increased the computational speed and spatial accuracy of suitability analysis and enabled the use of large, higher resolution raster data-sets. Raster data is represented using a grid format, similar to a JPEG image, where each individual grid-cell or pixel contains a unique value representing some feature in the real world. The concept of raster-based suitability analysis is simplified in Figure 1 which demonstrates how to find the most suitable location for a school based on two land use characteristics. When the two input rasters are combined the analysis shows the most suitable location for a school would be located in the area represented by the lower left grid-cell of the output raster with a value of 6.

Decisions on how to assign values to the data are made at two different levels during the suitability analysis. First, each dataset in the analysis must have a range of suitability values assigned to it, like the values of 1 to 3 (low to high) in the Figure 1 example that categorize the distances to major roads and residential areas. Secondly, each dataset must be weighted according to its relative importance in the overall analysis. In the example shown in Figure 1 an equal importance or weight is given to each dataset, that is, the


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distance of a location to a major road is of equal importance as its distance to residential areas. If, however, distance to major roads is considered more important, then the road raster would be weighted by multiplying its values by a factor representing this relative importance.

Figure 1: Simplified raster-based land use suitability analysis

When several decision makers are involved in weighting the input rasters there may be differing viewpoints on their individual importance in the overall analysis. Several methods exist for developing a weighting system that reflects the group’s preferences. These range from using a Delphi method where consensus among decision-makers is tallied using a voting system (Collins et al., 2001, p. 614), or simply by the use of expert advice (Carr and Zwick, 2007, p. 59-60). Another alternative is pair-wise comparison where each dataset is paired with another and the relative preference of one over the other is assigned by each decision-maker until all possible combinations have been compared. Values are then normalized and averaged to determine a weight for each dataset (Carr and Zwick, 2007, pp. 62-66).

LUCIS – Land Use Conflict Identification Strategy LUCIS is a form of land use suitability analysis that was designed as a suite of analytical

GIS models to determine where potential future conflicts may occur between competing land uses. LUCIS uses the Environmental Systems Research Institute’s (ESRI) software ArcGIS to perform raster-based land use suitability analysis (Carr and Zwick, 2007, p. 60) generally following the method described in the preceding paragraphs. It also uses role playing techniques where major stakeholders act as teams of experts who are charged with


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rating all available land according to relative suitabilities that best support a specific land use they are advocates for (Carr and Zwick, 2007, p. 60).

LUCIS uses three generalized land use categories that groups of stakeholders represent; urban, agriculture and ecological significance. Once each group of stakeholders rates the available lands according to their particular preference, the three results are compared to see where potential conflicts may occur. More specifically LUCIS follows a six step process represented in Table 1.

Table 1: The six-step LUCIS process

Step Description

1 Define goals and objectives that become criteria for determining suitability

2 Inventory data resources relevant to each goal and objective

3 Analyze data to determine a relative suitability for each goal

4 Combine the relative suitabilities of each goal to determine preference

5 Normalize and collapse the preferences for each land use into three categories – high, medium and low

6 Compare the ranges of land use preference to determine likely areas of conflict

(Carr and Zwick, 2007)

LUCIS uses a hierarchical set of goals, objectives and sub-objectives to guide the analytical process, with a unique set of goals created for each of the three land use categories. For each set, a GIS toolbox exists that contains spatial models to create the suitability rasters that represent and combine each of the sub-objectives, objectives and goals. Figure 2 shows an example of the urban suitability toolbox and Table 2 describes a subset of its contents.

Once goals and objectives have been identified and an inventory of data to be used is collected, suitability analysis is performed for all of the goals using ArcGIS. The modeling process begins by first running the sub-objective models, followed by the objective models and finally the goal models. The models within each of the suitability toolboxes are run independently of the others. Appendix A lists the goals, objectives and sub-objectives for each of the land use suitability toolboxes – agriculture, ecological significance and urban – and also includes an inventory of the datasets used in each of the models.


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Figure 2: Urban Suitability Toolbox example

Source: Carr and Zwick, 2007, p. 87

Table 2: Description of urban goals, objectives and sub-objectives (abridged)

Goal Objective Sub-objective 1 Identify lands suitable for residential development

1.1 Identify lands physically suitable for residential development

1.1.1 Identify quiet areas

1.1.2 Identify soils suitable for residential land use

1.1.3 Identify areas free of flood potential

1.1.4 Identify areas with good air quality

1.1.5 Identify areas distant to hazardous sites

The next step involves assigning preference to the suitability rasters, meaning each group of stakeholders must rate the individual goals according to their relative importance. LUCIS uses the technique of pair-wise comparison to assign weights to the different goals within each land use category. For example, using the urban goals shown in Figure 2, residential uses may be the most valued type of urban land use so residential use would be


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assigned a higher weight than industrial use that may be less valued by the urban stakeholders. The end result of this step is the creation of three separate rasters, one each for agriculture, urban and ecological significance suitabilities, with grid-cell values that represent the individual preference stakeholders assigned to them.

So that the three rasters may be compared each one is normalized and their values collapsed into three categories reflecting, high, medium and low preference. The final step of the LUCIS process, represented in Figure 3, involves the combination of the three preference rasters so that each grid-cell in the output raster has a unique value that represents the level of conflict between land uses in that location. Table 3 lists the combinations of preference rankings that represent the levels of conflict in the final raster dataset.

Figure 3: Combination of preference rasters to create land use conflict raster


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Table 3: Combination of preference rasters to create land use conflict raster

Dominant Land Use Preference Conflict

111 All Low Major

112 Urban Medium None

113 Urban High None

121 Ecological Medium None

122 Ecological / Urban Medium Moderate

123 Urban High None

131 Ecological High None


133 Ecological / Urban High Moderate

211 Agriculture Medium None

212 Agriculture / Urban Medium Moderate

213 Urban High None

221 Agriculture / Ecological Medium Moderate

222 All Medium Major

223 Urban High None



233 Ecological / Urban High Moderate

311 Agriculture High None


313 Agriculture / Urban High Moderate



323 Agriculture / Urban High Moderate

331 Agriculture / Ecological High Moderate

332 Agriculture / Ecological High Moderate

333 All High Major


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As shown in Table 3 there are three conflict classifications in LUCIS, none, moderate and major. When there is no conflict between the three land uses (none) a single land use type has the highest preference value and the other land uses in the conflict score have lower values. When two land uses have the same preference value and no other land use type has a higher value, the conflict is considered to be moderate. A major conflict is when all land use types have the same preference values. To further explain this concept an example is helpful. In the conflict raster displayed in Figure 3, the grid-cell in the bottom left corner with a value of 333 represents the level of land use conflict at that location. The first digit represents the preference for agricultural uses, the second digit represents the preference for ecological significance, and the third digit the preference for urban uses. In the example, each digit is a three, meaning that all three land use categories are highly preferred in that location (see bottom row of Table 3), highlighting the potential for a major land use conflict should any of the three uses be proposed there in the future. A more suitable and less contentious location for urban development would be in the location represented by the grid-cell value of 213, where agriculture is only moderately preferred, ecological significance is lowly preferred, and urban uses highly preferred.

LUCIS in the Heartland 2060 Sustainable Communities Regional Planning Grant

LUCIS was used to develop, or model, three scenarios, or Futures, of potential future development patterns for the Heartland Region in the year 2060. Data used to guide these Futures was created using the LUCIS methodology as described above. The following is a more detailed explanation of two specific aspects of the methodology that are important to clarify for the Heartland project; the assignment of relative utility to each of the suitability rasters, and the process of weighting the suitability rasters in the overall analysis. These are respectively steps three and four of the LUCIS process outlined in Table 1.

Utility Assignment for Suitability Rasters A fundamental step in suitability analysis is deciding how to categorize the utility or

usefulness of land for a particular purpose. In other words, determining what benchmarks should be used to determine how useful or suitable a location is in terms of its relationship to others in a region. For example, measuring a location’s proximity to a particular activity like shopping, or an environmental asset like a park, would influence its suitability for one or more land uses.

One alternative is to use expert advice, or scientific research that supports a particular benchmark. Another alternative, and one used in the Heartland project, is to assume that existing land uses offer the best guide for what is considered to be most suitable for that land use. For instance, if all property parcels with a multi-family land use in the region are located on average 0.8 miles to the nearest supermarket, the parcels that are located more than that distance to a supermarket are considered to be less suitable incrementally as the distance increases.


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Figures 4 and 5 provide an example to help demonstrate this process. To understand the existing distance relationship of multi-family parcels to supermarkets in a region, the GIS is used to measure the road-network distance of all multi-family parcels to their nearest supermarket. The GIS then calculates the mean distance to supermarkets for all multi-family parcels (Figure 4). Using the mean distance as the benchmark, all locations in the region are given a new value that represents their relative suitability or utility of driving to the nearest supermarket in this region. Locations that are the mean distance or closer are assigned the highest suitability, a value of 9. For all locations at a distance greater than the mean distance, the GIS categorizes their distances into 8 equal intervals and gives decreasing suitability to each category, with the most distant locations receiving the lowest suitability, a value of 1 (Figure 5). Suitability rasters are created using this method for each of the sub-objectives (see the toolboxes listed in Appendix).

Figure 4: Understanding existing relationships


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Figure 5: Assigning utility to create suitability raster

Weighting Suitability Rasters for Objectives and Goals The hierarchical modeling structure of LUCIS requires suitability rasters to be combined

in a cumulative way that represents the suitability for each of the three major land use categories in the region; agriculture, ecological significance, and urban. At the bottom of the hierarchy are specific sub-objectives which when combined represent a particular land use objective. Land use objectives are then combined to create the final goal representing that set of land uses. Figure 2 shows an example toolbox for urban land use suitability. For representative purposes the hierarchy is simplified but it highlights the general principle that there are three levels where decisions need to be made about the relative importance of the components in order to combine them to arrive at a final suitability layer. First, decisions must be made about how to weight the sub-objectives that make up an objective. Second, the objectives that make up the goals must be weighted according to the relative importance between them, and finally, each goal must be weighted in the overall suitability for each of the major land use categories.

A simple way to combine the layers at each of the levels (sub-objective, objective and goals) would be to equally weight them or use weights that represent some generally accepted standard. An alternative would involve asking community experts to assign weights to the layers so that the community’s values and preferences can be incorporated into the suitability analysis. For the Heartland project, a combination of these three approaches was used. Goals were weighted according to input from community representatives. Objectives and sub-objectives however were weighted using values from


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a similar long-range planning project undertaken by the University of Florida’s Urban and Regional Planning Department for east central Florida. The Appendix lists the goal, objective and sub-objective weights used in the project.

Community Weighting and AHP To develop community weights for combining goals into preferences for agriculture and

urban land uses, a day-long workshop was organized and community members were invited to provide expert advice on how best to weight the relative importance of these two land use categories for the region. Suitability analysis and weighting for the ecological significance land use however was performed prior to this workshop by local and regional experts as part of the Critical Lands and Waters Identification Project 2.0 (http://www.fnai.org/pdf/CLIP_2_Tech_Report.pdf) and the Heartland 2060 Ecological Prioritization Project (http://conservation.dcp.ufl.edu/Heartland_2060_Ecological_Report_Final.pdf).

Using a procedure known as Analytic Hierarchy Process (AHP), each community participant was asked to complete a pairwise comparison, ranking, in turn, all agriculture and then all urban goals. For example, participants was asked to evaluate the importance of each agriculture goal (i.e. row crops, livestock, specialty farms, nurseries, or timber) when compared individually to each of the remaining agriculture goals. On a scale of 1 (equally important) to 9 (extremely more important), each pair of goals is evaluated by each participant. Once all agriculture and urban goals have been compared in this way, participant’s rankings were converted to numerical values and statistically combined to create a weight representing the combined preference of all participants for both agriculture and urban land uses.

Assigning Preference and Creation of the Land Use Conflict Raster A final suitability layer was created for both agriculture and urban land uses by

combining each of their goals using the weights assigned to them at the community weighting workshop. The final suitability layer for ecological significance was created previously as described above. As outlined earlier in the description of LUCIS, each of the suitability layers must be finally collapsed into three broader categories of low, medium and high preference before being combined into the land use conflict raster (as represented in Figure 3). For the Heartland project, the preference layers were created using a quantile method, meaning that each of the final suitability layers was re-categorized into three equal-sized groupings, to represent low (1), medium (2) and high (3) preference.

The three preference layers are then combined as shown in Figure 3, so that a final raster is created that represents the level of conflict between the three major land use categories at any point in the region.

http://www.fnai.org/pdf/CLIP_2_Tech_Report.pdf

http://conservation.dcp.ufl.edu/Heartland_2060_Ecological_Report_Final.pdf


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Scenario Development The LUCIS method centers on the use of a final GIS raster that represents the study area

and into which future population and employment growth can be arranged or allocated in a pattern that reflects different visions of the future. The final raster, referred to as a Criteria Evaluation Matrix, or CEM, is a combination of several other independently generated layers each containing information about the study area that is useful to decision-makers and planners when considering potential future land use changes.

For the Heartland project, three different visions, or Futures, were developed by the Heartland 2060 Consortium with each emphasizing different types of economic activity. The “Current Economy” Future is one that reflects historical development patterns of population and employment, projecting them forward to show a “business-as-usual” future. This Future moved forward along present-day economic trends. The “Energy Economy” Future is one that prioritizes energy efficiency and conservation, renewable energy technology and alternative fuels development and manufacturing, and other high technology pursuits. And, the “Trade Economy” Future is one that focuses on logistics, advanced manufacturing and packaging, moving, warehousing, and handling freight. These Futures are detailed in another report.

By combining the land use conflict raster into a large spatial database (the CEM) with other layers that describe existing development patterns and where land use change is envisioned to occur in the future, population and employment patterns for each of the three Heartland Futures were created. The following sections describe in more detail how this process was undertaken in a GIS environment.

The Criteria Evaluation Matrix (CEM) A description of the ArcMap geo-processing tool ‘combine’ is necessary for explaining

the model used to create the Criteria Evaluation Matrix or CEM. Using the ‘combine’ tool, multiple rasters can be combined to create a single raster that retains all of the information associated with each of the individual input rasters. All GIS raster files contain a table in which information or attributes are stored about the geographic space the raster files represent. The attribute table of any GIS raster contains rows and columns, with each row containing a unique record relating to a geographic location or cell in the raster and each column a unique attribute or item of information about the raster cell.

The output raster of a combine process contains columns that represent a unique item for each input raster and rows that contain unique combinations of the values of the input rasters. Figure 6 illustrates how the values of multiple input rasters are attributed to an output raster during a combine process. The value column in the output raster contains a record for each unique combination of cells from the input rasters. For the example shown in Figure 6, the combination of values of both the input rasters creates in the output raster a unique value of 1, seen in both the top left and bottom right cells of the output raster. In the attribute table of the output raster this creates a row containing information about cells that contain the unique value of 1 (value = 1), of which there are two cells (count =2) and


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the original value from each input raster is retained in the columns bearing the names of the input rasters. Where a raster cell contains no data, meaning the location the cell represents in the raster contains no information in the geographic space it relates to, it has a value of NoData, shown as a grey-colored cell. When any input raster cell is combined with a cell containing NoData the corresponding cell in the output raster will also contain NoData.

Figure 6: The ‘combine’ process used to create the CEM

Source: “ArcGIS Desktop 9.3 Help – Combine” by ESRI, 2010.

Using the combine process described above, two different CEMs were created for the Heartland project, one for the allocation of future population, and another for the allocation of future employment. The following is a description of the multiple input rasters used in the creation each CEM.

Data Included in the Employment CEM The datasets included in the CEM used for allocating future employment fall into three

broad categories:

1. Suitability and conflict rasters;

2. Masks for including or excluding locations; and

3. Geographic identifiers for summarizing or relating the CEM to external datasets.

Suitability and Conflict Rasters Two conflict rasters are included in the CEM to begin to narrow the search for suitable

locations for future employment. The first is one that shows the conflict between the three


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major land use categories in LUCIS, namely Agriculture, Conservation, and Urban (ACU), used to broadly find the locations that are most preferred for urban land uses. The second conflict raster is used to additionally define urban locations to show a preference for mixed-use development. This raster shows the conflict between Retail, Commercial, and Multi-Family land uses (RCM). Both rasters contain values formatted in the three-digit conflict method described in the earlier section on LUCIS and shown in Figure 3.

Suitability rasters are included that reflect employment related uses, such as commercial, service, retail, industrial, and institutional land uses. The individual suitability rasters are originally in a floating-point data format, meaning they contain decimals (values ranging from 1.0 to 9.0). During the combine process of creating a CEM, individual rasters are automatically converted to an integer value. In the case of the suitability rasters, considerable information would be lost as decimal-places are rounded to whole integers. To counteract this problem, the suitability rasters values are re-calculated by multiplying them by 100 before they are included in the CEM. The end result is that each suitability raster in the CEM has a three-digit value that represents the original suitability value at two decimal places. For example, a value of 2.34 would become 234 in the CEM. Therefore, the suitability values in the CEM range from 100 to 900, instead of the original 1 to 9 suitability values.

Masks In total there are 11 individual rasters included in the employment CEM that work as

masks for guiding the allocation process. The term “mask” is used to describe how values in a raster can be used to show locations that are specifically inside or outside of particular areas of interest. For example, one raster mask included in the CEM shows the locations of places inside or outside of the existing urban areas of the region. The values in this raster are either a “1” for locations inside the urban areas or “0” for those outside. Masks such as this one are used to further refine spatial queries such as, in this example, find all suitable locations inside existing urban areas. Listed below are the 11 raster masks included in the employment CEM and a brief explanation of each.

1. Redevelopment mask Using property parcel land use data, parcels were selected that had land uses

considered to have potential for converting to other uses, such as mobile home parks, parking lots, drive-in theaters, and commercial/retail land uses. From these parcels, those with buildings more than 40 years old and where the parcel’s land value is greater than the building value were selected. The property just-value per acre was calculated for these selected parcels, and any with extremely high or low values (i.e. three standard deviations above or below the mean value) were excluded. These parcels were considered to have redevelopment value, based on their age and property values, and a raster mask was created with a value of 1 for the locations of these parcels, and a 0 for all other parcels.

2. Infill mask Infill parcels are those that have a land use categorized as vacant. These parcels

were selected from the property parcel data, and a raster mask was created where


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the following values represent the different types of vacant land (all other parcels have a value of 0):

1 = vacant residential parcels 2 = vacant commercial parcels 3 = vacant industrial parcels 4 = vacant institutional parcels

3. Existing urban mask Existing urban areas are those with property parcel land uses that are considered

non-agricultural and urban in nature, such as residential, commercial/retail, and some industrial and institutional/government uses. All agricultural land uses were excluded, as were some industrial uses such as extractive/mining, and government uses such as forests. A raster mask was created where existing urban parcels were assigned a value of 1 and all parcels a zero.

4. Developments of Regional Impact (DRI) mask Developments of Regional Impact, or DRI’s, are considered to be developments

that will have a significant impact on the health, safety and welfare of the residents of more than one county in Florida. Developments of this magnitude require special consideration from Florida Department of Environmental Protection. As such, this raster mask contains values (1 through 9) that relate to specific DRI’s in the region, and all other locations have a value of zero.

5. Transportation Corridors mask Major transportation corridors considered by the CFRPC to have regional priority

for guiding future development were included in the CEM as a raster mask. Areas within a one and two mile buffer of each corridor were assigned values of 1 and 2 respectively, and all other areas a value of zero.

6. Future Land Use mask Understanding where future employment has been allocated in terms its

designated future land use is an important analysis tool in the CEM. The Future Land Use mask represents land use designations according to a generalized future land use dataset provided by the CFRPC. Land use values are as follows, with all other locations having a value of zero.

1 = Agriculture 2 = Commercial/Office 3 = Conservation 4 = Industrial 5 = Institutional / Public 6 = Mixed Use 7 = Recreation / Open Space 8 = Residential, High Density 9 = Residential, Low Density 10 = Residential, Medium Density 11 = Residential, Very High Density 12 = Residential, Very Low Density


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13 = Transportation 14 = Unknown 15 = Water-body 16 = Residential, Unknown Density 17 = Mining / Extractive

7. Green Swamp / Mining / Military Influence Planning Area (MIPA) This raster mask is a combination of three different datasets representing areas

where development is to be discouraged. The values listed below represent each area, with all other locations being a zero.

11 = Green Swamp Rural Special Protection Area 13 = Green Swamp Rural Special Protection Area 21 = Mining Area - Polk County 22 = Mining Area - Hardee County 23 = Mining Area - Desoto County 31 = Military Influence Planning Area 1 32 = Military Influence Planning Area 2

8, 9, 10. Current, Energy and Trade Economy masks In order to concentrate future employment into specific areas of the region that

are consistent with the three future scenarios being investigating in the Heartland 2060 project, the CFRPC provided general boundaries around areas they anticipate such development might occur. These areas are known as Regional Employment Centers, and are detailed elsewhere. Each raster mask contains values where 1 represents areas where employment for that particular Future (Current Economy, Energy Economy, or Trade Economy) might happen in the future. All other locations have a value of zero.

11. Conservation mask

Conservation easements and other managed natural areas are locations that need to be protected from development. This raster mask represents public (and some private) lands that the Florida Natural Areas Inventory (FNAI) has identified as having natural resource value and that are being managed at least partially for conservation purposes. Along with more traditional conservation areas such as state parks and wildlife management areas, this dataset includes conservation easements. The raster mask contains values of 1 for conservation and managed lands, and a 0 for all other locations.

Geographic Identifiers A county raster is included in the CEM to serve two different purposes. Firstly, because

the allocation of employment is undertaken on a county-by-county basis it is necessary to find locations specific to each county. Secondly, to facilitate summaries of the employment allocation data once it has been finalized, it is useful to have a county raster. The following values represent each county:


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Desoto County = 24 Glades County = 32 Hardee County = 35 Hendry County = 36 Highlands County = 38 Okeechobee County = 57 Polk County = 63

So that the output data of the allocation process can be identified parcel by parcel, an identification number raster is included in the CEM that relates to a property parcel dataset external to the CEM that contains more detailed information from the Florida Department of Revenue for each property parcel. Additionally, through the use of the parcel identification number, the output allocation data can also be related to Traffic Analysis Zone boundaries, allowing the employment (and population) allocation data to be modelled by the CFRPC using the Florida Standard Urban Transportation Model Structure (FSUTMS).

Data included in Population CEM The datasets included in the CEM used for allocating future population also fall into the

same three broad categories (suitability/conflict rasters, masks, and geographic identifiers) as the employment CEM.

Suitability and Conflict Rasters The population CEM contains one conflict raster, the ACU mentioned above, which is

used in a similar way, i.e. to firstly broadly narrow the search for potential growth areas based on the three major land use categories of Agriculture, Conservation and Urban uses.

Four suitability rasters are included to guide the allocation process. Suitability for both urban and agricultural uses are used to provide additional depth to the allocation. For example, it is possible to differentiate between locations that are preferred equally for both urban and agriculture by further examining their individual suitabilities to choose those that have a higher urban suitability than agricultural suitability.

Two residential land use suitabilities (single and multi-family) are included to also further differentiate and add depth to the allocation process.

Masks The population CEM contains the same raster masks as described above for the

employment CEM, with the following exceptions. The Trade Economy and Energy Economy masks were not included. Only the Current Economy mask was included, as this is the only Future that was modelled for population growth. Additionally, a ‘Distance to Employment’ raster mask was included that was based on the locations of existing (2013) employment land uses. The values in this raster mask represent the distance in miles from employment, and range from 1 to 11.


22

Geographic identifiers The same geographic identifiers, a county raster and parcel identification raster were

included in the population CEM.

The Allocation Process The LUCIS method centers on the use of a final GIS raster that represents the study area

and into which future population and employment growth can be arranged or allocated in a pattern that reflects different visions of the future. The idea behind using the CEM is that the spatial database table can be queried using structured query languages (or SQL) to find specific locations suitable for future employment or population allocation based on a defined set of criteria. In the CEM table, each row in the table represents the different criteria attached to a group of cells in the raster. The ArcMap tool “selection by attribute” is used to find the cells that satisfy a certain condition or query. The user can build a query depending on the criteria in the CEM and the priorities and condition for allocation and mark the selected cells by the year of allocation. Automatic methods to generate query statements can be also used that utilize programming languages such as Python or Visual Basic.

As mentioned before, SQL can be used to build query sentences. For example, to allocate employment in Polk County according to suitability for service employment and to avoid allocation in existing urban, conservation and mining lands, a simple query would be:

Cty_zonal = 7 and Conserv = 0 and Mining = 0 and Exurb =0 and SerSuit >= 700

In the aforementioned query, “Cty_zonal” is the name of a raster in the CEM that has values of 1 to 7 representing the seven counties in the region, “Conserv, Mining, Exurb” are all allocation avoidance masks which are also raster grids in the CEM. The “SerSuit” is a service suitability raster in the CEM with values from 100 to 900. All of these raster grids and other criteria evaluation grids are components of the CEM. Thus, the meaning of the example query is to allocate service employment into land parcels that belong to Polk County (Cty_zonal=7), excluding areas that are conservation, mining or existing urban lands, and allocate in places that have a service suitability of more than 700 (out of 900). The allocation starts with higher priorities and then moves down to lower priorities in a sequential and iterative procedure.

The allocation procedure for the employment and population was performed using a custom allocation tool’s automatic allocation procedure and the scenario tables. The scenario table lists the priority and sequence of the allocation. The planners in the Heartland region summarized their future visions in draft scenario tables which were used as guidelines to create the scenario tables used by the allocation tool. Table 4 shows the draft scenario table created for the current economy allocation.


23

Table 4: Draft Scenario Table for the Current Economy Future Employment Allocation

Sequ

ence

of

Itera

tion

Sect

or (N

AICS

)CE

M jo

b na

me

Conf

lict t

o be

al

loca

ted

in (i

n or

der)

Ag-

Eco-

Urb

FLU

M c

ateg

ory

to b

e al

loca

ted

in

Cros

s wal

k La

nd u

se

Suita

bilit

y (L

UCI

S al

loca

tion

area

)

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tiona

l Cro

ss

wal

k La

nd u

se

Suita

bilit

y (L

UCI

S al

loca

tion

area

) <c

ombi

ned

with

"A

ND"

fu

nctio

nalit

y>M

ask(

s)1

23

45

67

8et

c?

1He

alth

Car

e an

d So

cial

Ass

istan

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alth

care

first

113

, the

n 12

3,

213,

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112

, th

en 2

23, e

tc. (

any

idea

s?)

Inst

itutio

nal,

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mer

cial

/ O

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e, M

ixed

Use

Inst

itutio

nal

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ice

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min

ing

land

s or

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ing

DRIs

, No

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n Sw

amp,

No

MIP

A 1,

X

2Re

tail

Trad

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tail

first

113

, the

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, th

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tc. (

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tail

No

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ing

land

s or

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, No

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n Sw

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3

Oth

er S

ervi

ces,

exce

pt P

ublic

Adm

inist

ratio

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Man

agem

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f Com

pani

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nter

prise

s /

Acco

mm

odat

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and

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Ser

vice

s /

Adm

inist

rativ

e an

d W

aste

Man

agem

ent

Serv

ices

/ Ar

ts, E

nter

tain

men

t, an

d Re

crea

tion

/ Rea

l Est

ate

and

Rent

al a

nd L

easin

g / F

inan

ce

and

Insu

ranc

e / I

nfor

mat

ion

/ Util

ities

/ St

ate

and

Loca

l Gov

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Fed

eral

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dera

l Civ

ilian

Serv

ice

first

113

, the

n 12

3,

213,

then

112

, th

en 2

23, e

tc. (

any

idea

s?)

Com

mer

cial

/ O

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e,

Mix

ed U

se, I

nstit

utio

nal

Serv

ice

No

min

ing

land

s or

min

ing

DRIs

, No

Gree

n Sw

amp,

No

MIP

A 1,

X

4Tr

ansp

orta

tion

and

War

ehou

sing

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ice_

ind

first

113

, the

n 12

3,

213,

then

112

, th

en 2

23, e

tc. (

any

Indu

stria

lSe

rvic

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dust

rial

No

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ing

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ing

DRIs

, No

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5M

anuf

actu

ring

/ Con

stru

ctio

n / M

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dust

rial

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113

, the

n 12

3,

213,

then

112

, th

en 2

23, e

tc. (

any

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rial

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DRIs

(kee

p th

is cr

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ific,

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Inst

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mm

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ixed

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ndus

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8Fa

rm /

Fore

stry

, Fish

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and

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311,

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, etc

…?

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ultu

ral,

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erva

tion

(if a

pplic

able

?)Ag

ricul

ture

No

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land

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.

Empl

oym

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data

sets

to u

se


24

The process of allocation starts by first allocating employment and then using employment locations as magnets for residential allocation. The allocation process for employment or for population follows three stages – infill, redevelopment and green-field development. Infill development is mainly allocated in suitable vacant land parcels using the CEM’s infill mask. Inside the masked areas, suitability is decided using LUCIS and is also directed by the future land use for the year 2035. The second stage finds locations that have both redevelopment potential (using the redevelopment mask) and suitability, and then allocates in these places. The third stage continues allocating in suitable undeveloped (or green-field) lands which were defined as land other than vacant and existing urban parcels. The allocation of infill, redevelopment and green-field development follows particular employment and population thresholds that were estimated from percentages of historical infill, redevelopment and green-field development for the region.

For the employment allocation, scenario tables were prepared according to the directions of the CFRPC included in draft scenario table for each of the Futures. A separate scenario table was prepared for each employment category in the Current Economy Future employment scenario and the specified allocation was performed for the amount of employment projected to occur by the year 2040. Table 5 is an example of the scenario table that was prepared for the institutional allocation. The allocation of 2060 employment started after finishing allocating 2040 for all the employment categories. For the 2060 allocation, a similar process was used. The main difference however, was that for the 2040 allocation, the future land use was used as a criteria in the CEM, while it was replaced by the use of suitability in the 2060 allocation process. Furthermore, the allocation process is an accumulated process which means that the land allocated in 2040 will not be available for allocation in 2060 allocation. Therefore 2060 and 2040 allocations are sequential.

Similar sequential procedures were followed for the Trade Economy Future and Energy Economy Future scenarios. The main difference between the two Futures is the employment magnet points (the Regional Employment Centers) used for each Future. These magnet points were converted to a “distance to employment” raster that prioritized the allocation process for each scenario. In the Trade Economy Future allocation, 35 percent of the service-industrial, industrial, institutional and commercial-service employees were allocated around the trade employment centers using the “distance to employment” raster. A similar method was used for the Energy Future scenario, with 35 percentage of employment for institutional, industrial and service-industrial categories being allocated using the “distance to employment” raster for energy employment centers.

The allocation for population followed the employment allocation. A new grid of proximity to employment allocated by the employment Current Economy Future is created and used as an additional criterion to direct the allocation of population around those employment areas. Another raster grid is also created to mask out the parcels that are used


25

for employment. The population allocation also followed the three stages of infill, redevelopment and green-field development.

Table 5: Sample Scenario Table for Institutional Employment Allocation


26

Data Used in the Allocation Process To create the employment and population patterns representing the three Futures for

the Heartland project, it was necessary to use data for the allocation processes that estimated the increase in jobs and people, and the rate of growth that would likely occur for each of them.

Population and Employment Projections The University of Florida’s Bureau of Economic and Business Research publishes annual

projections of population for the state and counties as part of its Population Studies Program. The program creates high, medium and low estimates for projected population growth to 2040. For the Heartland Project, an average value of the BEBR Medium and BEBR High estimates (from the 2011 projections report) was used for each county, and then projected forward to the year 2060. The Central Florida Regional Planning Council staff provided the population projections used in the LUCIS Heartland model. The development of these population projections is detailed elsewhere. Table 6 lists the county-level projections used in the allocation process.

Table 6: Population Projections – 2040 to 2060

County 2040 Population 2060 Population

Desoto 45,650 53,005

Glades 19,500 23,849

Hardee 32,500 35,494

Hendry 47,850 54,026

Highlands 137,850 163,052

Okeechobee 53,250 61,798

Polk 1,037,650 1,338,347

Total Region 1,374,250 1,729,571

Population and Employment Densities Knowing how many acres of suitable lands are needed to accommodate future growth in

employment and population for the region requires an understanding of densities, i.e. people and jobs per acre. For the Heartland project, future densities were based on existing ones which were calculated using the property parcel data to determine the


27

number of acres for residential and employment related land uses. The number of people and jobs were then divided into the number of acres for the appropriate land uses (residential or employment) to arrive at existing densities.

Population densities for the future (projected to 2060) were estimated by applying an annual growth factor. This was based on the change in residential acres between 2003/04 and the present day, calculated within a GIS environment using property parcel data from both time periods. The change in residential acres was determined for each county and an annual percentage increase was calculated and applied to the existing densities to project the future ones. The table below shows the existing population densities (i.e. people per acre) and their projected values for the years 2040 and 2060.

Table 7: Population Density (people per acre)

County 2013 2040 2060

Desoto 1.654 1.788 1.895

Glades 1.546 1.671 1.771

Hardee 1.876 2.029 2.150

Hendry 1.536 1.661 1.760

Highlands 1.848 1.998 2.117

Okeechobee 1.697 1.835 1.944

Polk See Figure 7: Polk County Population Allocation Densities

Polk County population allocation density maximums were handled differently due to previous research, planning, and information in this county. Previous work by the Polk County Transportation Planning Organization (TPO) has suggested that Polk County will need higher densities than other counties in the Heartland to support anticipated growth. These higher density areas have been planned around current and future transportation nodes. These higher densities are organized spatially by Traffic Analysis Zone (TAZ) and are presented in Figure 7. These higher density allocations were used in the population allocations for Polk County.


28

Figure 7: Polk County Population Allocation Densities (for year 2060)

Employment densities were calculated for 2013 by summarizing the number of acres in land uses that corresponded to each of the job categories of employment to be allocated. This was also done within the GIS using current property parcel land use data. No growth factors were applied for future densities. Due to there being little variation in existing employment densities across the six rural counties of the region, an average density was calculated for each employment category and used across all six counties. Table 8 shows the employment densities used in allocating future employment for 2040 and 2060.

The minimum population density used in the Polk County allocation is 1.775 people per acre. This is based on historical trends.


29

Table 8: Employment Density (jobs per acre in year 2060)

Counties Employment Categories

Commercial-Service Healthcare Industrial Institutional

Six Rural Counties 79.567 13.389 2.162 1.443

Polk 185.591 68.377 2.779 5.001

Retail Service Service-Industrial

Six Rural Counties 1.820 0.264 0.246

Polk 2.534 3.616 11.662

As Table 6 shows, the vast majority of current and future population and employment for the Heartland region occurs in Polk County, whereas the remaining six counties are by nature more rural with far less people and jobs. As such, during the allocation process, Polk County was allocated as a single (urban) sub-region, and the six rural counties (i.e. Desoto, Glades, Hardee, Hendry, Highlands and Okeechobee counties) were allocated separately as a single (rural) sub-region. Employment allocations were produced for all seven counties (performed in two sub-regions) for both 2040 and 2060 projected growth for all three Futures. Population growth for both 2040 and 2060 time periods, however, was only allocated for the Current Future.

Allocation Outputs Using the data and allocation process described above, population and employment

growth patterns for each of the three Futures were created and summarized in a series of maps shown in the Appendix. Additionally, a vector formatted geodatabase was created containing map and tabular data for the project which can be more readily used by the Heartland Consortium for future planning projects. Included in the geodatabase is a summary of the employment and population allocations by Traffic Analysis Zones (TAZs). This data will be modeled by the CFRPC using the Florida Standard Urban Transportation Model Structure (FSUTMS) to better understand how the development patterns resulting from each of the Futures may impact future transportation infrastructure for the region.


30

References Carr, M., & Zwick, P. (2007). Smart land-use analysis: The LUCIS model land use

identification system. Redlands: ESRI Press.

Collins, M.G., Steiner, F.R., & Rushman, M.R. (2001). Land-use suitability analysis in the United States: Historical development and promising technological achievements. Environmental Management, 28(5), 611-621.

Malczewski, J. (2004). GIS-based land-use suitability analysis: A critical overview. Progress in Planning, 62(1), 3-65.


31

Appendix:

Agriculture Models Toolbox and Weights

Map A-1: Today’s Population and Distribution


32

Map A-2: Future Population and Distribution (2060)


33

Map A-3: Today’s Employment and Distribution


34

Map A-4: Future “Current Economy” Employment and Distribution


35

Map A-5: Energy-Focused Economy Employment and Distribution


36

Map A-6: Trade-Focused Economy Employment and Distribution


37

Goal 1: Table A-1: Row Crops

Obj. S.Obj. Description Weights 1.1 Determine lands physically suitable for row crops/citrus 0.5 1.1.1 Identify the soils suitability for row crops/citrus Conditional 1.1.2 Identify land uses suitable for row crops/citrus Conditional 1.2 Determine lands proximally suitable for row crops/citrus 0.2 1.2.1 Identify lands proximal to markets 0.3 1.2.2 Identify lands proximal to major roadways 0.7 1.3 Identify land values suitable for row crops/citrus 0.3

Goal 2 Table A-2: Livestock

Obj. S.Obj. Description Weights

2.1 Determine lands physically suitable for high-intensity livestock 0.5

2.1.1 Identify land uses suitable for high intensity livestock Conditional 2.1.2 Identify lands away from open water Conditional 2.1.3 Identify geology suitable for high intensity livestock Conditional 2.1.4 Identify soils suitable for high intensity livestock Conditional 2.1.5 Identify lands away from existing urban areas Conditional

2.2 Determine lands economically suitable for high-intensity livestock 0.25

2.2.1 Identify lands proximal to markets 0.65 2.2.2 Identify lands proximal to major roadways 0.35

2.3 Determine lands physically suitable for low-intensity livestock 0.5

2.3.1 Identify land uses suitable for low-intensity agriculture Conditional 2.3.2 Identify lands away from open water Conditional 2.3.3 Identify geology suitable for low intensity agriculture Conditional 2.3.4 Identify soils suitable for low intensity agriculture Conditional

2.4 Determine lands proximally suitable for low-intensity agriculture 0.25

2.4.1 Identify lands proximal to markets 0.5 2.4.2 Identify land proximal to major roads 0.5

2.5 Identify land values suitable for high intensity livestock 0.25 2.6 Identify land values suitable for low intensity livestock 0.25

Goal 2 High Intensity Livestock 0.125 Low Intensity Livestock 0.875


38

Goal 3 Table A-3: Specialty Farms

Obj. S.Obj. Description Weights 3.1 Determine lands physically suitable for specialty farms 0.4

3.1.1 Identify land uses suitable for specialty farms 0.25 3.1.2 Identify lands away from open water 0.25 3.1.3 Identify geology suitable for specialty farms 0.25 3.1.4 Identify lands with soils suitable for specialty farms 0.25

3.2 Determine lands proximally suitable for specialty farms 0.4 3.2.1 Identify lands proximal to processing plants 0.5 3.2.2 Identify lands proximal to major roads 0.5

3.3 Identify land values suitable for specialty farms 0.2

Goal 4 Table A-4: Nurseries

Obj. S.Obj. Description Weights 4.1 Determine lands physically suitable for nurseries 0.4

4.1.1 Identify land uses suitable for nurseries Conditional 4.1.2 Identify land parcels large enough for nurseries (+10 ac.) Conditional

4.2 Determine lands proximally suitable for nurseries 0.4 3.2.1 Identify lands proximal to markets 0.5 3.2.2 Identify lands proximal to major roads 0.5

4.3 Identify land values suitable for nurseries 0.2

Goal 5 Table A-5: Timber Production

Obj. S.Obj. Description Weights 5.1 Determine lands physically suitable for timber production 0.4

5.1.1 Identify land uses suitable for timber production 0.4 5.1.2 Identify lands proximal to open water 0.1 5.1.3 Identify geology suitable for timber production 0.2 5.1.4 Identify soils suitable for timber production 0.1 5.1.5 Identify land parcels large enough for timber production (+10 ac.) 0.2

5.2 Determine lands proximally suitable for timber production 0.4 5.2.1 Identify lands proximal to markets 0.5 5.2.2 Identify lands proximal to major roads 0.5

5.3 Identify land values suitable for timber production 0.2


39

Community Weights for Combining Agriculture Goal

Table A-6: Community Weights for Combining Agriculture Goal

Agriculture Goal

Community Weight

Goal 1 – Row Crops 0.178

Goal 2 – Livestock 0.309

Goal 3 – Specialty Farms 0.289

Goal 4 – Nurseries 0.156

Goal 5 – Timber Production 0.068

Agriculture Models - Data USDA STATSGO soils shapefile and associated “compyld” (component crop yield) table

USGS 1:100,000 Hydrography shapefile

USEPA DRASTIC Surficial Aquifer Vulnerability shapefile

USDA SSURGO soils shapefile and associated “comp.DRAINAGE” table

USDA SSURGO soils shapefile and associated “comp.WTDEPH” table

Florida property appraiser parcel shapefiles – Desoto, Glades, Hardee, Hendry, Highlands, Okeechobee and Polk counties:

- Land uses - Tax code boundaries – city limits - Property just values


40

Urban Models Toolbox and Weights

Goal 1a Table B-1a: Multi-Family Residential


1.1 Determine lands physically suitable for multi-family residential use 0.06

1.1.1 Identify lands distant to noise generating land uses 0.06

1.1.2 Identify lands with soils suitable for residential construction 0.15

1.1.3 Identify lands not in flood prone areas 0.52 1.1.4 Identify lands distant to air polluting land uses 0.06 1.1.5 Identify lands distant to hazardous sites 0.22

1.2 Determine lands proximally suitable for multi-family residential use 0.15

1.2.1 Identify lands proximal to service amenities 0.05 1.2.2 Identify lands proximal to transportation amenities 0.13 1.2.3 Identify lands proximal to environmental amenities 0.05 1.2.4 Identify lands proximal to retail and shopping 0.04 1.2.5 Identify lands proximal to existing multi-family 0.38 1.2.6 Identify lands proximal to infrastructure amenities 0.23 1.2.7 Identify lands proximal to entertainment opportunities 0.05 1.2.8 Identify lands based upon suitable land values 0.07

1.3 Identify lands with suitable residential densities 0.51 1.4 Identify lands with historic growth in multi-family uses 0.06

1.5 Identify lands with potential for change to multi-family uses 0.22


41

Goal 1b Table B-1b: Single Family Residential


1.6 Determine lands physically suitable for single family residential use 0.16


1.6.2 Identify lands with soils suitable for residential construction 0.15


1.7 Determine lands proximally suitable for single family residential use 0.30

1.7.1 Identify lands proximal to service amenities 0.07 1.7.2 Identify lands proximal to transportation amenities 0.13 1.7.3 Identify lands proximal to environmental amenities 0.05 1.7.4 Identify lands proximal to retail and shopping 0.04 1.7.5 Identify lands proximal to existing single family 0.38 1.7.6 Identify lands proximal to infrastructure amenities 0.05 1.7.7 Identify lands proximal to entertainment opportunities 0.05 1.7.8 Identify lands based upon suitable land values 0.23

1.8 Identify lands with potential for change to single family uses 0.17

1.9 Identify lands with suitable residential densities 0.19

1.10 Identify lands with historic growth in single family uses 0.18


42

Goal 2 Table B-2: Commercial


2.1 Determine lands physically suitable for commercial uses 0.10


2.1.2 Identify lands with soils suitable for commercial construction 0.35


2.2 Determine lands proximally suitable for commercial uses 0.50

2.2.1 Identify lands proximal to service amenities 0.04 2.2.2 Identify lands proximal to transportation amenities 0.25

2.2.3 Identify lands proximal to existing commercial land uses 0.18

2.2.4 Identify lands proximal to existing residential uses 0.13

2.2.5 Identify lands proximal to existing support infrastructure 0.12

2.2.6 Identify lands proximal to existing entertainment uses 0.02 2.2.7 Identify lands proximal to existing institutional uses 0.08 2.2.8 Identify lands based upon suitable land values 0.18

2.3 Identify lands with suitable commercial densities 0.20

2.4 Identify lands with potential for change to commercial uses 0.20


43

Goal 3 Table B-3: Retail

Obj. S.Obj. Description Weights 3.1 Determine lands physically suitable for retail uses 0.09

3.1.1 Identify lands distant to noise generating land uses 0.06 3.1.2 Identify lands with soils suitable for retail construction 0.35 3.1.3 Identify lands not in flood prone areas 0.46 3.1.4 Identify lands distant to air polluting land uses 0.07 3.1.5 Identify lands distant to hazardous sites 0.06

3.2 Determine lands proximally suitable for retail uses 0.47 3.2.1 Identify lands proximal to service amenities 0.04 3.2.2 Identify lands proximal to transportation amenities 0.24 3.2.3 Identify lands proximal to schools 0.03 3.2.4 Identify lands proximal to existing retail land uses 0.18 3.2.5 Identify lands proximal to existing residential uses 0.12


3.2.7 Identify lands proximal to existing entertainment uses 0.02 3.2.8 Identify lands proximal to existing institutional uses 0.08 3.2.9 Identify lands based upon suitable land values 0.17

3.3 Identify lands with suitable retail densities 0.24 3.4 Identify lands with potential for change to retail uses 0.20


44

Goal 4 Table B-4: Industrial

Obj. S.Obj. Description Weights 4.1 Determine lands physically suitable for industrial uses 0.15

4.1.1 Identify lands with soils suitable for industrial construction 0.25

4.1.2 Identify lands not in flood prone areas 0.75 4.2 Determine lands proximally suitable for retail uses 0.50

4.2.1 Identify lands based upon suitable land values 0.07


4.2.3 Identify lands proximal to existing industrial uses 0.14 4.2.4 Identify lands proximal to existing residential uses 0.36 4.2.5 Identify lands proximal to existing commercial uses 0.13 4.2.6 Identify lands proximal to existing institutional uses 0.11 4.2.7 Identify lands proximal to existing retail uses 0.13 4.2.8 Identify lands proximal to transportation amenities 0.03

4.3 Identify lands with suitable industrial densities 0.15

4.4 Identify lands with potential for change to industrial uses 0.20

Goal 5 Table B-5: Service

Obj. S.Obj. Description Weights 5.1 Determine lands physically suitable for service uses 0.15

5.1.1 Identify lands with soils suitable for service construction 0.25

5.1.2 Identify lands not in flood prone areas 0.75 5.2 Determine lands proximally suitable for retail uses 0.50

5.2.1 Identify lands based upon suitable land values 0.16


5.2.3 Identify lands proximal to existing service uses 0.32 5.2.4 Identify lands proximal to existing retail uses 0.16 5.2.5 Identify lands proximal to existing commercial uses 0.16 5.2.6 Identify lands proximal to transportation amenities 0.12

5.3 Identify lands with suitable service densities 0.25

5.4 Identify lands with potential for change to service uses 0.10


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Goal 6 Table B-6: Institutional


6.1 Determine lands physically suitable for institutional uses 0.20

6.1.1 Identify lands with soils suitable for institutional uses 0.35 6.1.2 Identify lands distant to air polluting land uses 0.15 6.1.3 Identify lands not in flood prone areas 0.50

6.2 Determine lands proximally suitable for retail uses 0.50 6.2.1 Identify lands based upon suitable land values 0.15 6.2.2 Identify lands proximal to existing institutional uses 0.50 6.2.3 Identify lands proximal to transportation amenities 0.35

6.3 Identify lands with suitable institutional densities 0.20

6.4 Identify lands with potential for change to institutional uses 0.10

Community Weights for Combining Urban Goals

Table B-7: Community Weights for Combining Urban Goals Agriculture

Goal Community

Weight Goal 1a – Multi-Family Residential 0.106

Goal 1b – Single Family Residential 0.064

Goal 2 – Commercial 0.157

Goal 3 – Retail 0.127

Goal 4 – Industrial 0.352

Goal 5 - Service 0.111

Goal 6 - Institutional 0.083


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Urban Models - Data Florida property appraiser parcel shapefiles – Desoto, Glades, Hardee, Hendry,

Highlands, Okeechobee and Polk counties:

- Land uses - Tax code boundaries – city limits - Property just values - Year Built - Acreage

Water treatment facilities in Florida shapefile – Florida Department of Environmental Protection

Sewer treatment facilities in Florida shapefile - Florida Department of Environmental Protection

Solid waste facilities in Florida shapefile - Florida Department of Environmental Protection

Habitat and Landcover shapefile- Florida Fish and Wildlife Conservation Commission

Electricity Generating Power Plants in Florida shapefile – U.S. Environmental Protection Agency

Transportation Network

- Major Roads shapefile – Florida Department of Transportation - Major Highways shapefile – Florida Department of Transportation - Interstates shapefile – Florida Department of Transportation - Active Rail shapefile – U.S. Department of Transportation - Airports shapefile – U.S. Department of Transportation

SSURGO soils shapefile and associated “comp.DRAINAGE” table – U.S. Department of Agriculture

SSURGO soils shapefile and associated “comp.WTDEPH” table – U.S. Department of Agriculture

SSURGO soils shapefile and associated “comp.CORCON” table – U.S. Department of Agriculture

Flood Insurance Rate Maps shapefiles – Federal Emergency Management Agency

Documents

Land Use Conflict Identification Strategy (LUCIS ...heartland2060.org/download/scenario_modeling/lucis_technical... · LUCIS Technical Report 1 Land Use Conflict Identification Strategy