SOCIOECONOMIC & DEMOGRAPHIC DATA UPDATE

Transcription

1 SOCIOECONOMIC & DEMOGRAPHIC DATA UPDATE

2 Lead Agency: Metropolitan Planning Organization Transportation Policy Committee: Cory Glassburn, Mayor Dennis Wilde, NORTEX Regional Planning Commission Jeff Watts, Wichita County Commissioner Larry Tegtmeyer, P.E., TxDOT - District Engineer Michael Smith, City Council Representative Consultant Team: Russell Schreiber, P.E., Director of Public Works Stephen Santellana, Chairperson, Mayor Jesse Brown, City Council Representative MPO Technical Advisory Committee: Allan Moore, P.E., TxDOT - Director of Construction David Rohmer, P.E., TxDOT - Area Engineer Davis Powell, P.E., City of - City Engineer Jim Dockery, City of - Assistant City Manager and Director of Community Development Karen M. Gagne, City of - Community Development Larry Wilkinson, City of - Traffic Superintendant Lin Barnett, MPO Director and TAC Chairperson Glenn Allbritton, P.E., TxDOT - Director of Operations

3 TABLE OF CONTENTS 1. INTRODUCTION...1 STUDY AREA...2 TAZ BOUNDARY CHANGES BASE YEAR DEMOGRAPHICS...3 APPROACH AND DATA ANALYSIS...3 POPULATION AND HOUSEHOLDS GROUP QUARTER POPULATION MEDIAN INCOME EMPLOYMENT SPECIAL GENERATORS SUMMARY FORECAST YEAR DEMOGRAPHICS...12 APPROACH AND DATA ANALYSIS...12 CONTROL TOTAL DEVELOPMENT CARRYING CAPACITY ANALYSIS...13 LAND SUITABILITY ANALYSIS...2 LAND SUITABILITY SUMMARY FORECAST DEMOGRAPHIC ALLOCATION...27 POPULATION AND HOUSEHOLDS GROUP QUARTER POPULATION MEDIAN INCOME EMPLOYMENT SPECIAL GENERATORS SUMMARY MODEL NETWORK UPDATE...33 APPENDIX A...39

4 1. INTRODUCTION The Metropolitan Planning Organization (WFMPO) is coordinating with the Texas Department of Transportation (TxDOT) in updating the Travel Demand Model for the Wichita Falls region. This report summarizes the efforts to update the socioeconomic base year data to 215 and a process of allocating the socioeconomic data to the forecasted years: 22, 225, 23, 245, and 25. Burkburnett Cashion Transportation decisions in the Metropolitan Planning Organization area are determined through an evaluation of a number of different factors. In Texas, and across the nation, MPOs use travel demand modeling as a tool to assist in the transportation decision-making process. The travel demand model uses current mobility data to forecast the potential transportation impacts in the future. The split between different travel modes, number of lanes on existing roadways, and the location of future mobility connections are just a few of the outcomes of the travel demand model. Socioeconomic data is one of the primary inputs in the travel demand model. Iowa Park Dean Clay County The demographic data that is used for the base year and forecast year travel demand model assists in determining how many trips are generated, where the trips are destined, and where trips are produced. The following documentation summarizes the efforts to develop the base year socioeconomic data and the methodology in determining the forecast year demographics. Holliday Wichita County Archer County Counties Cities Figure 1: Metropolitan Planning Organization Modeling Area MPO and Model Boundary 1

5 229 STUDY AREA The WFMPO travel demand model covers an area that includes the a portion of Wichita County and a portion of Archer County. The MPO modeling area and WFMPO boundary is shown in Figure 1. Within the WFMPO Travel Demand Model, traffic analysis zones (TAZ) are the geography units that are used to develop socioeconomic data. TAZs are typically separated at key transportation, political, and/or environmental boundaries or features. The WFMPO MPO travel demand model consists of 322 unique TAZs and 2 external zones. TAZ BOUNDARY CHANGES Since the last travel demand model update for the base year of 25, 2 additional TAZs have been created in the region. Of those 2 TAZs, three of the TAZs (318, 319, and 322) included the expanded MPO Planning Area in Archer County between US 281 on the east, Wells Road on the west, and FM 1954 on the South as shown by the darkest shaded areas in Figure 2. TAZ 161 was expanded to include new areas along SH 79 south to FM 1954 In addition to the new geography included in the travel demand model in Archer County, a number of TAZs were split along key roadways to better represent the current transportation conditions in the region. A few TAZs had minimal boundary adjustments to account for changed roadway alignments. Each of these changes are identified in Figure 2 with color coding to represent the type of change that has occured. The detailed description of each TAZ boundary change is identified in the Appendix under Table A Traffic Analysis Zone Changes TAZ Changes Split TAZ TAZ Boundary Adjustment Figure 2: MPO TAZ Changes New TAZ Geography 2

6 2. BASE YEAR DEMOGRAPHICS APPROACH AND DATA ANALYSIS Several sources were used to develop the base year demographics for the MPO (WFMPO) modeling area. These include 21 census (block, block group, and tract levels), and American Community Survey (ACS), 215 InfoGroup business data, 215 County Business Patterns (CBP), Texas Comptroller of Public Accounts, 25 zonal socioeconomic and demographic data, and Texas Demographic Center (TDC) control totals document. The MPO has also given invaluable input in identifying special generators and noticeable changes in population and employment. Figure 3 shows the WFMPO modeling area, which includes a portion of Wichita County and a portion of Archer County. The modeling area is comprised of 322 internal traffic analysis zones (TAZs) and 2 external zones. The following sections outline the procedures that were followed to update the travel demand model for the 215 base year demographic data in the WFMPO modeling area. Figure 3: MPO Modeling Area TAZ Boundaries TAZ BOUNDARIES 3

7 POPULATION The 21 census block population and housing data was used to obtain 21 zonal population and housing data. As evident from the small difference between the 21 and 215 population control totals in the TDC document (19,798 versus 11,142), there is little growth in the WFMPO modeling area. Consequently, the 215 zonal population data is based on the 21 zonal population distribution scaled to match 215 control totals. The resulting 215 population using the ACS control totals is 11,15. Figure 4 shows the 215 population distribution in the WFMPO modeling area. Figure 4: WFMPO Modeling Area 215 Population 215 POPULATION Population < 25 1,251-2, > 2,5 4

8 5 HOUSEHOLDS The 21 census block data provides information on 21 total housing, but not on tenure. Hence, the ACS block group household data was used to determine occupancy rate for blocks. It was assumed that all blocks within a block group have the same occupancy rate as that block group. The zone would then have an average occupancy rate based on the blocks it contains. This transformed 21 housing to 21 households. Similar to population growth, it does not seem that there is much growth in households in the WFMPO modeling area between 21 and 215. Therefore, the 215 zonal household data is based on the 21 zonal household distribution scaled to match 215 control totals. The resulting 215 households using the ACS control totals is 4,51. Figure 5 shows the 215 household distribution in the WFMPO modeling area. WFMPO also provided lists of housing permits issued for every year between 21 and 215. Those have been geocoded and mapped in Figure 6. The bulk of the housing growth in the last 5 years has occurred south of US 277 in the southern portion of the MPO Planning Area. Figure 5: WFMPO Modeling Area 215 Households Figure 6: Housing Permits for Years HOUSEHOLDS Households < > HOUSING PERMITS

9 GROUP QUARTER POPULATION The 21 census tract data provides information on institutionalized and non-institutionalized group quarter population in the WFMPO modeling area. This, coupled with satellite imagery, yields zonal group quarter population information in 21. The 21 group quarter population was then scaled to match the 215 ACS control totals. The 21 total group quarter population was 11,871. Major contributors to that population are, Department of Criminal Justice, and Midwestern State University. It is generally not expected that population in group quarters decrease over the years, especially not from 11,871 in 21 to 9,91 in 215 as recommended by the TDC. Consequently, and because the TDC control totals were too low, potentially inflating zonal average household size, the ACS control totals were adopted. For consistency purposes, ACS control totals were adopted for population and household totals as well. The resulting 215 group quarter population based on the ACS control totals is 12,61. Figure 7 shows the group quarter population distribution in the WFMPO modeling area. 215 GROUP QUARTER POPULATION Population 51-1 < > 35 Figure 7: WFMPO Modeling Area 215 Group Quarter Population 6

10 HOUSEHOLD SIZE Household size was calculated by subtracting group quarter population from total population, then dividing by the total number of households. The resulting average household size is 2.46, which is slightly higher than 2.44 in the ACS control totals. It is important to note that the household size in the geographic file reflects the simple division of zonal population by number of households. Larger household sizes can be indicative of the presence of group quarters. MEDIAN HOUSEHOLD INCOME The ACS block group income data was used to assign median income to zones contained within a block group. Zones which intersect multiple block groups are assigned an average median income. Median income is reported in 215 dollars. The resulting average median household income is $49,152, which is slightly higher than $43,655 in the ACS control totals. Figure 8 shows the distribution of the 215 median household income in the WFMPO modeling area, with higher incomes concentrated to the west and south of the modeling area. 215 MEDIAN INCOME (215 Dollars) Households < $2, $2,1 - $4, Figure 8: WFMPO Modeling Area 215 Median Household Income (215 dollars) $4,1 - $6, $6,1 - $8, > $8,1 7

11 EMPLOYMENT The InfoGroup business data was received from TxDOT through the WFMPO. The employment dataset was processed by removing duplicate employers and noting those that appear in satellite imagery but are absent in the dataset. Table 1 shows how the resulting employment is distributed. Table 1: InfoGroup Employment by Type in WFMPO Modeling Area for Year 215 Employment Type # of Employees Basic 1,152 Retail 14,953 Service 3,839 Education 3,135 Total 59,79 The total employment in Table 1 is greater than what is recommended in the TDC control totals document 48,236 (based on TWC data). Moreover, the 215 CBP data estimates civilian employment in Wichita County at 55,53 and in Archer County at 4,239. Applying the same employment splits as in the TDC document yields an estimate of 5,387 civilian employees in the modeling area. Scaling the employment total to match the CBP estimate, keeping employment in in tact, results in the totals in Table 2. Table 2: Scaled InfoGroup Employment by Type in WFMPO Modeling Area for Year 215 Employment Type # of Employees Basic 9,485 Retail 13,51 Service 28,953 Education 2,431 Total 54,37 EMPLOYMENT TO POPULATION RATIO It is generally recommended that employment to population ratio be between.3 and.5. Consequently, it is recommended that the civilian employment be scaled to the CBP estimate. It is also possible to keep employment exactly equal to that in the 25 dataset, as it is more in line with the Texas Comptroller of Public Accounts data. However, that would inflate the employment to population ratio to 55%. Moreover, according to the MPO, around 2,5 jobs were transferred from to Lackland Air Force Base in San Antonio in 26. Consequently, it is expected that the 215 Sheppard Air Force Base employment be less than that of 25. Figure 9 through 13 show the scaled 215 employment distribution by type in the WFMPO modeling area. 8

12 Figure 9: WFMPO Modeling Area 215 Total Employment 215 TOTAL EMPLOYMENT Employment < , > 1,25 Figure 1: WFMPO Modeling Area 215 Basic Employment 215 BASIC EMPLOYMENT Employment 11-2 < > 5 9

13 Figure 11: WFMPO Modeling Area 215 Retail Employment 215 RETAIL EMPLOYMENT Employment 11-2 < > 5 Figure 12: WFMPO Modeling Area 215 Service Employment 215 SERVICE EMPLOYMENT Employment < > 1 1

14 POTENTIAL SPECIAL GENERATORS The employers listed in Table 3 are potential special generators based on the number of workers they employ (unscaled). Table 3: Potential Special Generators in WFMPO Modeling Area Employment Name Employer Description # of Employees TAZ Air Force Base 3, 299 United Regional Hospital Hospital 2, 71 rth Texas State Hospital Hospital 1, James Allred Unit Prison Kell West Regional Hospital Hospital Sike Center Shopping Mall 1,2 152 Alcoa Howmet Manufacturer Midwestern State University University SUMMARY The first section of this report described the process involved in developing the 215 base year demographics for the MPO modeling area. The base year data provides the information needed to create the forecast demographic database. This data is used as input into the MPO travel demand model. The following section describes the process of developing the forecast year demographic dataset. 215 EDUCATION EMPLOYMENT Employment 51-1 < > 15 Figure 13: WFMPO Modeling Area 215 Education Employment 11

15 3. FORECAST YEAR DEMOGRAPHICS APPROACH AND DATA ANALYSIS The development of forecast year demographics involves a much different process than the development of the base year data. While the base year data revolves around information collected through surveys, geographic data, aerial photography, and the U.S. Census; forecast data is determined based on anticipated growth trends and planning efforts. The forecast year demographics for the MPO were developed using a sophisticated model that anticipates the future growth potential based on socio-geographic factors together with the amount of available area for development or redevelopment. This section summarizes the methodology to develop the forecast year demographics for the MPO Travel Demand Model. CONTROL TOTAL DEVELOPMENT The Texas Demographic Center (TDC) estimates and forecasts future population growth for every county in the State of Texas. The TDC uses different factors including fertility rates, mortality rates, and migration rates to determine multiple demographic scenarios for each county. For the region, however, there are many different complexities to estimate the control totals to be used in the travel demand model. With the help of the TDC, population and employment projections were developed for the modeling area of the region. Using the data provided by the TDC, control totals were developed for the MPO model area for the forecast years of 22, 225, 23, 245 and 25. The population and employment control totals are summarized in Table 4. Table 5 provides the employment control totals by percentage of each category. Table 4: Forecast Years Control Totals Population: 11,15 113, , ,89 124,31 126,538 Employment: 54,372 56,7 57,729 59,512 65,142 67,125 Table 5: Percent of Employment by Category Basic Retail Service Education MPO Area 18% 25% 52% 5% 12

16 CARRYING CAPACITY ANALYSIS A carrying capacity analysis of the region was developed to estimate the area of land available for future development or redevelopment and the capacity of that land based on a number of assumptions or characteristics. DEVELOPABLE AREAS The areas in the region with the potential for development were initially determined by aggregating the parcel data from the Wichita County and Archer County appraisal districts into one shapefile. Parcels that contained an existing structure on it and smaller than five acres within any city limit were determined to be developed, while any parcel without an existing structure were considered undeveloped. In addition to determining the developed and undeveloped parcels in the region, areas of constraint were removed from the carrying capacity analysis. Factors that were considered as constraints were water features and floodways. Figure 14 identifies the development status of the areas considered in the carrying capacity analysis. Development Status AREAS OF CONSTRAINT DEVELOPED UNDEVELOPED Figure 14: Developed, Undeveloped, and Areas of Constraint 13

17 REGIONAL PLACETYPES Placetypes were developed for the MPO area to represent current and future development patterns. Placetypes represent more than just land use and zoning and consider other implications such as building types, household size, or number of employees. Each placetype developed for this study considers unique characteristics such as residential density, floor-area ratio, and percent residential versus non-residential. These characteristics or assumptions assist in the development of the potential carrying capacity of each developable parcel or area. Each placetype category and their related development assumptions are summarized. 1% Residential 1% Residential 1% Residential 1% Townhomes 1% Single-Family 1% Single-Family 9% Single-Family Agricultural Rural Residential Suburban Neighborhood Agricultural areas are categorized by substantial farming or ranching and are primarily located in rural areas of the MPO area. Although work is generated through farming and ranching in this placetype, only residential numbers are represented in this placetype. The residential density in this placetype is very low, on average one unit per 1 acres. Rural residential areas are characterized by very large lots, abundant open space and a high degree of separation between buildings. City water and sewer services can be provided, however, in most cases, homes are served through localized water and septic systems. Residential home sites are located randomly throughout the countryside, which helps to maintain the rural character, scale, and scenic views. Suburban neighborhoods are the most common residential placetype in the region. Most new residential developments are built in a suburban form. Suburban neighborhoods have access to municipal water and sewer services and are in close proximity to schools and commercial services. Gross residential density in these neighborhoods are on average 5 units per acre. 14

18 15% n-residential 85% Residential 15% Mult-Family 15% 3% Residential n-residential 85% n-residential 3% n-residential 1% n-residential 25% Townhomes 6% Single-Family 1% Multi-Family 25% Office (Service) 75% Retail 5% Industrial 25% (Basic) Office (Service) 7% Retail 1% Office (Service) 9% Industrial (Basic) Urban Neighborhood Urban neighborhoods support a mix of low to medium density housing options. These neighborhoods are relatively compact and walkable. The design and scale of the development in an urban neighborhood encourages active living with a complete and comprehensive network of walkable streets. Commercial District A commercial district is characterized by retail stores or multi-tenant commercial centers located in a centralized area. Commercial districts are primarily accessible by automobile. Buildings are typically set back from the road to provide large surface parking lots at the storefront, with little or no connectivity between adjacent businesses. Industrial Park Industrial parks provide basic employment and keep people in the city during normal work hours. They typically locate near major transportation corridors and may include manufacturing centers, transportation hubs, or technology centers. 15

19 3% n-residential 1% n-residential 3% 5% n-residential 5% n- Residential Residential 4% n- Residential 6% Residential 25% Townhomes 1% Townhomes 75% Multi-Family 9% Single-Family 6% Industrial (Basic) 4% Office (Service) 6% Office (Service) 4% Retail 3% Office (Service) 7% Retail Business Campus Urban Core Neighborhood Commercial Areas with business campuses provide a high concentration of service employment in the region. Densities in this placetype can vary, however 1 employee for every 3 5 square feet of building floor area is typical. Common uses include professional offices, corporate campus, research and development and technology centers. Urban core is the hub of employment, shopping, entertainment, civic, and cultural activities, with a mix of housing types and common open space for active living. The compact, walkable environment and a mix of residential and nonresidential uses in an urban core support multiple modes of transportation. Neighborhood commercial placetypes are a mix of low density residential in a suburban form with focused employment at primary intersection roadway intersections. Employment characteristics can include both neighborhood retail and neighborhood services. 16

20 4% nresidential 3% n-residential 1% n-residential 6% Residential 5% Multi-Family 5% Townhomes 2% Office (Service) 1% Retail 5% Industrial (Basic) 8% Office (Service) 8% Retail Suburban Regional Activity Center - Suburban regional activity centers are a low density mix of land uses in a focused area. The development of these areas can be anchored by a shopping mall, movie theatre, or other activity. These regional centers include a variety of housing types, as well as having a retail component that serves the region. Institutional - Institutional place types are primarily made up of areas that serve the community s social, educational, health, and cultural needs. Schools, colleges, medical campuses, and government facilities make up a large portion of the current institutional areas in the region. 17

21 GEOSPATIAL ASSIGNMENT OF PLACETYPES WITHIN THE REGION Determination of the total carrying capacity of the MPO modeling area required assignment of specific placetypes to the entire study area, whether a parcel had development potential or not. The entire MPO modeling area was overlayed with a 2 acre square grid to use as the base geographic unit for the analysis. The 2 acre grid throughout the region was then split along county lines and along TAZs. Splitting the grid along these boundaries provided a simple transition to TAZ boundaries at the conclusion of the forecast allocation. Within each municipality, placetypes were assigned to the 2 acre grid based on available future land use and zoning plans. This ensured that the placteypes assigned to each grid cell matched the planning efforts currently in place by cities within the MPO modeling area. Outside of the municipalities and their respective extra-territorial jurisdiction (ETJ), placetypes were assigned based on existing land use conditions and aerial imagery. Figure 15 demonstrates the assignment of placetypes across the MPO modeling area. CARRYING CAPACITY SUMMARY After the developable land is assessed and the placetypes are assigned, the population and employment carrying capacity can be determined in the region. This capacity gives the projected build out based on the assumptions within each placetype and the distribution of placetypes throughout the region. The potential developable land and the placetype distribution are the primary factors to determine the future amount of population and employment that can be accommodated in the region. Table 6 summarizes that the potential new capacity in the MPO planning area is 142,8 people and 54,7 employment. capacity in build out for the region as summarized within the MPO modeling area. In future updates, the potential build out or carrying capacity can be adjusted by redistributing the placetypes or by changing the assumptions within each placetype. Although the carrying capacity seen in Table 6 indicates a high number of potential population and employment, the actual growth projections are not specified between the base year and the forecast year(s). Table 6: Carrying Capacity or Build Out Summary 215 Demographics 25 Control Total Total Build Out Capacity Households 4,51 45,87 96,589 5,719 Population 11,15 126, ,95 126,412 Employment 54,37 67,125 16,22 39,77 Basic Employment 9,32 12,153 32,43 2,277 Retail Employment 13,684 16,54 3,224 13,684 Service Employment 28,516 36,88 43,548 7,46 Education Employment 2,431 2,477 N/A N/A Difference (Capacity - 25 Control Total) 18

22 Placetypes Agriculture Business Campus Commercial District Institutional Industrial Park Neighborhood Commercial Rural Residential Suburban Neighborhood Suburban Regional Activity Center Urban Core Urban Neighborhood Figure 15: Assigned Placetypes in the Region 19

23 LAND SUITABILITY ANALYSIS Land suitability uses spatial analysis to score the desirability of geographic units within the region. The suitability scoring for the MPO model used the same revised 2 acre grid as described in the previous section. The desirability of each unit was used to help determine the areas of the region that are more or less suitable for new development. Two suitability analyses were conducted for this study: one for residential demographic allocation and one for non-residential demographic allocation. Each geographic unit within the MPO modeling boundary was given a score between and 1 based on a number of different weighted factors to demonstrate relative suitability. A number of unique factors were used to assist in the development of the suitability scoring. These factors were developed from geographic data and input from MPO staff and Technical Advisory Committee members. In August 217, 13 potential suitability factors were discussed with staff to determine their importance in the region as it is related to property desirability. Feedback received during this meeting resulted in the removal of some of the proposed factors and the inclusion of others. In addition, the participants were asked to weigh the suitability factors between a score of and 1 to determine how important that factor was in the region. As a result, 15 suitability factors were used and weighted in the analysis to score land desirability in the region. The following describes each suitability factor in detail and their respective weighting. Proximity to Highways Limited access highways are some of the most important roadway facilities in the nation and in the region. They help connect commerce across the State and they provide access to employment and for customers to businesses. Distance from Interstate 44, U.S. Highway 287, U.S. Highway 277, and U.S. 281 were used as a suitability factor n-residential Weight (+) Proximity to Major Arterials Major arterials provide the access from homes to businesses throughout the region. Businesses enjoy being located on major arterials because of the visibility of pass by traffic and also to make access easier for customers and employees. Properties closer to major arterials were provided a higher suitability factor Residential Weight (-) n-residential Weight (+) Legend Highways Major Arterials Congested Arterials Proximity to Congested Arterials Congestion is often perceived negatively among transportation users, however businesses enjoy being located on busy, high volume corridors. Congested corridors in the region were identified and properties located close to these congested corridors given a higher suitability score. Properties closer to congested arterials were provided a higher suitability factor Residential Weight (-) n-residential Weight (+) 2

24 Legend 1/2 Mile School Buffer Legend Water Lines Sewer Lines Proximity to Schools (Elementary, Middle, and High Schools) Proximity to existing schools is one of the most important factors influencing where people choose where to live. Areas within a half mile radius of elementary schools, middle schools, and high schools were scored due to this proximity factor. Residential suitability was scored higher based on these factors, however non-residential was scored lower based on proximity to schools. Proximity to Water Service Without access to water, development can be limited for both residential and non-residential uses. There are many areas that don t have water service, and thus rely on well-water for providing clean drinking water. This suitability factor scores the areas high that are in close proximity to existing water lines Residential Weight (+) Residential Weight (+) n-residential Weight (-) n-residential Weight (-) Proximity to Sewer Service Similar to water service, access to sewer service can provide ease in development and lower costs for development implementation. This suitability factor scores the areas high that are in close proximity to existing sewer service Residential Weight (+) n-residential Weight (-) 21

25 Legend 1/4 Mile Transit Buffer Legend 1/4 Mile Parks and Trails Buffer Transit Transit service in the region is important to help people travel from their homes to school and employment. This is especially important for those working on the and attending Midwestern State University. Locating homes and businesses close to transit provides additional travel options. This suitability factor gives areas closer to transit service higher scores Residential Weight (+) n-residential Weight (+) Trails and Parks Trails and parks provide excellent recreational opportunities and amenities that benefit both residential and non-residential uses. Businesses and homebuyers continue to locate in close proximity to these amenities. The Circle Trail System in provides a well connected network of pedestrian and bicycle paths in the region. This suitability factor gives areas a higher score that are within 1/4 mile of an existing or future trail or existing park Residential Weight (+) n-residential Weight (-) 22

26 Legend Freight Rail Airports Legend Boundary Boundary Boundary Airport Airports can be a positive or negative in a suitability analysis. For residential development, airports can be loud throughout the day. However some businesses rely on airports for daily commerce and travel. For this suitability factor, residential suitability is negatively affected by its proximity to the airports and non-residential suitability is positively affected to its proximity to the airports Residential Weight (-) Rail Freight rail service in the region is an important logistics element for businesses. Proximity to rail can attract the location of businesses. Residential development typically is located further away from freight rail because of safety and noise and therefore residential suitabiliy has a negative relationship Residential Weight (-) Within Municipal Boundaries Areas within existing municipal boundaries provide a benefit of proximity and increases services to that new development. This suitability factor scores areas within the region higher if they are within the municipal boundary Residential Weight (+) n-residential Weight (+) n-residential Weight (+) n-residential Weight (+) 23

27 Legend 1 Year Flood Legend Employment Areas Water Features Within Floodway Floodways are areas of beauty; however, they are prone to hazards such as flooding. Development in these areas are typically restricted and incur higher insurance costs. This suitability factors scores areas lower that are within floodways in the region. Proximity to Water Features Although it is difficult to develop in a floodway, location in close proximity to lakes and other water features are desired in the region. Residential and non-residential were given higher suitability scores in closer proximity to water features. Employment Areas Employment areas include both current and future areas with a concentration of employment based on the future land use plans in the region. Areas with current or future employment tend to attract more jobs as employment is needed in the future. Downtown areas are a perfect example of this type of growth Residential Weight (-) Residential Weight (+) Residential Weight (+) n-residential Weight (-) n-residential Weight (+) n-residential Weight (+) 24

28 TIF Districts Tax Increment Financing (TIF) districts are locations in the region that receive special funding from property tax increases to help spur growth in strategic locations. These areas can be considered as focus areas that result from comprehensive planning efforts. Areas that are within each TIF district score higher for this suitability factor Residential Weight (+) n-residential Weight (+) Legend TIF Districts LAND SUITABILITY SUMMARY The land suitability analysis combines the results of all of these different factors and considers the weighting impact as was developed through staff input to determine the land suitabilty score. A suitability score was developed for residential growth and a separate score was developed for non-residential growth. The weighting of each factor differed for each suitability analysis. Additional suitability factors were included in the analysis to determine proximity to water features and overlap with existing TIF districts. Figures 16 and 17 display the resulting suitability score in the region for residential and non-residential suitability. 25

29 Residential Suitability n-residential Suitability Figure 16: Residential Land Suitability Map Figure 17: n-residential Land Suitability Map 26

30 FORECAST DEMOGRAPHIC ALLOCATION The development of the carrying capacity and the land suitability scoring provided the opportunity to begin the final task of allocating the forecast population and households to the TAZ level within the region. This allocation was an iterative process that used the CommunityVIZ model to assign the forecast demographics by each control total for population and employment. In addition, the relative suitability scoring placed the population and employment increment from the base year in the most desirable areas of the region first. The model forecast years of 22, 225, 23, 245, and 25 were all allocated to the TAZs based on their desirability score and also their carrying capacity. This process allowed for each successive forecast year to add to the previous forecast year growth, while subtracting the potential capacity from each geographic unit. This process ensured that no TAZ had more potential growth than the actual capacity based on the future land use plans of the cities within the MPO area. The following section summarizes the allocation, however only the 245 results are provided in the figures. For maps of the additional forecast years, please refer to Appendix A. POPULATION AND HOUSEHOLDS Using the data generated from the suitability analysis, the population forecast were distributed throughout the region. The largest increase in population was allocated in the southwest portions of the region. The allocation in the southwest portion of the region was a result of high desirability of the area and the development patterns that are currently occuring. Modest growth was projected in other areas of the region and high population concentrations were maintained in and in close proximity to and at the prison located in the northwest portion of the region. The number of households were calculated based on the average household size developed for the 215 base year demographics. In some instances, the 215 data set did not include household size information for TAZs that did not have population. However as the growth occurred in the forecast years in those same TAZs without existing population, a household size is required. Thus, the household size for those TAZs without population in 215 were determined based on the household size of adjacent TAZs and U.S. Census data in proximity to those TAZs. MEDIAN INCOME The median income attributes for each forecast year are based on constant dollars using 215 as the base year. For the region, there is an anticipation that median income will not encounter any significant changes. Thus, the forecast year median income values remained consistent with the base year data. 27

31 Figure 18: 245 Total Population by TAZ 245 POPULATION Population < 25 1,251-2, > 2,5 Figure 19: 245 Total Households by TAZ 245 HOUSEHOLDS Households < > 75 28

32 EMPLOYMENT Similar to the allocation of population to the forecast years, employment was also allocated based on the suitability results. However a separate suitability analysis was completed for employment to differentiate the desirability from residential uses, understanding that employment attraction differs from residential desirability. The employment allocation was divided into the three main employment types: basic, retail, and service. Based on the carrying capacity analysis, each TAZ had an available capacity based on the future land use for each employment type. Figures 2-23 shows the distribution of employment for 245. Figure 2: 245 Total Employment by TAZ 245 TOTAL EMPLOYMENT Employment < , > 1,25 Figure 21: 245 Basic Employment by TAZ 245 BASIC EMPLOYMENT Employment 11-2 < > 5 29

33 Figure 22: 245 Retail Employment by TAZ 245 RETAIL EMPLOYMENT Employment 11-2 < > 5 Figure 23: 245 Service Employment by TAZ 245 SERVICE EMPLOYMENT Employment < > 1 3

34 SPECIAL GENERATORS Special generators are those population and employment demographics that are not considered typical population and households or typical forms of basic, retail, and service employment. They are characterized as having unique rates of trip generation. The special generator population for the base year of 215 was determined based on data gathered from the U.S. Census and the ACS. Some of the group quarters population was included as special generators, however not all of the group quarters populations identified in the U.S. Census was considered special generators. Due to the complexity of predicting future special generator growth in each of the forecast years, an annual growth rate of.5% was used to allocate population. See Table 7 for the forecast special generator population by TAZ. Table 7: Special Generator Population Forecast TAZ Special Generator ,53 5,67 5,813 5,96 6,423 6, rth Texas State Hospital James Allred Unit 3,68 3,773 3,868 3,966 4,274 4, Midwestern State University Special generator employment for the base year of 215 was determined based on the employment data provided by the InfoUSA. The employers identified as special generators demonstrated unique characteristics that are not typical of travel patterns such as hospitals, universities, prisons, and large employment centers. For the forecast year special generator allocation, an annual growth rate was applied to the existing special generators of.5% for basic employment,.5% for retail employment, and.5% for service employment. Table 8 provides the special generator employment summary. Table 8: Special Generator Employers Forecast TAZ Special Generator , 3,76 3,153 3,233 3,484 3, United Regional Hospital 2, 2,51 2,12 2,155 2,323 2, rth Texas State Hospital 1,775 1,82 1,866 1,913 2,61 2, James Allred Unit ,68 1, Kell West Regional Hospital Sikes Center 1,2 1,27 1,53 1,8 1,164 1, Alcoa Howmet ,66 1,93 14 Midwestern State University

35 EMPLOYMENT TO POPULATION RATIO The employment population ratio for the MPO planning area is approximately 5% for the region. The employment population ratio was consistently around 5% throughout all of the forecast years. Table 9 summarizes the population and employment totals for the MPO area. Table 9: 215 MPO Modeling Area Statistics Population 113,48 115,24 117,28 122, Employment 55,786 57,242 58,74 63,495 65,172 Employment/ Population Ratio 49.19% 49.69% 5.19% 51.76% 52.3% SUMMARY The forecast year demographicsection of this report described the process involved in developing the data for the forecast years of 22, 225, 23, 245, and 25 for the MPO modeling area. Additional figures for the forecast years are found in Appendix A. The forecast year data provides the information needed to understand the traffic implications as growth occurs in the future. This data is used as input into the MPO travel demand model. The following section describes the process used in updating the travel demand model network for the base year and each forecast years. 32

36 4. MODEL NETWORK UPDATE For the base year of 215, the model network was updated to reflect the changes that have occured since the last model update. The model network determines the supply or capacity of the transportation system in the region. This capacity is determined through the number of lanes on each roadway, the area type that the roadway is within, and the functional classification of the roadway. The factor in the model network that affects travel time is the length of the roadway and the speed. Figure 24 identifies all of the roadways considered part of the model network for 215. Each has been coded and updated with the appropriate functional class, area type, number of lanes, and speed. In addition to the update of the 215 base year model network, the forecast years of 22, 225, 23, 245, and 25 were updated to include all of the capacity projects that have been included in the previous MPO planning studies such as the Transportation Improvement Program (TIP) and the Metropolitan Transportation Plan (MTP). For each of the forecast years, the model network is displayed with the recommended network changes highlighted red to indicate the projects that have been included in that forecast year network. The project descriptions for each year are included below each map consisting of Tables 1 through 14. Wichita County Archer County Legend Interstate/Freeway Principal Arterials Minor Arterials Collectors Figure 24: 215 Base Year Network Functional Classification Map 33

37 Table 1: 22 Model Network Project Travel Demand Model Year 22 Maplewood Ave Project Location Description Lawrence Rd to McNiel Ave Design and Construct 4 Lane Roadway Wichita County Archer County Legend Figure 25: 22 Model Network Changes Base Year Network 22 Network Changes Cities Model Boundary 34

38 Table 11: 225 Model Network Projects Travel Demand Model Year 225 FM 89 IH 44 to SP BU 287J FM 1814 to SL US 82 Parker Ranch Rd to Clyde Morgan Rd Project Location Description Widen Roadway & Overlay Widen Roadway Provide Turn Lanes at Intersections Wichita County Archer County Figure 26: 225 Model Network Changes Legend Base Year Network 225 Network Changes Cities Model Boundary 35

39 Table 12: 23 Model Network Project Travel Project Location Description Demand Model Year 23 US 82.7 Mi West Widen Roadway of FM 369 to Archer County Line 23 FM 369 US 287 to BU 287J Widen Roadway Wichita County Archer County Figure 27: 23 Model Network Changes Legend Base Year Network 23 Network Changes Cities Model Boundary 36

40 Travel Demand Model Year 245 Midwestern Pkwy Table 13: 245 Model Network Project Project Location Description Brewster St to Old Windthorst Rd Extend and Construct 2 Lane Roadway Wichita County Archer County Figure 28: 245 Model Network Changes Legend Base Year Network 245 Network Changes Cities Model Boundary 37

41 Travel Demand Model Year 22 Maplewood Ave Table 14: Model Network Summary Project Location Description Lawrence Rd to McNiel Ave 225 FM 89 IH 44 to SP BU 287J FM 1814 to SL US 82 Parker Ranch Rd to Clyde Morgan Rd 23 US 82.7 Mi West of FM 369 to Archer County Line 23 FM 369 US 287 to BU 287J 245 Midwestern Pkwy Brewster St to Old Windthorst Rd Design and Construct 4 Lane Roadway Widen Roadway & Overlay Widen Roadway Provide Turn Lanes at Intersections Widen Roadway Widen Roadway Extend and Construct 2 Lane Roadway Wichita County Archer County Figure 29: 25 Model Network Changes Legend Base Year Network Improvements Cities Model Boundary 38

42 APPENDIX A Table A1: TAZ Boundary Changes TAZ Boundary Change 84 Split along future Midwestern Pkwy Extension - TAZ 84 on rth; TAZ 32 on South 98 Split along McKinley Rd - TAZ 98 on rth; TAZ 314 on South 124 Boundary changed from Arthur to Santa Fe 125 Boundary changed from Arthur to Santa Fe 13 Split along Ave O - TAZ 13 on rth; TAZ 37 on South 134 Split into 3 TAZs - TAZ 35 South of Maplewood, 36 split on Grant Rd and Hamptead Ln 156 Boundary reduced to not include Lake Wichita 158 Boundary changed to align with Taft Blvd 16 Boundary changed to align with Taft Blvd and Stone Lake Dr 161 Expanded to include new MPO area; also added portion of TAZ 163 in Archer County 162 Removed portion of TAZ 162 in Archer County and added it to TAZ Boundary changed to align with Stone Lake Dr and Rathgeber Rd 187 Realigned with Cottonwood Rd 188 Realigned with Cottonwood Rd 26 Split along Loop 11 - TAZ 26 on East; TAZ 312 on West 22 Split into 3 TAZs - TAZ 22 rth of Borton St; 315 South of Borton St; 316 East of Kenley Ave 223 Split along Valley View Rd - TAZ 223 on East; TAZ 311 on West 229 Boundary move from Shallowfield Rd 243 Split along Lebanon Rd - TAZ 313 on East; TAZ 243 on West 245 Split along Future Maplewood Extension - TAZ 245 on rth; TAZ 34 on South 246 Split along University Ave - TAZ 33 on the East; TAZ 246 on the West 261 Boundary realigned to Lake Wichita shore 262 Split along Langford Ln - TAZ 317 on rth; TAZ 262 on South TAZ Boundary Change 263 Added new geographyin Archer County but split on County Line to create new TAZ (321) 276 Geography removed on west side of MPO Boundary 277 Split along Horshoe Lake Rd - TAZ 38 on East; TAZ 277 on West 278 Split along Huntington Ln - TAZ 278 on East; TAZ 39 on West 279 Split along Huntington Ln - TAZ 279 on East; TAZ 31 on West 281 West boundary reduction to Peterson Rd 33 Split from TAZ Split from TAZ 245; Realigned along Lawrence Rd 35 Split from TAZ Split from TAZ Split from TAZ Split from TAZ Split from TAZ Split from TAZ Split from TAZ Split from TAZ Split from TAZ Split from TAZ Split from TAZ Split from TAZ Split from TAZ New Geography 319 New Geography 32 Split from TAZ Split from TAZ 263; New Geography 322 Split from TAZ 265; New Geography 39

43 215 BASE YEAR SUPPLEMENTAL TABLES Table A2: 215 Group Quarters Populations TAZ GQ Group Quarter Name Pop (215) Wichita County District Court; County Jail Wichita County Jail Annex Vernon College Century City Center; Brookdale Sikes Lake University Park Nursing & Rehabilitation United Regional Hospital Senior Care Health & Rehabilitation 91 4 Salvation Army Promise Skilled Nursing Facility Nursing Home; Texhoma Christian Care Center Monterey Care Center Ministry of Hope; Daybreak Community Services Midwestern Healthcare Center, Faith Refuge 5 14 Juvenile Probation Downtown Office 14 1 Juvenile Probation Heritage Medical Gables at Rolling Meadows 1 25 First Step Inc. Domestic Abuse Treatment Center Faith Mission; Daybreak Community Service 7 54 Courtyard Gardens Brookdale Lake Wellington Arbor House Assisted Living N/A 4

44 Table A3 shows the variables found in the 215 SED. There are a number of items to note: 1. TxDOT s socio-economic guidelines state that group quarter populations should not be included in a field separate from the rest of the population unless they reside in a special generator (e.g. university dorms, military barracks and jails). All other group quarter populations should be included with household population. Zones that have group quarter populations can be identified by a larger than usual average household size. 2. The field EMP_15 represents the total of basic, retail, and service employment that do not count as special generator employment. Education employment is not included in this figure as stated in the TxDOT document. 3. The service special generator employment SGZ_SER_15 includes education as a special generator employment. These two types of employment are consolidated because of TripCAL5 functionality. 4. The CMT_15 field contains information on special generators and group quarters. Table A3: 213 Data Structure Field Name Type Width Decimal ID Integer (4 bytes) 1 Area Real (8 bytes) 1 2 ACRES Real (8 bytes) 1 2 TAZ Integer (4 bytes) 1 LIST Integer (4 bytes) 1 CHECK Integer (4 bytes) 1 MPO_BOUNDARY Character 16 COUNTY Character 16 TAZ_7 Integer (4 bytes) 1 EDIT Character 6 COMMENT Character 48 POP_15 Integer (4 bytes) 1 HH_15 Integer (4 bytes) 1 HHSIZE_15 Real (8 bytes) 1 2 MEDINC_15 Integer (4 bytes) 1 EMP_15 Integer (4 bytes) 1 BASIC_15 Integer (4 bytes) 1 Field Name Type Width Decimal SERVICE_15 Integer (4 bytes) 1 EDU_15 Integer (4 bytes) 1 SGEN_15 Integer (4 bytes) 1 SGZ_15 Integer (4 bytes) 1 SGZ_SIZE_15 Real (8 bytes) 1 2 SGZ_POP_15 Integer (4 bytes) 1 SGZ_HH_15 Integer (4 bytes) 1 SGZ_AVGHH_13 Real (8 bytes) 1 2 SGZ_MEDINC_15 Integer (4 bytes) 1 SGZ_EMP_15 Integer (4 bytes) 1 SGZ_BAS_15 Integer (4 bytes) 1 SGZ_RET_15 Integer (4 bytes) 1 SGZ_SER_15 Integer (4 bytes) 1 CMT_15 Character 2 TOT_POP_15 Integer (4 bytes) 1 TOT_HH_15 Integer (4 bytes) 1 TOT_EMP_15 Integer (4 bytes) 1 41