Chester County Land Use and Suitability Analysis by Shelly Zhu

Assignment 4

501: Quantitative Methods Analysis Professor Hsu 12/12/12

CHESTER COUNTY: Land Use and Suitability Analysis

Report Submitted by: Wesley Vaughn Shelly Zhu

Chester County: Land Use and Suitability Analysis Part I: 2020 Land Use Program

The 2020 Chester County Land Use Program projects that the county’s population, housing demand, and employment sectors will grow during the 2010-2020 period and seeks to manage this growth as sustainably as possible. Chester County is located in the Delaware River Valley region of Pennsylvania, consists mostly of farmland and forests, has a population of around 500,000, and has grown fairly regularly each decade since 1930.1 Increasing density within the county’s urban areas and conserving its rural areas serve as the two major goals of this land use program. This program produces two different scenarios that draw from the same growth projections, but handle the growth in divergent fashions with revealing results. As Berke et al suggest, a “business as usual” scenario is contrasted to an ideal scenario to demonstrate the benefits of desired policies.2 The ideal scenario encompasses the smart-growth goals of denser urban development and the protection of farmland and open space. By calculating and comparing the required additional acreage necessary to accommodate “business as usual” development and ideal development, this proposed land use program presents the impacts of allowing Chester County to continue growing at its historical rate without prescribing targeted development restrictions. Chester County residents decidedly support this program’s goals, according to a recent survey. During the county’s 2007 comprehensive planning process, the Chester County Planning Commission found that 88 percent of respondents view the county’s rural character as its most important asset and that 84 percent preferred that future development occur in existing urban centers.3 Therefore, this land use program’s recommendations have a strong connection to community values. The direction of the ideal scenario is also built on environmental and fiscal reasoning. With regards to the environment, Landis argues that setting higher density requirements is necessary because the private market cannot serve alone “as a means to protect and preserve valuable resource lands, open space and habitat.”4 This land use program sets increased density standards for residential units and commercial buildings to facilitate more infill development rather than new suburban development. In addition, conservation zoning, which mandates a large-minimum lot size of 20 to 40 acres per dwelling unit, serves as another regulatory tool for this ideal scenario to safeguard Chester County’s farmland from unwanted development.5 The goals of high-density urban development and rural preservation also promote fiscal responsibility. The results of these goals save local governments from extending infrastructure to accommodate spreading suburban growth. As Landis discusses, the cost per resident of servicing a larger population is less than a smaller population.6 Thus, this land use program seeks to constrain development within already developed areas with smaller average lot sizes for residential units and commercial properties to minimize the need to extend services into new areas with less dense populations.7 Meanwhile, the agricultural residential areas function as a conservationist boundary and buffer around the areas slated for development.

2020 POPULATION PROJECTIONS

Chester County’s population has grown steadily since 1930, and each projection model forecasted varying degrees of additional growth for 2020, as shown in Appendix 1. This land use program chose to base itself off the Delaware Valley Regional Planning Commission’s 2020 projection, but additional models were constructed for additional information and were not used due to their respective limitations. Figure 1 presents the different projections of every model in relation to Chester County’s past growth. Although the most-recent share model incorporates the county’s 2010 relationship with the region into a 2020 projection, it fails to account for the county’s historically increasing share of the region’s population – as seen in step-down-trend model’s data. As a result, the most recent share model projects only a modest population growth relative to the DVRPC projection. The average population change rate model found the county’s average population percentage-growth per decade since 1940 and projected 2020’s population according to the calculated average. This model serves as the high-end option relative to the other models, even though the calculated 19 percent average 1

growth per decade isn’t unreasonable. However, it is unlikely that Chester County will grow by almost 100,00 residents in the next 10 years, which this model erroneously projects. The time-trend model uses Chester County’s population levels from 1930 to 2010 and the Excel trend function to project 2020’s population. The county’s consistent growth over this time span and the short 10-year projection bolsters the model’s effectiveness, but this model lacks a connection with the surrounding region and external trends.8 The step-down-trend model projects the county’s 2020 population based on a linear regression of the county’s share of the region’s population from 1930 to 2010. Other than the DVRPC population projection, this model is the most reliable since the county and region grew comparably in this time period and the county’s population share grew steadily. The step-down-trend model even projected a 2020 population only a few thousand off of the DVRPC projection. However, this land use program chose the more professional DVRPC projection. The DVRPC projects that Chester County’s 2020 population will be 538,809, an increase of 39,923 residents2 from 2010.9 This land use program chose this projection because it unites several factors, trends, and models into its forecast. This population projection ultimately serves as the foundation for the additional household, housing unit, and residential acreage projections.

n Region

608

2020 Population Projection Models Chester County Population Time Trend Most Recent Regional Share Step-Down Step-Down Trend w/ Delaware Valley Region Average Population Change Rate DVRPC Population Projection

593,544 593,544 538,809 530,943 525,289 512,264

498,886 433,501

498,886

376,396 316,660 277,746

433,501

210,608 126,629

1930

135,626

1940

159,141

1950

1960

1970

538,809 530,943 525,289 512,264

1980

376,396

1990

2000

2010

2020F

316,660

Figure 1: 2020 Population Projection Models

277,746

2020 HOUSEHOLD PROJECTIONS

A 2020 household projection for Chester County was calculated in order to project housing tenure rates, housing structure type demand, and residential acreage requirements. The average household size model divided the DVRPC population projection by Chester County’s 2010 average household size, and projected a total of 203,324 households in the county by 2020, as shown in Appendix 2. This model assumes that the county’s average household size will remain constant, which is reasonable. The national average household size has only slightly shifted in past decades and this projection is only for a 10-year interval.10

1970 1980 PROJECTIONS 1990 2020 HOUSING UNIT TENURE

2000

2010

2020F

This land use program projects the 2020 split of owners and renters by applying the projected households to two tenure rate models. The 2010 tenure rate model assumes that the 2010 owner-renter percentage split will hold constant for 2020, while the 2000-2010 tenure change-split model uses the changes in owners relative to household changes in Chester County between 2000 and 2010 to project the 2010-2020 change in owners, as shown in Appendix 2. The ownership projections of the two models only differ by a few hundred, but this land use program uses the 2000-2010 tenure change-split model because it incorporates more data within a 10-year window. This model unfortunately includes the tumultuous housing market bubble of 2007, but there is no way to know when its effects will subside or by how much. The chosen model projects a total of 154,722 owners and 48,522 owners by 2020. 2

2020 HOUSING STRUCTURE-TYPE PROJECTIONS

The housing structure-type projections find the 2010-2020 forecasted changes in single-family and multi-family units, as shown in Appendix 2. This model calculates these changes by multiplying the 2020 projected numbers of owners and renters by the 2010 owner and renter-occupation rates for single-family units to find the total projected number of owner and renter-occupied single-family units in 2020. Subtracting the 2010 number of single-family units in the county from this total provides the projected change in single-family units between 2010 and 2020. The projected 2020 multi-family total is found by subtracting the 2020 projected number of single-family units by the 2020 projected number of households, while also accounting for the mobile home population that comprises 2.7 percent of households in Chester County.11 Finally, the 2010-2020 changes in multi-family units is found by subtracting the 2010 number of multi-family units by the projected 2020 number of multi-family units. This model projects that single-family units will increase by 11,672 and that multi-family units will increase by 2,088 by 2020. This model assumes that the owner and renter share of single-family units will remain constant for 2020. However, the rates of owners and renters occupying single-family units may increase by 2020 since Chester County’s median age increased from 36.9 to 39.3 years old between 2000 and 2010, according to the 2010 Census. Older residents typically live in single-family units; thus, if this aging trend continues over the next decade, Chester County may have a larger change in single-family units and smaller change in multifamily units than projected by this model.

2010-2020 PROJECTED ADDITIONAL RESIDENTIAL ACREAGE

With the projected 2010-2020 residential unit changes, this land use program projects the amount of land required to accommodate Chester County’s residential growth. Five models were developed to project varying residential acreage increases, but the net densities by unit type and alternative plan densities represent the “business as usual” and ideal scenarios, respectively. All of the models found or supplied various acres per residential unit measures before multiplying these figures by the projected unit change to project the additional residential acres required, as shown in Appendix 3. The gross residential density estimate, the net residential density estimate, and recent market net density models provide useful information, but this land use program discards them due to various limitations. The gross residential density estimate incorporates all developed acres into its acres per residential unit measure. This is a crude method because it accounts for non-residential uses that will be calculated separately in this land use program. The net residential density estimate applies the same concept as the gross estimate, but only includes residential acreage. Thus, the acres per residential unit are less than that of the gross density estimate. However, the net estimate fails to separate between single-family and multi-family units, which generally have different acres per unit standards. Lastly, the recent market net density does divide residential units into single-family and multi-family units, but it only considers the densities of two projects.12 The net densities by unit type or “business as usual” scenario represents Chester County’s current development pattern. This scenario calculates the 2010 average acres per single-family and multi-family unit by dividing the land devoted to single-family and multi-family uses by the number of single-family and multifamily units.13 This separation reveals and accounts for the high relative density of multi-family units to singlefamily units on average. Despite this model’s sophistication, it still doesn’t account for all types of single-family units. This oversight partially explains why this model’s average acreage per single-family unit (.59) is less than the recent market net density’s (1.7). By not including attached single-family units, the net density by unit type model groups these typically denser units with the less dense detached single-family units.14 Even still, the “business as usual” scenario still has a higher average acreage per single-family and multi-family unit than the ideal scenario. This land use program bases its projected residential acreage requirements on the alternative plan densities model, or “ideal scenario,” because this scenario incorporates each type of residential development and sets average acres per unit that achieve this land use program’s goals – higher urban density and rural area protection. Unlike the other models, this “ideal scenario” calculates and projects the acreage for agricultural single-family units, traditional single-family units, detached single-family units, and multi-family units, as shown in Appendix 3. 3

The complex method, as explained and cited in Appendix 3, finds the 2010 percentage breakdown of single-family units by the three single-family types and calculates the proportional 2010-2020 projected unit changes from the previously projected 2010-2020 projected single-family unit change. Then, these projected single-family unit changes and the previously projected multi-family unit change are multiplied by ideal density averages to find the projected additional required residential acreage. The decision to include an agricultural residential section was influenced by Chester County’s composition and values. Agricultural comprises 37 percent of the county’s land and is valued by its residents.15 This method assumes that dwelling units on agricultural land are included in single-family detached unit totals, whereas the acreage amounts are kept separate. As it will be shown in the suitability analysis, this sector serves as an active form of land conservation and as a buffer between incompatible land uses. The ideal agricultural residential acres per unit stems from legally supported conservation-zoning policies that require a minimum of 40 acres per dwelling unit on farmland.16 The average farm size in Chester County is actually 88 acres, but this ideal density just serves as an average.17 By also calculating and projecting single-family attached housing, this scenario allows for denser residential opportunities. Attached single-family housing comprises 22 percent of the single-family unit market in Chester County.18 An ideal density of 0.2 acres per attached-unit follows an urban residential pattern for single-family units and minimizes the land necessary for single-family unit growth (as shown in Appendix 3 and Figure 3).19 The ideal densities of this scenario for traditional single-family and multi-family units also diverge from the “business as usual” scenario. The ideal density for traditional single-family units, which encompass typical suburban homes, is set at 0.5 acres per unit rather than the current net density of 0.59. This half-acre standard still supports the traditional style of single-family units while remaining consistent with this land use program’s goals.20 This scenario also increases the density of multi-family units from the current 0.12 acre per unit to 0.1 acre per unit. As a result, 10 dwelling units can be developed on an acre, which is regarded as the minimum standard for supporting mass transit and walkability.21 Figure 2 contrasts the two scenarios’ residential density for the projected new population on the projected additional acres, illustrating the ideal scenario’s success in creating denser development.

2010-2020 PROJECTED INDUSTRIAL AND COMMERICAL EMPLOYMENT

2020 Projected Density Comparison 6.00

5.31

To find Chester County’s projected 5.00 3.87 additional acreage for businesses by 2020, this 4.00 land use program first had to project the county’s 3.00 Business as employment growth for the 2010-2020 period. 1.83 Usual Scenario 2.00 1.33 This land use program considered four Ideal Scenario 1.00 employment projection models, but ultimately (excluding agriculture) 0.00 chose the trend model, as shown in Appendix New Dwelling Unit New Population 4. The other three models included an external Density on Additional Acres Density on Additional Acres projection, a step-down model, and a shift-share (Residents per Acre) (DUs per Acre) analysis model, and were not chosen for a variety Figure 2: 2020 Projected Density Comparison of reasons. The DVRPC’s 2020 employment projection for Chester County wasn’t broken down into industry sectors and was based on a faulty 2010 employment total that didn’t match County Business Patterns’ data.22 The step-down model could not be calculated because a reliable 2020 employment projection for the Delaware Valley Region or Pennsylvania with industry sector breakdowns wasn’t available. A shift-share analysis wasn’t calculated because the model’s national growth, proportional shift, and differential shift factors would be difficult to predict accurately. The chosen trend model assumes that the 2000-2010 rate of change for industry sector employment can be applied to the 2010-2020 period, and this assumption is fairly reasonable. The trend model projects a 2020 employment total of 265,362, an increase of 40,731 jobs from 2010. The projected losses in the 4

construction and manufacturing industry and the large gains in the services and information and the finance, real estate, and insurance industries are results of the 2000-2010 trends. The FIRE industry grew 217% in Chester County between 2000 and 2010, which may be the result of a headquarters or business expansion in the area. Thus, it’s realistic to assume that this growth in the FIRE sector will likely not continue at such a pace. It’s also likely that a job-losing sector such as construction will begin to recover during the 2010-2020 period once the housing market recovers from the 2007 bubble.

2010-2020 PROJECTED ADDITIONAL COMMERCIAL AND INDUSTRIAL ACREAGE

With a projected employment growth for the industrial and commercial sector, an additional acreage requirement was found for each by multiplying the growth by the current or an ideal job-density standard, as shown in Appendix 4. This land use program decided not to calculate a projection using an industry-standard density model because the national job density standards could not be found and these standards would not be related to Chester County anyway. The current net densities model, or the “business as usual” scenario, found the projected additional acreage for 2020 by first calculating the 2010 jobs per acre for the industry and commercial sectors. These figures were then multiplied by the projected employment growth in each sector to find the additional acreage required to accommodate “business as usual” development. This scenario projects a large additional need for commercial land – a 30% increase from 2010 – but only a small additional need for industrial land, as influenced by the employment sector projections detailed before. The alternative plan densities model, or the ideal scenario, found its projected additional acreage for 2020 by multiplying its ideal acres per job by the projected 2010-2020 employment growth in each sector. The ideal scenario chose to apply the same industrial density average because of the sector’s need for large properties and the relatively small growth projected for the sector. The ideal scenario did set a denser average (0.03 acre per job) for the commercial sector than the “business as usual” scenario (0.05 acre per job), though. With denser commercial development, Chester County can avoid big box stores and large office parks that consume large tracts of land outside of urban centers. Especially considering the immense growth in the FIRE sector, the county would benefit from concentrating jobs in this sector rather than allowing them to spread out away from other development. In sum, the ideal scenario conserves 917 acres that the “business as usual” scenario projects for development.

2010-2020 PROJECTED ADDITIONAL COMMUNITY FACILITY ACREAGE

The projected additional community facility acreage needed in Chester County between 2010 and 2020 was calculated by finding the 2010 total number of community facilities, the 2010 average units per resident, the 2010-2020 community facility growth based on the DVRPC 2020 population projection, and the 2010 average acres per facility unit, as explained in Appendix 4. Due to data limitations, the projections could only be made for community facilities as a whole instead of for each facility type. Projected population demographics and detailed facility acreage data would be needed to calculate more reliable figures. Although this is a current net density model, its acreage projections are used for the “business as usual” and ideal scenarios. This model projects a need for 360 additional acres to accommodate 2010-2020 community facility growth. Because of the small size of this need and the benefits of community facilities, this land use program decided that setting higher density standards for community facilities was not necessary.

THE SUMMARY OF THE LAND USE PROGRAM

The 2020 ideal scenario for Chester County implements the community-supported and environmentally and fiscally sustainable goals set forth by this land use program. This land use program seeks to increase development density in urban areas and protect the county’s open spaces and rural areas, and the ideal scenario demonstrates the benefits of accommodating growth with these goals in mind. The “business as usual” scenario, on the other hand, demonstrates the projected impacts of continuing to accommodate growth with the county’s current net densities. The Summary of the Land Use Program compares the projected results and reveals the success of the ideal scenario in achieving its goals, as shown 5

in Appendix 5 and Figure 3. The summary 2010-2020F Additional Acres Required applies a zero percent reserve level for Business as Usual Scenario Ideal Scenario (excluding agriculture) both scenarios because their projections 6845 have already calculated acreage 4997 requirements without the need for an arbitrary estimation. 2548 The density comparison table 1630 360 360 325 325 246 209 presented in Figure 2 and calculated in Appendix 6 provides a clearer picture of Single-Family Multi-Family Commercial Industrial Community the ideal scenario’s success. Excluding Units Units Facilities the acres dedicated to agricultural Figure 3: 2010-2020F Additional Acres Required residential units, the ideal scenario saves a projected 2,802 acres that the “business as usual” scenario would have developed. In addition, the ideal scenario has higher projected residential and dwelling unity densities for the projected population growth on its additional acres than the “business as usual” scenario. The ideal scenario’s dense development occurs in already developed urban areas – one of this land use program’s goals – with the criteria set by the suitability analysis. As a result, the cost of providing services should decrease per resident due to decreasing marginal returns, and the county’s governments should become more fiscally sustainable – another goal of this land use program. Unlike the “business as usual” scenario, the ideal scenario also ensures the communitysupported protection of rural land through the agricultural residential calculations and projections. Chester County residents value the area’s farmland, and the ideal scenario guarantees its protection from unwanted development. In sum, the ideal scenario of this land use program achieves its purpose of acting upon the community’s preferences and creating a more fiscal and environmentally sustainable future.

Part 2: Development Suitability Analysis

Assets and Constraints Residential

Agriculture

Commercial

Industrial

Conservation

A development suitability analysis Farms C A C C determines the appropriateness areas to Forest C C C A devote for each land use type. Suitability Pastures C A C C modeling calculates optimal site locations Urban A C A C C by identifying possible influential factors, City Boro A C A C C creating new data sets from existing data, Hwy 500 C A C A reclassifying data to identify high-suitability Hwy 1000 C A C C areas, and aggregating these data into Lacus Wetland C C C C A one logical result of optimal suitability. Palus Wetland C C C C A The basic premise is that each aspect of Parks C C C C A the landscape has intrinsic characteristics Rail Stop A C A C that are either suitable or unsuitable for Slope >10 C C C C A the planned land uses. Coupled with the Slope >5 C C C C proposed land use program, the use of Slope <5 A A A GIS analysis made it possible to consider Slope <2 A A A areas for potential development based on Stream Buff 100 C C C C A a variety of factors and criteria such as Stream Buff 500 A A A C physical location, hydrology, and slope. To Urban Footprint A C A A C identify locations within Chester County Figure 4: Assets and Constraints suitability criteria that could serve as feasible locations for future development, the summary model examined both constrains and opportunities. The metrics for suitability preferences listed in above in Figure 4 were organized according to binary criterion in terms of constraints or opportunities for five major land use and development types: residential, agriculture, commercial, industrial, and conservation. 6

CONSTRAINT MAPS

0 2 4

Methodology: A binary ranking method was used to produce constraint maps. This procedure Is based on a standard principle: 1=yes, 0=no. In other words, this assumption results in two very delineated responses of criteria as either yes, a constraint, or no, not a constraint. Furthermore, each land type is assumed to be mutually exclusive, i.e. if an area is farmland, it cannot also be pasture. In this way, maps were produced through a dialogue where all criteria identified as constraints ranked as 1 for that land use or development type. This resulted in hard edges defining areas of constraints, producing highly restrictive development maps. A binary constraint model is easy to calculate, but does not place weighted importance on the various layers; each criteria has equal influence on the final output.

Criteria: All urban areas

Conservation

Criteria Farmland Forest Pasture Urban Area City Boros Hwy Dist 1000 Wetlands Parks Rail Stop 500 Slope >10 Slope >5 Stream Buff 100 Stream Buff 500

Criteria: Farmland Forest Pastures Hwy Dist 500 Hwy Dist 1000 Wetlands Parks Slope >10 Slope >5 Stream Buff 100

Residential

Industrial

Criteria:

Farmland Forest Pastures Hwy Dist 500 Hwy Dist 1000 Wetlands Parks Slope >10 Slope >5 Stream Buff 100

Urban Area City Boros Urban Footprint Wetlands Parks Rail Stop 500 Slope >10 Slope >5 Stream Buff 100

Agriculture

Commercial 7

Miles

OPPORTUNITY MAPS Methodology: While a binary method was used to define constraints, a conditional suitability model was deemed a more appropriate method to compare areas of opportunity. In order to achieve a transect that supports our proposed land use program for Chester County, opportunities are defined primarily in terms of criteria based on location and land type, and are separated into two fundamental classifiers: urban locations and non-urban locations. For example, for non-location indicators such as highway buffers, wetlands, slope, etc. that were identified as a possible opportunity, the conditional model sets these criteria to be dependent on its preferred urban or non-urban location in order to return a value. This results in overlaying binary conditions with â&#x20AC;&#x153;if-thenâ&#x20AC;? conditions to produce a single, weighted, opportunity map. Depending on the number of non-location indicators used as criteria, resulting outputs ranged from 0-7, with 0 indicating areas not suitable for an opportunity, and the return values as areas that are suitable. These values were then reclassified back into a simple binary map showing only 0 and 1 rankings. Rather than producing maps showing every opportunity for any given land use or development type, this approach only shows practical opportunity for development, and results in fairly restrictive, but realistic opportunity maps.

0 2 4

Criteria: Forest Wetlands Parks Slope >10 Stream Buff 100

Conservation

Criteria:

Criteria

Urban Area City Boros Rail Stops 500 Slope <5 Stream Buff 500 Urban Footprint

Hwy Dist 500 Slope <2 Slope <5 Urban Footprint

Residential

Industrial

Criteria:

Urban Area City Boros Rail Stops 500 Slope <2 Slope <5 Stream Buff 500 Urban Footprint

Farmland Pasture Hwy Dist 500 Hwy Dist 1000 Slope <2 Stream Buff 500

Agriculture

Commercial 8

Miles

SUMMARY MAPS

0 2 4

Methodology: After independent analyses, both constraint and opportunity maps displayed values of 0 and 1. In order to sum both maps to produce a final summary map, constraint values were reclassified to 0 and 2. The end result are five generalized maps showing areas of the county that are ranked in a hierarchical order from 1 to 3 as either areas of high suitability (1), no suitability (2), or areas of possible tension (3) for each type of land use and development type. Areas of tension are pixels where both constraints and opportunities are met. Due to the method of analysis, it is unclear the magnitude of which layer (constraint or opportunity) would have more impact. This simple summary effectively shows constrained land to avoid sprawl and guides future Chester County growth in desirable patterns per the proposed land use program.

2020 Projected Total Acreage

Agriculture

Commercial

Industrial

Conservation

96,806

184,686

9,930

3,125

186,333

Areas of Tension: 11,938 acres

Conservation

Constraints: 343,813 acres Opportunities: 25,938 acres

Constraints: 355,375 acres Opportunities: 3,000 acres

Areas of Tension: 16,313 acres

Areas of Tension: 31,688 acres

Residential

Industrial

Constraints: 80,625 acres Opportunities: 192,438 acres

Constraints: 340,875 acres Opportunities: 28,375 acres

Areas of Tension: 47,063 acres

Areas of Tension: 13,875 acres

Agriculture

Commercial 9

Miles

Constraints: 29,750 acres Opportunities: 158,688 acres

Ideal Scenario Residential

Part 3: Allocation Discussion

The development suitability analysis generates suitability maps consistent with the urban-growth and rural-conservation goals of the land use program. However, due to the limitations of information and resources, the analysis maps and the land use program’s ideal scenario do not share equal calculations for the desired acreage to accommodate Chester County’s growth for 2020. The binary nature of the summary maps – as explained before – provides explicit opportunities and constrains, and no preferable or not preferable options. This policy matches the land use program’s decision to use 0% reserve levels for each development type. Consequently, the summary map results need little interpretation, other than comparisons to the results of the land use program.

RESIDENTIAL AND COMMERCIAL

Rural Rural Center Suburban Suburban Center Urban Natural Resources Residential Suitability

Acknowledging the limitations of information and resources, the suitability model and land use program abandons the unrealistic goal of Figure 5: Chester County Livability Map producing an exact prediction of the future for the preparation of a range of alternative scenario-based forecasts which reveal a range of potential futures. The map results generated by considering the most ideal scenario provide an understandable expression of the scenarios’ underlying goals, smart-growth policy choices, and assumptions. For instance, the summary models demonstrate that there is pressure on land available for residential and commercial growth. This resulted from identical criteria set for both land uses, as outlined in Figure 4. Residential and commercial development opportunities were restricted to the existing urban footprint near rail stops, so urban areas increase in density but not in area, ultimately halting the growth of the current suburban fringe, as compared in Figure 5. Areas on the urban fringe typically experience rapid and uncontrolled urbanization, and face difficult and complex issues of sustainability, sprawl, growth management, and maintaining a sound fiscal base. As a result, the proposed land use program and suitability model is particularly useful in promoting and supporting compact, mixed-use growth. The suitability map reveals 25,938 acres of residential opportunity, however, due to GIS spatial data limitations, this does not include the current developed suburban acreage, and would likely make up a large portion of the difference in projected 2020 residential acreage needs of 96,806. Because the projected population growth rate of Chester County is low and consistent, there is no need to develop more land faster than the population is growing. Rather, this induced residential and commercial land pressure stems from the desire to maintain strict environmental protection policies of the county’s rural character, as well as to encourage higher residential density standards within the existing urban core.

AGRICULTURE RESIDENTIAL

In accordance with the land use program’s ideal scenario, the suitability analysis also planned for an agricultural residential land use. As the land use program discusses, the agricultural residential use is considered an important asset by the county’s residents and serves as an actively used buffer and conservation tool to constrain development in urban areas. This use’s summary map seems expansive, but since dwelling units on this type of land typically use on-site septic and wells, public infrastructure extensions are usually not necessary.23 Thus, this land use achieves each goal of the land use program – promoting the values of the community and planning for fiscal and environmental sustainability. The suability analysis set the opportunity criteria as current farmland, pastures, areas surrounding highways, slope less than 2 degrees, and areas within 500 meters of streams, as shown in FIgure 4. Pastures and areas within 500 meters of streams were included to amplify the conservation role of agriculture and to provide additional growth areas. By preferring farms beside highways, this suitability analysis seeks to prevent wasteful, strip commercial development on the outskirts concentrated along highway exits. The agriculture land use is only slated for areas with slopes less than 2 degrees this slope range provides the most fertile soil for farming.24 With these criteria, the suitability analysis finds 192,438 acres suitable for agriculture. This figure 10

only a little more than the projected 184,686 required acres by the land use program’s ideal scenario, and this oversupply provides additional room for growth.

INDUSTRIAL

As indicated previously in the proposed land use program, the industrial sector, based on the ideal scenario, projects acreage needs of only 3,125 total. This is in large due to the relatively modest projected employment growth for the sector from 2010 to 2020. With strict criteria as outlined in chart xx, the suitability analysis reveals possible opportunities of 3,000 acres, extremely close to the total projected acreage needs. By restricting industrial development to its current locations within the urban core, the rural character of the county can be maintained by disallowing large amounts of impervious surfaces created by single-story, low-density industrial parks, which otherwise would creep on the urban fringe. Furthermore, continuing a policy of compact industrial development strongly supports fiscal responsibility, as the benefits of industrial agglomeration include access to transportation infrastructure already in place as well as substantially more productive workers. 25

CONSERVATION

Conservation and development are two sides of the same process and both endeavors necessarily lead to trade-offs with each other. In areas where sprawl is not controlled, the concentration of urban residential, commercial, and industrial uses may lead to disruption of sensitive ecosystems. The reach of uncontrolled development into natural areas such as forests and wetlands is a primary force of habitat loss and fragmentation of wildlife habitats. In the ideal land use scenario, land set aside for conservation accounts for 186,333 total acres. By setting opportunity criteria of primarily natural land uses such as forest, wetlands, parks, and stream buffers, approximately 160,000 acres of land can be conserved. Areas of conservation are especially In alignment with Chester County resident’s values, a report released in 2007 revealed the state of Pennsylvania among 7 others in the country identified uncontrolled development as the greatest threat to open space and precious wildlife, and cites smart growth and careful conservation planning as useful solutions to the problem. In keeping conserved land as buffers between land uses, the impacts of development and encroachment on sensitive land can be reduced, and can help reach the ultimate goal of reduced sprawl, maintain already suburban areas, and conserve natural habitats

Part 4: Trip Generation and Mode Split Regression Analysis

Using various 2000 Philadelphia County data for the county’s 380 tracts, this analysis developed six regression models to study the effects of demographics, housing, density, employment accessibility, and transit availability on work-trip generation and work-trip mode split shares. Each model involved multiple versions that eventually resulted in the “best” model, which was as parsimonious as possible. This analysis attempted to conclude with final models that tested each individual hypothesis and used the fewest possible number of variables while remaining statistically significant. To ensure proper analysis, all y-values – or “predictors” – were standardized to scale each variable on standard deviation changes. In addition, the standard deviation, mean, minimum, and maximum of the 2000 Philadelphia County data were calculated in order to gauge the variables’ validity. This analysis judges each model’s results for statistical and practical significance.

MODEL 1: TOTAL WORK TRIPS

This analysis predicted that population, dwelling unit density, median household income, the percentage of college graduates in the community, and percentage of the community below the poverty line would most affect a community’s total work trips. We expected that an increase in population would increase work trips, since a larger population is more likely to generate more work trips than a smaller population. We expected that an increase in dwelling unity density would somewhat decrease total work trips because residents may not need to travel as much. We expected that total work trips would increase alongside median income because wealthier residents have greater means to travel more frequently. For the percentage of college graduates in the community, we expected a similar result as median income. Lastly, we expected 11

that a greater share of residents below the poverty line would decrease the total number of work trips. Our final model explained 84% of the total work trip variations between the communities, according to its R-square, by only incorporating total population and percentage of the community below the poverty line, as shown in Appendix 7. The population and poverty figures were both found to be statistically significant, as both had 0% p-values and small standard errors that didn’t include zero. The regression model found that total population had the highest coefficient calculated at 909 – meaning that one increase in standard deviation (2,415 residents) generated an additional 909 work trips, as expected. This analysis views the total population variable also to be practically significant since an increase of 2,415 residents in a community and the projected work trip increase is reasonable and can be planned for. It is assumed that this model’s R-square is so high because of the high correlation between population and total work trips, which was found to be 84%. As for the poverty measure, the model found that each standard deviation increase (16% of the community below the poverty line) decreased the total work trips by 289, as the analysis predicted. We struggle to consider the poverty variable practically significant because one standard deviation (16%) is almost the mean (22%), and this drastic change would have to be the result of many other changes in a community. Dwelling unit density, median income, and the percentage of college graduates in the community fell out of the model for statistical reasons as shown in Appendix 7. The density variable had the highest p-value (0.13%) in the model’s first run and the least impact (a coefficient of 74). Although density may decrease the total work trips per person, it could not be judged this way due to the given data. The median household income variable had the second-highest p-value (0.05%) and the second smallest impact (a coefficient of -91). This analysis removed it from the model because of its statistical insignificance, which may have been related to its 61% correlation with percentage of college graduates. The percentage of college graduates variable was taken out before the final model because, even though it was statistically significant, it didn’t contribute much to the R-square and overlapped with the poverty variable’s impact.

MODEL 2: TOTAL PRIVATE CAR TRIPS

This analysis predicted that total households, median household income, percentage of the community below the poverty line, percentage of single-family dwelling units, percentage of whites, percentage of Hispanics, and percentage of dwelling units built before 1950 would impact the total number of private car trips in a community. We expected that total households would account for most of the private car trips as a quantity measure. We expected that a higher median household income would generate more private car trips because wealthier individuals are more likely to use private automobiles. We expected that an increase in poverty would decrease private car trips since poorer individuals have less accessibility to cars. A higher share of singlefamily units was expected to increase private car trips because single-family units are associated with suburban, auto-oriented living. For percentage of whites and Hispanics, this analysis had no real expectations other than curiosity. Lastly, it was expected that a higher percentage of pre-1950 dwelling units would be associated with older, transit-oriented suburbs that generated less private car trips. Our final model included the total households, percentage of dwelling units built before 1950, and percentage of single-family unit as its variables, and according to the R-square, it explained 73% of the variation among the Philadelphia County communities, as shown in Appendix 8. These three statistically significant variables had 0% p-values and standard errors that didn’t include 0. According to the model, the household totals have the highest impact on the total private car trips. Each increase in standard deviation (992 households) generates 573 additional private car trips, as hypothesized. As with the population variable in Model 1, total households explains most of the variation by itself since it exhibits a high correlation (77%) with total private car trips. This analysis considers the household variable to be practically significant since the standard deviation is a reasonable figure relative to the county mean (1,548 households) and is a reasonable change to plan for. The model found that for each standard deviation increase (27%) in the percentage of dwelling units built before 1950, 279 less private car trips are generated – as predicted. Unfortunately, this variable cannot be considered practical since it is essentially impossible to increase this percentage. Also as expected, each standard deviation increase (27%) single-family dwelling units generates 224 additional private car trips, which this analysis expected. Unlike the pre-1950 dwelling units built variable, the single-family unit variable is practical because altering the share of single-family units in a community is 12

possible and the standard deviation (27%) is reasonable relative to the county’s mean of 67%. This analysis removed several of the original variables before the final model due to statistical insignificance and other reasons, as shown in Appendix 8 and 9. Median household income and percentage of Hispanics had calculated p-values above 30% and were cut as a result. In addition, the Hispanic variable’s standard deviation of 14% is larger than the county mean of 7%, which obfuscates any data analysis regarding the variable. The percentage of whites and the percentage of the community below the poverty line could be considered statistically significant, but their calculated impacts were less than the final model’s chosen variables and their practical significance was suspect.

MODEL 3: DRIVE-ALONE MODE SHARE

This analysis included five variables believed to help explain the variations of the percentage of residents who drive alone in Philadelphia County communities. A higher Normalized Access Index was expected to cause a decrease in the drive-alone mode share since residents would have other transportation options due to closer proximity to employment opportunities. More dwelling units per square mile was expected to decrease driving alone as well because a higher density could support mass transit, walking, and biking. We expected that a larger total population would generate a greater share of driving alone due to the likelihood that the additional residents would follow the national trend of driving alone. This analysis had no expectations for percentage of whites and African-Americans, but was interested to find out their calculated impacts. The final model had an R-square of 40% when incorporating the density, access, and total population variables, as shown in Appendix 10. This R-square is low relative to Models 1 and 2, but this discrepancy is explained by the first two models’ use of whole-number outputs rather than percentage outputs. This relatively low R-square will be the case for Models 4, 5, and 6 as well, because explaining the variation between the communities is almost impossible with the given data. The y-intercept of the final model is 45%, which means that even if all included variables were 0, 45% of the community would drive alone. This unreasonable calculation demonstrates that this portion of the equation is outside the model’s scope and helps explain the low R-square. The three final variables all had p-values of 0% and standard errors that didn’t include 0 and are considered statistically significant. The impacts of the variables all followed what was expected and fell within a tight range. An increase in the standard deviation of total population increases the drive-alone share by 6%, as predicted. As other models have explained, the total population is considered practically significant. An increase in the standard deviation (6,041) of dwelling units per square mile results in a -5% decrease in the drive-alone share, as hypothesized. This variable is practically significant since the standard deviation is reasonable relative to the county mean (8,162) and maximum (46,907) and is a variable that can be planned for. As predicted, a standard deviation increase (11) in the Normalized Employment Access Index lowers the drive-alone share by 4%. The access variable passes the test for practical significance due to the same reasons as the density variable. Although these two variables may seem related, it was found that their correlation fell below 50%. The percentage white and African-American variables fell out of the model because of their overlapping impacts, even though they were statistically significant in terms of their p-values and standard errors. The first version of the model found that both variables had coefficients above 10%, as shown in Appendix 9 and 10, but since it is essentially impossible to change one variable without impacting the other, it was decided that neither was impactful enough for inclusion in the final model.

MODEL 4: RAIL-TRANSIT MODE SHARE

This model calculated the impacts of five variables that were expected to best explain the variations in rail-transit mode share between Philadelphia County communities. We predicted that dwelling unit density would not affect the share much because even low population densities can support rail transit, unlike other mass transit options. Increases in median home value were expected to increase the use of rail transit since it was assumed that the county’s outlying suburbs have higher home values and use the train to commute to the urban core. We assumed that larger tract areas would have more available land and need for rail transit, thereby increasing the mode’s share. More SEPTA stations in a tract should also increase rail-transit use, 13

according to our hypothesis. Lastly, we believed that a higher Normalized Employment Access Index would decrease rail use since residents would opt to take other, short-range transit options. The final model could only explain 7% of the variation in rail use for the county’s communities when the SEPTA station and tract size variables were used as shown in Appendix 10. This insignificant R-square value is a result of similar data limitations, as explained for Model 3. Similar to Model 3, the results of this final model have a y-intercept of 8% – meaning 8% of the community would ride the train even if there were no SEPTA stations. Also, the y-intercept of 8% is equal to the county’s average rail-transit mode share of 8%. Although the model as a whole explains little, the two variables are statistically significant. Each variable has a p-value below 1%, and their standard errors don’t include 0. A standard deviation increase of SEPTA stations (0.78) results in a 1% bump for rail-transit use in a community, and a standard deviation increase of tract area size (0.36 square miles) results in a 2% decrease. The analysis expected the SEPTA station’s effect, but not the impact of tract area size. However, even though the SEPTA station variable may be practical, the tract area size is not because changing one tract’s boundaries would change another and that isn’t beneficial. The three other variables were not included in the final model because of p-values exceeding 10%, minuscule coefficients, perceived overlaps, and high standard deviations relative to variable means and maximums.

MODEL 5: BUS MODE SHARE

We chose the number of bus routes, the percentage of the community below the poverty line, the number of residents older than 65-years-old, the percentage of multi-family dwelling units, and dwelling unit per square mile as the variables to explain the variation between the percentages of bus usage in Philadelphia County. Our hypothesis was that more bus routes would increase bus usage because of increased accessibility. We expected that bus usage would increase alongside poverty since poorer residents cannot typically afford other means of transportation. Since the Pennsylvania lottery funds mass transit use for senior citizens, we predicted that a community with older residents would have higher bus ridership. Denser communities were expected to have higher bus ridership figures as well due to accessibility and preferences. Finally, we included the multi-family unit variable out of curiosity. The final model explained 50% of the bus ridership variation, according to its R-square, when accounting for density, poverty, and multi-family unit variables, as shown in Appendix 12. This percentage falls in line with that of Model 3’s due to the same data limitation reasons. All three variables had p-values of 0% and standard errors that didn’t include 0. The percentage of residents below the poverty line exhibited the largest impact of the variables. For each increase in its standard deviation, bus ridership increases by 9%, as expected. However, as explained previously, it’s difficult to consider the poverty variable as practically significant. An increase in the standard deviation of density and multi-family units impacts bus ridership by 2% and -2%, respectively. We predicted that increased density positively affects bus ridership, but we didn’t think that more multi-family units would decrease bus ridership. This discrepancy may be a result of the multi-family unit variable incorporating or representing other data that forces it to act as the negative coefficient in the equation. Regardless, both these variables are considered because of their tangibility. The number of bus routes and population over 65-years-old were excluded from the final model. Even though the number of bus routes would seem to be the most likely indicator of bus ridership, the variable had a high p-value (13.2%) and a coefficient of -0.8% in a prior version of the model as shown in Appendix 11 and 12. It’s assumed that its interaction with other included variables, such as density and multi-family units, caused this unexpected result. An initial p-value of 73% forced us to take out the older population variable from the model, since it was too likley that it did not affect bus ridership.

MODEL 6: BIKE-PEDESTRIAN MODE SHARE

To explain the variation of bike-pedestrian mode shares, this analysis chose to examine the percentage of college graduates in a community, the Normalized Employment Access Index, and median home values. We expected that a higher percentage of college graduates would indicate a more progressive community with the means to walk and bike to work. We thought a higher index rating would indicate more opportunities to bike or walk to work since employment options were closer to residents. Lastly, we believed that higher median home 14

values would decrease the bike-pedestrian mode share since residents likely live in wealthy suburban communities and have the need and means to drive cars instead. Including the percentage of college graduates and the Normalized Employment Access Index developed a final model that explained 45% of the bike-pedestrian mode share variation in Philadelphia County, ss shown in Appendix 13. Both variables have 0% p-values and standard errors that do not include 0, so they are considered statistically significant. The access index variable has the largest coefficient (7.5%) compared to the college graduateâ&#x20AC;&#x2122;s variable of 3.5%. As it has been explained before, the access index is practically significant for several reasons. The college graduate percentage, however, is not as practical because itâ&#x20AC;&#x2122;s difficult to control for this variable. The median home value variable was not included in the final model because of its 8.34% p-value and 76% correlation with the percentage of college graduates, as shown in Appendix 12 and 13.

CONCLUSION

Limitations of the 2000 Philadelphia County data prevent making substantial conclusions on the validity of the effects of transit-oriented development, but in theory, the development model should discourage work trips by car and encourage the use of mass transit, biking, and walking to work. Higher density residential development around rail transit stations would create walkable areas around the station and opportunities to commute to other areas via rail, thereby encouraging rail transit use for work trips rather than by car. Additional bus routes in an area would provide greater mass transit accessibility, but these routes must be configured effectively for maximum route speed and coverage. Similar to additional bus routes, building more rail transit lines and stations should encourage rail use and discourage work trips by car if the lines and stations are configured properly. Three development policies would likely encourage additional walking or biking to work for an area. By lowering the minimum parking requirements for buildings, cities can create more infill opportunity on land previously allocated to parking.27 This policy also discourages car use because of the decreased accessibility of parking, which would hopefully encourage biking or walking instead. In addition, incentive zoning is a development policy that could encourage walking and biking to work by incentivizing helpful development patterns within an area. Incentive zoning provides floor-area-ratio bonuses to properties in return for public enhancements such as sidewalk extensions and the provision of bike racks.28 As a result, walking and biking would be encouraged due to the improved infrastructure provisioned by private property owners. An area could also encourage biking to work by constructing bike lanes or adding to a bike lane network already in place.29 Although this simple policyâ&#x20AC;&#x2122;s effects range between communities, bike lanes that connect key areas and provide a perceived sense of safety generally increase number of bike users.

Endnotes “Community Profiles,” Chester County Planning Commission, 2012, http://www.landscapes2.org/ccpc/profiles/communitypro files.html#. 2 Philip Berke, David Godschalk, Edward Kaiser and Daniel Rodriquez, Urban Land Use Planning, 5th edition (Champaign, Illinois: University of Illinois Press, 2006), 216-217. 3 Robert E. Walker, “Land Use Plan for Chester County” (steering committee meeting, Chester County Planning Commission, 2007), 3-5, http://www.landscapes2.org/pdf/Land101807.pdf. 4 John Landis, “Characterizing Urban Land Capacity,” in Land Market Monitoring for Smart Urban Growth, ed. Gerrit Knaap (Cambridge: Lincoln Institute, 2001), 5. 5 Thomas Daniels, The Small Town Planning Handbook (Chicago: American Planning Association, 2007), 220. 6 Landis, 8 7 Daniels, 326. 8 Philip Berke and David Godschalk, Urban Land Use Planning, (Champaign, Illinois: University of Illinois Press, 2006), 124. 9 “Report for Population Projections,” Delaware Valley Regional Planning Commission, 2012, http://www.chesco.org/DocumentCenter/ View/6674. 10 MarketingCharts Staff, “Average US Household Size Declines to 2.6,” Marketing Charts, 2009, http://www.marketingcharts.com/topics/demographics/census-data-average-us-household-size-declines-to-26-10679/. 11 “Community Profiles,” http://www.landscapes2.org/ccpc/profiles/communityprofiles.html#. 12 “Yearly Subdivision and Land Development Proposals,” Chester County Planning Commission, 2011, http://www.chesco.org/index. aspx?NID=1227. 13 “Community Profiles,” http://www.landscapes2.org/ccpc/profiles/communityprofiles.html#. 14 Dan Reed, “More homebuyers want walkable, transit-served communities,” Greater Greater Washington, 2011, http://greatergreaterwashington.org/post/11722/more-homebuyers-want-walkable-transit-served-communities/. 15 “Community Profiles,” http://www.landscapes2.org/ccpc/profiles/communityprofiles.html#. 16 Daniels, 220. 17 “Chester County, Pennsylvania (PA),” City-Data, 2012, http://www.city-data.com/county/Chester_County-PA.html. 18 “Community Profiles,” http://www.landscapes2.org/ccpc/profiles/communityprofiles.html#. 19 Daniels, 326. 20 Daniels, 326. 21 John Holtzclaw, “Community Characteristics Promoting Transit and Walking,” The Sierra Club, 2007, http://www.sierraclub.org/sprawl/ articles/characteristics.asp. 22 “Chester County, PA: 2040 DVRPC Forecasts,” Delaware Valley Regional Planning Commission, 2012, http://www.dvrpc.org/asp/ CountyProfiles/Chester.aspx. 23 Daniels, 109. 24 “Examination and Description of Soils,” National Resources Conservation Service, 2012, http://soils.usda.gov/technical/manual/contents/chapter3.html. 25 Diego Puga, “The Magnitude and Causes of Agglomeration Economies,” Journal of Regional Science 50, no. 1 (2009): 1. 26 Lenore Fahrig, “Effects of Habitat Fragmentation on Biodiversity,” Annual Review of Ecological and Evolutionary Systems 34, (2003): 496. 27 Josh Stephens, “Parking Reform Measure Strains Relationship Between Infill Developers, Housing Advocates,” California Planning & Development Report, 2011, http://www.cp-dr.com/node/3033. 28 “Tool: Incentive Zoning,” Pudget Sound Regional Council, 2012, http://www.psrc.org/growth/hip/alltools/incent-zoning/. 29 Eric Jaffe, “Do Bike Paths Promote Bike Riding,” The Atlantic Cities, 2012, http://www.theatlanticcities.com/commute/2012/02/do-bikepaths-promote-bike-riding/1318/. 1

Model

Chester County 2020 Population Projections

DVRPC Population Projection Step-down with the Delaware Valley Region (Most Recent Share) Average Population Change Rate

Time Trend (1930 - 2010)

Step-down Trend with the Delaware Valley Region

Source

538,809

DVRPC

Chester Countyâ&#x20AC;&#x2122;s 2010 Share of Region

8.9%

Census, DVRPC

2020F Region Population

5,777,060

DVRPC

Chester County 2020F

512,264

2010 Share * 2020F Region

Chester County 1940 Growth

107%

Census

Chester County 1950 Growth

117%

Census

Chester County 1960 Growth

132%

Census

Chester County 1970 Growth

132%

Census

Chester County 1980 Growth

114%

Census

Chester County 1990 Growth

119%

Census

Chester County 2000 Growth

115%

Census

Chester County 2010 Growth

115%

Census

Chester County 2020F Growth

119%

Average

Chester County 2010

498,886

Census

Chester County 2020F

593,544

2010 * 2020F Growth

Chester County 1930

126,629

DVRPC

Chester County 1940

135,626

DVRPC

Chester County 1950

159,141

DVRPC

Chester County 1960

210,608

DVRPC

Chester County 1970

277,746

DVRPC

Chester County 1980

316,660

DVRPC

Chester County 1990

376,396

DVRPC

Chester County 2000

433,501

DVRPC

Chester County 2010

498,886

DVRPC

Chester County 2020F

525,289

Trend Function

Chester County Share 1930

3.8%

DVRPC

Chester County Share 1940

4.0%

DVRPC

Chester County Share 1950

4.1%

DVRPC

Chester County Share 1960

4.6%

DVRPC

Chester County Share 1970

5.4%

DVRPC

Chester County Share 1980

6.3%

DVRPC

Chester County Share 1990

7.3%

DVRPC

Chester County Share 2000

8.0%

DVRPC

Chester County Share 2010

8.9%

DVRPC

Chester County Share 2020F Share

9.2%

Trend Function

2020F Region Population

5,777,060

DVRPC

Chester County 2020F

530,943

2020F Share * 2020F Region

Appendix 1

Chester County 2020 Household Projections Model

Source 2020F Population

2010 Average Household Size

538,809

2010 Avg. Household Size

2.65

2020F Households

DVRPC Projection 2010 Census

203,324

2020F Population / Avg. Household Size

Chester County 2020 Housing Unit Tenure Projections Model

Source

2010 Tenure Rate

2000-2010 Tenure Change Split

2020F Households

203,324

From Above

2010 % Owners

76.2%

2010 Census

2020F Owners

154,933

2020F Households * 2010 % Owners

2010 % Renters

23.8%

2010 Census

2020F Renters

48,391

2020F Households * 2010 % Renters

2000-2010 Change in Owners

18,900

2000 & 2010 Census

2000-2010 Change in HHs

24,995

2000 & 2010 Census

2000-2010 Tenure Change Split (owners)

75.6%

2000-2010 Change in Owners / Change in HHs

2010-2020F Change in HHs

20,424

2020F HHs (from above) - 2010 HHs (Census)

2010-2020F Change in Owners

15,444

Tenure change split * 2010-2020F Change in HHs

2010 Owners

139,328

2010 Census

2020F Owners

154,772

2010 Owners + F-Change in Owners

Chester County 2020 Housing Structure Type Projections Model

Source 2010 Single-Family Units

149,205

2010 Census

2010 Multi-Family Units

34,869

2010 Census

2010 % Owners occupying SF-Units

94.2%

2010 ACS Table B25032

2010 % Renters occupying SF-Units

31.1%

2010 ACS Table B25032

2020F Owners

154,772

From Above

2020F Renters

48,552

2020F Households (from above) - 2020F Owners

2020F Owner-Occupied SF Units

145,754

2020F Owners * 2010 %owners in SF-homes

2020F Renter-Occupied SF Units

15,123

2020F Renters * 2010 %renters in SF-homes

2020F Single-Family Units

160,877

2020F Owner-occupied + Renter-occupied SF Units

2020F Multi-Family Units

36,957

2020F HUs - (2020F SF Homes + (2010 Mobile Home 2.7% * 2020F HHs))

2010-2020F Change in SF-Units

11,672

2020F SF homes - 2010 SF homes

2010-2020F Change in MF-Units

2,088

2020F MF units - 2010 MF units

2010 Tenure Splits

Appendix 2

Model Gross Residential Density Estimate

Chester County 2020 Projected Acreage Requirements 2010-2020F Unit Change

13,760

12/11/2012 DVRPC estimates, 2010 Census

2010-2020F Additional Residential Acres

567,671,495 2010-2020F Unit Change * 2010 Total Acres per Unit 13,760

2010-2020F SF Unit Change

Sum of 2010-2020F Unit Changes Above

0.50

DVRPC estimates, 2010 Census

6,848

2010-2020F Unit Change * 2010 Net Acres per Unit

11,672

From Above

Recently-built Acres per SF-Unit

1.7

2010-2020F SF Additional Acres

19,379

2010-2020F SF Unit Change * Recent Acres per SF-Unit

2011 Market Net Density 2010-2020F MF Unit Change

Ridgecrest Subdivision, East Fallowfield Township

2,088

From Above

Recently-built Acres per MF-Unit

0.01

220 Chestnut Associates, West Chester Borough

2010-2020F MF Additional Acres

2010-2020F Unit Change * Recent Acres per MF-Unit

2010-2020F Additional Residential Acres

19,400

2010-2020F SF + MF Additional Acres

2010-2020F SF-Unit Change

11,672

From Above

2010 Average Acres per SF-Unit

0.59

2010-2020F SF Additional Acres

6,845

DVRPC, 2010 Census

2010-2020F MF-Unit Change

2,088

From Above

2010 Average Acres per MF-Unit

0.12

DVRPC, 2010 Census

2010-2020F MF Additional Acres

246

2010-2020F Additional Residential Acres

7,091

2010-2020F SF + MF Additional Acres

2010-2020F SF-Unit Change

11,672

From Above

2010 % of SF Homes that are Detached 2010-2020F SF-Detached Unit Change 2010 Agricultural Acreage

78%

Chester County Planning Commission

9,139

Calculated

178,344

2010 Average Acres per Farm

2010 SF-Detached Farm Residential Units

Alternative Plan Density (Ideal Scenario)

Sum of 2010-2020F Unit Changes Above

2010 Total Acres per Unit

2010-2020F Unit Change Net Residential Density 2010 Net Acres per Unit Estimate 2010-2020F Additional Residential Acres

Net Densities by Unit Type (Business as Usual Scenario)

Source

2,027

Chester County Planning Commission Chester County City-Data 2010 Farm Acreage / Average Acres per Farm

2010 % of SF-Detached that are Farms

Calculated, Chester County Planning Commission

2010-2020F Farm Residential Unit Change

159

2010-2020F SF-Detached Unit Change * 2010 % of SF-Det. that are Farms

Ideal Agricultural Residential Acres per Unit

Conservation Zoning Standard

2010-2020F Ideal Ag. Residential Acreage Increase

6,342

2010-2020F Farm Unit Change * Ideal Acres per Unit

2010 % of SF-Detached that are Traditional Units

98%

Chester County Planning Commission

2010-2020F SF-Detached Traditional Unit Change

8,981

2010-2020F SF-Detached Unit Chang * 2010 % of SF-Detached that are Traditional Units

Ideal SF-Detached Traditional Acres per Unit

0.5

2010-2020F Ideal Traditional SF-Detached Acreage Increase

4,490

2010-2020F SF-Detached Traditional Unit Change * Ideal Acres per SF-Detached Traditional Unit

2010 % of SF-Units that are Attached

22%

Chester County Planning Commission

2010-2020F SF-Attached Unit Change

2,533

2010-2020F SF-Unit Change * 2010 % of SF-Units that are Attached

Ideal SF-Attached Acreage per Unit

0.2

2010-2020F Ideal SF-Attached Acreage Increase

507

2010-2020F MF-Unit Change

2,088

Dense Suburban Standard

Urban Residential Standard From Above

Ideal Acreage per MF-Unit

0.1

Mass Transit Supporting Residential Standard

2010-2020F Ideal MF-Unit Additional Acres

209

2010-2020F MF-Unit Change * Ideal Acreage per MF-Unit

2010-2020F Ideal Additional Residential Acres

11,547

Appendix 3

Ideal Acreage Increase: SF-Farms + SF-Attached + SFDetached + MF-Units

Chester County 2020 Employment Projections DVRPC Employment Projection

312,460 2000 Chester County Employment

Trend Model

2010 Chester County Employment

190,152

Total

224,631

Industrial

2020F Chester County Employment

2000-2010 Rate of Change 118%

2010-2020F County Change

265,362

40,731

47,696

Mining, quarrying, and oil and gas extraction

5,539

207

Construction

11,051

8,986

Manufacturing

19,509

14,971

Wholesale trade

10,548

14,302 9,339

161%

15,001

Transportation & Public Utilities

5,814

Commercial

-52

47%

81%

7,307

-1679

77%

11,489

-3482

136%

19,392

5,090 5,662

177,046

54,341

Retail trade

25,175

26,312

105%

27,500

1,188

Finance, Insurance, Real Estate

11,594

25,210

217%

54,817

29,607

105,697

125,524

119%

149,070

23,546

Services & Information Source

County Business Patterns

2010 Employment * 2000-2010 Rate of Change

2010 Employment / 2000 Employment

2020F Employment 2010 Employment

Chester County 2020 Business-Sector Acreage Projections Current Net Densities (Business as Usual Scenario)

2010 Acres

2010 Employment

2010-2020F Employment Change

2010 Acres per Job

2010-2020F Additional Acreage

Industrial Development

2,800

47,696

0.06

5,539

325

Office and Retail Development

8,300

177,046

0.05

54,341

2,548

Source

DVPRC, Chester County Planning Department

County Business Patterns

Alternative Plan Densities (Ideal Scenario)

2010-2020F Employment Change

Ideal Acres per Job

Industrial Development Office and Retail Development Source

2010 Acres per Job * Employment Trend 2010-2020F Employment Model Change

2010 Acres / 2010 Employment

DVPRC, Chester County Planning Department

County Business Patterns

Ideal

2010-2020F Additional Acreage

0.06

5,539

325

0.03

54,341

1,630

Ideal Acres per Job * Employment Trend 2010-2020F Employment Model Change

Chester County 2020 Community Facility Acreage Projections Current Net Densities (Business as Usual Scenario and the Ideal Scenario) Total

2010 Units per Resident

2010 Units

258

Colleges/Universities

2010 Public Schools and Community Facility Acreage

2010-2020F Unit Growth

0.001

0.000

Public Schools

110

0.000

Public Libraries

0.000

Fire Stations

0.000

Hospitals

0.000

Police Stations

0.000

Source

Chester County Education Dept., Polic Dept., and Planning Commission

2010 Census

External projection change * Units per Resident

Appendix 4

4,500

DVRPC

2010 Average Acres per Unit 17

2010 Acreage / 2010 Units

2010-2020F Additional Acreage 360

2010 Acres per Unit * 2010-2020F Unit Growth

0.59

6,845 0% 94,345

2010 Average Density (acres per unit/job)

2010-2020F Additional Acres Required

Reserve Level

2020 Projected Total Acreage

6,342

184,686

Reserve Level

2020 Projected Total Acreage

Ideal Density (acres per unit/ job)

2010-2020F Additional Acres Required

159

Additional Housing Units or Jobs

507

0.2

2533

4,490

0.5

8981

92,497

4,997

Activity or SF-Detached Single Family Residential SF-Attached Development Traditional Units (Attached Agricultural Units Type Units and Detached)

11,672

Single-Family Units

Additional Units or Jobs

Activity or Development Type

10,848

4,309

209

0.1

2088

Multi-Family Units

2,548

0.05

54,341

Commercial (Office and Retail)

Appendix 5

9,930

1,630

0.03

54341

Commercial (Office and Retail)

Ideal Scenario

4,346

246

0.12

2,088

Multi-Family Units

Business as Usual Scenario (Current net Densities)

3,125

325

0.06

5539

Industrial

3,125

325

0.06

5,539

Industrial

The Summary of the Land Use Program

4,860

360

Community Facilities

8,660

360

Community Facilities

299,407

13,863

Total Developed Land

121,323

10,323

Total Developed Land

186,333

471,877

Conservation (Undeveloped Land)

2020 Projected Density Comparison 2010-2020F Additional Acres Required

Acres Saved

New Population Density on Additional Acres (Residents per Acre)

New Dwelling Unit Density on Additional Acres (DUs per Acre)

Business as Usual Scenario

10,323

3.87

1.33

Ideal Scenario (excluding agriculture)

7,521

2,802

5.31

1.83

Source

From Above

Scenario

Additonal Acres: Ideal - Business 2010-2020F Population Growth 2010-2020F DU Growth / Additional as Usual / Additional Acres Acres

Appendix 6

Model 1, Run 1: Total Work Trips Regression Statistics R R Square Adjusted R Square S Total number of observations

94% 89% 89% 339.29679 380

Work Trips = 1494.9974 + 896.7719 * POP - 91.4706 * Med_Inc - 283.4334 * Pct_BlPov + 229.6239 * Pct_ColGrad + 73.6909 * DU_pSqM ANOVA

d.f.

Regression Residual Total Intercept POP Med_Inc Pct_BlPov Pct_ColGrad DU_pSqM

T (2%) LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

SS 343,248,168 43,055,745 386,303,913

5. 374. 379.

Coefficients 1,494.99737 896.77187 -91.47064 -283.43341 229.62395 73.69085 2.33636

Standard Error 17.40556 21.71013 26.22196 21.52156 23.87276 22.7537

F 596.3191

68,649,634 115,122

LCL 1,454.33172 846.0492 -152.73457 -333.71551 173.8486 20.53002

p-level 0.E+0

UCL 1,535.66301 947.49455 -30.2067 -233.1513 285.39929 126.85168

t Stat p-level 85.89195 0.00% 41.30661 0.00% -3.48832 0.05% -13.16974 0.00% 9.61866 0.00% 3.23863 0.13%

H0 (2%) rejected? Yes Yes Yes Yes Yes Yes

Model 1, Run 2: Total Work Trips

Regression Statistics R

94%

R Square

88%

Adjusted R Square

88%

351.77039

Total number of observations

380 Work Trips = 1494.9974 + 932.5495 * POP - 225.2730 * Pct_BlPov + 202.5910 * Pct_ColGrad

ANOVA d.f.

Regression

339,776,767

113,258,922

Residual

376.

46,527,146

123,742

Total

379.

386,303,913

Coefficients Intercept

Standard Error

p-level 915

LCL

0.E+0

UCL

H0 (2%) rejected? 0.00% Yes

POP

933

890

975

0.00% Yes

Pct_BlPov

-225

-270

-181

-12

0.00% Yes

203

158

248

0.00% Yes

Pct_ColGrad

1,537

p-level

T (2%)

1,453

t Stat

1,495

2.33631

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Model 1, Final: Total Work Trips

Regression Statistics R

92%

R Square

84%

Adjusted R Square

84%

400

Total number of observations

380 Total Work Trips = 1494.9974 + 909.3180 * POP - 288.6600 * Pct_BlPov

ANOVA d.f.

Regression

326,054,820

163,027,410

Residual

377

60,249,093

159,812

Total

379

386,303,913

Coefficients Intercept

Standard Error

LCL

p-level 1,020

UCL

t Stat

H0 (2%) rejected?

Population

909

861

958

0.00% Yes

% Below Poverty Line

-289

-337

-240

-14

0.00% Yes

UCL - Upper value of a reliable interval (UCL)

Appendix 7

0.00% Yes

LCL - Lower value of a reliable interval (LCL)

1,543

p-level

1,495

T (2%)

1,447

Model 2, Run 2: Total Private Car Trips Regression Statistics R

90%

R Square

80%

Adjusted R Square

80%

337.3346

Total number of observations

380

Private Car Trips = 927.6684 + 559.7908 * HHs - 18.7277 * Med_Inc - 197.5973 * Pct_DU_b50 - 180.3210 * Pct_BlPov + 18.3802 * Pct_Hisp + 82.8907 * Pct_White + 192.1199 * Pct_SF_DU

ANOVA d.f.

Regression

171,555,557.79161

24,507,936.82737

Residual

372.

42,331,604.42944

113,794.63556

Total

379.

213,887,162.22105

Coefficients

Standard Error

LCL

215.36988

UCL

p-level 0.E+0

t Stat

p-level

Intercept

927.66842

17.3049

887.23701

968.09983

53.60727

HHs

559.79083

17.65333

518.54535

601.0363

31.71022

Med_Inc

-18.72766

23.14608

-72.80648

35.35115

-0.80911

Pct_DU_b50

H0 (2%) rejected? 0.00% Yes 0.00% Yes 41.90% No 0.00% Yes

-197.59734

20.53326

-245.57154

-149.62314

-9.62328

Pct_BlPov

-180.321

25.41534

-239.70174

-120.94026

-7.09497

Pct_Hisp

18.3802

19.61199

-27.44152

64.20192

0.93719

Pct_White

82.89071

21.53804

32.56893

133.21248

3.84857

0.01% Yes

Pct_SF_DU

192.11986

20.47492

144.28197

239.95775

9.38318

0.00% Yes

T (2%)

0.00% Yes 34.93% No

2.33641

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Model 2, Run 2: Total Private Car Trips Regression Statistics R

90%

R Square

80%

Adjusted R Square

80%

337.14223

Total number of observations

380

Private Car Trips = 927.6684 + 560.8624 * HHs - 199.1935 * Pct_DU_b50 - 162.5841 * Pct_BlPov + 80.6155 * Pct_White + 191.0128 * Pct_SF_DU

ANOVA d.f.

Regression

171,376,495.07406

34,275,299.01481

Residual

374.

42,510,667.147

113,664.88542

Total

379.

213,887,162.22105

Coefficients Intercept HHs

927.66842

Standard Error 17.29503

LCL

UCL 887.26101

p-level

301.54695

0.E+0

t Stat

p-level

H0 (2%) rejected?

968.07584

53.63786

0.00% Yes

560.8624

17.55001

519.85926

601.86553

31.95796

0.00% Yes

Pct_DU_b50

-199.19352

20.47869

-247.03911

-151.34793

-9.72687

0.00% Yes

Pct_BlPov

-162.58405

21.06737

-211.80501

-113.36309

-7.71734

0.00% Yes

Pct_White

80.61548

20.35491

33.0591

128.17187

3.96049

0.01% Yes

Pct_SF_DU

191.01284

19.50867

145.43358

236.5921

9.79118

0.00% Yes

T (2%)

2.33636

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Appendix 8

Model 2, Final: Total Private Car Trips Regression Statistics R

85%

R Square

73%

Adjusted R Square

73%

393

Total number of observations

380 Private Car Trips = 927.6684 + 573.2613 * HHs - 278.5865 * Pct_DU_b50 + 223.5675 * Pct_SF_DU

ANOVA d.f.

Regression

155,888,422

51,962,807

Residual

376

57,998,741

154,252

Total

379

213,887,162

Coefficients

Standard Error

p-level 337

LCL

UCL

t Stat

p-level

H0 (2%) rejected?

Intercept

928

881

975

0.00% Yes

Total Households

573

526

621

0.00% Yes

% of DUs Built Before 1950

-279

-331

-226

-12

0.00% Yes

% of Single-Family DUs

224

171

276

0.00% Yes

T (2%)

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Model 3, Run 1: Drive-Alone Mode Share

Regression Statistics R

77%

R Square

59%

Adjusted R Square

58%

0.12415

Total number of observations

380

Drive-Alone% = 0.4536 + 0.1184 * Pct_AfAm + 0.0553 * POP - 0.0576 * DU_pSqM - 0.0411 * Normalized Employment Access Index (0-100) + 0.1680 * Pct_White ANOVA d.f.

Regression

8.14421

1.35737

Residual

373.

5.74901

0.01541

Total

379.

13.89322

Coefficients Intercept

45.36%

Pct_AfAm

11.84%

POP

5.53%

Standard Error

LCL

0.64%

p-level

88.06709

UCL

0.E+0

t Stat 71.22982

p-level

H0 (2%) rejected? 0.E+0 Yes

43.88%

46.85%

1.31%

8.79%

14.89%

9.06091

0.000% Yes

0.81%

3.63%

7.43%

6.79569

0.000% Yes

DU_pSqM

-5.76%

0.89%

-7.85%

-3.67%

-6.4392

0.000% Yes

Normalized Employment Access Index (0-100)

-4.11%

0.83%

-6.04%

-2.18%

-4.97719

0.000% Yes

Pct_White

16.80%

1.32%

13.72%

19.88%

12.73243

0.000% Yes

T (2%)

2.33639

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Appendix 9

Model 3, Final: Drive-Alone Mode Share Regression Statistics R

63%

R Square

40%

Adjusted R Square

39%

0.14916

Total number of observations

380 Drive-Alone% = 0.4536 - 0.0503 * DU_pSqM - 0.0445 * Normalized Employment Access Index (0-100) + 0.0634 * POP

ANOVA d.f.

Regression

Residual

375.

Total

379.

Coefficients

Standard Error

p-level 62

LCL

UCL

t Stat

p-level

H0 (2%) rejected?

Intercept

45%

0.765%

44%

47%

0.00% Yes

Dwelling Units per Square Mile

-5%

1.043%

-7%

-3%

-5

0.00% Yes

Normalized Employment Access Index (0-100)

-4%

0.959%

-7%

-2%

-5

0.00% Yes

Total Population

0.967%

0.00% Yes

T (2%)

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Model 4, Final: Rail-Transit Mode Share

Regression Statistics R

26%

R Square

Adjusted R Square

0.068

Total number of observations

380 Rail_Transit% = 0.0794 + 0.0107 * SEPTA_Stns - 0.0153 * Tr_Area_sq

ANOVA d.f.

Regression

0.124

0.062

Residual

377.

1.747

0.005

Total

379.

1.870

Coefficients Intercept

Standard Error

LCL

13.346

UCL

# of Septa Stations

0.4%

Tract Area (Square Miles)

-2%

0.4%

-2%

T (2%)

p-level

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Appendix 10

t Stat

p-level

H0 (2%) rejected?

22.7452

0.00% Yes

3.04377

0.25% Yes

-1%

-4.37913

0.00% Yes

Model 5, Run 1: Bus Mode Share Regression Statistics R

71%

R Square

50%

Adjusted R Square

50%

0.09974

Total number of observations

380

Bus% = 0.1862 + 0.0237 * DU_pSqM + 0.0848 * Pct_BlPov + 0.0020 * Pop_Over65 - 0.0090 * Bus_Routes - 0.0215 * Pct_MF_DU

ANOVA d.f.

Regression

3.7563

0.62605

Residual

373.

3.71083

0.00995

Total

379.

7.46713

Coefficients Intercept

Standard Error

18.6%

62.92841

LCL

0.51%

p-level 0.E+0

UCL 17.42%

t Stat

19.81%

p-level

H0 (2%) rejected?

36.38724

0.0% Yes 0.0% Yes

DU_pSqM

2.4%

0.58%

1.01%

3.73%

4.08015

Pct_BlPov

8.5%

0.66%

6.95%

10.02%

12.93554

Pop_Over65

0.2%

0.57%

-1.13%

1.53%

0.34976

Bus_Routes

-0.9%

0.53%

-2.14%

0.35%

-1.68493

9.3% No

Pct_MF_DU

-2.1%

0.53%

-3.39%

-0.90%

-4.02959

0.0% Yes

T (2%)

0.0% Yes 72.7% No

2.33639

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Model 5, Run 2: Bus Mode Share

Regression Statistics R

71%

R Square

50%

Adjusted R Square

49%

0.09981

Total number of observations

380 Bus% = 0.1862 + 0.0897 * Pct_BlPov + 0.0250 * DU_pSqM - 0.0079 * Bus_Routes - 0.0221 * Pct_MF_DU

ANOVA d.f.

Regression

3.73118

0.9328

Residual

375.

3.73595

0.00996

Total

379.

7.46713

Coefficients Intercept

18.6%

Standard Error

LCL

0.51%

p-level

93.63043

UCL

0.E+0

t Stat

17.42%

19.81%

36.36181

p-level

H0 (2%) rejected? 0.00% Yes

Pct_BlPov

9.0%

0.53%

7.73%

10.22%

16.88203

0.00% Yes

DU_pSqM

2.5%

0.53%

1.25%

3.74%

4.67517

0.00% Yes

Bus_Routes

-0.8%

0.53%

-2.02%

0.43%

-1.51001

Pct_MF_DU

-2.2%

0.53%

-3.45%

-0.97%

-4.15071

T (2%)

2.33633

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Appendix 11

13.19% No 0.00% Yes

Model 5, Final: Bus Mode Share Regression Statistics R

70%

R Square

50%

Adjusted R Square

49%

0.09998

Total number of observations

380 Bus% = 0.1862 + 0.0249 * DU_pSqM + 0.0900 * Pct_BlPov - 0.0238 * Pct_MF_DU

ANOVA d.f.

Regression

3.70846

1.23615

Residual

376.

3.75866

0.01

Total

379.

7.46713

Coefficients Intercept Dwelling Units per Square Mile

Standard Error

p-level

123.65952

LCL

0.E+0

UCL

t Stat

p-level

H0 (2%) rejected?

18.6%

17%

20%

0.00% Yes

% Below Poverty Line

10%

0.00% Yes

% of Multi-Family DUs

-2%

-4%

-1%

-5

0.00% Yes

T (2%)

2.33631

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Model 6, Run 1: Bike-Pedestrian Mode Share

Regression Statistics R

0.68258

R Square

0.46592

Adjusted R Square

0.46022

0.09481

Total number of observations

380

Bike_Ped% = 0.0952 - 0.0133 * Med_Value + 0.0454 * Pct_ColGrad + 0.0685 * Normalized Employment Access Index (0-100) ANOVA d.f.

Regression

2.94057

0.73514

Residual

375.

3.37074

0.00899

Total

379.

6.3113

Coefficients

Standard Error

LCL

p-level

81.78577

UCL

0.E+0

t Stat

p-level

H0 (2%) rejected?

Intercept

9.52%

11%

19.57047

0.00% Yes

Med_Value

-1.33%

-3%

-1.73592

8.34% No

Pct_ColGrad

4.54%

5.87371

0.00% Yes

Normalized Employment Access Index (0-100)

6.85%

12.65626

0.00% Yes

T (2%)

2.33633

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Appendix 12

Model 6, Final: Bike-Pedestrian Mode Share Regression Statistics R

67%

R Square

45%

Adjusted R Square

45%

0.09556

Total number of observations

380 Bike_Ped% = 0.0952 + 0.0352 * Pct_ColGrad + 0.0748 * Normalized Employment Access Index (0-100)

ANOVA d.f.

Regression

2.86895

1.43448

Residual

377.

3.44235

0.00913

Total

379.

6.3113

Coefficients Intercept

Standard Error

9.5%

LCL

0.5%

p-level

157.10134

UCL 8.4%

t Stat 10.7%

19.4174

p-level

H0 (2%) rejected? 0.00% Yes

% of College Graduates

3.5%

0.5%

2.4%

4.7%

7.10458

0.00% Yes

Normalized Employment Access Index (0-100)

7.5%

0.5%

6.3%

8.6%

15.08508

0.00% Yes

T (2%)

LCL - Lower value of a reliable interval (LCL) UCL - Upper value of a reliable interval (UCL)

Appendix 13