4 land use program, suitability analysis, travel demand regressions

Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

Land Use Program Suitability Analysis Travel Demand Regressions Chester county is located in the state of Pennsylvania. It was one of the three original Pennsylvania counties created by William Penn in 1682. Now, it is part of the Delaware Valley region. The county seat is West Chester. It is the highest-income county in Pennsylvania and 24th highest in the nation as measured by median household income (2010). Eastern Chester County is home to many communities that comprise the Main Line western suburbs of Philadelphia, while part of its southernmost portion is considered suburban Wilmington, Delaware, along with southwest Delaware County.

Figure 1.1 Location of Chester county

The tables of Land Use Program and their data sources are presented in the Appendix.

Below is an index of these tables.

Table 1-1 Appendix tables for Question 1

Name

Content

Table a-1

2025 Chester County Population Projection

Table a-2 Table a-3 Table a-4

Households Projection

Households Projection by Tenure

Structure Type Projection by Tenure

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions Table a-5 Table a-6-1

Table a-6-2 Table a-6-3 Table a-7-1 Table a-7-2 Table a-8

Residential Acreage Requirements Projection Using Development Densities

Employment Change Projection, Chester County, 2010-2025 (External Projection and Step-down Projection) Employment Change Projection, Chester County, 2010-2025 (Trend Projection)

Employment Change Projection, Chester County, 2010-2025 (Shiftshare Projection) Acreage Requirements Using Development Densities (Apply Industry Standards)

Acreage Requirements Using Development Densities (Apply Current Net Densities) Summary Land Use Program

Land Use Program Population projection (Appendix table a-1)

For the 2025 Chester county population projection, the external projection comes from Delaware Valley Regional Planning Commission, January 2012, which is latest and convincing. Its projection is 573,108. There are three other methods to project Chester county’s 2025 population. For the first one, it is assumed that Chester county’s population share of 5 SE PA counties(Bucks county, Chester county, Delaware county, Montgomery county and Philadelphia county) and 9-county Greater Philadelphia region(besides the five above, other four are Burlington county, Camden county, Gloucester county and Mercer county) remain unchanged in the future. The projected population are 526,598 and 526,183 respectively. They are smaller to the external projection.

For the second approach, the Time trend regression model is used to figure out the population changing trend from 1970 to 2010, with a time interval of five years. Its changing trend is quite linear, as shown in the graph below. This regression model is very satisfactory, with an R square of 0.992. The projected population is 573,166, which is very close to the external projection. If Chester county’s population growth rate stays the same, especially stays the same as most recent years, from 1990 to 2010, its 2025 population will be even larger. However, a more practical assumption is that its population growth rate will slow down gradually. Hence it is much likely that the future population will be a little smaller than the trend-model result.

Chester county population trend

600,000 500,000 400,000 300,000 200,000 100,000

Chester county population

0 1970

1975

1980

1985

1990

1995

2000

Figure 1.2 Chester county population trend

2005

2010

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

The third method is the Step-down trend regression model(step-down to Philadelphia MSA from 1975 to 2010, with a time interval of five years). It assumes a constant share of Chester county’s population of Philadelphia MSA’s population. This regression model is also very satisfactory, with an R square as high as 0.994. The projected share in 2025 is 0.088 and the projected population is 496,551. It is smaller than former projections because the projected Philadelphia MSA population is 5,635,000, which is smaller than the population in 2010. This decreasing regional population may undermine the accuracy of the projection and the actual future population will be higher than its projected result.

Chester county share trend 9.00% 8.00% 7.00% 6.00% 5.00% 4.00% 3.00% 2.00% 1.00% 0.00%

Chester county share 1975

1980

1985

1990

1995

2000

2005

2010

Figure 1.3 Chester county population share trend

Based on the analysis above, the external projection made by DVRPC is the most convincing one and it will be used in following projections.

Household, household by tenure, and structure type projections

Two methods are used to project the number of future households. First, from 2010 census data, I calculated the average household size of Chester county and deem it remains same in the future. Hence, the 2025 projected household will be 216,267. Secondly, I split 2010 headship rate to four age groups—under 24 years old, 25 to 44 years old, 45 to 64 years old and 65 years old and over. I calculated the headship rates for each age group and assume that the 2025 population has the same structure as that in 2010. Then, by applying the four headship rate separately, I get the number of household for each age group. The final total household is 208,301, which is very close to the first projection. These two projections are both reasonable. I choose the bigger one because surplus land is better than overcrowded development or land shortage. (Appendix table a-2)

Then, I use three methods to convert households to households by tenure. The first method is very simple and direct that I assume the ownership rate in 2025 is the same as that in 2010. So the number of projected owners in 2025 is 164,796. The second method assumes the rate of ownership change to total household change between 2000 and 2010 is constant.

Hence, its projected 2025 owners is 164,559, which is very close to the first projection. The third method split ownership rate by age and it assumes the ownership rate by age is constant in the future. Its projected number of owners in 2025 is 160,533, which is smaller

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

than the former two. I choose the second one as the most convincing one. (Appendix table a3) As to the tenure split household projection, I apply the 2010 tenure rate by structures.

Based on 2010 census data, I can calculate the percentage of owners who occupy singlefamily homes and the percentage of renters who occupy single-family homes. By multiplying numbers of owners and renters in 2025 separately, I can get the owneroccupied single-family homes. The final projected single-family homes is 170,931 and the projected apartments is 45,336. (Appendix table a-4)

Residential acreage requirements projection (Appendix table a-5)

There are five methods to convert household projection to residential acreage requirements projection. Using data from DVRPC, I apply the gross residential density(0.542 acres per unit) to 2010-2025 additional residential acres and the result is 18,958 acres. The second approach is to apply net residential density. Its result is 17,656 acres in total. The third approach splits net densities by unit type. It is more accurate than the former two because houses of different types need different acres per unit. The final single-family additional acreage is 15,972 and the additional multi-family acreage is 955 acres. This method assumes current construction standards or customs will last to the future. The fourth approach applies recent market net densities. I find the data from Chester County Table 1-2 2011 Chester county new housing summary

Planning Commission that average single-family density in 2010 is 1.2 units per acre. Thus, each unit occupies 0.833 acre of land. Then, I use the ratio of single-family unit density to multi-family unit density in the third approach, and generate the multi-family unit density is 0.154 acres per unit. Hence, the projected single-family additional acreage is 21,987 acres and the projected multi-family additional acreage is 1,321 acres. This recent ratio is actually very high that more land will be consumed in the future. It is not good for smart growth and sustainable development. So an alternative plan densities are provided that I assume the future net acres per single-family unit is 0.45 and the net acres per multi-family unit is 0.08.

Thus, the projected single-family additional acreage is 11,873 acres and the projected multifamily additional acreage is 685 acres. It consumes much less land than the third and the fourth methods.

However, this better alternative plan density may not happen in the future. Thus, I will choose the third projection. It is much more practical on the on hand, on the other, it avoids possible land shortage in the future.

Employment projection (Appendix table a-6-1, a-6-2, a-6-3)

There are four approaches to predict future employment. The first one is an external projection, which I find in DVRPC. Its 2025F employment is 330,020. The second approach

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

is using the step-down model: step down Chester county’s employment by sectors to Pennsylvania state’s employment. I can find Pennsylvania state’s employment projection, 2010—2020. Since its employment data by sectors for 2010 is different from the data I find in United Census Bureau, I first convert its 2010 data to the data from U.S. Census Bureau.

Then convert its projected 2020 employment proportionally. Now, I calculate the growth rate of Pennsylvania’s employment by five years and use this rate to predict its 2025 employment. When I generate the results, I apply the 2010 Chester county’s step-down ratio to 2025 Pennsylvania’s employment by sectors and thus get the projected employment for Chester county by sectors. The total employment in 2025 for Chester county is 300,080, with an increase of 74,892 from 2010.

The third approach is the trend model method. I assume that the annual employment change rate is stable during 2000—2010 and 2010—2025. The total employment is 388,049, with an increase of 162,801 from 2010. This approach is less convincing because it may enlarge the effect of some accidental changing rate. Besides, some employment kinds and types may vary dramatically during 10 years or even shorter period. So this annualized changing rate is not so predictable for future employment. The last approach is the shiftshare model method. I assume Pennsylvania state is the regional economy as to Chester county. Instead of using the 2000—2010 actual economic growth rate, which is -1.17%, I use the 2010—2025 projected economic growth rate—6.36%, as the macro economic growth factor. And I assume the industry mix factor stays the same. The future local competiveness factor’s is half of that during 2000 and 2010. Thus, the total employment in 2025 for Chester county is 323,300, with an increase of 98,052 from 2010.

Acreage requirements projection by different sectors (Appendix table a-7-1, a-7-2)

I use two methods to do the acreage requirement projection. The first one is to apply industry standards. Based on Urban Land Use Planning(5ed), by multiplying acreage per job and then divided by floor area ratio for different kinds of industries, I can get the additional acres. Since the number of manufacturing employment decreases in the future, no land is needed for manufacturing or perhaps some industrial land could be transformed to other kinds of land use. 1,077 acres of land is needed for wholesale, transport and utilities. No more land for financial investment, insurance and real estate & rental & leasing is needed also because of the employment reduction. Services need a lot of land in the future, about 6,465 acres in total.

The second approach is to apply current net densities. By using the rate of employment for public schools of Philadelphia, I get the employment of Chester’s public school both for 2010 and 2025. The results show that -24 acres are need for industrial land use, 327 acres are needed for commercial land use and 2,366 acres are needed for schools and public facilities.

Summary of land use program(alternative land use projections) (Appendix table a-8)

Based on the Land Use Rules of Thumb and Commercial Densities for compact development below and considering that Chester county is a fast growing county that it may act as a working destination, I set higher land use density for retail, office and industrial land use.

There are 40 retail jobs, 75 office jobs and 10 industrial jobs per acre. In the alternative scenario, these standards are set even higher. This is beneficial to land conservation.

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

Compact land use arrangement is also helpful to reduce auto dependency and pollution. The Land Use Plan for Chester County(10/2007) by Chester County Planning Commission also propose such measures to reduce land consumption and preserve the natural environment of Chester county.

Figure 1.4 Commercial Densities Source: Land-Use Densities, Guide for Transit-Oriented Development Table 1-3 Land-Use Densities: Rules of Thumb

Source: Land-Use Densities, Guide for Transit-Oriented Development

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

Besides, land use projection should provide some flexibility. For retail, office and industrial land use, certain level of reservation should be set that developers will have choices of where to allocate in the future. This is not only an economic consideration, but also allows better allocation between different land use to avoid mutual interference. For residential land use, however, this reservation is not so necessary because it is not as elastic as commercial or other land use that to certain location.

Suitability Analysis

When selecting the attractions and discouragements for Question 2 and deciding the weights of different factors, I make references to the two tables below.

Table 2-1 Attractions and Discouragements for Industrial, Residential, and Commercial Development

Source: Development and Calibration of the UPlan Land Use Planning Model, Delaware Valley Regional Planning Commission, 07/2005 Table 2-2 GIS Variable Buffer Size in Feet and Weight

Source: Development and Calibration of the UPlan Land Use Planning Model, Delaware Valley Regional Planning Commission, 07/2005

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

The table below shows the total acreage of the most suitable areas for each kind of land use, and the total available land for these three types of land use(part of them are overlapped).

Table 2-3 Land use acreage summary

Grid cell Acreage

Residential

Commercial

10,250

9,563

164

153

Industrial

336

21,000

Total

365

22,813

As shown in the three summary maps above and heir overlapped map below, these three kind of land use all show an allocation consistency with current construction, traffic system, with consideration of the natural environment. Residential and commercial land use are close to rail stops, allocated along 4-lane roads, rail lines and highways. They are also adjacent to built up areas, the current city or borough. All of these are beneficial for future development because new constructions can take advantages of existing infrastructures.

Better accessibility is good to develop commerce. And residents can make use of the trail line to commute. Most advocated areas are mixed-used that residents can walk or bike to work or stores, rather than use a car. Development is compact that more land can be reserved.

Farms, parks, forests, wetlands and most pastures are preserved that no construction is advocated(but not totally forbidden). Constructions are also arranged with a distance of rivers and streams, in order to avoiding harm to eco-system. Areas with high slope percentage are also not advocated for development, especially for commercial and industrial land use. While as to residential land use, certain level of slope is harmless.

Design can integrate natural landscape with future construction possibilities and it is also helpful to form the sense of place.

Commercial and residential land use are mostly adjacent to each other while industrial land use is not always close to them. On one hand, this provides more flexibility for industrial land use; on the other hand, in case of some industries that may have negative effect on residents or other daily activities, some industrial land use should be allocated far away from new construction areas and current built up areas. Besides, the space between them can act as green buffers that possible pollution(like air or noise) can be reduced or eliminated. Besides, industrial land use is more consistent with traffic system than the other two land uses. It is because that some industries depends more on traffic system.

Figure 2.1 Overlapped summary map

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

Considering current development mode of Chester county and future development principles, I choose the first scenario in Table a-8 in Appendix to do this land allocation analysis. The table below is a summary of land in both demand and supply sides.

Table 3-1 Land Allocation—Demand and Supply Residential Demand acreage Grid cells

Supply acreage Grid cells

Commercial

Industrial

Total

9,770

1,302

1,656

12,728

10,250

9,563

21,000

22,813

156 164

21

153

26

336

204 365

As shown in the table, commercial and industrial land use both have high flexibility of where to locate for new constructions, while the demand of residential land is just a little less than its supply. However, since many grid cells are overlapped by residential and commercial land use, and there is much less demand for commercial land use than residential, I should consider the allocation of commercial land use first. This will guarantee that commercial land use could get allocated in good places without taking up too much good places for residential land allocation.

I will allocate commercial land use in urban areas, near existing built up areas. Urban area will provide available infrastructures and near existing built up area will make it easier to attract customers. Also, avoid sprawling to distant rural area will reduce the consumption of environmental-valuable land and generates less auto usage. Even though rural land is cheaper than that near city or borough, the profit it loses can be made up by more consumers and less construction investment. At the same time, considering developer’s profit, commercial land should be allocated near highway or 4-land roads. This will improve the accessibility of commercial services. Or new commercial centers should be allocated near current residential area. Thus, people can walk to stores rather than using a car. This is beneficial for both the developer and the public. For the developer, less land will be needed to build parking space, which usually cost much, and residential communities will provide more customers for the commercial center. For the public’s well being, more land will be reserved that they can enjoy more green space, less auto traffic will be generated that local traffic condition will not be affected, and they can enjoy better convenience of shopping. I will also consider some commercial land be allocated near rail stops. Since rail stops are place of high-density population flow, such areas have high possibilities to attract customers. And this will also encourage people to use public transit. At the same time, considering the natural topography, areas with slope bigger than 5% are not advocated for development. It is good to help preserving natural landscape while at the same time reduce the development cost. In summary, most of the commercial land will be allocated in flat areas, along the rail line, in the northeast part of Chester county. Some commercial land will be allocated near existing residential areas, like infill to these areas.

Next, I want to mainly consider about the allocation of residential land use. New residential units should not extend greatly to rural areas. I will allocate them in urban areas with existing infrastructures and close to city or borough. Mixed land use should be advocated

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

that residential land use ought to be close to commercial, office land use, public facilities, like school etc. This will encourage less car trips. Residential land can also be allocated along the rail line, near rail stations, which will encourage people to use more public transit.

Some residential land will be allocated near valuable natural resources, like river, streams and wetlands, but with certain distance from them to avoid the possible negative effect of construction. This will increase the diversity of houses for people to choose, which have better access to natural resources. In sum, most residential land will be allocated along 4lane roads and rail lines, close to existing commercial, office, or public facility land use or close to future commercial areas. There is a moderate extension to rural areas, considering the rapid population growth in Chester county, but the extension should take future development possibilities into consideration, which means that their allocation should provide possibility for future mixed land use allocation and transit system construction.

Industrial land use allocation should obey three basic principles. First, from the owner’s perspective, industrial areas should be adjacent to major roads to guarantee economic profits; secondly, from residents’ perspective, industrial areas should not have negative effect on current/adjacent neighborhoods; thirdly, from the perspective of the public, they should not be too far away from employees, which will generate more traffic and pollution, and it should have least negative effect on natural environment. Hence, what I need to do is to find a balance between different groups. The location of industrial land use should also consider current abandoned industrial places which will reduce new land consumption.

Based on these principles, I will allocate industrial land use mostly in the northeast and east edge of Chester county. These areas are adjacent to highways or 4-lane roads. They are also infill to urban areas, with good accessibility for residents while have green buffer around.

Some industrial land use, if the industry has obvious negative effect, will be allocated in the middle south area of Chester county. This area is close to major roads while with relatively longer distance with residential areas.

Travel Demand Regressions

The tables of six final “best” model regression results are presented in the Appendix. Below is an index of these tables.

Table 4-1 Appendix tables for Question 4

Name

Content

Table d-1

Work-trip In Total Regression Result

Table d-2 Table d-3 Table d-4 Table d-5 Table d-6

Work-trip By Private Car Regression Result

Drive Alone Rate Regression Result Bus Rate Regression Result

Rail Transit Rate Regression Result Bike&Ped Rate Regression Result

The table below shows the regression process. First, I select out some hypothesized independent variables for each regression model and input them for the first round.

Variables in red font are expected to have positive effect on their dependent variables while variables in blue font are expected to have negative effect on their dependent variables.

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

11

There are some other independent variables that I am not sure about their effect. So I include them also to see if they have effect on their dependent variables. In each round, the most insignificant independent variables are removed from its former independent variables group. When all the remaining independent variables are significant(t Stat>2 or t Stat<-2), I stop the regression. The final significant independent variables for each regression are in table cells with green background.

Table 4-2 Regression rounds, Hypothesized independent variables and their expected signs, and the “Best” model results

Regression

Work-trip in total

Work-trip by private car

Drive alone%

Bus%

Rail transit%

Bike&ped%

POP

POP

Pop_Over65

Pop_Over65

POP

POP

Pct_White

Families

Pct_White

Pct_AfAm

Pop_Over65

Pop_Over65

Pop_Over65 Pct_AfAm Pct_Hisp

Pct_Asian Hypothesized Independent Variables.

First round

Avg_HHSze

Pct_ColGrad Med_Inc

Pct_SF_DU

Pct_DU_b50 Med_Rent

Med_Value DU_pSqM

Tr_Area_sq

N_EmAc_Ind POP

Pop_Over65 Second round

Pct_White Pct_Hisp

Pct_Asian

Avg_HHSze

Pct_ColGrad

Pop_Over65 Pct_White Pct_AfAm

Pct_ColGrad Med_Inc

Pct_SF_DU

Pct_DU_a90 Med_Rent

Med_Value DU_pSqM

SEPTA_Stns Bus_Routes Tr_Area_sq

N_EmAc_Ind POP

Pop_Over65 Families

Pct_White Pct_AfAm

Pct_ColGrad Med_Inc

Families

Pct_AfAm Pct_Hisp

Avg_HHSze

Pct_ColGrad Med_Inc

Pct_SF_DU

Pct_DU_a90 Med_Value DU_pSqM

SEPTA_Stns Bus_Routes Tr_Area_sq

N_EmAc_Ind Pop_Over65 Families

Pct_White Pct_AfAm Pct_Hisp

Avg_HHSze

Pct_ColGrad

Pct_White Pct_Hisp

Pct_Asian Med_Inc

Pct_BlPov

Pct_MF_DU

Pct_DU_a90 Med_Rent DU_pSqM

SEPTA_Stns Bus_Routes Bus_Len_In Tr_Area_sq

N_EmAc_Ind Pop_Over65 Pct_White Pct_AfAm Pct_Asian Med_Inc

Pct_BlPov

Pct_MF_DU

Pop_ Under5 Pct_White Pct_AfAm Pct_Hisp

Avg_HHSze Med_Inc

Pct_BlPov

Pct_MF_DU

Pct_DU_b50 Med_Rent DU_pSqM

SEPTA_Stns Tr_Area_sq

N_EmAc_Ind POP

Pop_ Under5 Pop_Over65 Pct_White Pct_AfAm Med_Inc

Pct_BlPov

Pop_ Under5 Pct_White Pct_AfAm Pct_Hisp

Pct_Asian

Pct_ColGrad Med_Inc

Pct_BlPov

Pct_MF_DU

Pct_DU_a90 Med_Rent DU_pSqM

Tr_Area_sq

N_EmAc_Ind POP

Pop_ Under5 Pop_Over65 Pct_AfAm Pct_Asian Med_Inc

Pct_BlPov

Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions Med_Inc

Pct_SF_DU

Med_Inc

Pct_DU_a90

Pct_MF_DU

Pct_MF_DU

Med_Value

Med_Value

DU_pSqM

SEPTA_Stns

SEPTA_Stns

Med_Rent

Pct_DU_b50 DU_pSqM

N_EmAc_Ind POP

Pop_Over65 Pct_White Pct_Hisp

Avg_HHSze Third round

Pct_ColGrad Pct_DU_b50 Med_Value DU_pSqM

N_EmAc_Ind

Fourth round

Fifth round

Pct_DU_a90 DU_pSqM

SEPTA_Stns

N_EmAc_Ind Pop_Over65 Families

Pct_White Pct_AfAm

Pct_ColGrad Med_Inc

Pct_SF_DU

Med_Value DU_pSqM

N_EmAc_Ind

POP

Pop_Over65

Pct_Hisp

Pct_White

Pct_ColGrad

Med_Inc

DU_pSqM

Med_Value

Pct_White

Families

Avg_HHSze

Pct_ColGrad

Pct_DU_b50

Pct_SF_DU

N_EmAc_Ind

DU_pSqM N_EmAc_Ind

Pct_SF_DU SEPTA_Stns Tr_Area_sq

N_EmAc_Ind

DU_pSqM

N_EmAc_Ind

Pct_DU_b50 Tr_Area_sq

N_EmAc_Ind

12

Pct_DU_a90 DU_pSqM

N_EmAc_Ind

Pop_Over65

Pct_White

POP

POP

Pct_White

Pct_Asian

Pct_White

Pop_Over65

Pct_MF_DU

Pct_Asian

SEPTA_Stns

Pct_BlPov

N_EmAc_Ind

DU_pSqM

Families

Pct_AfAm Pct_Hisp

Avg_HHSze

Pct_ColGrad Med_Inc

Pct_SF_DU DU_pSqM

Pct_AfAm Med_Inc

Pct_BlPov

Pct_MF_DU DU_pSqM

SEPTA_Stns

N_EmAc_Ind

Pop_ Under5

Pop_ Under5

Pct_AfAm

Pct_AfAm

Pct_DU_b50

Med_Inc

Tr_Area_sq

Pct_MF_DU

Tr_Area_sq

N_EmAc_Ind Pop_Over65

Pct_White

Pct_White

Families

Pct_AfAm

Pct_AfAm

Pct_White

Med_Inc

Pct_MF_DU

Pct_AfAm

Pct_BlPov

Pct_DU_b50

Pct_Hisp

Pct_MF_DU

SEPTA_Stns

Avg_HHSze

DU_pSqM

Tr_Area_sq

Pct_SF_DU

N_EmAc_Ind

N_EmAc_Ind

DU_pSqM

Tr_Area_sq

POP

N_EmAc_Ind

Pct_White

Pct_White

Families

N_EmAc_Ind

Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions Pct_Hisp

Pct_AfAm

Avg_HHSze

Pct_Hisp

Pct_ColGrad

Avg_HHSze

Pct_DU_b50

DU_pSqM

DU_pSqM

Tr_Area_sq N_EmAc_Ind

R squares of these six regressions range from 0.16 to 0.89. The least fitting regression is train transit regression. The best ones are work-trip in total regression(0.89) and work-trip by private car regression(0.82). The other three are moderately fitting: bus rate(0.71), bike & ped rate(0.67), drive alone rate(0.63).

There are many factors that would affect the fitting degree of regression models. First, the result is much depended on the available data. For example, the demographic, social and physical characteristics available have more direct influence on the total work-trips and the work-trip by private cars, so their fitting degree are more satisfactory. On the other hand, for the train transit regression, the available data is not enough or not so appropriate for running the regression model. Some data, like the accessibility of a train station from homes, the distance between train station and working place, and the level of comfort or safety of using train transit might be helpful to explain the train transit rate. For bike&ped rate, whether there are consistent bike lanes, what degree the land is mixed-used etc will affect the percentage that people bike or walk to work.

Secondly, some data are ambiguous that they should be split up to be considered in each regression. For instance, the independent variable N_EmAc_Ind(normalized employment access index) is a comprehensive variable that it cannot show the direct and independent effect on each different regression. A high-value employment access may mean that both cars can buses can arrive at employment location quickly and safely that whether people choose car or bus cannot be explained by this variable. Thus, it should be split to several different accessibility indexes, like the accessibility for cars, buses, or bikes that could contribute independently to the dependent variables and generate better regression results.

Thirdly, some data are not “pure” enough that they may have overlapping effect on dependent variables. For example, the variable Tr_Area_sq(tract area in square miles) itself can have either positive or negative effect on one dependent variable. If the tract is very large while the built-up area is concentrated in one corner of this area which is very close to the employment center, the variable might have negative effect on the total work-trips.

However, if the tract is small while constructions are scattered greatly, the variable might have positive effect on the total work-trip. Hence, the relationship between N_EmAc_Ind and total work-trip is not so consistent. Some other data are needed, like the construction concentration ratio, to help mitigate the variable effect generated by the “impure” independent variables.

The work-trip in total regression (Appendix table d-1)

The R square of this regression is 0.89, indicating that the regression is very fitting. For independent variables, total population has a very significant positive effect(t Stat=39.81)

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

on the total work-trip. It makes sense that larger population have relatively larger number of employment. Also, a higher significant degree indicates a higher employment rate. The percentage of white population and the percentage of college grads also contribute positively to the total work trips, with t Stats of 8.31 and 8.27 separately. It shows that white population or people with high education tend to generate work trips. Dwelling density has a small significant effect(t Stat=2.41), which shows that higher residential density can generate more work trips. On the other hand, percentage of Hispanic, average household size and percentage of homes built before 1950 have moderate negative effects on total work trips(-6.44, -4.45, -2.75 respectively). Hispanic population may either has low employment rate or they prefer working at home. Higher percentage of homes built before 1950 may indicate relatively inactive economic environment that less work trip will be generated.

Many independent variables fall out of the initial model run. The number of elderly people and the percentage of African-Americans or Asians have no significant effect on the total work trip. Probably, people over 65 years old tend to work rather than retire in Chester county. The percentage of African-Americans changes dramatically among different tracts, this may lead to inconsistent effect for different tracts that itâ&#x20AC;&#x2122;s final result is insignificant. Or it is because African-Americans generate neither more nor less total work trip in all. For the percentage of Asian, since it is very small in most tracts, its effect is insignificant to the total work trip. Median household income, percentage of single-family dwelling units, median rent, median home value, tract area, normalized employment access index are also insignificant to the total work trips. It seems that total work trips has little relationship with economic factors, like income and home value. It also has little relationship with geographic factors like tract area and employment accessibility. Whether there are more work trips or not depends on largely on the demographic factors.

The work-trip by private car regression (Appendix table d-2)

This regression also has a high fitting degree(R square=0.82). The most positively significant variables are number of families(t Stat=18.91) and percentage of white population(t Stat=10.16). The other three positively significant variables are population over 65(3.55), median income(3.29) and percentage of college grads(2.85). It is reasonable that white population with high median income and high education degree tend to use private cars to go to work. As to the number of families, since the independent variable-total population falls out in the first round, more families might indicate stable economic status of residents. Hence, there might be a higher auto ownership rate that people tend to use private cars to commute. The number of population over 65 tend to use private cars to commute may indicate that private cars are more comfortable or safer for them rather than bus or trail transit, compared to these two regression models. The most negatively significant variables are percentage of single-family dwelling units(-6.57) and dwelling units per square mile(-5.21). The other two negatively significant variables are normalized employment access index(-3.93) and median home value(-2.92). It is kind of strange that more single-family units generate less work trips by private car. As to the unit density, it makes sense that higher density is good to reduce private car commuting. And when employment accessibility is good, people will use less private cars. The median home value may have two kinds of effects on work-trip by private car. On one hand, higher median home value means residents in this place are richer that they can afford more cars and donâ&#x20AC;&#x2122;t care the expense of oils and other car-related consumptions; on the other hand, from the perspective of economics, higher home value will urge people consume less other

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

commodities that people use public transit more to reduce consumption. This regression shows the accordance with the latter possibility.

Independent variables like total population, percentage of African-Americans, median household income, percentage of dwelling units built after 1990, median rent, SEPTA stations, bus routes, tract area fall out of the original hypothesis. It seems that the condition of public transit won’t affect residents’ preference of using private cars. And whether residents are poor or rich has little effect on what kind of vehicles they choose.

The drive alone rate regression (Appendix table d-3)

This regression has a less fitting degree whose R square is 0.63. The positively significant independent variables are percentage of white population(13.88), percentage of AfricanAmericans(9.05), number of families(7.59) and percentage of Hispanic(4.11). It shows that nearly all kinds of people tend to drive alone to work if they can. The negatively significant variables are dwelling units per square mile(-8.18), normalized employment access index(6.96), average household size(-3.98) and tract area(-3.95). Higher unit density, again helps to decrease car uses. And good employment accessibility is helpful to reduce drive alone rate. As to the tract size, it indicates that larger tracts will urge people carpool to work or use other vehicles.

Independent variables like population over 65, percentage of college grads, median household income, percentage of single-family dwelling units, percentage of dwelling units built after 1990, median home value, number of SEPTA stations, bus routes fall out the original hypothesis. Again, the condition of public transit has little effect on drive alone rate.

Most social or economic factors also have little effect on people’s decision about drive alone or not.

The bus rate regression (Appendix table d-4)

This regression’s R square is 0.71, which shows a basically fitting result. The most positively significant independent variables are percentage of African-Americans(11.05) and percentage below poverty line(9.38). It makes sense that poor African-Americans tend to use bus to commute. The other three positively significant independent variables are percentage of white population(3.11), dwelling unit per square mile(2.68) and normalized employment access index(2.02). Higher unit density and better employment accessibility will promote the use of public transit. The negatively significant independent variables are percentage of multi-family dwelling unit(-5.83) and median household income(-2.53). This shows that richer household tend not to use bus to commute and more multi-family units will also decrease the bus rate, which is consistent with the work-trip by private car regression that more single-family units will generate less work trips by private cars.

Independent variables like population over 65, percentage of Hispanic, percentage of Asian, percentage of dwelling units built after 1990, median rent, number of SEPTA stations, bus routes, bus length, and tract area fall out of the original hypothesis. It shows again that whether people will choose public transit are not depended on the current condition of public transit system, at least its physical condition. And whether there are more newly built dwelling units will not affect the bus rate. It may indicates that recently built units have little consideration about take advantage of existing public transit system. They may be too far away from SEPTA stations.

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

The rail transit rate regression (Appendix table d-5)

This regression is least fitting, with an R square 0.16. It indicates that these available independent variables are not the most appropriate ones to build this model, as I mentioned in the first part of this question. The significant level of its independent variables are also not high enough, compared to other regression models. The positive ones are percentage of dwelling units built before 1950(3.64), SEPTA stations(3.31), percentage of white population(3.19), percentage of African-Americans(2.92) and percentage of multi-family dwelling units(2.69). More SEPTA stations will promote people use more rail transit. And higher percentage of units built before 1950 is positively significant may indicates that these units have good access to SEPTA stations or it may indicates an inactive economic condition of this place that people choose to use public transit rather than private cars. The negative variables are tract area(-2.29) and normalized employment access index(-2.15).

This indicates that the current rail routes may be not reasonable enough. For example, it may be not long enough that it couldnâ&#x20AC;&#x2122;t provide service for long-distance commuting.

However, since this regressionâ&#x20AC;&#x2122;s R square is so small that if other more appropriate factors are considered into this regression, the results I get before may be changed. Hence, we should not depend so much on this regression result.

Independent variables like total population, population under 5 or over 65, percentage of Hispanic, average household size, median income, percentage below poverty line, median rent and dwelling unit per square mile fall out of the original hypothesis. It shows that rail transit rate has little relationship with economic factors. Unit density also has no contribution to this rate.

The bike & ped rate regression (Appendix table d-6)

This regressionâ&#x20AC;&#x2122;s R square is 0.67, which is moderately satisfactory. The most positively significant independent variables are total population(9.19) and normalized employment access index(6.93). Other positively significant independent variables are percentage below poverty line(5.72), percentage of multi-family dwelling unit(5.03), dwelling unit per square mile(3.19), percentage of Asian(2.32) and median household income(2.24). Better accessibility and higher unit density, again will promote bike & ped rate. Higher household median income is also helpful to bike & ped rate. However, as shown in former regression models, higher income people also tend to generate more private-car work trips while tend not to use bus to commute. This indicates that public transit system needs to be improved, perhaps to improve the service, if based on current transit system. The negatively significant independent variables are population under 5(-9.67), population over 65(-8.47) and percentage of African-Americans(-6.33). More population under 5 may implies that there are more young parents in this place. They tend not to use bike or by foot to commute.

More population over 65 is negative to bike & ped rate is perhaps due to their inability to use bikes safely or quickly, or due to the long distance they need to commute. AfricanAmericans also tend not to use this method to commute while use bus and trail transit. This is because of the cultural customs, just like Asians that tend to use bikes or walk to commute, rather than use trail transit.

Independent variables like percentage of white population, Hispanic, percentage of college grads, percentage of dwelling units built after 1990, median rent and tract area fall out of the original hypothesis. It shows that recent built houses may have little consideration about being close to working places. Tract area is also insignificant shows that whether the tract is big or small will not promote or restrain bike & ped commuting.

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Chester County, PA Land Use Program; Suitability Analysis; Travel Demand Regressions

Summary Based on the six regression models, I can conclude that higher density residential development near rail transit will significantly encourage fewer work trip by private cars and drive alone rate, and greater bus, bike & ped commuting. However, higher density has little effect on trail transit rate. It is probably because of the low fittingness of this trail transit regression. More SEPTA stations will encourage greater trail transit while it has little effect on other commuting approaches. Number of bus routes and bus route length have little effect on what kind of commuting approaches people choose. In all the six regression models, these two independent variables both fall out of the final best models. This is caused by several possible reasons. First, the available data are not appropriate to find the relationship between commuting approaches and them, as I mentioned in the beginning; secondly, the current bus transit system is so bad(the routes are unreasonable or bus service is bad) that people hold indifferent attitude toward it, that whether it has more routes or longer length will not affect peopleâ&#x20AC;&#x2122;s commuting choice; thirdly, the current system is not so bad but people have better commuting approaches that they tend not to use bus. In order to get a better understanding of the condition of bus transit system, more detailed data and analysis are needed.

As to the policies intended to encourage walking or biking to work, first, new residential units should be built with higher density and close to working places. New working places should also be allocated near residential land use to provide easy accessibility for people, especially for elderly people. Also, schools should be allocated near working places. For young parents, one possible reason of their low bike & ped rate is that they need to drive their children to school every day and then go to work. Even though their houses are close to their working places, they still need to drive if their childrenâ&#x20AC;&#x2122;s school is not walkable. Hence, if schools, working places and residential areas are arranged properly, more young parents will be attracted to walk or bike to work. Promoting education degree is a long-term measure to encourage walking and biking to work. Higher education degree may lead to higher income which will also increase the bike & ped rate.

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34th & Market Street, Philadelphia Land Appraisal

the small offices. From the latter study, the result shows that in the office sector, an efficiency ratio of 85% represents a good ratio of tenant area to landlord area from a landlord’s point of view, and an ratio of 80% represents an efficiently designed office building from a tenant’s point of view, where primary circulation does not reduce the usable area unreasonably.

Based on these research, and considering the new technology in architecture design as well as the high land value in Philadelphia University City, I assume the building on this site will have a high efficiency rate to increase the leasable area or add more housing units. So I assume the efficiency factor for apartment, retail and office are 90%, 95% and 90% separately.

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Average vacancy rate:

The assumption of vacancy rate for apartment is based on what I’ve researched for the pencil-out model. For retail building, it will be leased by one or two big tenants for a longterm, so once it is leased out, the vacancy rate will be 0%. For office building, I assume the vacancy rate is 10%, which is an average level of Philadelphia’s office market.

Conclusion/recommendations Based on this property appraisal of three different types of buildings, the land value varies dramatically. If there will be an apartment building, the land value will be $18,960,450; if an retail building is developed, the land value will be $13, 330,827; and if an office building is built here, the land value will be -$5,334,643. The land value per square foot is $873, $613, $245 respectively. This result shows that if the land owner sell this parcel to an apartment developer, he can ask for higher price for the land. For office developers, they might don’t want to buy this parcel since the development cost is too high that couldn’t be covered by the rent. Hence, I would recommend the land owner to sell the land to an apartment developer and ask for about $870/S.F. or an retail developer with an asking price of $600/S.F. for the land.

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